NASA Contractor Report 185637

National Aeronautics and Space Administration (NASA)/

American Society for Engineering Education (ASEE)

Summer Faculty Fellowship Program--1990

Volume 2

Richard B. Bannerot, Editor

University o['Houston _ University Park

Houston, Texas

Stanley H. Goldstein, Editor

University Programs Office

L yndon B. Johnson Space Center

Houston, Texas

_ %_

Grant NGT 44-005-803

December 1990

(NA_A-CR-I85037-VoI-2) NASA/AS_E SUM_ER

FACULTY FFLLGWSHIP PROGRAM, 1990t VqLUM_

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PREFACE

The 1990 Johnson Space Center (JSC) National Aeronautics and Space Administration

(NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program

was conducted by the University of Houston--University Park and JSC. The 10-week program was

operated under the auspices of the ASEE. The program at JSC, as well as the programs at other

NASA Centers, was funded by the Office of University Affairs, NASA Headquarters, Washington,

D.C. The objectives of the program, which began nationally in 1964 and at JSC in 1965, are

1. To further the professional knowledge ofqualifled engineering and science faculty members

2. To stimulate an exchange of ideas between participants and NASA

3. To enrich and refresh the research and teaching activities of participant's institutions

4. To contribute to the research objectives of the NASA Centers

Each faculty fellow spent at least 10 weeks at JSC engaged in a research project commensurate with

his/her interests and background and worked in collaboration with a NASAJJSC colleague. This

document is a compilation of the final reports on the research projects done by the faculty fellows

during the summer of 1990. Volume 1 contains reports 1 through 14, and volume 2 contains reports 15

through 28.
4_
CONTENTS

Volume I

1. Akundi, Murty A.: "LIF and Emission Studies of Copper and Nitrogen" . .............. 1-1

2. Barnes, Ron: "Compound Estimation Procedures in Reliability" . .................... 2-1

. Bishop, Phillip A.: "Techniques for Determining Total Body

Water Using Deuterium Oxide" . ................................................ 3-1

. Boyd, Ernest J.: "Mathematical Modeling of the Flow Field and Particle

Motion in a Rotating Bioreactor at Unit Gravity and Microgravity" . ............... 4-1

. Hart, Maxwell M.: "An Air Revitalization Model for Regenerative Life

Support Systems (RLSS)" . ..................................................... 5-1

6. Hite, Gerald E.: "Photographic Image Enhancement" . .............................. 6-1

. Hooker, John C.: "Nonlinear Systems Dynamics in Cardiovascular

Physiology: The Heart Rate Delay Map and Lower

Body Negative Pressure" . ...................................................... 7-1

. Huntsberger,Terry:"IncorporationofShuttleCCT Parameters

inComputer SimulationModels" ................................................8-1

. Johnson, Debra Steele: "Training Effectiveness of an Intelligent

Tutoring System for a Propulsion Console Trainer" . .............................. 9-1

10. Karimi, Amir: "Re-Examination of Metman, Recommendations on

Enhancement of LCVG, and Development of New

Concepts for EMU Heat Sink" . ................................................ 10-1

11. Kurdila, Andrew J.: "A Nonrecursive Order N Preconditoned Conjugate

Gradient]Range Space Formulation of MDOF Dynamics" . ........................ 11-1

12. Lacovara, R. C.: "Applications of Formal Simulation Languages in the

Control and Monitoring Subsystem of Space Station Freedom" . ................... 12-1

13. Lawless, DeSales: "A Study of Murine Bone Marrow Cells Cultured in

Bioreactors Which Create an Environment Which Simulates Microgravity" . ....... 13-1

14. Layne, Charles S.: "The Effects of Deuterium on Static Posture Control" . ............ 14-1

Volume 2

15.

16.

Morgan, Paul: "Evaluation of Geophysical Properties of the Lunar Regolith

for the Design of Precursor Scientific Missions for the

Space Exploration Initiative" . ................................................. 15-15I

Morris, Robert A.: "A Design for an Intelligent Monitor and Controller for

Space Station Electrical Power Using Parallel Distributed

Problem Solving" . ............................................................ 16-1

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

Murphy, Michael G.: "Fuzzy Logic Control System to Provide Autonomous

Collision Avoidance for Mars Rover Vehicle" . ...................................

Navard, Sharon E.: "Evaluating the Effect of Accuracy Ratios on the

Percent of Calibrations Which Are Out of Tolerance" . ............................

Neubek, Deborah J.: "Developing a Taxonomy for Mission

Architecture Definition" . .....................................................

Radhakrishnan, Ramalingam: "Design of a Lunar Oxygen Production Plant" . .......

Rajan, Periasamy K.: "Design of Correlation Filters for Hybrid Vision Systems" . .....

Ricles, James M.: "Development of Load-Dependent Ritz Vector Method

for Structural Dynamic Analysis of Large Space Structures" . .....................

Squires, William G.: "Mechanics, Impact Loads and EMG on the

Space Shuttle Treadmill" . .....................................................

Strait, Melissa M.: "Solutions to Problems of Weathering in

Antarctic Eucrites" . ..........................................................

Tietze, Karen J.: "Assessment of the Pharmacodynamics of Intranasal,

Intravenous and Oral Scopolamine" . ...........................................

Uhde-Lacovara, J.: "High Accuracy Optical Rate Sensor" . .........................

Williams, Raymond: "Planetary Spacecraft Cost Modeling Utilizing

Labor Estimating Relationships" . .............................................

Yin, Paul K.: "Structural Concept Studies for a Horizontal

Cylindrical Lunar Habitat and a Lunar Guyed Tower" . ..........................

17-1

18-1

I9-I

20-1

21-1

22-1

23-1

24-1

25-1

26-1

27-1

28-1

N 9 1- 2 7 160_

EVALUATION OF GEOPHYSICAL PROPERTIES OF THE LUNAR

REGOLITH FOR THE DESIGN OF PRECURSOR SCIENTIFIC MISSIONS

FOR THE SPACE EXPLORATION INITIATIVE

Final Report

NASA/ASEE Summer Faculty Fellowship Program--1990

Johnson Space Center }_

t/t

Prepared By:

Academic Rank:

University & Department:

Paul Morgan, Ph.D.

Professor

Northern Arizona University

Geology Department, Box 6030

Flagstaff, Arizona 86011-6030

= , r

NASA/JSC

Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Space and Life Sciences

Solar System Exploration

Space Resources Utilization Office

Wendell W. MendeU, Ph.D.

August 7, 1990

NGT-44-005-803

15-1
ABSTRACT

A working knowledge of the physical parameters of the upper ten meters of

the lunar regolith is crucial to-the planning of experimentation, mining, resource

utilization, and construction associated with future lunar bases. Scientific research

literature pertaining to studies Of the lunar surface, including results of Apollo

experiments and exploration, have been reviewed to produce a summary of our

present knowledge of the'physical propertihS of the]unar regolith. Particular

emphasis was placed on gaps in our understanding. The primary data source for this

re viewhas been Carrier et aZ (1). _

The purpose of this review is to address the following questions: 1) With what

frequency may boulders be encountered that represent hazards to lunar operations?;

2) How easy may lunar soil be excavated?; 3) How may explosives be used in

excavation operations?; 4) What is the trafficability of the regolith?; 5) What

problems may be encountered in mining (probably strip mining) of the regolith?; 6)

What angle(s) of repose will be stable in excavation of the regolith?; 7) what

layering may be encountered in the subsurface?; 8) Is our knowledge of the regolith

site specific, or may it be applied genetically to any site on the lunar surface?; 9)

What data will be needed to characterize a site for a lunar base?; 10) How may the

regolith properties modify the design of geophysical experiments from the lunar

base?; 11) Are there terrestrial analogues for the geophysical properties of the lunar

regolith?

Available data indicate that there is significant lateral heterogeneity in the

lunar regolith. This heterogeneity exists both with respect to small-size particle

distributions (< 1 mm), which may affect bulk material properties, and with respect

to the random distribution of large-sized particles (> 100 mm), which need to be

considered for mining and excavation operations. Vertical layering of the regolith

is expected at most sites, and considerable vertical heterogeneity may also be

expected. Detailed site-specific surveys are recommended at all sites where mining

and excavation operations are planned. These surveys should include sub-surface

profiling, for which radar techniques appear to be most easily adapted, to determine

the density and distribution of large, consolidated particles. This profiling should

also determine the degree of subsurface layering. Core samples will be required to

determine small-sized particle properties if significant layering is recorded.

Trafficability appears to be primarily restricted by surface topographic features,

although problems of raising the loose surface regolith must be considered. These

problems appear to be associated with 1Jallistic trajectories associated with surface

activities which disturb the surface, and with electrostatic attraction by the lighter

particles to charged surfaces. No terrestrial analogue adequately covers the range

of properties likely to be experienced in the lunar regolith, but analogues for

different physical and mechanical properties may be found.

|

15-2
INTRODUCTION

J

The lunar regolith is a an essentially continuous layer, typically several meters

in thickness, which covers the entire lunar surface (2). Any surface activities on the

Moon, and any geological studies of the Moon are therefore strongly affected by the

properties of the regolith. It is a mantle of fragmental and unconsolidated rock

material of highly varied character, ranging from very fine dust to blocks of several

meters in diameter. All lunar landings (Apoll-o and Luna), and all photographic

studies of the Moon indicate that the regolith completely covers the underlying

bedrock, except perhaps where bedrock may be exposed on some very steep-sided

crater walls and lava channels. The finer components of the lunar regolith (< 10

mm) are often informally referred to as lunar "soil", but this term must be used

carefully as there are fundamental differences between terrestrial regoliths, of which

soils are one group, and the lunar regolith (see DIFFERENCES AMONG THE

MODES OF FORMATION OF LUNAR AND TERRESTRIAL REGOLITHS

below).

Basically

comparison with

regolith:

1)

the constitution of the lunar regolith is relatively simple in

terrestrial regoliths. There are five basic particle types in the lunar

Crystalline rocks (fragments of multi-mineral rocks which formed

by crystallization from silicate melt)

2) Minerals (fragments of individual mineral crystals)

3) Breccias (fragments of rocks which are themselves composed of

an aggregate of other rock and mineral fragments)

4) Agglutinates (pulverized rock, mineral and glass fragments

bonded together with glass)

5) Glasses (fragments, irregular masses and beads of amorphous

silicates formed by rapid cooling of silicate melts)

Fewer than a hundred minerals have been identified in lunar samples, compared with

several thousand minerals that have been identified on Earth. While we may expect

new minerals to be identified with continuing lunar exploration, we may confidently

extrapolate present knowledge of lunar mineralogy to indicate that the mineralogy

of lunar crust is at least an order of magnitude simpler than terrestrial crustal

mineralogy. The physical properties of the lunar regolith also fall into relatively

limited ranges compared to those of terrestrial regoliths due to the relatively simple

mineralogy, the absence of a significant atmosphere, and the lack of water, clay

minerals, and organic material in the lunar regolith. The primary factors that affect

the physical properties of the lunar regolith are the packing of the regolith (bulk and

relative densities) and temperature.

Current scenarios for the establishment of a permanent manned base on the

Moon require specific activities which interact with the regolith (3). These activities

include: 1) Use of regolith material to shield long-term working and habitation

modules sufficiently to reduce radiation from the largest recorded solar flares to safe

15-3
levels; 2) Surface transportation across the regolith; 3) Excavation of regolith

material for oxygen production, and in the future for other mining operations such

as the production of rare isotopes, such as 3He, implanted in the regolith by the solar

wind. A number of specific questions need to be answered before the mechanics of

these activities can be evaluated. These questions include: 1) With what frequency

may boulders be encountered that represent hazards to lunar operations?; 2) How

easy may lunar soil be excavated?; 3) How may expl0_sjyes beused in excavation

operations?; 4)Mat is the trafficability of the regolith?; 5) What problems may

be encountered in mining (probably strip mining) of the regolith?; 6) What angle(s)

of repose will be stable in excavation of the regolith?; 7) What layering may be

encountered in the subsurface?; 8) Is our knowledge of the regolith site specific, or

may it be applied generically to any site on the lunar surface?; 9) what data will be

needed to characterize a site for a lunar base?; 10) How may the regolith properties

modify the design of geophysical experiments from the lunar base?; 11) Are there

terrestrial analogues for the geophysical properties of the lunar regolith? Physical

properties of the lunar regolith have been measured in situ by robots and astronauts,

in the laboratory on returned samples, and by remote sensing from the Earth's

surface and from lunar orbit. This report summarizes these results as they apply to

the establishment of a permanent manned lunar base, and uses these results to

propose precursor scientific studies for the Space Exploration Initiative. For the

purposes of this report, the discussion of physical properties has been limited to

mechanical (geotechnical) properties, which include parameters which describe grain

size and shape, densities, and other geotechnical properties. These data are primarily

summarized from Carrier et al. (1). Other physical properties, which include elastic

(seismic), electrical and electromagnetic, magnetic, and thermal properties will be

considered in future work. The section on mechanical properties is followed by a

discussion of the implications of these properties, the mode of formation of the

regolith, and considerations for lunar base activities.

I

MECHANICAL (GEOTECHNICAL) PROPERTIF.S

The mechanical properties of the lunar regolith were of vital importance to

design of the Apollo Lunar Module and there was significant concern regarding the

implications of these prope_es until the first human footsteps were made on the

Moon (1). Some of the major uncertainties resulted from lack of knowledge of the

behavior of fragmental lunar material in the lunar vacuum. Predictions varied from

a very hard surface due to "cold welding" of the fragments in the vacuum, to a very

soft surface due to the very small grain size of the fragments. Our present

experience is that the lunar surface is not very soft, nor very hard. The range of

geotechnical properties of the lunar regolith are less than occur in the terrestrial

regolith: There appear to be three sources of this difference: 1) Terrestrial geologic

exogenic processes tend to produce well-sorted sediments; The primary lunar soilforming process is meteorite impact, which produces a poorly-sorted regolith;

15-4
2) Geotechnical properties of terrestrial soils vary by orders of magnitude as a result

of water, clay minerals, and organic materials, not present on Moon; 3) The range

of minerals in lunar soils much less than on Earth, in fact a large portion of the soil

is glass.

Geotechnical properties of lunar soil tend to fall in a fairly narrow range, and

the most significant variable is the relative density. Most other physical properties

of the lunar regolith also tend to fall in a relatively narrow range, the largest

variations occurring in those properties dependant on the relative density.

Particle Size Distribution

The particle size distribution of a regolith controls to various degrees its

strength, compressibility, optical, thermal, and elastic (seismic) properties. The size

distribution of selected samples have been summarized by Carder et al. (1). Care

should be exercised in using these data as they were derived from biased samples in

the sense that only the soil fraction of the regolith, fragments smaller than I cm, have

been included in the particle size distribution analysis. The consequences of this

biased sampling are discussed below.

Carrier (4) has interpreted these results to indicate that the bulk of lunar soil

samples fall in fairly narrow range of particle size distribution. In general, these

results indicate that the soil is a poorly sorted, silty sand to sandy silt. The median

particle size in the < 1 mm fraction is 40 to 130 microns, with an average of 70

microns. In practical terms this means that about half of the lunar soil by weight is

finer than human eye can resolve. Roughly 10-20% of soil finer than 20 microns, and

a thin layer of dust appears to adhere electrostatically to everything that comes in

contact with the soil: spacesuits, tools, equipments, lenses. Carder et al. (1) warn

that "Housekeeping is a major challenge for operations on the lunar surface."

Regolith dust is clearly a factor which must be taken into account in lunar

surface operations, but potential problems with large regolith fragments is not so

clearly illustrated by these data. Even some of the samples listed by Carrier et al.

(1) have a significant fraction of particles with grain sizes greater than 1 cm. The

bulk of samples 15400 and 70180 consist of these large particles, which were excluded

from the median grain size calculations. The magnitude of this problem is clear from

an examination of almost any of the photographs taken on the lunar surface (e.g., (5),

Figures 4.1 and 4.3). Many centimeter- to meter-sized fragments are visible in these

photographs, and in places large fragments clearly constitute a significant portion of

the regolith. There is a need for greater study of the size and frequency distribution

of the larger regolith particles.

Particle Shapes

Shapes of the lunar regolith particles are highly variable, ranging from

spherical to extremely angular. In general, particles are somewhat elongated and are

15-5
sub-angularto angular. Because of this elongation, particles tend to pack together

with a preferred orientation of the long axes, and physical properties in situ are

expected to be anisotropic. Particles are not compact, but have irregular, re-entrant

surfaces, a feature which especially affects the shear strength of the regolith.

Studies of the average shape properties of the finer fraction of the regolith

indicate that the particles have an average elongation of about 1.35 (ratio of the

major and intermediate axes of the fragments), and aspe_ ratios for most of the

particles are in the range of 0.4 to 0.7 (ratio of minor and major axes of an ellipse

fitted to the particle shape by least squares). Most particles are therefore described

as slightly-to-medium elongated. Measurements of particle roundness indicate that

the particles are sub-angular to angular in shape.

Specific Gravity

The specific gravity of a soil particle is defined as the ratio of its mass to the

mass of an equal volume of water at 4°C. Values for lunar soils range from 2.3 to

>3.2 and Carder et 02 (1) recommend a representative value of 3.1 for general

scientific and engineering purposes. In contrast, many terrestrial soils have a specific

gravity of 2.7. The average specific gravity of a given lunar soil is related to the

relative proportions of the different particle types which constitute the soil, i.e., the

relative proportions of basalts, mineral fragments, breccias, agglutinates, and glasses.

Bulk Density and Porosity

The bulk density of a soil is defined as the mass of the material contained in

unit volume (usually given in g crn "3 or Mg m "3) Porosity is defined as the volume

of void space in the material divided by its total volume. The in situ bulk density of

a lunar soil is a fundamental property, influencing its bearing capacity, slope stability,

seismic velocity, thermal conductivity, electrical resistivity, and the depth of

penetration of ionizing radiation. Lunar regolith bulk density is highly variable, not

only from site to site, but also from station to station, and even with depth in a single

core. At present, the best estimate for the average bulk density of the top i50 mm

of lunar soil is 1.50 + 0.05 Mg m "3, and of the top 600 mm, 1.66 + 0.05 Mg m"3 (1).

The average porosity in the top 300 mm of the surface is 49%. Bulk densities tend

to be less variable below about 1 m, and the mean bulk density of a long core from

Apollo 17 from a depth of 1 to 2 m is approximately 1.8 Mg m3. A variability of

+ 0.15 Mg m 3 is shown in bulk density measurements on cores from Apollo 15, 16

and 17 at these depths (1), however, and this variability could be significant in lunar

base engineering activities.

Relative Density

Relative density is a measure of the degree of particle packing of a granular

15-6
soil, such as lunar soil. In situ relative density is another parameter which influences

the physical and geophysical properties of a soil, and some of these properties may

vary over several orders of magnitude between relative density values of 0% to

100%. In situ relative density of lunar soil has been found to be about 65% (medium

to dense) in the top 150 mm, increasing to more than 90% (very dense) below a

depth of 300 mm. These relative densities are very high when compared to a relative

density of 65-75% which is the practical limit for field compaction of terrestrial soils.

This high relative density of lunar soils was probably caused by the shaking effects

of shock waves generated by innumerable meteoroid impacts.

Compressibility

Compressibility describes the volume change that occurs when a confining

stress is applied to soil. It is quantified by the compression index, the decrease in

void ratio that occurs when the stress is increased by an order of magnitude. The

compression of lunar soils, measured in a vacuum, has been found to vary from 0.01

to 0.11, depending on the initial density and the applied stress. The compressibility

curves for soils of different initial densities converge when the stress exceeds 100 kPa,

which corresponds, on the Moon, to a depth of more than 30 m below the lunar

surface.

Shear Strength

The shear strength consists of two components, a cohesive component, that

is independent of the applied stress, and a frictional component that is directly

proportional to the normal stress. Based upon a variety of data the following

estimates for these components have been derived:

Cohesion = 0.1 to 1 kPa

Friction angle = 30 ° to 50 °

The higher shear-strength parameters correspond to higher values of relative density

of the lunar soil. These values are relatively high, probably due to the interlocking

of irregular and re-entrant soil particles under the low stresses present near the lunar

surface. These values are thought to be useful for near-surface lunar structure and

construction activities, but for heavy structures or deep excavations, a more complete

study of lunar soil shear strength should be made.

Permeability and Diffusivity

The coefficients of permeability and diffusivity are used to evaluate the

movement of fluid through a porous medium, and no direct measurements of these

parameters have been made on lunar soil. Based on a test firing of the Surveyor 5

vernier engine on the lunar surface, the permeability of the soil to a depth of

250 mm was estimated to be 1 to 7 x 1012 m 2. Dif_sion rates for noble gases

15-7
through lunar soil have been measured in the laboratory yielding values of 7700,

2300, and 1800 mm 2 s"t for He, Ar, and Kr, respectively.

Bearing Capacity, Slope Stability and Trafficability

These three parameters control important engineering properties of the

regolith. The ultimate bearing capacity defines the maximum possible load that can

be applied without causing gross failure, and has been determined to be

approximately 6,000 kPa for a one-meter footing on the lunar surface. The allowable

bearing capacity defines a lesser load that can be applied without exceeding a given

amount of settlement. For a maximum settlement of 10 mm for a load with a footing

of less than 500 nun, data from the depths of astronaut boot-prints indicate, that for

a 95% confidence level, the allowable beating capacity should not exceed 2 kPa.

The slope stability of lunar soil is expected to vary widely from naturally

compacted material to excavated material. Calculations indicate that a vertical cut

to 3 m could be make in naturally compacted lunar soil, and a slope of 60 ° could be

maintained to a depth of about 10 m. In contrast, a pile of uncompacted excavated

soil could be raised to height of 10 m at an angle of nearly 40 °.

TrafficabiLity is defined as the capacity of a soil to support a vehicle and to

provide sufficient traction for movement. From the experience of Apollo and

Lunokhod missions, we know that almost any vehicle with round wheels will perform

satisfactorily on the lunar surface provided that the ground contact pressure is no

greater that about 7 to 10 kPa. The primary limitations on the trafficability of the

lunar soil are speed and slope. The steepest slope that the Apollo Lunar Roving

Vehicle could climb was 19° to 23 °. Additional slope climbing capability could be

attained by adding more aggressive tires to the vehicle. Particularly soft soil

conditions were encountered by both Apollo and Lunokhod vehicles when

approaching craters, particularly on the inside wall. Local problem areas may

therefore exist for trafficability.

DIFFERENCES AMONG THE MODES OF FORMATION OF

TERRESTRIAL AND LUNAR REGOLITHS

In order to understand tile differences in phySi_prOperties among lunar and

terrestrial regoliths one must first understand_ebasic differences in the modes of

formation of these materials. Both regoliths are ultimately formed by breakdown of

bedrock' Ten'es_al regolidiS ai-e fo/'med by a range of processes wh|cfi commo-nly

involve physical disintegration and some chemical change of the bedrock,

transportation of the sediment so formed by wind, water or ice, commonly with

additional physical and/or chemical modification, and redeposition of the sediment.

These processes typically result in the production of a sediment which is relatively

well-sorted both physically and chemically, and which is characterized by rounded

particles. Sediments generally become smaller in grain size, more rounded, and more

15-8
chemically mature (stable in a hydrous environment at standard temperatures and

pressures) as the distance of transportation from the bedrock source increases. The

major exception to this generalization is erosion and direct deposition from ice which

produces very poorly sorted sediments (grain sizes from rock flour to boulders), with

little or no chemical change from the parent bedrock. The ice-transported sediments

are rounded, however, and if ice melting occurs before sediment deposition, the

sediments sizes are significantly sorted by transportation in the melt-waters. The

sediments may then be further modified by soil formation, which causes further

vertical physical and chemical differentiation and modification of the sediments and

the addition of organic material to the upper regolith. The result is a regolith which

is well-sorted with a primary vertical stratification typically modified by lateral facies

changes.

In contrast, the lunar regolith is primarily the result of a single physical

process, hypervelocity impacts (projectiles with impact velocities in excess of the

speed of sound in the target material) of particles ranging in size from dust to small

planets. These impacts cause major disintegration of the target material, typically

forming angular fragments ranging in size from rock flour to large boulders, and

transportation of these materials from the impact site is limited to ballistic projection

of the fragments. Ballistic fragments with an initial velocity of greater than 2.4 km s"1

may be ejected from the Moon; particles with lower velocities are redeposited on the

lunar surface, the larger particles forming secondary craters.

There is no sorting of particle size during ballistic transportation on the Moon

as there is no significant atmosphere to cause differential hydrodynamic drag on

particles of different sizes and shapes, as occurs on Earth. Chemical changes are

limited to phase-transformations by high shock-pressures produced by impact, and

changes in mineralogy associated with impact melting. These processes results in a

suite of characteristic high-pressure minerals found only associated with impact

structures. Impact melting results in a high proportion of rounded beads of glass in

the lunar regolith, formed by ballistically projects melt droplets which rapidly solidify

during flight to form amorphous glass beads before landing.

Although the result of repeated impacts is to continue to decrease grain size,

reworking of the lunar regolith has not in general proceeded to a state of dynamic

equilibrium in which the decrease in grain size due to impact fracturing is equal to

the increase in grain size associated with melt solidification. The lunar regolith

therefore consists of a poorly sorted aggregate of angular brecciated fragments and

glass spherules. Layering in the regolith is associated with the overlap of ejecta

blankets from different impact targets, and this layering tends to be modified by

subsequent impact events (gardening). There is no lunar soil-forming processes

equivalent to the hydro-chemical and organic processes of terrestrial soil formation.

However, there is surficial regolith modification by exposure to the solar wind and

micro-meteorite impacts.

15-9
DISCUSSION OF THE IMPLICATIONS OF REGOL1TH PHYSICAL

PROPERTIES FOR LUNAR BASE ACTIVITIES

From this preliminary review of the physical properties of the lunar regolith,

I have identified two areas of potential concern for lunar base activities. These areas

concern the probability of encountering cobbles and boulder in the regolith

subsurface, and "dust" and heterogeneity in the fine fraction of the regolith.

Most of the published discussions on particle-size distributions are based upon

data only from sieved samples of fines (< 1 ram) or the coarse and fine fraction (< 10

ram), ignoring the "lumps" (> 10 ram) in the regolith. Coarse fragments of the

regolith samples were hand-picked from the samples in the Receiving Laboratory and

given their own numbers - one may therefore be able to reconstruct the original

sample particle-size constitution, but apparently these data are not readily available

at present. There is also the problem that meter-sized boulders were not sampled

en masse for obvious practical reasons, so this particle size is completely missing from

the sample analyses. Photo-geologic studies have been made of the larger boulder

distributions, and more work is needed on this topic. In a conservative approach, we

may not ignore the possibility of excavating large boulders even in the marie regolith

distant from large, young impacts. Cobbles and boulders are visible on many of the

lunar surface photographs, and if similar conditions exist in the subsurface, this largesize component of the regolith could cause engineering hazards.

Problems of "dust" from the lunar regolith have been briefly discussed by a

number of authors (e.g., (1)). There appear to be inconsistencies in some of these

reports, however. Although there are many references to "dust permeating the

samples" and "how quickly things were made dirty" in the astronaut voice

communications of the Apollo lunar surface activities, the rock samples that were

returned to the Lunar Receiving Laboratory at the Johnson Space Center were

considered to be covered in dust only to the extent that would be expected from the

packing of friable samples and transportation back to Earth (Don Morrison, personal

communication, 1990). Some appearance of dusty-surfaces could have been the

effects of darkening by exposure. The magnitude of the problems of dust associated

with lunar surface activities remains unresolved at present. The main dust problems

associated with lunar surface activities appear to be associated with porous surfaces,

such as the space suits, or charged surfaces, camera lenses and other insulators.

Other materials may still be mantled in dust by activities which cause ballistic

disturbances of the regolith, but there is no evidence dust jumps onto all exposed

surfaces. This dust may still be a major problem, and more study of this problem is

required before extensive lunar surface activities are planned.

I am also concerned that not enough attention has been given to heterogeneity

in particle-size in the fines. There is considerable variability in the mean grain size

of the < 1 nun fraction (41-123 microns), and I suspect that this variation may result

in variations in physical properties that are being homogenized in the averages which

are quoted. For example, I suspect that the significance of re-entrant surfaces

15-10
becomes less as the particle size decreases, which could have measurable effects

upon soil cohesiveness and related properties. This topic should be studied in

greater detail.

RECOMMENDATION FOR SIrE-SURVEYS

Available data indicate that there is significant lateral heterogeneity in the

lunar regolith. This heterogeneity exists both with respect to small-size particle

distributions ( < 1 mm), which may affect bulk material properties, and with respect

to the random distribution of large-sized panicles (> 100 mm), which need to be

considered for mining and excavation operations. Vertical layering of the regolith

is expected at most sites, and considerable vertical heterogeneity may also be

expected. Detailed site-specific surveys are recommended at all sites where mining

and excavation operations are planned. These surveys should include sub-surface

profiling to determine the density and distribution of large, consolidated panicles.

This profiling should also determine the degree of subsurface layering. Core samples

will be required to determine small-sized particle properties if significant layering is

recorded.

These surveys would be desirable, and perhaps essential for engineering

purposes if the boulder problem is thought to be significant. They would be

desirable for geophysical studies under any circumstances, primarily because the

rapid change in physical properties of the upper regolith with depth (increase in

compaction, etc.). Lateral variations in regolith thickness may cause significant

uncertainties in local corrections for regional/global measurements, as in static

corrections for seismic data, shallow refraction and conductor corrections for heat

flow and electrical data, etc. For engineering considerations, a high resolution survey

with a penetration of about 10 m would be desirable. For geophysical site surveys

a lower resolution with greater penetration, of the order a km should suffice. Ideally

these surveys should give some three-dimensional resolution of subsurface structure

without the need for a dense two-dimensional grid of surface profiles. Technology

analogous to side-scan sonar or imaging radar would be useful in this respect.

There are three likely candidates for site-survey geophysical methods, active

seismic, electrical or electromagnetic techniques. The magnetic contrast among

boulders and regolith, and bedrock/consolidated regolith/regolith without a strong

inducing field is likely to be insufficient to produce a useful magnetic signal.

Similarly, gravity anomalies associated with long-wavelength, shallow density contrasts

or small, shallow density contrasts are unlikely to be resolvable from instrumental

noise and deeper source anomalies. Electrical methods could probably be devised

with sufficient sensitivity to locate shallow buried boulders, but these techniques

would probably require interpretation from an electrical section of the regolith to a

structural section. Direct imaging techniques such as reflection seismic or radar

techniques would probably have the best resolution for this problem and could be

most easily adapted to profile sweeps in a lunar environment.

15-11
Both radar and seismic methods have been successful in revealing layering in

the lunar regolith. Under the high vacuum conditions of the lunar surface,

difficulties may be encountered in coupling a seismic source and receivers to the

regolith without stopping the rover and "planting" both source and receivers for each

profiling point. Thus, radar probably offers the most attractive option for shallow

"rover profiling" with a sideways scanning radar system mounted perpendicular to the

rover traverse direction.

Trafficability appears to be primarily restricted by surface topographic

features, although problems of raising the loose surface regolith must be considered.

These problems appear to be associated with ballistic trajectories associated with

surface activities which disturb the surface, and with electrostatic attraction by the

lighter particles to charged surfaces.

TERRESTRIAL ANALOGUES

No terrestrial analogue adequately covers the range of properties likely to be

experienced in the lunar regolith, but analogues for different physical and mechanical

properties may be found. Dry desert conditions in which eolian sands and fluvial

gravels are interfingered may present a useful analogue for the lunar regolith for

tests of subsurface profiling, both for the detection of boulders and to identify

subsurface layering. The properties of the lunar regolith dominated by the fine-sized

fraction of the regolith are unlikely to be reasonably represented by any terrestrial

regolith materials, primarily due to the different modes of formation of the terrestrial

and lunar regoliths. These lunar regolith properties may by simulated by suitably

crushed basalt, mixed perhaps mixed with a fine silt component to simulate the

effects of the glass component in the lunar regolith.

15-12
(/)

(2)

(3)

(4)

(5)

REFERENCES CITED

Carder, W. D., III, Olhoeft, G., and Mendell, W. (1990) Physical properties of

the lunar surface. In Lunar Source Book (G. Heiken, D. T. Vaniman and B.

M. French, eds.), Cambridge University Press, in press.

McKay, D. S., Heiken, G., Basu, A., Blanford, G., Simon, S., Reedy, R., French,

B. M., and Papike, J. (1990) The lunar regolith. In Lunar Source Book (G.

Heiken, D. T. Vaniman and B. M. French, eds.), Cambridge University Press,

in press.

Aired, J., Bifldn, A., Kennedy, K. J., Petro, A., Roberts, M., Stecldein, J., Sturm,

J. (1989) Lunar Outpost. Systems Definition Branch, Advanced Program

Office, Johnson Space Center, 60 pp.

Carrier, W. D., III, Mitchell, J. K., Mahmood, A. (1973) The relative density of

lunar soil. In Proceedings of the Fourth Lunar Science Conference (Supplement

4, Geochimica et Cosmochimica Acta) 3, 2304-2411.

Taylor, S. R. (1982)Planetary Science: A Lunar Perspective, Lunar and Planetary

Institute, Houston, 481 pp.

15-13

- <=.9. _/_

N9 1 - 27 10 5f_

A DESIGN FOR AN INTELLIGENT MONITOR AND CONTROLLER FOR SPACE

STATION ELECTRICAL POWER USING PARALLEL DISTRIBUTED

PROBLEM SOLVING

/'<i

//

E

Final Report

NASA/ASEE Summer Faculty Fellowship Program--1990

Johnson Space Center

/

Prepared By:

Academic Rank:

University & Department:

NASA/JSC Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Robert A. Morris

Assistant Professor

Florida Institute

of Technology

Dept. of Computer Science

Melbourne, FL 32901

Engineering

Propulsion and Power

Power

Mr. Tom Jeffcoat

August 21, 1990

NGT-44-005-803

16-1
ABSTRACT

This paper describes work leading to the development of an

automated electrical power monitor and controller for spacecraft

secondary power systems such as the one anticipated for Space

Station Freedom. This work integrates current research in

parallel processing and distributed models for problem solving,

with current research and development being conducted at NASAJohnson Space Center on computer automation of spacecraft

secondary power distribution and control. Although the emphasis

here is on secondary power control, the computational model and

representational framework is designed with generality,

modularity, and extendibility in mind, and hence could be used

in the design of a complete spacecraft monitor and control

device.

This research is a response to the need for increased

autonomy in spacecraft subsystem control. Electrical Power

System (EPS) autonomy is defined to be the capability designed

into the EPS which permits onboard task execution through

monitoring, computation, command, and control functions with

minimal or no ground intervention. The reason for EPS autonomy,

especially on based missions with technologically sophisticated

spacecraft such as Space Station Freedom, is clear. Already,

autonomous load scheduling and fault detection and correction

have proved to be plausible applications of intelligent (AI)

models for decision making. At the same time, there has been a

notable lack of success in integrating intelligent techniques

into real time software. To make matters worse, the throughput

requirements of electrical power control create even more

difficult challanges to automation. It has generally been

accepted that for spacecraft applications such as control,

advanced software must be integrated with hardware and firmware,

as exemplified in recent developments in microprocessor

technology. In addition, developments in intelligent systems

architecture, such as distributed knowledge representation and

parallel processing, should be considered.

The starting point for this research is the Advanced

Electrical Power Management Techniques for Space Systems

(ADEPTS) project at NASA-JSC. The initial success of ADEPTS has

demonstrated the viability of integrating knowledge based

techniques for monitoring and control into a parallel processing

environment. This research proposes to extend the ADEPTS model

with three major goals in mind: first to include more

intelligent decision-making capabilities; second, to more fully

exploit the computational advantages of parallel processing; and

third, to make the ADEPTS model for power management more

generic, and hence capable of being applied to future spacebound vehicles such as space station. To fulfill these goals, a

cognitive model based on cooperative experts is defined.

16-2
17 _ _ __

. 7: _: ......

INTRODUCTION

The emphasis in this report is on defining a set of

communicating processes for intelligent spacecraft secondary

power distribution and control. The computer hardware and

software implementgtion platform for this work is that of the

ADEPTS project at JSC. The Electrical Power System design which

was used as the basis for this research is that of Space Station

Freedom, although the functionality of the processes defined

here generalize to any permanent manned space power control

application.

This report is organized as follows. First, the Space

Station Electrical Power Subystem (EPS) hardware to be monitored

is described, followed by a set of scenarios describing typical

monitor and control activity. Then the parallel distributed

problem solving approach to knowledge engineering is introduced.

There follows a two-step presentation of the intelligent

software design for secondary power control. The first step

decomposes the problem of monitoring and control into three

primary functions. Each of the primary functions is described in

detail_ in Bubsequent sections. This Teport concludes with

suggestions for refinements and embelishments in design

specifications;_. _ .-r._

SPACE STATION SECONDARY ELECTRICAL POWER CONTROL

Monitoring and Control (M&C) involves o_serving and guiding

the behavior of a large physical system towards some objective

(ref. I). Intelligent monitoring and Control involves the

integration of perceptual, cognitive andreactive knowledge into

the monitor and control process. M&C _asks that require such

knowledge include: interpretation and prediction of system

behavior, reasoned response to impo[fant events, diagnosis of

exceptional events, and planning long term courses of action.

Secondary electrical power distribution hardware on space

station comprises the following eiements: DC-to-DC converter

units (DDCU), which output 120 volts of DC power, Secondary and

Remote Power Distribution Assemblies (SPDA, RPDA) which contain

a collection of Remote Power Controller Modules (RPCM) for

switching, 1553 buses for transmitting data, power lines, and a

set of loads. These elements are arranged in a hierarchical

framework to allow for more effective hardware control.

Space station M&C offers challenges which do not arise on

either ground-based system control or control on short-term

missions such as Space Shuttle. In particular, electrical power

will be a limited resource, requiring scheduling on the basis of

load priority. In addition, EPS hardware maintenance will need

to be performed on potentially dangerous electrical equipment by

relatively inexperienced crew members. Finally, the 30-year

duration of space station makes continuous ground monitoring

16-3
economically and logistically inefficient. Each of these

considerations strongly suggest a need for an increased role for

automation.

Current design for space station involves dividing the

overall EPS M&C process into a hierarchical configuration of

tiers (Figure I). Tier I consists of Operations Management

System (OMS) control, including software and hardware for

overall ground and onboard management of Space Station

operations. Tier II consists of a set of Standard Data

Processors (SDP) containing 80386- or 486-based processing

capabilities, running concurrently to perform functions such as

Guidance, Navigation, and Control (GN&C), data management (DMS),

storage unit control, and Node and ITA management. Currently,

the Node/ITA SDP and DMS SDP are jointly responsible for Tier II

secondary power control. Communication among the Tier II

managers and the OMA are via a Fiberoptic Distributed Data

Interconnect (FDDI) bus. A Tier III secondary power controller

(SPC) provides local control and protection for the various

Space Station subsystems. Each SPC, as currently defined, Will

consist of a Multiplexer-Demultiplexer (MDM), and functions for

controlling input/output and self-monitoring. Finally, Tier IV

control consists of a hierarchy of Secondary Power Distribution

Assemblies (SPDAs) and Remote Power Distribution Assemblies

(RPDAs) which are collections of Remote Power Controller Modules

(RPCMs). The RPCM switches and distributes 120-Vdc power via a

set of unidirectional, solid-state switches. Each RPCM provides

deadfacing features, remote manual off capabilities, hardware

setpoint programmability and built-in testing features (ref. 2).

Each RPCM is assigned a 1553b data bus which responds to

external control signals and transmits data and status

information within 200 ms of application of control power. These

local features of the RPCMs allow for a real time response to

fault conditions.

Any proposed advanced automation for space station must

integrate with standard control firmware and software. In

particular, this report describes software that would naturally

migrate to Tier III MDM operations, although certain functions

could more properly be assigned to the Tier II level. Certain

constraints have been placed on application software at this

level, due to two primary factors: the desire to preserve the

main function of the MDM, which is high throughput of sensor and

effector i/o to the higher control levels, and to maintain

compatibility with MDM software. The primary reason for adding

intelligence to the MDM is to cut 1553 bus traffic to the higher

Tiers (viz., to the SDPs and OMA) (ref. 3). These constraints,

among others, are major factors influencing the design decisions

made in this report.

To summarize, the intelligent secondary power system

controller proposed here, if used on space station, would

monitor, evaluate and control performance from the DDCU units

to the interface to the loads. It should be viewed as an

16-4
!

FDDI

NODE/ITA MANAGER

OMA

l

SYSTEM MANAGER

SECONDARY POWER,

CONTROLLER i

I

w

1553

FROM

DDCU

120 VDC

I

SPDA

RPCM

RPCM

RPCM

I

i

l

RPDA

RPCM

- RPCM

- RPCM

LOAD

LOAD

LOAD

FIGURE I. SPACE STATION POWER DISTRIBUTION

AND CONTROL HIERARCHY

16-5
extension of Tier II and Tier III control operations. This

controller would therefore have one instantiation for each space

station node or element.

SCENARIOSFOR CONTROL

In this section we present four scenarios involving the

secondary power distribution hardware for which M&C activities

are required. These scenarios are based on a four-fold

classification of power system states into normal secure, normal

insecure, emergency, and restorative. These states are defined

in terms of two sets of constraints: load constraints and

operating constraints (ref. 4). Load constraints are

requirements for power to the loads. Operating constraints are

upper and lower limits on voltage, current, and other parameters

of the system.

NORMALSECURESTATE. In a normal secure state, both operating

and load constraints are met. While in this state, load

schedules are being dispatched and executed. Status information

from the RPCMs and other hardware is monitored and analyzed to

check for potential fault conditions.

NORMALINSECURE STATE. In this state, both load and operating

constraints are met by the power system, but there is a

likelihood of a transition to an emergency state. Some

violations caused by a contingency cannot be corrected without

load constraint violations. Preventive rescheduling of loads may

be needed to return the system to a secure state.

EMERGENCYSTATE. Transition to this state is caused by major

violations of operating constraints. Redundant power is

currently being used if available, but there is serious loss or

degradation of power. While in this state, remedial action or

corrective rescheduling is needed to return the system to a

normal state.

RESTORATIVESTATE. In this state, only the load constraints are

violated _, typically because of a need for load shedding and

reconfiguration. Actions are required to restore power to the

effected loads.

The functionality of the intelligent power controller is based

on the need to perform the activities described in these

scenarios. It is clear that a number of perceptual, cognitive,

and responsive capabilities are involved. When performed by

mission control personnel, spacecraft M&C is a cooperative

distributed activity performed by a hierarchical committee of

flight controllers led by the "'front room'' controllers, with

support and backup by power system experts in the "'back

rooms.'' In addition, the power system hardware will be

16-6
geographically distributed on space station, making distributed,

localized on-board automated control a logical approach to

intelligent automation. For these reasons, we propose a software

model of this activity in terms of a collection of processing

agents. This model is presented in the next section.

GENERIC CONTROLMODELUSING DISTRIBUTED PROBLEMSOLVING

AND PARALLELISM

Distributed problem solving involves cooperation by a

decentralized and loosely connected group of processing units

called agents (ref. 5). The problem solving is cooperative in

that communication of data and task sharing are required in

order to come up with a solution. The problem solving is

decentralized in that knowledge, computational resources,

control, and data are distributed.

Many distributed problem-solving architectures have

appeared in recent years. Among these are blackboard systems,

contract net systems, and multi-agent planning systems. These

systems are based upon metaphors of social, economic, or

biological organization. There are many dimensions along which

to categorize these systems. These include:

I. system metaphor (individual, society, committee);

2. grain size (how much processing power is in each of the

smallest units);

3. system size (small, medium, large);

4. agent characteristics (controlled or independent,

restricted or unrestricted in their resources,

interactions simple or complex); and

5. techniques for attaining results (problem solving

methods).

Space limitations forbid a detailed discussion of each approach

and methodology. Rather, we proceed to present a specific

distributed model for intelligent monitoring and control.

For reasons relating to problem domain, implementation,

and space station design constraints, the model proposed here is

based on the idea of a small, loosely-coupled group of semiautonomous agents cooperating to maintain the proper

distribution of electrical power to each of a set of loads. The

actual number of agents will depend on factors such as desired

throughput, the number of independent subtasks of the primary

tasks that can be discerned, and hardware restrictions. These

factors will emerge as the proposed model is refined and

instantiated in further research. For now, the proposed model

recognizes at least three separate communicating agents.

Partitioning work activity should insure an equal

distribution of processing labor; otherwise, the benefits of the

distributed approach is lost. The organizational topology of our

control model consists of a two-level decomposition of tasks

(Figure 2). On the first level, the problem of secondary power

16-7
INTELLIGENT

POWER CONTROL

FAULT MANAGER

*FAULT DETECTION

*FAULT ISOLATION

*FAULT RECOVERY

POWER MANAGER

*MONITORING

*SWITCHING

*RECONFIGURATION

I

LOAD MANAGER

*LOCAL PLANNING

*HEALTH ANALYSIS

FIGURE 2. POWER CONTROL TASK PARTITIONING

16-8
control is divided into three primary tasks: power management,

fault management, and load management. This division is based on

a decomposition of monitoring and control functions in terms of

a three-fold temporal partitioning of the events that make up

the behavior of the power system. Events that occurred in the

past include exceptional or urgent events that require

diagnosis, and are of interest to the fault manager. Events that

are presently occurring include the flow of electrical power and

relay switching. These events need to be monitored and

controlled, and will be the assigned task of the power manager.

Finally, future events are those which need to be planned or

predicted; in particular, loads need to be scheduled and

possible unhealthy conditions predicted. This will be the job of

the load manager.

On the second level, each primary task is subdivided into

a collection of perceptual and cognitive operations. For

example, the fault manager consists of procedures for gathering

and evaluating voltage and current information in order to

perform diagnostics. Each of these subfunctions are described

further in the next section.

As mentioned, in this report, we will view each primary

task as an intelligent agent. Further refinements of this model

may result in assigning to each primary task a network of

agents, depending on whether certain subtasks within a primary

task may be viewed as independent perceptual or cognitive

activities. Furthermore, each primary agent can be viewed as

either operating concurrently on a separate processor, or as

sharing processing resources. In the discussion that follows, we

will assume for simplicity that the agents run continuously on

their own processors. This assumption allows for a consideration

of the potential for parallelism in the monitor and control

process.

Two sorts of parallelism can be distinguished: in one, a

set of necessary and independent tasks are identified and

processed concurrently (AND-parallelism). The other (ORparallelism), is the result of the non-determinism involved in

intelligent problem solving, i.e., in the potential for solving

the same problem in a number of different ways. As will be

shown, both kinds of parallelism are inherent to the problem of

monitoring and control.

As mentioned, in choosing this preliminary model, a number

of constraints have been applied. Some of these involve

integrating distributed conventional control hardware and

software with intelligence. Other constraints involve the nature

of the process of spacecraft monitoring and control itself,

which, when solved by humans, typically involves a small group

working cooperatively on the problem. Finally, the desire to

implement the model using the transputer environment, as

envisioned by the ADEPTS designers, was a consideration in

constructing the model.

16-9
PARTITIONING KNOWLEDGE AND EXPERTISE AMONG LOCAL AGENTS

This section contains a generic description of each of the

three primary agents in terms of four characteristics: the

agent's functional components; responsibilities; resource

requirements (software, hardware, and communication); and

interactions (i.e., which agents talk to which others, and what

do they exchange). In ad_t_on, there is a brief informal

discussion of the potential for AND- and OR-parallelism within

each agent's activity. Finally, we describe briefly how each of

these generic functions could be instantiated on space station.

Local Fault Management

Responsibilities. A fault manager is responsible for

isolating, identifying, and aiding in the recovery from faults

involving power system components. The problem of fault

diagnosis can be stated thusly: given a set of symptoms, find a

set of faults to account for them. The problem of fault

identification is complicated by the fact that different faults

may share symptoms, and also by virtue of the possibility of

multiple faults. The amount of reasoning skills required by

fault diagnostic software is inversely proportional to the

amount of sensor data to be processed. Therefore, by adding an

intelligent fault manager to system software, data processing

can be potentially reduced.

Functional Components. Every intelligent fault manager

consists of the following:

I. A procedure for gathering data

2. A simululator of the behavior of the artifact

3. A diagnostic problem solver, consisting of:

i. A procedure for recognizing discrepencies

ii. A procedure for generating hypotheses

iii. A procedure for testing hypotheses

To perform fault management, data must be constantly

gathered and tested against the expectations generated by the

simulator. When a discrepency is identified, a symptom set is

generated. The model is consulted to generate hypotheses about

the cause of the discrepency. Each hypothesis is tested by

observing further data, or by injecting the hypothesis into the

simulated model of the system, generating simulated symptoms

which are further matched to the actual data. This procedure is

repeated until a unique hypothesis is generated.

Resource Requirements. Input to this agent will consist of

voltage and current data from remote controller modules. The

knowledge sources of this agent include the model of the

components of the subsystem under its control, including the

buses, switchgear, and loads. Its primary local data are current

symptom sets.

16-10
Interactions. This agent can perform its major functions

locally. Diagnostic information, i.e., data identifying faulty

components; the nature of the fault; and recommendations for

recovery procedures; should be communicated to the local power

manager, as well as the load manager. The load manager must use

this information in plan generation. In addition, the human user

of the system should be alerted and recommendations for recovery

enumerated.

Potential for Parallelism. AND-parallelism is inherent in

the relative independence of the subtasks that are performed by

the fault manager. For example, the data analysis and problem

solving phases can be run concurrently. The fault manager

activity also allows for some degree of OR-parallelism, e.g.,

when multiple hypotheses are generated from symptom sets. Each

hypothesis can be tested separately against current data. This

parallelism could be useful if multiple fault detection is

required by the fault manager.

Space Station Instantiation. Distributed diagnosis is

particularly suited to space station design. The point of local

diagnosis is to reduce the flow of status data from the

component controllers to the upper level managers. Furthermore,

the purpose of intelligent automated diagnosis is to reduce the

number of points that need to be sensed in order to make a

diagnosis, thus reducing the data processing requirements.

Power Manager

Responsibilities. The power manager is responsible for

maintaining the proper flow of electrical power to the loads.

This involves controlling the allocation of power resources to

meet the current system load. The cognitive requirements for the

power manager consist in the ability to compute changes in

configuration of the system on the basis of load priority, which

includes the ability to determine which loads must be shed. It

is also envisioned that the power manager will share the local

monitoring tasks with the fault manager.

Functional Components. The activities of the power manager

can be broken down into the following functions:

I. Configuration Processor

2. Resource Controller

3. Health Monitor

The configuration processor computes switch settings for

the remote power controllers based on current load demands and

load priorities. These load demands may either result from

normal execution of a load schedule, or by an exceptional event

such as a component fault or addition of a load. For example, if

power to a critical load has been switched automatically to a

redundant path, and if a part of that path has already been

fully allocated to other loads, then the power manager will

compute which loads to shed along the redundant path.

16-11
The other functions of the power manager may consist of

more conventional algorithmic software. The resource controller

distributes commands to remote controllers (RPCMs) for switch

settings. The health monitor will periodically perform analysis

of the data from the remote power controllers.

Resource Requirements. Like the fault manager, the power

manager will contain component and behavior knowledge of the

power system. In addition, it will require at least two small

databases: one, consisting of current switchgear configuration

information, which will be updated as load requirements change;

the other, a temporal database, which will allow for the

maintenance of health information. Finally, it must contain load

priority data, in order to compute power allocation.

Interactions. The power manager will interact both with the

fault manager, as described above, and the load manager. The

load manager will request information about current power

availability in order to verify or update a load schedule. In

addition, configuration updates will be reported to users.

Potential For Parallelism. The three power manager subtasks

are relatively independent, which suggests a source of ANDparallelism. In particular, the monitoring task can be performed

concurrently with the other tasks. OR-parallelism may be found

in the computation of configurations.

Space Station Instantiation. The power manager will allow

for more load shedding decisions and health monitoring decisions

to be made locally, thus reducing the volume of OMA-directed

activity.

Load Manager.

Responsibilities. Scheduling loads can be a highly complex

problem. Distributing this responsibility by localizing the

processing would lessen the bottleneck associated with

generating load schedules. A promising approach to partitioning

the problem solving knowledge and control is by distinguishing

between the static and dynamic aspects of scheduling. Static

scheduling involves taking a set of loads and constraints and

generating a consistent, usable schedule. Parameters and

constraints which are used to develop a static schedule involve

global knowledge of each EPS function, from generation and

storage to distribution. As such, the design and implementation

of the static scheduling procedure would occur on Tier I or Tier

II level control. Consequently, this problem is beyond the

current scope of the subsystem controller being designed here.

The dynamic scheduling procedure has the function of

modification of proposed schedules on the basis of the addition

or deletion of resource or load constraints. These new

constraints may arise as the result of damage to EPS equipment,

additional load requirements, or other resource constraints such

as crew or instrument availability. This, we propose, is a

function that can be localized and distributed to a Tier III16-12
type controller. This dynamic load scheduler will be called a

(local) load manager.

Functional Components. A load manager will verify and

modify schedules and plans based on current resources. The need

for such a function arises due to expected frequent changes in

resource availability. Schedule verification consists of

ensuring that the power andother resource expectations demanded

by the static schedule can be currently met. For example, the

static schedule may assume that a certain activity A, can be

assigned an interval of time T with power requirements P,

instrument I and crew members C D and E. The contingency handler

will verify that no changes to any of these resource

requirements has occured since the static schedule was

generated. If current resources do not match load requests, the

load manager will attempt to modify the schedule in order to

satisfy the requests.

Resource Requirements. The data and knowledge sources for

this module include a load profile database for each load, a

resource database of currently scheduled resources, and a

database consisting of current schedule information.

Interactions. Rescheduling may involve reconfiguration and

load shedding. Therefore, it will be necessary for the load

manager to request data and share tasks with the power manager.

It will also receive and send schedule information from/to human

planners.

Potential for Parallelism. Scheduling can be viewed as a

search problem. Parallel versions of depth-first search

algorithms are being developed, which are essentially examples

of exploiting OR-parallelism.

Space Station Instantiation. Because of the complexity of

space station planning, it is less clear to what extent a local

planner can be employed on Tier III-level software. It is likely

that automated planning on station would be a Tier I or II

activity. It was suggested that a Tier III load manager could be

an "'intelligent interface'' to human planners, notifying them

that planned loads can or cannot be powered, and perhaps

suggesting contingency short-term schedules.

Summary of Preliminary Model

The model described here is in a preliminary stage of

development. More is required in order to define the knowledge

sources of each agent, the communication and synchronization

mechanism among the primary agents, and the potential for

concurrency among the agents' subfunctions.

A useful metaphor for distributed power control is human

organization. To avoid control bottlenecks among upper level

managers, it is useful to d_stribute intelligent decision making

among lower-level managers. On the other hand, too much local

control could result in the lack of a common direction among the

different local units. The issue of the amount of local

16-13
automated control will need to be addressed in applying the

generic model proposed here to space station.

SUMMARY AND FUTURE DIRECTIONS

This paper has sketched a design for an intelligent

secondary power controller, based on current Space Station

Freedom secondary power distribution hardware specifications, as

well as on current developments in cooperative problem solving

methodology. The model has arisen from conversations with

station designers of EPS hardware, the OMS, data management

system (DMS), and application software. These developers were

helpful in detailing the functionality of the different software

modules, and suggesting avenues for intelligent automation.

A number of improvements and extensions of this work are

needed. First, as mentioned, the model is in need of significant

further development. A prototype of the model should be built

and tested, and ported to the ADEPTS transputer implementation

environment. Currently, an implementation of a prototype of this

model is being developed.

Second, more agents should be added as needed. For example,

a separate data pre-processing agent could monitor sensors and

translate and filter sensed data to the appropriate manager. In

addition, an intelligent user interface could be added for

displaying data to users, such as crew members, with varying

expertise in the problem domain.

Third, more functional autonomy could be added to each

agent. Each agent could possess more sophisticated cognitive and

decision-making capabilities. Our design favored a more modest

approach to agent autonomy, in line with current opinions

regarding SPC MDM processing capabilities. As early prototypes

are tested and validated, more functionality can be added.

Fourth, a more detailed investigation of the potential for

AND- and OR-parallelism should be undertaken. Finally, the

committee of agents defined in this report could be extendned to

include more global (i.e., Tier I or Tier II) control

operations, such as static scheduling and user interfacing. With

few modifications, the generic model could also be instantiated

for primary as well as secondary control operations.

It is believed that the model defined here could be useful

to space station designers in the automation of space station

power 6on£rol. Ins£antiating the generic model for space station

will imply tailoring the roles of each manager to the

requirements of a Tier III or Tier II controller. As the model

proposed here and the space station automation process progress,

the proposed software could "'migrate'' on board in later growth

stages. Finally, the overall model advanced here can be applied

to future automation projects such as for lunar and Mars bases.

16-14
ADEPTS

DDCU

DMS

FDDI

FDIR

ITA

M&C

MDM

OMA

RPCM

RPDA

SDP

SPC

SPDA

ACRONYMS

Advanced Electrical Power Management Techniques for

Space Systems

DC-to-DC Converter Units

Data Management System

Fiberoptic Distributed Data Interconnect

Fault Detection, Isolation and Recovery

Integrated Truss Assembly

Monitoring and Control

Multiplexer/Demultiplexer

Operations Management Applications

Remote Power Controller Module

Remote Power Distribution Assembly

Standard Data Processor

Secondary Power Controller

Secondary Power Distribution Assembly

REFERENCES

I. Hayes-Roth, B.; Washington, R.; Hewett, R.; Hewett, M.; and

Seiver, A.: Intelligent Monitoring and Control, (Proceedings of

the International Conference on Artificial Intelligence, IJCAI89), Morgan Kaufmann Publishers, Inc., 1989, pp. 243-249.

2. Interface Development Document: Remote Power Controller

Module Standard (Preliminary), Space Station Freedom Electrical

Power System, Work Package 04 Document, SSP 44004, March 20,

1990.

3. Systems Engineering and Integration Data: Multiplexer/

Demultiplexer (MDM) DMS PDR#3 Support Data, Work Package 2 (WP2) MDC H4778, March, 1990.

4. Talukdar, S.; Cardozo, E.; Leao, L.: Toast: The Power System

Operator's Assistant, Proceedings of the IEEE, Vol. , No.

July, 1986, pp. 53-59.

5. Gasser, L.; Huhns, M.H.: Distributed Artificial Intelligence

Volume II, Morgan Kaufmann Pub., Inc., 1989.

16-15

D_ -m7

N9 1- 27 l_6f_

FUZZY LOGIC CONTROL SYSTEM TO PROVIDE AUTONOMOUS

COLLISION AVOIDANCE FOR MARS ROVER VEHICLE

Final Report

NASA/ASEE Summer Faculty Fellowship Program--1990

t

Johnson Space Center )-{

Prepared By:

Academic Rank:

University & Department:

Michael G. Murphy, Ph.D.

Professor

University of Houston - Downtown

Department of Applied Math. Sciences

Houston, Texas 77002

NASA/JSC

Di rectorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Information Systems

Information Technology

Software Technology

Robert N. Lea, Ph.D.

August 10, 1990

NGT-44-005-803

17-1
ABSTRACT

NASA is currently involved with planning unmanned missions to Mars to

investigate the terrain and process soil samples in advance of a manned

mission. A key issue involved in unmanned surface exploration on Mars is that

of supporting autonomous maneuvering since radio communication involves

lengthy delays. It is anticipated that specific target locations will be designated

for sample gathering. In maneuvering autonomously from a starting position to

a target position, the rover will need to avoid a variety of obstacles such as

boulders or troughs that may block the shortest path to the target. The physical

integrity of the rover needs to be maintained while minimizing the time and

distance required to attain the target position.

Fuzzy logic lends itself well to building reliable control systems that

function in the presence of uncertainty or ambiguity. Systems already

developed by Robert N. Lea of NASA/Johnson Space Center and others test

out well for shuttle proximity operations and for Mars rover trajectory control in

the absence of obstacles. One of the tools investigated for developing a fuzzy

logic obstacle avoidance control system is the Togai infraLogic TILShell fuzzy

logic expert system shell that provides a software development tool to express

fuzzy logic rules as well as crisp rules and procedural code segments, then

generates standard C code for implementation. This shell has been

successfully used in the proximity operations and rover trajectory systems.

Fuzzy logic control systems have been underutilized in American application

areas but have been very successfully employed in a variety of commercial and

industrial applications in Japan. An additional technology that shows promise

of applicability to a variety of control system environments (such as the one

addressed here) is that of learning and adaptation based on neuronlike

elements. - -

This report addresses three major issues;

1. the nature of fuzzy logic control systems and software tools to

implement them;

2. collision avoidance in the presence of fuzzy parameters; and,

3. techniques for adaptation in fuzzy logic control systems.

In addition to current work on these issues, possible directions for future work

are given.

17-2
INTRODUCTION

The Space Exploration Initiative of the United States includes plans for

unmanned missions by NASA to Mars to investigate the terrain and to process

soil samples in advance of a manned mission. Path planning is a crucial

element in providing for this exploration. More specifically, surface exploration

of Mars involves an environment where uncharted obstacles are likely to

interfere with _ma_neuvering of a rover vehicle and where radio transmissions to

and from Earth involve lengthy delays, hence the need for autonomy of

operation of the vehicle. In moving autonomously from a starting position to a

target position, the rover will need to avoid a variety of obstacles such as

boulders or troughs that may block the shortest path to the target. The problem

is to maintain the physical integrity of the rover while minimizing the time and

distance required to attain the target position.

Although the problem of autonomous path planning including collision

avoidance is not new, the research into the use of fuzzy logic in the decision

and control process is new. The theory of fuzzy sets and fuzzy logic has

matured since 1965 when it was introduced by Professor L. A. Zadeh of the

University of California, Berkeley (1). The book by Klir and Folger (2) gives a

good treatment of the fundamentals of this field. What has been lacking until the

last few years are significant applications of this body of knowledge to practical

endeavors in business and industry. This lack has been partially addressed by

Japan, where a variety of applications involving fuzzy logic have been

developed. One of the most important types of application has been fuzzy logic

control systems (3,4). Togai Infralogic, Inc., of California has recently marketed

a fuzzy logic expert system shell to facilitate the production of software for fuzzy

logic applications(5). Robert N. Lea of NASA/JSC and others have been

working on fuzzy logic applications to space vehicle rendezvous and proximity

operations (6,7,8). Since autonomous path planning with collision avoidance is

structurally a control problem and since fuzzy parameters are likely to be

present in the Mars rover maneuvering environment, a fuzzy logic control

system would be a promising avenue of investigation.

FUZZY LOGIC CONTROL SYSTEMS AND SOFTWARE TOOLS

The purpose of control systems is to achieve and then maintain a desired

state of a process by monitoring the output and state of the process then taking

responsive control actions. An example would be monitoring the temperature

and pressure of a chemical process then raising, maintaining, or lowering the

temperature to achieve or keep an optimal state for the process. Since

temperature and pressure are likely to change during operation, this monitoring

needs to be continued at regular time intervals. It is also desirable that the

control be automated so that operator intervention is minimized (but permitted).

Traditional automatic controllers usually require a sufficiently precise

mathematical model of the process. For many complex control environments, it

is very difficult if not impossible to construct a satisfactory model due to issues

such as nonlinearity, time variance, or poor quality of available measurements.

17-3
In cases such as these, a human operator may be able to effect superior control

over traditional automatic controllers. An expert human operator consciously or

unconsciously uses heuristic control rules or "rules of thumb" based on

experience to control the process. When articulated by the operator, these rules

usually involve linguistic descriptions with vague or ambiguous concepts. An

example could be "if the speed of a conveyor belt is very fast, then the likelihood

of an accident is high" with the terms "very", "fast", and "high" being defined only

approximately. Terms such as "fast" in this context are considered values of the

linguistic variable "speed", where the degree of membership p could be given

by means of a graph, such as the one in Figure 1 below, where the horizontal

axis takes on possible values for speed, and the vertical axis gives the degree

(0 to 1) that a particular speed is a member of the linguistic value above it. Of

course, the membership function could be of any form and not necessarily

piecewise linear. The term "very" would usually be interpreted as the square of

the membership of "fast" in our context.

o o.s .1. 1.5

Figure 1. - Graphical representation of linguistic values of "speed" for conveyor.

A rule-based fuzzy logic controller consists of a finite set of fuzzy control

rules which are processed by applying what is called approximate reasoning. A

typical fuzzy logic control rule is of the form:

ff x is A and y is B, then z is C.

Figure 2 below indicates how the memberships of x in A and y in B yields the

membership level of z in C.

PA

_ A

o

X

_C

0 0

Y

!

p

_an,

Figure 2. - Graphical representation of approximate reasoning for one rule.

17-4
One of the ways used to combine the recommended control actions (in

membership form) for several rules is to determine the center of area for all of

the actions, thus giving a crisp control action as indicated in Figure 3 below.

This is sometimes called conflict resolution or inferencing.

O

Figure 3. - Conflict resolution (inferencing) for more than one control rule.

Figure 4 below gives the architecture for a typical fuzzy logic control

system. Simple systems of this type are usually called fuzzy logic controllers.

k.owl,(l_}e

Io9;c

Figure 4. - Basic architecture for fuzzy logic control.

In addition to straightforward programming from scratch and analog fuzzy

logic chips, there are now a limited number of software tools to facilitate the

development of fuzzy logic expert systems, with a fuzzy logic control system as

an example of such. One of the most readily available and most conveniently

used tools is the fuzzy logic expert system shell called TILShell from Togai

InfraLogic Inc. (5). Technically, TILShell is a graphical editor that creates a

database for Togai InfraLogic Inc.'s Fuzzy-C Compiler, which then generates C

language code with the data structures and inference engine required to

implement the system. This code may then be compiled and linked to other

application code. Additional software/hardware hybrid tools are being

developed by Apt Instruments Inc. and should be available by the early part of

1991.

17-5
Fuzzy logic control systems have been implemented effectively for a

variety of applications in Japan. A video teleconference on July 24, 1990,

linked the close of the International Conference on Fuzzy Logic and Neural

Networks (IIZUKA'90 at the Kyusu Institute of Technology in Japan) with

interested parties at the Johnson Space Center, and it was made clear that this

is very likely the dawn of an era of vigorous and expanded growth in fuzzy logic

applications.

COLLISION AVOIDANCE UNDER CONDITIONS OF UNCERTAINTY

This section represents an original approach to collision avoidance

under conditions of uncertainty. The first case to be considered for an

avoidance system is a fuzzy obstacle that is not moving. The objective is to

have the vehicle maneuver from a source position to a destination position (for

sample gathering, etc.) in the shortest amount of time and avoid collision with

the obstacle. Figure 5 below gives the scenario for this problem.

c(¢_k;,_=t;o,,

& (,xol>_ h`¢1¢ .,,o _A,yd)

• Y.)/

/ / /_/

(y_,y_ _ e ek

5o_C_

Figure 5. - Avoidance of a static fuzzy obstacle.

Table of symbols for diagram:

(xs,Ys) = coordinates of source

(xd,Yd) - coordinates of destination

(xo,Yo) ,=coordinates of obstacle "center"

w = fuzzy "radius"

0 - angle of desired trajectory to destination

01= lower angle for fuzzy avoidance

Oh . higher angle for fuzzy avoidance

d - distance covered in 1 time unit

The symbol "w" is used to denote the fuzzy "radius" of an obstacle; that is,

the radius about the obstacle "center" for which the membership in "is an

obstacle" is above some threshhold (e.g., 0.1). Both the fuzzy "radius" and the

fuzzy "center" for the obstacle are assumed to be available from current sensor

data. This represents a significant relaxation of requirements for object

identification and object extent determination. In general, the objective is to

take fuzzy information regarding a potential obstacle, determine whether it is an

obstacle, take evasive action if necessary, then continue until the destination is

reached. The algorithm for collision avoidance in the case of this static fuzzy

obstacle is:

17-6
.

2.

t

,

Receive (xo,Yo,W) from sensor processing.

Check for obstruction:

if 91 < 6 < Oh

then change (] to 01 if J0 - ell < I e-eh I,

otherwise change (] to Oh

else no change in 6.

Advance vehicle one time unit :

new Xs = old xs + d cos 0

new Ys = old Ys + d sin 0.

Repeat steps 1., 2, 3. until destination is reached.

Once the vehicle is sufficiently close to the destination, it may be necessary to

enter a new control environment for positioning, etc., so step 4. above may

actually cease when some critical minimum distance to the destination is

reached.

The second case to be considered for an avoidance system is an

amorphous fuzzy obstacle system that is not moving. The term "amorphous" in

this setting means that there may be more than one obstacle or perhaps even a

mass of obstacles that have not been separated or may not be separable. Once

again this represents a simplification of the requirements for sensor processing

while providing a satisfactory environment for collision avoidance. The

objective again is to have the vehicle maneuver from a source position to a

destination position (for sample gathering, etc.) in the shortest amount of time

and avoid collision or damage to the vehicle. Figure 6 below gives the scenario

for this problem.

......

I _.=., ._. __c..,,_, , t. I collision envelooe within critical distance = d°

_P.I') __ within critical sector = [e-AG.e+&o] w* = clearance within width criticalforheight safe =passage [£.h]

di = closest distance _ d*) from source to a

/,__= _.-c¢- P.i= membership "significant" of feature in frame i

frame i in "is an obstacle"

_-cg4t-q^¢_, v-e,_¢r.t_c¢, t-c._¢r-e_c_-

p,,:,,'c +t. poi,,e o _,_,_-l:-L

Figure 6. - Avoidance of a static amorphous fuzzy obstacle system.

]7-7
The idea in this avoidance process is to provide a corridor for safe passage of

the vehicle. If 0 is the angle from the source to the destination, then the angular

interval [e-_0,I_-A0] is broken up into an odd number of angular sectors of

width at least 2w*. The 0th sector is centered about e at a distance of d* from

the source on the way to the destination. The first sector to the right of center is

labelled 1, then the second sector to the right is labelled 2, and so forth. The

first sector to the left of center is labelled -1, then the second sector to the left is

labelled -2, and so forth. The sensor data is processed for each sector by

scanning the region from the source to the sector boundary as indicated in

Figure 6. The reference point is moved back from the source position to a

position d* units behind the source along a line extended from the center of the

sector through the center of the vehicle. This ensures that the entire sector path

is evaluated for safe passage. Each sector may be considered a frame of

reference which is scanned with sensors and assigned a distance and

membership in "is an obstacle" which is a generalized form of obstacle

detection. In essence, what happens is that the closest distance to a

"significant" obstacle is determined; significant would mean above a certain

threshhold of membership. The exact form of sensor processing Is not

addressed here. A simplified version of the algorithm for collision avoidance in

the case of a static amorphous obstacle system is:

1. Receive (di,l_i) for each frame from sensor processing.

2. Select path if possible:

if Po < threshhold

then

else

else

3. Repeat steps

proceed in direction 6

pick i with smallest fiI and _i < threshhold

and proceed in direction of center of frame i

expand angular interval by Ae on both

sides and check for safe corridor there

until one is found.

1., 2. until destination is reached.

Once the distance to the destination is sufficiently close (e.g., < d*), it is likely

that a proximity positioning subsystem would take over for the final approach.

One rather natural adjustment to the algorithm would be to scale the

membership value of each frame j with a "significant" obstacle by di/dj where i

is the frame with the closest "significant" obstacle. Another feature to consider

adding is that of speed control (e.g., speed up for safer passage such as the 0th

sector with a low membership value and slow down for riskier passage such as

a higher numbered sector with a membership value near the threshhold). A

simplified form of this obstacle system and algorithm has been implemented by

Larry Wakers of Lincom Corporation using the TILSheli software tool. Further

work is needed to make a verifiable autonomous avoidance system for the case

of a static amorphous fuzzy obstacle system.

17-8
The third case considered is a single fuzzy obstacle with fuzzy

movement. Work to date on this case is limited and represents a preliminary

framework upon which to build in the future. The situation is that an obstacle

with an estimated "center" and fuzzy "width", fuzzy magnitude of speed, and

fuzzy direction may cross the path of the vehicle. The problem is to generate

the range of positions that the obstacle may take and then determine if the

vehicle is likely to be endangered; if so, evasive action must be taken. Figure 7

gives the scenario for the problem. This is a much more complicated problem

than that of a single fuzzy obstacle, and it may be that the amorphous static

approach may provide the basis of an approach for this situation as well.

C_,VD

aC=÷,3 (_.o,y.) /o ¢jesE;,_=+_;o, _

c._--,,_,_,_,_-, / "1' /

E_,Y_)

/"/

fuzzy speed: [m - &m, m + Am]

fuzzy direction: [$ - &_, ¢ + &_]

fuzzy radius: w

time increment: &t

calculate positions a,b,c,d:

(xa,Ya) = (Xo+ (m - &m)&t cos($ + A¢),

Yo + (m - Am)At sin(¢ + AS))

etc.

d* = rain { distance from (xs,Ys) to (xa,Ya),

....distance from (xs,Ys) to (xd,Yd)}

ThetaSet = {angle from (xs,Ys) to (xa,Ya),

....angle from (Xs,Ys) to (xd,Yd)]

eI = rain of ThetaSet

Oh = max of Theta Set

&e = adjustment for fuzzy width (w)

check for 61- &e < 6 < (]h + 49, which

subject to time issues indicates

the presence of an obstacle

Figure 7. - The situation for avoidance of a moving fuzzy obstacle.

17-9
ADAPTATION IN FUZZY LOGIC CONTROL SYSTEMS

The issue of developing adaptive fuzzy logic control systems is important

and promises to permit the building of control systems that operate under

conditions of uncertainty or ambiguity while projecting robustness of operation

as conditions change and the likelihood of graceful degradation of operation

under loss of control functionality. Adaptive in this setting means developing

the system adaptively from training sets of typical data or even permitting "on

the fly" modification of the system to reflect changing environments as they

occur. The book by Widrow and Stearns (9) provides a framework for adaptive

systems in general while the book by Kosko (10) addresses environments

closer to those in this report including fuzzy logic systems and adaptive

techniques associated with neural networks. Adaptive rule-based control

systems that use neuronlike elements in a crisp environment have been

addressed by Barto, Sutton, and Anderson (11) and then extended to systems

with fuzzy logic rules by Lee and Berenji (12, 13, 14, 15).

The architecture for one type of trainable adaptive fuzzy logic control

system is given in Figure 8. It makes use of coupled neurons that learn based

on system performance, thus updating the rule-base for enhanced performance.

The flow of calculations is given in Figure 9. A good reference for this approach

is the paper by Lee (13).

Figure 8. - Architecture for adaptive fuzzy logic control system.

17-10
/

Figure g. - Flow of calculations for adaptive fuzzy logic control system.

CONCLUSION AND FUTURE DIRECTIONS

Based on the literature identified this summer and additional topics

explored from scratch, it is clear that the area of fuzzy logic control is one of

significant opportunity for applications. In particular, it is likely to work well

under conditions of vagueness or ambiguity and in settings where autonomy is

important since fuzzy logic control supports approximate reasoning and model

simplification. The specific area of collision avoidance for a Mars rover is a

natural setting for the application of these techniques. Further work is needed

to explore fuzzy logic collision avoidance in more general situations than those

explored in this report. Examples would be fuzzy obstacles with fuzzy

movement, collision avoidance in three-space (e.g., involving the space shuttle

or other non-surface space vehicles), and developing a working adaptive

mechanism for collision avoidance.

17-ll
.

2.

.

.

.

°

.

.

.

10.

11.

12.

REFERENCES

Zadeh, L.A.: Fuzzy sets. Information and Control, vol. 8, pp. 338-353.

Klir, George J.; and Folger, Tina A.: Fuzzy Sets, Uncertainty, and

Information. Prentice-Hall, 1988.

Lee, Chuen Chien: Fuzzy Logic in Control Systems: Fuzzy Logic

Controller-Part I. IEEE Transactions on Systems, Man, and

Cybernetics, vol. 20, no. 2, March/April, 1990, pp. 404-418.

Lee, Chuen Chien: Fuzzy Logic in Control Systems: Fuzzy Logic

Controller-Part II. IEEE Transactions on Systems, Man, and

Cybernetics, vol. 20, no. 2, 1990, pp. 419-435.

TILShell User's Manual, Release 1.1. Togai InfraLogic Inc., Jan., 1990.

Lea, Robert N.: Automated Space Vehicle Control for Rendezvous

Proximity Operations. Telematics and informatics, vol. 5, no. 3,

1988, pp. 179-185.

Lea, Robert N.: Applications of Fuzzy Sets to Data Monitoring and

Process Control. Proceedings of the ISA'88 International

Conference and Exhibit, October, 1988, pp. 1495-1502.

Lea, Robert N.; Togai, Masaki; Teichrow, Jon; and Jani, Yashvant:

Fuzzy Logic Approach to Combined Translational and

Rotational Control of a Spacecraft in Proximity of the Space

Station. Proceedings of the Third International Fuzzy Systems

Association Congress, August, 1989.

Widrow, Bernard; and Stearns, Samuel D.: Adaptive Signal Processing.

Prentice-Hall, 1985.

Kosko, Bart: Neural Networks and Fuzzy Systems: A Dynamical

Systems Approach to Machine Intelligence. Prentice-Hall, 1990.

Bart,, Andrew G; Sutton, Richard S.; and Anderson, Charles W.:

Neuronlike Adaptive Elements That Can Solve Difficult Learning

Control Problems. IEEE Transactions on Systems, Man, and

Cybernetics, vol. 13, no. 5, 1983, pp. 834-846.

Lee, Chuen-Chien; and Berenji, Hamid R.: An Intelligent Controller

based on Approximate Reasoning and Reinforcement Learning.

Proceedings of the IEEE International Symposium on Intelligent

Control 1989, pp. 200-205.

17-12
13.

14.

15.

Lee, Chuen-Chien: Intelligent Control Based on Fuzzy Logic and Neural

Net Theory. Proceedings of the 1990 Workshop on Fuzzy Logic

and Neural Networks, NASA/J SC.

Lee, Chuen-Chien: A Self-Learning Rule-Based Controller Employing

Approximate Reasoning and Neural Network Concepts.

International Journal of Intelligent Systems, vol. 5, 1990.

Lee, Chuen-Chien: Modeling behavioral substrates of associative

learning and memory: adaptive neural models. IEEE

Transactions on Systems, Man, and Cybernetics, vol. 20, 1990.

17-]3

N9 1-27

EVALUATING THE EFFECT OF ACCURACY RATIOS (1I

ON THE PERCENT OF CALIBRATIONS

WHICH ARE OUT OF TOLERANCE

Final Report

NASA/ASEE Summer Faculty Fellowship Program--1990

Prepared by:

Academic Rank:

Johnson Space Center

Sharon E. Navard, Ph.D.

Assistant Professor

_-_2_

|

=

=

University & Department: Virginia Commonwealth University

Department of Mathematical Sciences

Richmond, Virginia 23284-2014

NASA/JSC

Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Safety, Reliability and Quality Assurance

Quality Assurance and Engineering

Flight Systems Quality Engineering

Otho T. Dickerson

August 10, 1990

NGT 44-005-803

18-1
ABSTRACT

The standard practice in calibration laboratories across the

country, including the Measurement Standards and Calibration

Laboratory (MSCL) at the Johnson Space Center, is to use accuracy

ratios to determine if instruments are in-tolerance rather than

computing the actual uncertainty associated with the instruments. In

the past the accepted practice was to use an accuracy ratio of 10:1, but

then state-of-the-art advanced to the point where the 10:1 ratio could

no longer be maintained, and the ratio was arbitrarily lowered to 4:1.

It is now becoming increasingly difficult to maintain the 4:1 accuracy

ratio, and in some cases 1:1 is the best that can be achieved. However,

the effect of using these small accuracy ratios on the number of

mistakes made in classifying instruments as in- or out-of-tolerance is

completely unknown.

In order to assess the effect of using accuracy ratios in

calibration, a simulation program was written to compute the

proportion of instruments determined to be out-of-tolerance which

were actually in, denoted by a, and the proportion of instruments

determined to be in-tolerance which were actually out, denoted by .13.

This was done for accuracy ratios of 1:1 to 10:1, for one to five

progressive calibrations, under varying standard and instrument

conditions. Selected results are presented and explainedin this

report;__ the full set of results, as well as the simulation program itself,

can be obtained from the author.

18-2
INTRODUCTION

The Measurement Standards and Calibration Laboratory (MSCL)

at the Johnson Space Center is responsible for calibrating all of the

instruments used at JSC and by offsite contractors. Calibration

involves determining if an instrument measures accurately within its

tolerance as specified by the manufacturer and, if it does not, adjusting

or repairing it so that it does. The time interval between calibrations

for each instrument is determined so that, in theory, it will be recalibrated before it drifts far out of tolerance.

Each calibration that is performed must possess a property

known as traceability. This means that the standard which was used

to do the calibration must be traceable back to either an intrinsic

standard or to the national standards maintained by the National

Institute of Standards and Technology (NIST). Instrinsic standards

are those which are based on physical laws of nature or naturally

occurring phenomena, such as the speed of light or the triple point of

water. These standards are created or maintained within the

Reference Standards Laboratory at JSC, and no interaction with the

NIST is necessary for these calibrations. For those quantities for

which no intrinsic standard is known, however, the national standards

maintained by the NIST are the legal basis for a measurement system

in the United States.

While the intrinsic and national standards are by definition the

"true" values, any measurements made on them are, unfortunately,

subject to error. In order for a calibration to be meaningful, the

magnitude of the possible error must be known. There are two

characteristics of the measurements which are of interest: the

accuracy, which determines how close the measurements are, on

average, to the true value, and the precision, which determines how

close repeated measurements made with the same instrument are to

each other. An instrument can be accurate but imprecise, and vice

versa.

There are several levels of accuracy and precision of standards

used in the MSCL. The most accurate are the reference standards,

which are the most precise standards available, and are calibrated

either in the MSCL directly from the intrinsic standards or by the

NIST directly from the national standards. The reference standards

are recalibrated periodically to maintain their level of accuracy, a

process which can be very expensive and time consuming, especially if

it involves physically returning the standards to the NIST. Thus, the

integrity of the reference standards is closely guarded. Since merely

handling the standards can reduce their accuracy, the reference

standards are used only to calibrate the transfer standards, which in

turn are used only to calibrate the working standards. It is the

working standards which are actually used to calibrate customers'

18-3
instruments. They are periodically recalibrated by the transfer

standards, which are in turn recalibrated by the reference standards.

Thus the accuracy of the reference standards is maintained as long as

possible.

The problem with using this progression of standards is that

accuracy is lost at each successive step. When a reference standard is

used to calibrate a transfer standard, the measurements given by the

reference standard are taken to be the actual values; but due to the

inaccuracy and imprecision of the reference standard, these values

will differ from the "true" values, thus making the reference standard

inaccurate by this amount. Also, the imprecision of the transfer

standard itself contributes to the inaccuracy, increasing its magnitude.

When the transfer standard is then used to calibrate the working

standard, its measurements are taken to be "true," and hence this

larger inaccuracy is passed on, and is in turn increased by the

imprecision of the transfer and working standards.

The result of this is that, when calibrating an instrument which

has a certain precision as stated by the manu amfamfacturer,the calibrating

standard must be even more precise. The precision of an instrument

is stated as a tolerance, which is theoretically the largest possible

magnitude of the difference between the measured and "true" values,

assuming the instrument is accurate. The instrument is said to be

calibrated, or in-tolerance, if a very high percentage (usually more

than 99%) of measurements that it gives are within its stated

tolerance of the "true" value. Thus to determine ff an instrument is

calibrated, it is necessary to know the precision and accuracy of the

standard; and since the "true" value is unknown, mistakes will be

made. The aim of this report is to determine how many mistakes are

made under certain circumstances.

DETERMINING THE TOLERANCE OF AN INSTRUMENT

Determining the precision and accuracy of an instrument, and

thus the uncertainty associated with it, can be a very difficult and time

consuming problem. Extensive literature has been devoted to the

subject--see, for example, Abernethy and Benedict (1985); Cameron

(1976); Colclough (1987); Croarkin (1984); Eisenhart (1963); Ku

(1966); Ku and Judish (1986); and Schumacher (1981).

Unfortunately, due to the time involved in computing uncertainties,

the common practice in calibration laboratories across the country is

to employ the use of accuracy ratios rather than actually computing the

uncertainty. A brief description of both methods follows.

18-4
Random and Systematic Errors

There are two types of errors which must be evaluated in order

to compute the uncertainty of an instrument--the random error,

which is a measure of precision, and the systematic error, which is a

measure of the accuracy or offset. The total uncertainty, or tolerance,

of the instrument is then the sum of the random and systematic

errors.

Since the random error is a measure of precision, it can be

estimated by taking repeated measurements with the instrument,

under all of the different operational and environmental conditions

with which the instrument will be used. The random error is then

taken to be some multiple of the standard deviation of the

measurements. When the manufacturer specifies a tolerance for an

instrument, this tolerance is an estimate of the random error of the

instrument; for most of the instruments used in the MSCL, the

tolerance is set at three standard deviations. When there is more than

one source of random error, such as in progressive calibrations or very

complex calibrations, the random errors from the different sources

can be combined in quadrature.

The systematic error is much more difficult to compute because,

unlike the random error, it depends on the unknown "true" value and

cannot be ascertained by repeated measurements. One method of

estimating the systematic error is to use the tolerance of the standard

used to do the calibration. When there is more than one source of

systematic error, there is no general agreement on a method of

combining them. Because they _e not random, they cannot

necessarily be expected to cancel each other out, so the most

conservative method of combining them is a direct sum. However,

some sources (see, for example, Schumacher (1988)) argue that since

systematic errors from different sources are independent of each

other, they can be viewed as random observations from another

process and can thus be treated as standard deviations and combined

in quadrature.

The ideal situation in doing progressive calibrations would be to

compute the random and systematic errors at each step and keep

track of them, combining them at each successive calibration. If this

were done, the MSCL could report to each customer the actual

uncertainty associated with his instrument, and also would be able to

determine what proportion of instruments will be classified

incorrectly as being in- or out-of-tolerance. Unfortunately, due to the

time involved with computing random errors, this is not done;

instead, accuracy ratios are employed to determine whether or not the

instrument reads accurately within the manufacturer's specifications.

18-5
Accuracy Ratios

Calibration by using accuracy ratios is much faster and simpler

than the method described above because no repeated measurements

are taken and the overall uncertainty is not computed. If a test

instrument with tolerance T is to be calibrated using an accuracy ratio

of R: 1, then the standard used to calibrate it must have a tolerance of

T/R. A quantity is then measured using the standard, and the

resulting reading is taken to be the "true value." The same quantity is

then measured using the test instrument, and if the reading is within

T of the assumed "true value," the test instrument is said to be intolerance; otherwise, it is said to be out-of-tolerance. Any time the

reading on the test instrument is more than (0.7}T from the assumed

true value, the test instrument is adjusted to read the same as the

standard.

Since this method of calibration is much faster than the other, it

is the method that is used in the calibration labs of all of the NASA

centers as well as other calibration labs throughout the country. The

JSC Metrology Requirements Manual (1990) specifically advocates the

use of a 4:1 accuracy ratio. The reason for using it is expediency; the

vast majority of the theory of calibration in the literature is about

computing uncertainties. Very little work has been done on

determining how well the use of accuracy ratios actually works. It is

undesirable from a theoretical point of view because it loses all

information about the actual uncertainty of the instrument.

Furthermore, the proportion of mistakes made in determining if an

instrument is in- or out-of-tolerance is unknown.

In the past, no one worried about what was lost by using

accuracy ratios because the accepted practice was to use a 10:1 ratio,

which intuitively seemed to guarantee accurate calibrations. However,

as the technology improved, customers were able to obtain more

precise instruments and it became impossible to maintain the 10:1

ratio. At this point, about twenty years ago, the accuracy ratio was

arbitrarily lowered to 4:1 by tacit agreement among the calibration

community. The effect of lowering the ratio was unknown, but the

consensus seemed to be that it still did a good Job and still no one

worried about it; theoreticians continued to ignore what was being

done in practice and continued to research computing uncertainties.

However, the state-of-the-art has now improved to the point where

the 4:1 ratio can no longer be maintained in many disciplines and

pressure is being felt in the calibration community to lower the

accuracy ratio to 3:1; the U. S. Navy has already done so. Even this

won't solve the problem in some disciplines, however, where the best

accuracy ratio that can be maintained is 1:1. Metrologists are finally

starting to worry.

18-6
Because the calibration community has only recently become

aware that it has a problem, very little work has been done on

determining the effect of using accuracy ratios on the proportion of

instruments which are incorrectly determined to be in- or out-oftolerance. Nothing on the subject has been published in the major

statistical journals, although some papers have recently been

presented on the problem at metrology conferences. See, for

example. Capen (1988): Capell (1989); and Schumacher (1988). Of

these, only Schumacher addresses the specific problem of computing

the proportion of instruments which are incorrectly classified. He

actually computes the proportion of products which are incorrectly

determined to meet or not meet specifications when the measuring

instrument has a tolerance that is a fraction of the specifications, but

this is equivalent to the case of using an accurate standard to calibrate

a customer's instrument.

THE QUESTION

The specific question to be addressed is this: What proportion

of instruments are incorrectly determined to be in- or out-oftolerance, after one to five progressive calibrations, using accuracy

ratios of 1:1 to 10:1? This should be answered for different

proportions of the instruments being calibrated being actually out-oftolerance, and for the standards being both in-tolerance and out-oftolerance.

Notation

The following notation will be used in the remainder of this

report.

a = proportion of instruments determined to be out-of-tolerance

which are actually in tolerance

j3 = proportion of instruments determined to be in-tolerance

which are actually out-of-tolerance

Io = the reference standard

li = the i th instrumen t progressively calibrated after the

reference standard

Ei = the error distribution of li

k -- the number of standard deviations of El defining "intolerance"

o - standard deviation of reference standard error distribution,

EO

18-7
100p = percent that calibrating standard is out-of-tolerance

r: I -- accuracy ratio

Employing this notation, and assuming that the accuracy ratios are

exactly correct, the reference standard 10will have tolerance k¢_,while

Ii will have tolerance rik¢), i = 1, 2..... 5. If Ii is in-tolerance when it

is used as a standard, i -- 0, .... 4, then the standard deviation of the

error distribution El is ria; otherwise it is (1 + p)ria.

Assumptions of the Model

In order to compute a and .6, it is first necessary to define the

specific probability model that is being observed. When working with

measurement errors, a standard assumption to make is that the errors

have a normal distribution with mean zero. It is also assumed that an

r: 1 accuracy ratio is exactly maintained throughout the progression of

calibrations. Therefore,

Ei ~ N(0, [r_]2), i = 0 ..... 5.

If Ii is actually 100p% out-of-tolerance, then

Ei ~ N(0, [(1 + p)ria]2).

Note that if p = 0, then this distribution reduces to that given when

the standard is in-tolerance. Henceforward, the general form of the

distribution of Ei will be used.

It is necessary to know not only the error distribution, but also

the distribution of the measurements themselves. With Ei defined as

above, the distribution of measurements Xi taken from Ii will also have

a normal distribution with some mean tti and the same standard

deviation; that is,

Xi ~ N(_i, [(1 + p)ria]2), i = 0 ..... 5.

Thus far the assumptions have been very straightforward, but at

this point one is faced with a dilemma: what is _i? Ideally, _i Will be

equal to the "true" value at each step, which would mean that the

instruments are all exactly accurate. Unfortunately, this will almost

certainly not be the case; a systematic error will probably be present.

But what is the magnitude of the systematic error? The metrologists

at the MSCL were unable to give any practical insight into this

problem; systematic errors are the ones which are nearly impossible

18-8
to estimate, even if the time and resources to do so are available. It is

thus necessary to use some intuitive reasoning.

The reference standard Io must be treated differently from the

others because it is calibrated using actually known values, that is, the

intrinsic or national standards. For simplicity's sake, denote the "true"

value by _. When Io is being calibrated, it should be made to register

exactly _. However, due to the imprecision of the instrument itself, it

will be set to read a value slightly different from _. Since E0 - N(0, [(1

+ p)a]2), _iO will on the average be equal to _, but will vary from it an

amount determined by the distribution of E0. Thus,

]io - N(_t, [(1 + p)o]2).

The exact value of _L0will depend on the particular observations made

by the instrument at the time of the calibration. In practice,

calibration of the reference standard is done at the NIST, if national

standards are used, or in the Reference Standards Lab of the MSCL, if

intrinsic standards are used. In either case, repeated measurements

are made, and ¢_ is then estimated. At this step, accuracy ratios are

not used.

Determination of _i, i = 1..... 5, requires further reasoning.

The situation is not as simple as that of determining _io because,

instead of actually setting _i based on observations from li, it is now

necessary to know what }li is before any observations are ever taken.

This is because of the method associated with using accuracy ratios--

one observation will be taken from the standard Ii. 1 and the test

instrument Ii. and these observations will then be compared to

determine if li is in-tolerance.

At this point, it is necessary to make some assumptions, which

may and may not be correct. First, assume that Ii is, on the average,

in-tolerance. However, a certain percentage of the instruments being

calibrated are found to be out-of-tolerance; at the MSCL, this

percentage is 10% or more. Thus the possible values of }ll must be

allowed to shift about _ in either direction, out to a distance that is

determined by the percent of instruments coming in which are

actually out of tolerance. For simplicity, it will again be assumed that

_i has a normal distribution with mean _, but now the standard

deviation must be larger than that of E i. In fact, it is assumed that

_i - N(_t, [D(I + p)ri¢_]2),

where D is the drift factor that determines what percent of the

instruments coming in to be calibrated are actually out of tolerance.

18-9
Similar to the case of _o, the exact value of _i will depend on the

particular instrument which is being calibrated.

Defining the measurement distributions as above, the values of a

and JS will vary depending on the values of p and D. However, due to

the nature of the normal distribution, the particular values of _ and a

are unimportant; the probabilities will be the same no matter what

values are used. Therefore it is assumed without loss of generality that

= 0 and a = .01.

THE EFFECT OF USING ACCURACY RATIOS

Now that the specific model has been defined, it is now possible

to compute a and J_. Unfortunately, it is possible to obtain an

analytical solution only in a limited situation--when both the standard

and test instrument are accurate. Even then it is only possible to.

obtain the solution for one calibration, because when this calibration is

performed a systematic error is introduced. Therefore, in order to

evaluate a and .13, it is necessary to perform a simulation.

Simulation Program

The simulation program was written in SAS/IML, the Interactive

Matrix Language of the statistical package SAS. The random number

generator used was the SAS function RANNOR. The parameter values

used were:

k = 2.4, 3;

p = 0, .1, .25, .5, 1, 2, 3; and

D = 15%, 32%, 65%, 92%.

For each combination of these parameters, 10,000 iterations were

performed using accuracy ratios of 1:1 to 10:1, and progressing 5

steps down from the reference standard.

The algorithm for the program is as follows. At each iteration,

for i = 1 to 5, observations xl-i and xl are obtained from li-1 and Ii by

first generating the means, and then the observations themselves.

Once the mean is generated for an instrument when it is the test

intnnnent, the same mean is used at the next step when that same

instrument becomes the standard. Since the "true" value is zero, Ii is

defined to be actually in-tolerance if Ixil < kr%, where r is the

accuracy ratio. Since the perceived true value is xl-I, Ii is perceived to

be in-tolerance if Ixi-1 - xll < krla. There are therefore four possible

outcomes: the instrument is actually in-tolerance, and is also

perceived to be in-tolerance; the instrument is actually out-of18-I0
tolerance and is also perceived to be out-of-tolerance; the instrument

is actually in-tolerance but is perceived to be out-of-tolerance (an a

error); or the instrument is actually out-of-tolerance but is perceived

to be in-tolerance (a j3 error). The outcome for each step is observed,

and the total number occurring of each outcome out of the 10,000

iterations is counted. In keeping with the procedure actually

practiced in the MSCL, if Ixi-i xil > (.7)kri¢_, Ii is adjusted to read

the same as li-I. This procedure is done for accuracy ratios of 1:1 to

10:1.

Results

The output of the program for selected parameter values is

shown in Tables 1 - 4; output using the other parameter values follows

a similar pattern. In general, it can be seen that, as one would expect,

no matter what the parameter values are, a and _6 decrease as the

accuracy ratio is increased. For a particular accuracy ratio, a is

generally larger than .6, with the magnitude of the difference

decreasing as the accuracy ratio is increased. This is desirable

because a is a less severe error than .6; it is also the same pattem that

was observed in Schumacher (1988). When the accuracy ratio is 1:1, a

and .6 both increase as progressive calibrations are made from one

step to the next, but this increase vanishes as the accuracy ratio is

increased. For accuracy ratios of 2:1 and 3:1, there is an increase in a

and .6 from the first step to the second, but they remain constant

afterward; for accuracy ratios of 4:1 and higher, a and .6 remain fairly

constant for all five steps down.

Comparing Tables 1 and 2, it can be seen that as "in-tolerance"

is changed from 3¢_ to 2.4¢_, the percent of errors made increases.

This is to be expected, because errors are more likely to be made

when the observation is near the edge of the tolerance range, and

when the tolerance range is reduced, a larger proportion of

observations will be near the cutoff points. Comparing Tables 1 and 3,

it can be seen that, as one would expect, if the standard is out-oftolerance, more mistakes will be made. The farther out-of-tolerance

the standard is, the more mistakes will be made. Finally, comparing

Tables 1 and 4, one can see that as more instruments are out-oftolerance coming in, fewer mistakes are made. While this may seem

surprising at first glance, it makes sense because the way the model

was defined, fewer observations are near the edge of the tolerance

range. The farther the ins_ents drift out-of-tolerance, the easier it

is to detect, and fewer mistakes will be made.

18-11
TABLE I.- a AND _6WHEN TOLERANCE = 36, STANDARD IS IN-TOLERANCE, AND

15% OF INSTRUMENTS COMING IN ARE OUT-OF-TOLERANCE

Accuracy RaUo

I:I _;I 3:1 4:1 _;I

___ ___ __m_ __L __L____ _m_ _-i- __m___h_

1 .131 .043 .050 .026 .030 .022 .023 .017 .018 .015

2 .210 .052 .063 .033 .037 .023 .026 .016 .020 .014

3 .257 .054 .063 .031 .036 .025 .025 .017 .020 .015

4 .286 .056 .068 .033 .036 .022 .029 .022 .021 .013

5 .322 .058 .065 .034 .036 .022 .026 .016 .020 .017

Accuracy Ratio

6:1 7; I _: 1 9:1 10:1

_tzn _.m__h_ __m__L _n.___A_ __m__A_ ____li_

1 .014 .013 .011 .010 .01 .008 .010 .008 .012 .007

2 .015 .016 .013 .011 .011 .009 .010 .009 .009 .009

3 .016 .012 .013 .011 .012 .009 .008 .010 .009 .007

4 .018 .012 .013 .010 .012 .010 .010 .009 .008 .010

5 .017 .013 .013 .010 .012 .010 .010 .009 .010 .007

TABLE 2.- aAND_6 WHEN TOLERANCE = 2.4¢_, STANDARD IS IN-TOLERANCE. AND

15% OF INSTRUMEN2_ COMING IN ARE OUT-OF-TOLERANCE

Accuracy Ratio

I:I _:i 9;1 4:1 5:1

I .167 .055 .066 .035 .041 .027 .030 .022 .022 .017

2 .246 .058 .085 .039 .050 .031 .033 .021 .023 .018

3 .304 .067 .084 .038 .045 .030 .034 .021 .027 .019

4 .342 .059 .084 .041 .047 .027 .029 .020 .025 .019

5 .365 .055 .084 .038 .048 .029 .033 .022 .024 .016

Accuracy Ratio

6:1 7:1 _:1 Q:I _ 1Q;1

_.ca__h__ _n___It_ __L__h_ _.n___h_ _____.L

1 .020 .016 .016 .012 .017 .01 .012 .012 .011 .009

2 .019 .016 .016 .011 .016 .013 .011 .011 .012 .009

3 .020 .015 ,019 .015 .015 .011 .014 .012 .011 .011

4 .019 .014 .017 .012 .014 .016 .015 .012 .011 .01

5 .017 .016 .013 .014 .016 .011 .014 .012 .011 .010

18-12
TABLE 3.- a AND -6 WHEN TOLERANCE = 3o, STANDARD IS 50% OUT-OFTOLERANCE, AND 15% OF INSTRUMENTS COMING IN ARE OUT-OFTOLERANCE

Accuracy Ratio

1;1 2;1 3;1 4;1 _;1

st_an .__._.£._ __n___L __n_._h_ __.m....IL ____...._

1 .171 .048 .074 .036 .040 .023 .028 .019 .024 .016

2 .262 .052 .081 .035 .047 .025 .033 .022 .028 .016

3 .319 .055 .083 .037 .045 .030 .034 .019 .026 .019

4 .364 .052 .087 .036 .047 .028 .036 .018 .025 .020

5 .394 .054 .085 .035 .044 .028 .034 .024 .026 .018

Accuracy Ratio

_:I 7;1 8:1 9;1 I0:I

__a_ __h_ ____ _IL _m_ ___ _m_ ___ _m_ _IL

1 .018 .013 .014 .014 .013 .011 .011 .009 .013 .008

2 .022 .017 .016 .013 .013 .012 .014 .011 .014 .008

3 .020 .014 .017 .013 .016 .012 .012 .012 .011 .008

4 .022 .015 .019 .011 .016 .012 .013 .011 .011 .009

5 .020 .016 .016 .014 .016 .012 .011 .01 .011 .011

TABLE 4.- a AND .6 WHEN TOLERANCE = 30, STANDARD IS IN-TOLERANCE. AND

32% OF INSTRUMENTS COMING IN ARE OUT-OF-TOLERANCE

Accuracy Ratio

I:I 2:1 3:1 4:1 5:1

1 .110 .067 .047 .039 .037 .028 .023 .022 .019 .016

2 .180 .084 .067 .052 .039 .031 .029 .024 .022 .020

3 .212 .099 .066 .050 .038 .031 .030 .022 .021 .017

4 .247 .099 .068 .050 .037 .033 .029 .025 .021 .017

5 .273 .102 .060 .046 .038 .036 .029 .024 .023 .021

Accuracy Ratio

6:1 7; ] _; 1 9: I I0:1

_____£_ _____IL _-L__L __m__IL __m______

1 .015 .014 .014 .013 .013 .013 .011 .010 .010 .010

2 .019 .018 .017 .013 .012 .011 .012 .013 .011 .011

3 .016 .018 .016 .013 .015 .013 .010 .013 .011 .010

4 .019 .016 .017 .016 .012 .011 .010 .010 .011 .011

5 .018 .017 .016 .016 .015 .014 .011 .011 .010 .011

18-13
CONCLUSIONS

When using the results of this simulation to determine the effect

of accuracy ratios on the percent of instruments which are incorrectly

determined to be in- or out-of-tolerance, it is necessary to remember

that the results obtained apply only to the specific model that was

used. While most of the assumptions seem fairly reasonable, the

method of determining the means of the instrument measurement

distributions may not closely approximate what is actually happening

in practice. Furthermore, the definition of "in-tolerance" when using

accuracy ratios is not the same as the standard definition. When using

accuracy ratios, the instrument is said to be in-tolerance if the one

observation taken is within a certain tolerance of the "true" value.

However, the one observation taken may be an extreme value from the

measurement distribution, and in fact most observations taken from

that instrument would be outside of the tolerance. This possibility was

not considered in this program.

With this in mind, it is now possible to answer the questions

that were asked. First of all, the effect of lowering the accuracy ratio

from 4:1 to 3:1 is, in almost all cases, an increase of approximately

0.01 in both ¢xand iS. While this may sound like a small price to pay, if

one considers the percent increase in the number of errors made, it

ranges from approximately 25% to 50°/6, depending on the parameter

values--a substantial increase. Secondly, using a 1:1 accuracy ratio is

substantially worse, and the magnitude of the errors increases as the

progressive calibrations are made. The size of the errors made using

the 1:1 ratio is almost certainly unacceptable in any situation.

Nevertheless, situations do exist where accuracy ratios of 1:1 to

3:1 are the best that can be maintained. What can be done in these

situations? One suggestion is to abandon accuracy ratios altogether

and keep track of the systematic and random errors at each successive

calibration. The metrologists will immediately counter this suggestion

with the fact that they have neither the time nor the personnel to do

this for every instrument they calibrate.

Perhaps a compromise can be reached. Since the transfer and

working standards are not calibrated very often, it may be practical to

take the time to make repeated observations on them to obtain an

accurate estimate of their uncertainties. This might lead to a smaller

tolerance for the working standards than that obtained by using the

accuracy ratios, which would in turn make larger accuracy ratios

possible for calibrating customers' instruments. If the tolerance gets

bigger instead of smaller, this would demonstrate that the problem

with calibration is even more serious than previously believed. In

either case, the MSCL would have a better idea of how accurate its

calibrations are than is now known.

18-14
REFERENCES

Abemethy, R. B.; and Benedict, R. P.: Measurement Uncertainty: A

Standard Methodology. ISA Transactions, vol. 24, no.l, 1985, pp.

75-79.

Cameron, Joseph M.: Measurement Assurance. Journal of Quality

Technology, vol. 8, no. 1, Jan. 1986, pp. 53-55.

Capell, Frank: From 4:1 to SPC. Paper presented at the National

Conference of Standards Laboratories 1988 Workshop and

Symposium (Washington, D. C.), Aug. 14 - 18, 1988.

Capell, Frank: More on 'From 4:1 to SPC.' National Conference of

Standards Laboratories 1989 Workshop and Symposium Technical

Presentations, July 9-13, 1989, pp. 8-1 - 8-15.

Colclough, A. R.: Two Theories of Experimental Error. Journal of

Research of the National Bureau of Standards, vol. 72, no. 3, MayJune 1987, pp. 167-185.

Croarkin, Carroll: Measurement Assurance Programs Part II:

Development and Implementation. National Bureau of Standards

Special Publication 676-II, Apr. 1984.

Eisenhart, Churchill: Realistic Evaluation of the Precision and

Accuracy of Instrument Calibration Systems. Journal of Research of

the National Bureau of Standards - C. Engineering and

Instrumentation, vol. 67C, no. 2, April-June 1963, pp. 161-187.

JSC Metrology Requirements Manual. JSC M 8070C, April 1990.

Ku, Harry H.: Notes on the Use of Propagation of Error Formulas.

Journal of Research of the National Bureau of Standards - C.

Engineering and Instrumentation, vol. 70C, no. 4, Oct.-Dec. 1966,

pp. 263-273.

Ku, Harry H.; and Judish, Robert M.: Fundamentals of Error Analysis.

Proceedings of the IEEE, vol. 74, no. 1, Jan. 1986, pp. 25-27.

Schumacher, Rolf B. F.: Systematic Measurement Errors. Journal of

Quality Technology, vol. 13, 1981, pp. 10-24.

Schumacher, Rolf B. F.: MIL-STD 45662: Conflicting Positions on Outof-Tolerance Measurements. Proceedings of the 1988

Measurement Science Conference, Jan. 1988, pp.99-105.

18-15

Developing a Taxonomy for Mission

N9 1 - 27 10_.

Architecture Definition

Final Report

NASA/ASEE Summer Faculty Fellowship Program-- 1990

Johnson Space Center _ _- 6_

Prepared By:

Academic Rank:

University & Department:

Deborah J. Neubek

Visiting Assistant Professor

University of Houston

College of Architecture

Houston, TX 77204-4431

NASA/JSC

Directorate:

Division:

JSC Colleague:

Date Submitted:

Contract Number:

Lunar & Mars Exploration

Program Office

System Engineering & Integration

Norman H. Chaffee

9-17-90

NGT-44-005-803

I9-1
Abstract

The Lunar & Mars Exploration Program Office (LMEPO) was tasked to

define candidate architectures for the Space Exploration Initiative to

submit to NASA senior management and an externally consitituted

Outreach Synthesis Group. A systematic, structured process for

developing, characterizing and describing the alternate mission

architectures, and applying this process to future studies was

developed. The work was done in two phases: first, National Needs

were identified and categorized into objectives achievable by the

Space Exploration Initiative. Secondly, a Program Development

Process was created which both hierarchically and iteratively

describes the program planning process.

--- Introduction

The Space Exploration Initiative (SEI) is the program being

developed in response to President George Bush's proposal to take

the U.S. back to the Moon then on to Mars. Whereas in 1961 it took a

crisis (the space race) to galvanize the nation to reach for the stars,

SEI is based on the President's belief that space holds an opportunity

for greatness.

After the initial speech given by the President on July 20, 1989,

NASA undertook a 90-Day Study which resulted in a potential

approach to achieving these goals. The National Space Council then

decided to broaden the input to this activity and requested that an

independent committee synthesize data collected from an

unrestricted range of public and private institutions and individuals

(Outreach). This committee, termed the Synthesis Group and chaired

by General Thomas Stafford, is due to produce a report in March,

1991 which suggests "at least two" alternatives for embarking on the

Space Exploration Initiative.

Determining Program Objectives from National Needs

To fully reap the benefits of SEI, it is important to identify the

National Needs which can be met by the program and a process by

19-2
which links may be forged. Linking National Needs to program

planning results in two separate but related products:

Program drivers (key goals and objectives to be

achieved) which are INPUTS to the program planning

process.

Program benefits which are OUTPUTS from doing the

program and are what the American public wants to

receive from their investment.

It was in this context that a team of Level I and Level II people

performed an analysis of national needs and policies which resulted

in the chart seen in figure 1. This chart lists National Needs in terms

of achievable goals and objectives which might guide program

planning. The group identified seven primary areas of National

Needs which may be addressed by the SEI. These are to: Invigorate

National Spirit and Pride; Expand Human Knowlege; Stimulate

Education; Strengthen the Economy; Improve the Quality of Life;

Improve International Relations; and Strengthen National Security.

Under each of these areas, specific objectives were identified which

might be set by a policymaker as an area of emphasis. For example,

if "Enhance and improve potential career opportunities" were

designated as a high priority objective, the SEI architecture

developed might show additional co_p programs included in the

management plan.

It is important to note that the SEI program will meet all the

objectives listed to some extent. However, the set of objectives

selected as program drivers will influence the final implementation

of the program. An alternate set of objectives would yield a program

which is different in its make-up. Therefore, different architectures

may be created which all meet the basic National Needs, however,

architecture "A" may have enhanced commercialization programs

whereas architecture "B" might place more of an emphasis on

scientific research.

19-3
I

Invigorate

National

Spirit &

Pride

_u_ycf

N_on_

purpose

Expand

Human

Knowledge

-IIoeggr_5

knowledge

Ethg_ Me

knowledge

I

Stimulate

Education

Impm_ &

system

Goals &

Objectives

Met by SEI

I

Strengthen

the Economy

!

Improve

Quality of

Life

Providespace

Oemenew md resources for

tmmj_ E_use

l_"vck_ptgw ln'qprovefood

ugh_ologm mdha_h age

ization

!

Strengthen

National

Security

Improve

International

Relations

md_

blue

Share _fas

ard _)sts with

parmer$

Figure 1" National Needs Stated as Policy Objectives

19-4
Developing a Process to Link National Needs to Implementations

Once the National Needs were identified, it was necessary to develop

a process by which they could be physically linked to specific

program goals and features. In the most basic of terms, three

questions must be answered to develop a program: Why go? What

will we do? How will we do it? The chart in figure 2 illustrates a

top-down, hierarchical approach for linking National Needs to

program implementation. This chart serves as a logical outline which

shows the different levels of definition needed to complete the

description of a program architecture.

Developing a program as complex as the SEI is not strictly

hierarchical, however. It is an iterative process with many interrelated steps. Figure 3 illustrates the primary steps and their

relationships.

The first step in developing a program is to define the program

purpose by determining the primary drivers: Why are we going?

What do we want to accomplish?

These questions may be answered through:

• understanding the overarching National Needs

• establishing Visions which will guide SEI planning and respond

to National Needs

• setting Program Goals which define what must be achieved to

fulfill the Visions and

• creating a Program Mission Statement which captures the

essence of the SEI Program Goals.

Each of these areas may be defined as follows:

National Needs are the same as those discussed previously and

shown in figure 1.

Six Visions have been enumerated by the Synthesis Group to guide

SEI planning. These are:

• Enhance knowledge of the planets including Earth

• Rejuvenate interest in science and technology

19-5
National Needs

Visions for SEI

Why go?

SEI Program Goals

What will

we do?

Objectives I -"-"

Functional System

Definitions

How will

we do it?

Implementations

LMEPO/Ne_bek/Ver. 2.0 7/25/90

Figure 2: Linking National Needs to Implementations

19-6
ProgramDevelopment Process

i

Program

"Mission Statement"

°° k.. J o

Figure 3: An Iterative Program Development Process

19-7
• Refocus the role of U.S. world leadership

• Develop technologies with terrestrial applications

• Facilitate further space exploration and privatization

• Boost the U.S. economy

SE! Program Goals are specific, space-oriented statements which

guide and direct the program so the visions are fulfilled. They serve

as the "missing link" between "fuzzy" national needs and "hard"

technical or programmatic requirements.

A "universal set" of goals, including some which may be incompatible

with others may be created. A subset of these goals which are

internally consistent may then be selected to create an architectural

theme. For clarity it is useful to break these goals into two categories:

• spacefadng ("of or engaged in life in Space"-- Webster's)

• administrative ("of or related to policies, procedures or

management issues")

The $EI Program Mission Statement captures the essence of the

primary drivers for a given program theme. It is made up of a

selected subset of SEI Program Goals.

Once the program purpose is established, a program framework

must be laid out to provide the foundation for requirements

definition. This consists of iteratively identifying program objectives,

strategies, constraints and key decisions, program Objectives are

those measurable program features which serve to frame what

should be done, specifically, to fulfill the program goals.

r_.tLaLC.gj_ are the internal, long range approaches which cut across

systems. They are methods or plans of action for accomplishing the

objectives. Program Constraints are those external limitations,

guidelines or policies which are imposed upon the program. These

may be technical, budgetary or programmatic in nature.

are those decision points identified by examining each of

the previously selected elements of the Program Purpose and

Program Framework and incorporating temporal considerations. A

key point in creating the Program Framework is to assure that these

decisions are top-level in nature, and deal with programmatic issues.

19-8
The next step is to begin requirements definition. Systems must

be defined in terms of their functional parameters while technology

readiness, strategies and constraints must be identified. The

Functional Definitign_ are descriptions which are expressed in terms

of capabilities and are grouped into like areas. For example,

planetary surface operations is a functional area. Early program

decisions may be made through the examination of Technology

Readiness. Strategies and Constraints. Once the critical technologies

are identified, they can be plotted on a temporal scale and

interrelationships drawn. This leads to the creation of a tool which

aids in determining which programs have the greatest impact on

others, as well as which programs should be started early on in the

overall SEI schedule.

Finally implementation trade spaces may be selected and studied,

leading to decisions for future technology and advanced development

programs.

Conclusions

The Program Development Process is a tool by which the SEI

program may be planned hierarchically, beginning with National

Needs and working down to implementations. A key factor in using

it successfully is to identify the appropriate levels of detail and

depth which should be addressed at each step. In general, the

program should be planned from the top down, defining what should

be done, then functionally how it should be done. Only after this has

been accomplished can the implementation studies be optimized.

This process is iterative, and not only entails interrelating

components at each step, but also assumes the entire process will be

repeated as new information is returned from studies. By

developing a program for the SEI that is guided by National Needs,

SEI can be optimized for returning the greatest benefits possible to

the nation.

19-9

N 9 1 - 2 7 _£()9A !

DESIGN OF A LUNAR OXYGEN PRODUCTION PLANT _ / I

Prepared By:

Academic Rank:

University

& Department:

Final Report

NASA/ASEE Summer Faculty Fellowship Program - 1990

JOHNSON SPACE CENTER

Ramalingam Radhakrishnan, Ph.D., P.E.

Assistant Professor _ 6

Prairie View A&M University

Department of Civil Engineering

College of Engineering and Architecture

Prairie View, Texas 77446

NASA/JSC

Division:

Branch:

JSC Colleagues:

Date Submitted:

Contract Number:

Solar System Exploration Division

Space Resources Utilization Office

David S. McKay, Ph.D.

Thomas A. Sullivan, Ph.D.

August 17, 1990

NGT=44-005-803

20-1
ABSTRACT

To achieve permanent human presence and activity on the Moon, oxygen is

required for both life support and propulsion. Lunar oxygen production using resources

existing on the Moon will reduce or eliminate the need to transport liquid oxygen from

earth. In addition, the co-products of oxygen production will provide metals, structural

ceramics and other volatile compounds. This will enable development of even greater

self-sufficiency as the lunar outpost evolves.

Ilmenite is the most abundant metal-oxide mineral in the lunar regolith. A

process involving the reaction of ilmenite with hydrogen at 1000*C to produce water,

followed by the electrolysis of this water to provide oxygen and recycle the hydrogen,

has been explored.

The objective of this 1990 Summer Faculty Project was to design a lunar

oxygen-production plant to provide 5 metric tons (mt) of liquid oxygen per year from

lunar soil. The results of this study describe the size and mass of the equipment, the

power needs, feedstock quantity and the engineering details of the plant.

20-2
INTRODUCTION

The primary objective of this project was to design a plant to produce oxygen on

the Moon from lunar materials. The Lunar Oxygen Production Plant is required to

produce 5 metric tons (mr) of oxygen per year. The plant process selected for this study

is the hydrogen reduction of ilmenite with soil feedstock. The plant consists of

equipment for mining, processing, oxygen storage, and hydrogen storage. After

construction or emplacement on the Moon, the plant will be teleoperated from Earth.

The plant is assumed to have a duty cycle of 90% and a continuous power supply.

A simplified schematic of the hydrogen reduction of ilmenite process is shown in

Figure 1. In this process, the bulk raw soil is mined and fed into the plant. The plant

separates the very large and fine fractions of the soil so that an acceptable size of the

soil can be processed. A system of storage hoppers then feeds the soil containing

ilmenite into the top of a three=stage fluidized bed reactor. The reactor heats the soil to

1000"C as the soil flows down the reactor's three stages. Hydrogen gas flows up through

the solids, mixes with the particles, and reacts with the ilmenite to form water vapor.

An electrolysis cell separates the water vapor into hydrogen and oxygen. The oxygen is

liquefied and stored. The hydrogen is fed back into the top section of the reactor. A

solids settler removes the reactor residuals (spent solids) from the bottom of the reactor.

This report describes the different plant components. The results of the design

are presented. The summary included shows the mass, power, and volume of the plant

components.

Assumptions

1. The required production capacity of the plant is 5 mt liquid Oxygen (LOX) per year

using bulk lunar soil on the surface.

2. The concentration of ilmenite in lunar regolith can be as high as 10 volume percent

(15 weight percent). However, a more likely value of 5 volume percent (7.5 weight

percent) will be used in this design. An alternate design based on 10 volume percent of

ilmenite (15% wt) concentration in regolith would be similar but the plant would be

slightly smaller in size than the current design. Another advantage would be that the

plant could produce more oxygen than the required 5 mt capacity if the feedstock

contains higher percentage of ilmenite.

3. No beneficiation. This means there will be no enrichment steps included in the plant

to increase the concentration of ilmenite in the soil.

4. Mining will be done only during the lunar day. At night, metals in the mining

equipment could become brittle unless heated. The hot noon time also will be avoided.

This requires the storage of sufficient mined regolith for operation during the night.

5. The plant operation is continuous with a 90 % duty cycle, allowing 10% of the time

for trouble=shooting, maintenance and unexpected problems.

6. The process yield (chemical reaction and ilmenite conversion) factor is 0.9.

7. Lunar surface temperature varies from =I00°C to +Ill°C. Assumed feedstock

temperature is =200C.

20-3
Hydrogen Reduction

Of Ilmenite

Reduction

FeTiO3+ H_ L-_ cFe + TiO_ +H_O

Electrolysis

H_O + Elec. ----> H2+ 1/20_

SOIL 100% _[ SIZE FEED

tLMENtTE

56.2%

TAILINGS _"

j43.5%

REACTOR

Moke-Up H2

gOY- Chemicol Y eld

H,

ELECTROLYSIS

-H_O_, I

02

0.3%

of

feed,

by

weight

Figure 1

Simplified Schematic of

Hydrogen Reduction of Ilmenite Process

20-4
LOX PRODUCTION PLANT

The oxygen production process is illustrated in Figure 2. Approximately 1620

metric tons of bulk soil feedstock is required per year. After removing the oversize and

undersize rejects, a useful feed of 710 mt is available. As shown in Figure l,

considering a 7.5% wt of ilmenite concentration in the feed and a 90% yield of oxygen

during the hydrogen reduction process, LOX production of 5 mt per year is obtained.

The important components of the LOX production plant are described in the following

sections.

Mining

The mining equipment assumed is a front end loader and hauler. The front

end loader (FEL) is a telerobotic vehicle for mining feedstock. The FEL can complete

soil mining and will have sufficient time remaining to perform additional base

operations.

One hauler is assumed. After being filled by the FEL, the hauler will travel to

the process feed bin, dump the load, collect a load of tailings from the discharge bin,

dump the tailings at the discharge area, and return to the mining site to start the new

cycle over again.

The mining operation is on a 35% duty cycle, equivalent to 3066 hrs. The

operation will be discontinued during lunar noon and night. The mining equipment has

excess capacity. Since there will be a need for the installation of additional units to

increase the oxygen production in the future, the excess capacity of the FEL and hauler

is justified.

Bulk Storage Feed Bin

Since the plant is run on a 90% duty cycle and the mining equipment is operated

on a 35% duty cycle, the feed bin is sized to hold enough soil to run the plant.du(ing

the lunar night (14 days). Using high-strength polyethylene fiber Spectra 1000 tTM) i,n

place of A1 2024 is recommended. The shell with 2 mm thick fiber Spectra 1000 tTM),

and 1-cm-thick low-density insulation to provide protection from radiation and

temperature effects reduces the mass of the bin considerably. Furthermore, this fabric

might be compacted into a much smaller volume for transport to the Moon.

Coarse Screen

The coarse Screen removes the oversize size unwanted rocks (material >0.5 mm)

in the soil to discard bin (but see recommendations for an alternative).

Fine Vibrating Screen

The minimum allowable feed size to the reactor is 0.45 ram.

was selected to remove fines.

A vibratory screen

Feed Hoppers (2)

Two hoppers will be used to minimize gas losses from the reactor. The capacity

of each hopper is adequate for 3 days of feedstock. Feedstock is fed into the reactor

continuously from the bottom hopper which is maintained at 10 atm. pressure. When

inventory in the bottom hopper is at an appropriate low point, the top hopper, which is

20-5
Cyctone

Separator

-20 °

Hea_ E_changer/

Clone

Heater

Gas /"

Recovery

COmpressor

B/Ower

1 arm. H2Mokeu p

"_ Compres

10 otto.

LOX Storage

-253 °

Tonk

Figure_ 2

HydrogenRed-iJeLion of I]me-nife

LOX Produefion Plant

20-6
operating normally in vacuum, is pressurized to 10 atm. Then a valve between the two

hoppers is opened to rapidly re-inventory the bottom hopper. After the valve between

the hoppers is closed, hydrogen gas in the top hopper is recovered. The design allows

continuous feed to the reactor and minimizes gas losses from the reactor.

Reactor

Ilmenite reduction takes place in a three-stage fluidized bed reactor (shown in

greater detail in Figure 3) at 1000*C and 10 atm. pressure. The reactor in the LOX pilot

plant (Ref. 1) was designed to use beneficiated feed rate of 71.29 kg/hr to produce 24

mt of oxygen per year. The reactor required in the current design will use

unbeneficiated feed at a rate of 90 kg/hr to produce 5 mt of LOX per year. Since the

feed quantity is nearly the same, the overall reactor dimensions of the pilot plant were

maintained in the current design. However, several specific details have been modified

as required to conform to the needs of the current design goals. The interior diameter

of the reactor is 0.31 m at middle bed section. In consideration of the temperature

variation existing in the middle (1000*C), bottom (809"C), and top (744"C) beds, the

diameter of the reactor is designed to be 0.279 m and 0.267 m at the bottom and top

beds, respectively, to maintain the gas velocity at 1 ft/sec in all the three beds. Reactor

input material is restricted to sizes greater than 0.045 mm to avoid excessive carryover

of fines to avoid clogging up of the system and less than 0.5 mm to allow fluidization to

occur. Thus, the residence time of solids is 50 minutes in the top bed, 1.4 hours in the

middle bed, and 50 minutes in the bottom bed. The total residence time of the solids in

the reactor is 3.08 hrs. The central 0.31 m core of the reactor was surrounded by 7.5 cm

of high-density (S.G. 2.24) super-duty firebrick to withstand the erosional nature of the

high temperature gas/particles in the fluidized beds. Surrounding this is 23 cm of lowdensity (S.G. 0.14) insulation. The height of each bed in the reactor has been

determined so that there will be enough space above the volume occupied by the solids

for disengaging entrained solids. The outside diameter and height of the reactor are 0.9

m and 6.6 m respectively.

Electric Heater

The electric heater heats the gas stream entering the reactor middle bed.

Electrolysis Cell

The solid-state electrolysis cell, operating at 1000*C, separates the hydrogen and

water vapor coming out of the reactor into hydrogen and oxygen. The hot hydrogen is

recycled to the top bed of the reactor.

Recycle Blower

The recycle blower circulates the hydrogen gas.

reactor at the bottom bed plenum chamber.

Gas from the blower enters the

Cyclone Separators

The cyclone separators separate the solids from the gases. The cyclone separators

contain no moving parts. Dust in the exit gas coming out of each of the reactor beds is

removed to prevent the clogging of the system. This dust is returned to the reactor

below the surface of each bed.

Solids Settlers

Two solids settlers have been proposed to allow continuous flow of "spent" solids

from the bottom bed. Each hopper is sized to allow 2 days residence time for the solids

20-7
Cyclone

Rector i+0. ot middle bed = 0.31 m

Figure 3

Reactor Details

20-8
to settle and separate trapped gases. A gas loop recovers the hydrogen gas and recycles

it through the H 2 make-up system. Solids will be allowed into one hopper while the

other would be reduced to 0.1 atm. or less to recover gases. This arrangement may also

facilitate the addition of a heat recovery system in the future. By putting compressors

in series, as shown in Figure 2, the power of each is minimized and redundancy is

provided.

Radiator/Thermal Control System (TCS) (not shown)

The radiator/thermal control system rejects the waste heat from oxygen

liquefaction equipment. The TCS uses heat exchangers and an appropriate cooling

medium. The radiator is positioned to keep the radiator permanently shaded from the

sun.

Oxygen Liquefaction and LOX Storage

The oxygen produced by the electrolysis cell is liquefied by a stirling cycle

refrigerator. The liquid oxygen is stored in two buried tanks. The re-use of tanks from

an abandoned lunar excursion vehicle (LEV) is recommended. This will reduce mass

and is a good substitute for new LOX storage tanks brought from Earth.

Hydrogen Make-up System

The hydrogen make-up system includes a tank, a heater, and a compressor. The

tank contains enough hydrogen for six months of process leaks and losses. Resupply will

be necessary at that time.

Discard Bin

This bin holds 15 days quantity of spent solids for disposal.

Support Structure (not shown)

The process support structure is mounted in

gravity flow of the solids in the reactor.

the vertical direction to allow

RESULTS

A list of mass, power and dimensions for the equipment required in a 5 mt per

year LOX production plant is given in Table 1. The process plant mass, not including

mining equipment (front-end loader and hauler) is 5600 kg. The electric power required

is 49.0 kW. Plant equipment was sized using the equations, methods and scaling

indicators described in Ref. 1.

SUMMARY AND CONCLUSIONS

1. While there are several ways to produce oxygen from lunar materials, the

process of reducing ilmenite with hydrogen was considered in this design.

2. Using bulk lunar soil as feedstock without beneficiation components reduces

total equipment mass significantly. The mass of the beneficiation components for the 24

mt pilot plant was 3879 kg (Ref. 1). The current design, without beneficiation

components, does not require a large-size reactor and the process plant is a viable

option. This trade needs to be evaluated for larger production rates.

3. Delivery of small, modular oxygen-production units is easy compared to delivery of a

large production plant. Several of the modular units can be deployed and

20-9
TABLE 1.

LUNAR OXYGENPRODUCTIONPLANT EQUIPMENT

5 mt/yr, H 2 Reduction of Ilmenite

Ilmenite in regolith --- 7.5_ wt. No benefieiation.

Equipment

Mining

Front-end Loader (1)

Hauler (I)

Minin 8 Total

Efed Prevaratlon

Feed Bin

Coarse Screen

Fine Screen

Process

Pressure Hopper 1

Pressure Hopper 2

Reactor

Electric Heater

Electrolysis Cell

Recycle Blower

Cyclone Separators (3)

Solids Settlers (2)

Discard Bin

Oxygen Liquefier

Oxygen Storage Tanks (2)

Radiatpr/TC$

Hy_drozen System

Liquid Hydrogen Tank

H 2 Heater

H 2 Blgwer

Process Pivin2

In reactor, 4 cm pipe

Others, 0,25 (;m pipe

Process Total

Total Total Dimensions

Mass Power W/D L H

(kg) (kW) (m) (m) (m)

Rate 1620 mt/yr

2000 0.5

)ooo o,2

3000 0.6

Rate 206 kg/hr

V

(ms)

Mar2in 30%

iincludes support structure)

PLANT TOTALS

2.1 3.3 2.3 16

2.5 4,0 2,_ 25

176 0 4.5 -- 3.0

15 0.5 0.5 0.8 0.4

190 _ 2,5 _,O O.5

ReactorFeedRate90kg/hr

140 -- 1.5 -- 2.0

140 -- 1.5 -- 2.0

1960 -- 0.9 -- 6.6

140 25 0.9 1.1 --

23 4 0.4 0.4 0.7

29 0.3 0.2 0.2 0.3

3 -- 0.1 -- 0.4

288 -- 1.2 -- 2.0

320 -- 3.0 -- 3.0

40 l 0.3 0.8 --

120 -- 1.O ....

400 -- 3.0 6.5 0.01

1 0.5 0.5 ....

1 0.1 0.1 0.1

0,I O,l -- 0,1

210 .... 50 --

lO0 .... _0 --

4298 37.5

1302 11.5

5600 49.0

40

0.2

3.8

3.5

3.5

4.4

0.7

0.11

0.01

0.01

2.26

21

0.06

0.5

0.2

0.05

20-10
erectedon the Moon to increase the total oxygen production in stages. This may not

result in economies of scale, but there are benefits in commonality, operations and

construction.

4. A single "point design" to produce 5 mt of LOX per year using

unbeneficiated lunar soil shows feasibility and indicates that the reduction of total plant

mass is not trivial comparing the 24 mt/yr pilot plant.

5. The 5 mt/yr plant was designed for the feed rate of 90 kg/hr with 7.5% wt

ilmenite concentration. If the soil contains a higher percentage of ilmenite (as much as

15% wt.), the process plant will be able to operate without any problem producing more

oxygen with the same rate of feed. However, modifications relating to quantity of soil

mining, feed rate, heat and power would make the plant operation efficient and

effective.

6. Payback of plant mass occurs in one year of operation.

RECOMMENDATIONS

1. The proposed use of a vibrating screen to remove fines of sizes less than 45

microns results in relatively large screen areas, power and mass. An alternate design

may use air classifiers which lift the fines and allow the collection of useful material as

a "heavy stock". The air classifier with aspirator, cyclone separator, fan and motor may

replace the vibrating fine screen. This system can be introduced between top and

bottom reactor feed hoppers. A substantial reduction in mass is expected. A detailed

pressure analysis and design to incorporate air classifier to the LOX plant is needed.

This will also provide the flexibility to change the minimum size limit of the feedstock

as required, without replacing any parts. This could be useful if problems occur during

operation, or if off-nominal operation is required, as in during start-up and shut-down.

2. Hands-on research to determine the optimum reactor configuration for

maximum ilmenite conversion, especially for soil with high ilmenite concentration, is

recommended.

3. More detailed design of dust removal and hydrogen removal from the process

stream (water and hydrogen) to the electrolysis cell is required to avoid clogging and to

reduce the load.

4. At least 90% of the feed to the reactor is rejected as waste at 809°C. Using

this waste product and providing only a little more heat, the material can be sintered or

even melted and cast into bricks and other products. Radiation and micrometeoroid

shield design for the lunar outpost will be economical using cast basalt since the density

could be as high as 2900 kg/m-'. Casting the co-products of a lunar oxygen plant to

form useable materials would be advantageous and may allow their use as radiation

protection. This must be explored. Alternately, recovery of this heat value could lower

the power requirements of the plant.

20-11
1.

.

.

.

REFERENCES

Christiansen, Eric L., et al. "Conceptual Design of a Lunar Oxygen Pilot Plant"

NAS9-17878. Eagle Engineering, Inc. EEl Report No. 88-182. Houston, Texas. July

1, 1988.

Christiansen, Eric L., et al. "Support to Extraterrestrial Propellant Leveraging Trade

Study" Eagle Engineering, Inc. EEl Report No. 89-251. Houston, Texas. August 15,

1989.

Gibson, M.A. and Knudsen, C.W. " Lunar Oxygen Production from Ilmenite", Lunar

Bases and Space Activities of the Twenty-First Century. W.W.Mendell, ed. Lunar

and Planetary Institute. 1985

Anderson, C., et al. "Conceptual Design of a Technology Verification Unit for

Lunar Regolith Volatiles Extraction", Senior Project Final Report, Rice University,

April 20, 1990.

20-12
57--..e.3

DESIGN OF OPTIMAL CORRELATION FILTERS FOR HYBRID VISION SYSTEMS

Final Report

NASA/ASEE Summer Faculty Fellowship Program -- 1990

Johnson Space Center

Prepared By:

Academic Rank:

University & Department:

Periasamy K. Rajan, Ph. D.

Professor

Tennessee Technological University

Department of Electrical Engineering

Cookeville, TN 38505

NASA/JSC

Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Engineering

Tracking and Communications

Tracking Techniques

Richard D. Juday, Ph. D.

August 15, 1990

NGT-44-005-803

7

_ff

f Y

21=I
ABSTRACT

Research is underway at the NASA Johnson Space Center on the development of vision

systems that recognize objects and estimate their position by processing their images. This is a crucial

task in many space applications such as autonomous landing on Mars sites, satellite inspection and

repair, and docking of space shuttle and space station. Currently available algorithms and hardware

are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance

in computing and control; however, they take too much time to carry out important signal processing

operations such as Fourier transformation of image data and calculation of correlation between two

images. Fortunately, because of the inherent parallelism, optical devices can carry out these

operations very fast, though they are not quite suitable for computation and control type of

operations. Hence, investigations are currently being conducted on the development of hybrid vision

systems that utilize both optical techniques and digital processing jointly to carry out the object

recognition tasks in real time.

The author of this report, during his tenure as a summer faculty fellow at the Johnson Space

Center studied the various aspects of this research. He collaborated with Dr. Richard juday and his

colleagues on developing algorithms for the design of optimal filters for use in hybrid vision systems.

Specifically, an algorithm was developed for the design of real-valued frequency plane correlation

filters. Further, research was also conducted on designing correlation filters optimal in the sense of

providing maximum signal-to-noise ratio when noise is present in the detectors in the correlation

plane. Algorithms were developed for the design of different types of optimal filters: complex filters,

real-valued filters, phase-only filters, ternary-valued filters, coupled filters. This report presents

some of these algorithms in detail along with their derivations.

21-2
INTRODUCTION

Recognition of objects by machines is a vital task in many applications related to NASA

missions. Docking of space shuttle and space station, extra-vehicular activities by robots, and

autonomous landing on Mars are some examples of such applications. To carry out the tasks

efficiently and accurately in real time, suitable technologies are being investigated. Most of these

tasks involve some form of processing of images acquired using a camera. A straightforward

approach is to use electronic digital hardware to carry out the necessary processing involved in the

recognition of objects. Researchers in many universities and research laboratories around the world

are working on the development and evaluation of algorithms for such processing and pattern

recognition. One major drawback of the electronic processing technique is that at the current

technology, the algorithms take too much time to be suitable for real time applications.

An alternate technology that is under investigation by many researchers is the use of optical

processing. Since through optical techniques processing can be done in a parallel mode, very high

speed can be achieved. Taking Fourier transformation, multiplication of a signal by a filter, and

obtaining correlation function of two images are some of the operations that optical processing can

efficiently perform. However, some operations such as storage and retrieval of data, arithmetic

operations, etc., cannot so easily be done using optical techniques. Hence, a hybrid system employing

optical techniques for correlation filtering and electronic digital techniques for image storage,

retrieval, correlation plane processing, etc., is being investigated for development and use in vision

systems.

One major area of investigation in the development of hybrid vision systems is the design of

optimal filters that yield high signal to noise ratio (SNR) facilitating accurate detection of objects

in the presence of noise and other extraneous objects. During the summer fellowship period, the

author collaborated with his JSC colleague Dr. Richard Juday and Dr. B.V.K. Vijaya Kumar of

Carnegie Mellon University and developed algorithms for the design of several classes of optimal

filters: complex filters, real-valued filters, ternary valued filters, phase only filters, and coupled

filters. Some of these algorithms and their derivations will be presented in the following sections.

CORRELATION FILTERS

The theory of correlation filters for the recognition of known objects has been well discussed

in the literature [1]. It basically involves the following steps. From a knowledge of the reference

image s(x) (we will use I-D notation for the sake of simplicity), a filter function h(x) is designed.

When an image t(x) is to be tested to find whether the reference image is present in the test image or

not, the image t(x) is convolved with the filter h(x) to yield c(x). If c(x) has a well pronounced peak

above a preassigned threshold, then it is concluded that the reference image is present in the test

image; otherwise it is not. If a peak is present, from the position of the peak, one may also estimate

the location of the reference image in the test image. When h(x) is equal to s(-x), the matched filter

of the reference image, it can be shown that c(x) will yield the cross correlation between the test

image and the reference image. Hence, these filters are called correlation filters.

The implementation of correlation filters in the space domain is quite time consuming. An

alternate approach is to transform the test and reference images into the Fourier frequency domain,

multiply the transforms and then perform the inverse transform to obtain the filter output c(x). If

one uses digital techniques using electronic hardware, the Fourier transformation is also equally time

consuming. However, Fourier transformation can easily and almost instantaneously be carried out

using optical techniques. As a result, considerable research is being conducted on the design of optical

correlators for real time pattern recognition purposes. Since the invention of the holographic matched

filter for optical correlations by VanderLugt [2] in 1964, many other filters with improved

performance have been proposed. The accuracy of detection of these correlation filters depends to

a large extent on the absence of noise in an image. It may be noted that the nonreference image

21-3
componentspresent in an image may also be considered as noise. To account for the presence of noise

and to measure the performance of a filter in the presence of noise, signal-to=noise ratio (SNR) has

been introduced and procedures have been devised to design optimal filters that maximize the SNR

and thus improve the accuracy of identification.

Derivation of maximum SNR filters of different types (complex, phase only, ternary and

coupled filters) in the presence of image=plane noise but no detector noise has been presented in

literature, For the case of real filters, derivation of optimal filters starting from the fundamental

expression for SNR has not been done so far. We first present such a derivation obtained during the

fellowship period. This result has been submitted for publication [3].

OPTIMAL REAL CORRELATION FILTERS

Let s(x) denote the reference image and S(f) its Fourier transform. Let H(f) denote the filter

transform. In the absence of input noise the resulting correlator output at the origin is given by

c(O) - fS(f)H(f)df

(1)

where the limits of integration are those implied by the bandwidths of S(f) and H(f) (whichever has

a smaller bandwidth.) A model for possible uncertainties in the input is the additive noise n(x). We

model this as a sample realization with mean un and power spectral density Pn(f). The additive noise

n(x) in the input leads to randomness in the output c(0). We can show that

E{c(O)} - gn H(O) + fS(f)H(f)df

(2)

and

Var(c(O)) - IPn(f) IH(f) 12d/ (3)

When the input to the correlator is only noise n(x), the output c(0) will be a random variable with

mean un H(0) and variance as given in Eqn. (3). For good detection, we need to separate the two

means as much as possible while keeping the variance small. A convenient measure for this is the

signal-to-noise ratio (SNR) defined as below:

SNR - I_S(f)H(.f) dfl 2

_/'_(DIH(D 19d/

(4)

We will next derive the optimum real filter H(') that maximizes the SNR. The derivation can be done

in more than one way. In the following we use discrete arguments and partial differential equations.

For the continuous argument case one may employ variational calculus and obtain identieai results.

One may also employ Cauchy=Schwartz inequality to obtain the same result. We use such a technique

in the next section where detector noise is taken into account.

21=4
Let us sample the frequency domain quantities at intervals Af to produce amplitudes A k and

phases Ck :

Akexp(j_k) - S(k Af)

Hk - H(kAf) (S)

" G(ka/)

Then

[ra::x,,.J,,,][ J'"]

_ H:Pnk

k

(6)

where t and k are summed over values appropriate to the filter bandwidth. For an extremum SNR,

each filter value H m is chosen so that the SNR is stationary; i.e., subject to the constraint

(SNR) - 0 (7)

OHm

Defining and B real _, 0 such that

Bexp(j_ ) -_ AkHkexp(j* k) (8)

k

and taking the partial derivative in (7) we get

HmPnmB2" Am[_k H:Pnk]B COS(' m- IJ)

(9)

We have, then,

A m

Hm ,, __ cos(era_ a ) (10)

Pnm

with proportionality constant independent of m. The SNR is independent of a constant multiplier of

Hk, SO we may as well make Eqn. (10) an equality. Then we need only solve for a. Eqn.(10) indicates

several interesting things. As might have been expected, the optimum-SNR real filter value is

directly proportional to the local amplitude of the reference signal transform and inversely

proportional to the local noise power. The novelty in this result is that the filter is also weighted

according to how well the signal's local phase (era) lines up with the phase (#) of the filtered result.

Now, to determine the filter completely, we need to find the value of a. For this we need to

solve the nonlinear equations

21-5
where

A m

Zm " COS(*m- #)

_ " Arg{_k HkAkexp(j#k) }

(11)

A traditional method of solving these equations is as follows: Choose a trial value for a = _r Solve

for Hra and see if the result is consistent. That is, if the # calculated using the second expression is

equal to #1, then we have a stationary SNR. This is similar to the previous [4] approaches where

calculating a filter giving an extremum in correlation intensity involved a search over a single

parameter. Relating to the present case, we would require a search over B. As a significant advance,

we have been able to separate ¢ in Eqn. (l 1). The search for # is over for real filters. Substituting

for H m in the expression for B in Eqn. (l l) we have

# - Arg [A, _

k

--_---COS(¢k-B)Akexp(JCk)

Fnk }

- Arg{ _k [exp(J*k)exp(/'nk :k -j# ) +exp( -J¢k)exp(jB )]Akexp(j¢k) }

(12)

A s _ A_ll

:--'I[

_nk ] k _nk

The above will be true if and only if

p - ( )Arg _a_t + n_;

where n is an integer

(13)

By substituting for # in the expression for SNR, we can show that n should be equal to zero for

maximum SNR.

We now give an abbreviated discussion of the derivation of the optimal real filter in the

continuous domain. Define u and v as follows.

v -IH 2(.f)Pn(f)df (I4)

SNR u

V

21-6
(Theregionof integration,ordinarilyasinglediffraction order,isimplicit.) Followingthevariational

calculusapproachgivenin moredetailelsewhere[4], wedenotethearbitrarydisturbancefunction

as u(-), so that the deviatedvalue of the optimum real filtering function is H(-)+ _(') for a

(presumedsmall)scalar,a. The deviated value of the correlation field is obtained by replacing H(.)

by H(.)+ _(-). Let ua be u evaluated with H(.)+ au(-) replacing H(') and similarly for v_. The

first-order variation in SNR induced by _(.) is

That a given function H(.) produces an extremum of SNR is stated as

(15)

*(SNR)-0 , implying v 6u-u ,v (16)

A relatively straightforward extension of the method shown earlier leads to a form very similar to the

discrete case.

H(/) - A(/) cos[, (f) - 111]

en(/) (17)

_ - ArgI H(f)A(/)exp[j_(/)ldf

We have consistency if B1 = a2. These expressions are of the same form as those obtained for the

discrete case with the replacement of integration in place of summation. Then applying the same

development as in Eqns. (12) - (13), we get

tl--rl,[S(f)_d[l

" (1)Argl[SR(f)?-[Sl(f)?Pn(f)

l SR(I)St(l)

d/ + j2 _ df[ 1,.(/) 1

(18)

We will now consider some special cases . The discussion in the following is restricted to the

continuous case. Results for the discrete case are exactly analogous, as is apparent from the

congruence between Eqns. (11) and (17).

i) s(x) is real. Then _ SR(f)SI(f)d/ -O" .'. B -0 or" (19)

[SR(/)] 2 [Sx(D]2

ii) s(x) is real and f Pn(f) df - _ Pn(--'---_df. Then _ is arbitrary.

(20)

21-7
[$I(/)_ dr. Then a - L

iii) s(x) is complex and _ Pn(f) 4

(21)

Cases i) and ii) correspond to the cases considered by Kumar [5] and case iii) is a new result.

The SNR of the real filter, obtained by substituting the optimal value of H(f) and simplifying

the expression, is given by

lr Is(/)P dl+ 1⁄21 [S(l)i dli S N RRMF " "2J -_nO0- TZfi "

(22)

where SNRRM F represents the SNR of the real matched filter.

of the real filter in comparison with the complex matched filter is obtained as

A relative measure of performance

I _ [S(/)]2 df I

SNRRM}- 1 1 Pn(/)

._ + (23)

SNR_M F 2 "_" j IS( f ) P df

where SNRcM F represents the SNR of the complex matched filter. In the above expression we see

that the SNR of the optimal real filter is less than or equal to the optimal complex filter. However,

the maximum loss that results from the use of our optimal real filter is 3 dB.

Figure 1 in the earlier paper [5] shows the loss in SNR (relative to the matched filter) when

using the optimal real filter. That is valid here also. Only what is meant by "normalized even-part

energy" changes depending on Pn(f)" As shown before, we lose at most 3 dB in SNR (compared with

the matched filter) when we use the optimum real filter. This discussion also shows that the optimal

real filter is not the amplitude part of the complex matched filter.

DETECTOR NOISE AND SNR

When the correlation output c(0) is detected by a photodetector, several things happen.

Detectors respond only to Ic(0)l, thus ignoring all phase information. Also, the detectors introduce

a gain and some noise. An accurate model for detector noise is complicated and must include the

signal dependent nature of detector noise. Instead, we will use the following simple model for y, the

detector output:

y - c(0) + nd. (24)

In this detector noise model, we assume (without loss of generality) that the detector gain is unity and

that the detector noise nd is additive. The noise nd is assumed to have a mean nd and variance Od2.

The additive assumption is somewhat questionable. However, it makes the analysis tractable and it

helps us to illustrate the main point we want to emphasize (i.e., we must trade-off input noise

tolerance for detector noise tolerance). We will now find the mean and variance of y in Eqn. (24)

for the two possible input cases.

21-8
Whenthe input contains only noise n(x) let the output be Yo.

Yo are given as follows:

Then the mean and variance of

and

E{Yo} = _d + _n (25)

Var{y o) =o sd + rpn(f)lH(f)12df (26) J

When the input contains signal s(x) corrupted by additive noise n(x), let the output be Yr Then the

mean and variance of Yl are given as below:

E{yl} - v.d + ttn + _S(f)H(f)df (27)

and

Var{yl} = o_ + IPn(f)lH(f)t2df (28)

Using the statistics in Eqns. (25)-(28), the SNR in the presence of detector noise can be expressed as

SNR A

IE{yl}-E{Yo}I s

1 (Var{Yl} Var{Yo} )

2

II S(f)H(f) d ]12

%s + ipn(f) lH(f)[Sd/

(29)

Note that the only difference between the SNR expressions in Eqn. (4) and Eqn. (29) is the extra Od2

in the denominator of Eqn. (29). However this makes the optimal filter choices for the two SNR's

different. When ads is very small (compared to the input noise term), the two SNRs are identical and

the previous optimal filters will still be optimal. However, when ads is very large ( such that the input

noise term can be ignored) SNR is simply proportional to IE{c(0}l'and we must simply maximize the

correlation value at the center. In the next section we derive expressions for H(f) that maximize the

SNR in Eqn. (29).

OPTIMAL FILTERS WHEN DETECTOR NOISE IS PRESENT

In this section we will determine H(f) that maximize the SNR in Eqn. (29). There are five

different cases: (1) Complex filters, (2) real filters, (3) phase only filters (4) binary phase only filters

and (5) coupled filters. In each case while determining these filters, we must use the condition

IH(f) l _, 1 (30)

21-9
since the transmittance of an optical filter can never exceed unity. Also, without this constraint, the

detector noise variance Od2 will not be meaningful.

Complex Filters

Let us allow H(f) to be complex. Let the filter energy Eh be defined as follows:

Eh - flH(f)t2df

(31)

Then the SNR in Eqn. (29) can be written as below:

SNR - 1:S(]')H(f)dfl2

[[.i2d+ e.(_]Inff)12d/

where

(32)

2

2 °d (33)

°id " _h

is the variance of an equivalent white noise at the input which gives the same effect at the origin of

the correlation plane as the detector noise with variance Od_ for the given filter H(f).

To find the optimal choice of H(f) we now apply the Cauchy-Schwartz inequality to the

numerator to get the following:

s(:)"(:)d::" , s_(& ÷:(,o ] [H(f)¢0i_ + Pn(f)]dr Is

(34)

Substituting Eqn. (34) in Eqn. (32), we obtain the following result:

SNR -< j" 2 _(f)12 df - SNRmax(a_d)

aid + Pn(f)

with equality occurring if and only if

H(f) - a

S "(/)

Old + e.(/)

(35)

(36)

21-10
wherea is chosen to satisfy the constraint that the maximum magnitude of the filter is unity.

the optimal filter H(f) can be written as

s'cr)/to ,,+e.(jo:

Hopt(JO - IS "(J0/t e_d + Pn(JO ] [max

Then

(37)

As aid 2 is related to the detector noise adz, this filter will also be optimal for a detector noise adz _-

Oid2Eh where

Eh - flH(f)l 2 df (38)

Thus starting with aid2, we can design an optimal filter and find the corresponding detector noise for

which this filter is the optimal one. To design a filter for a given oa2, we may use either an

optimization scheme or do the following: Obtain a graph relating aid_ and Od2 by designing a number

of filters with different values of aids. Then from the graph, a suitable Oids is picked for a given Ods.

Once we know aid_, we can design the optimal filter. It may be verified that for the special case of

Pn(f) = a constant, the classical matched filter is still the optimal filter.

Real Filters

Let us now consider the case when H(f) is real. Then using the same notation for E h as above, we

have

I_S(JOH(JOa/Is I_S(JOH(JOa/t2. cc" SNR - - -

"_d+ f Pn(JOlH(f)Isdf 2 IPnt(jo IH(f)l 2 df (39)

I (_-_ + Pn(JO)IH(JOlSdf

where

0

Expressing cc as

we get

S

ad +Pn"

c - f S(f)H(f)df, and Pnt "

ICC _' + C*clS cc" - I, 21cl

I r c S "(JOH'(f)+c "S(JOH(f)air

SNR - I,.I 21ci J

_P.t(JO [H(JO[s a/

(40)

(41)

(42)

21-11
Asfor real filters H* = H, we can write the expression for SNR as

I_(cS'(f)+c'S(f)) l

SNR - 2tcl H(f) d/

IPnt(f) [H(f) I2 d/

Applying Cauchy-Schwartz inequality we get

[I cS *(J') +c.__*S(J') P_nt(f) H(f) d f l

SNR - 21cVPnt(f) , _cS'(/)+c'S(f)rdf

'(21cl#.t(:) J

Equality (and hence maximum SNR) is achieved iff

cS'(/)+ c'S(/). Is(/)l

H(f) - a 2IclPnt(f) a _cos(¢,(f)pnt(/) -B)

(43)

(44)

(45)

where %(f) = Arg{S(f)), IJ= Arg{c) and a is a real constant such that the maximum response condition

is satisfied. To determine a, substitute the optimal H(f) in the expression for c (Eqn. (40)) and

simplify as

[S(f)]2 eja

IcleJa - _lfIS(.f)l:_d[ 2 [,JPn--'_ -

+ e-i2a _ d/ }

(46)

The above condition will be satisfied if and only if

,,. Lf. r-rts(I)l'a. ÷,,,,j

(47)

where n is an integer. Further we can show that SNR will be larger if n = 0 (or any even integer) than

when n is odd. Thus # value is given by

B " 2Arg[ I P_t(_tS(f)]2d/]) (48)

With this B value the maximum SNR value can be shown tI be

.... 1 , IS(f)l 2 .. 1 l, ts(/)] _.]

°'V^m_ " _'J pn--'_a/ + "_/j pn-"--_ aq

(49)

21-12
The optimal H(f) satisfying the maximum condition is given by

Hopt(/)-

lS(f) l coS(¢s(/) _ #)

Pnt(/)

I IS(f)l cos(%(f) -a) _na_

Pnt(f)

(50)

The above H(f) is optimum for a given input noise. To determine H(f) for a given detector noise one

can follow a procedure similar to that used in the complex filter case of either using an optimization

scheme or setting up a graph between aid2 and ad2 and selecting a suitable aid2 for a given ad2.

Phase Only Filter

Let H(f) be a phase-only filter (POF) with a region of support R defined below:

e j_h(f) for f c R

H([) -

0 for / _ R

(51)

Substituting this in Eqn. (29) we obtain the following expression for SNR:

SNR -

2

od + df

(52)

The denominator in Eqn. (52) does not depend on Ch(f) and the numerator is maximized by choosing

Ch(f) = -_s(f), i.e., the conventional POF maximizes the SNR in the presence of detector noise. The

next task is to find the optimal region of support R. Let A R denote the area of the region R. Then

the SNR in Eqn. (52) can be rewritten as follows:

SNR - (53)

The SNR expression in Eqn. (53) is identicai to the SNR expression derived elsewhere [6] for the case

of no detector noise and input colored noise with spectral density [Pn(f) + ad2 / AR}. It is shown there

that the region of support R that maximizes the SNR must be of the form:

R m

(54)

21-13
whereT is an unknown threshold. Since A R depends on R, Eqn. (54) is an implicit equation. An

algorithmic procedure for determining R is given below:

To illustrate the workings of the algorithm, let us assume that S(f) is represented by an array

of 64 X 64 = 4096 pixels. Each frequency in this array may or may not be included in R. Thus there

are 24o90 possible choices of R from which-we must select the one that maximizes the SNR.

Exhaustive search is obviously impossible, but the characterization in Eqn. (54) proves useful. For

example, there are 4096 different choices for R with A R = 1. Among all of them, the best R is the

one that includes the highest value of IS(f)l/[Odz + Pn(f)]. Extending this to other A R values, we get

the following algorithm:

Step 0: Start with A R = 1.

Step 1: Compute

G(/) -

IS(f) I

2

Od + pn(f )

AR

(55)

Step 2:

Step 3:

Step 4:

Step 5:

Arrange sampled values of G(f) in the descending order as G 1 > Gz > ... > Gt_ > 0.

Construct the optimal R'(AR) of area A R by including pixels corresponding to G i, G 2.....

GAR.

Compute the SNR(AR) obtained by using R*(AR). If A R is greater than or equal to N, go to

Step 5. Otherwise go to step 2.

Determine the largest SNR(AR). This determines the optimal region of support.

The above algorithm involves N sorts where each sort is of N values. Since A R changes by

only 1 from one sort step to the next, we do not expect that the sorting order will change significantly

from one step to the next. This can be used to speed up the algorithm still further.

Ternary-Valued and Coupled Filters

Optimal ternary-valued filters (with values -1, +1, and 0) in the presence input and detector noises

have also been derived during the fellowship period. However due to lack of space, they are not

discussed in this report. Similarly, the design of optimal coupled filters in the presence of input and

detector noises have also been investigated. These results will be sent for publication in due course.

SUMMARY

In this report, the derivation of optimal real correlation filters when detector noise is not

present is first presented. Then a model for the detector noise is presented. Using this model, the

optimal filter design problem when detector noise is present is formulated. Derivations for the

optimal filter when the filter is an unconstrained complex filter, real filter, and phase only filter are

presented. Algorithms for their design are also presented. Implementation and testing of these

algorithms will be taken up during the follow-up grant period.

4

21-14
Ill

[21

[31

[4]

[51

[61

REFERENCES

H. Bartlet, Unconventional correlators, Chapter 2.2 in Optical Signal Processing, J.L. Homer

(Ed.), Academic Press, Inc., San Diego, 1987.

A. VanderLugt, "Signal detection by complex spatial filtering," IEEE Transacti0n_ on

Inf0rmatign Theory, Vol. IT-10, pp. 139-184, 1964.

Richard. D. Juday, B.V.K. Vijaya Kumar and P.K. Rajan, "Optimal real correlation filters,"

Applied Optics, in review, 1990.

Richard D. Juday, "Correlation with a spatial light modulator having phase and amplitude

cross coupling," Applied Optics, Vol. 28, pp. 4865-4869, 1989.

B.V.K. Vijaya Kumar, "Signal-to-noise ratio loss in correlators using real filters," Atgolied

Ootics, Vol. 28, pp. 3287-3288, 1989.

Zouhir Bahri and B.V.K. Vijaya Kumar, Fast algorithms for designing optimal phase-only

filters (POFs) and binary phase-only filters (BPOFs)," Applied Optics, Vol. 29, pp. 2992-2996,

July 1990.

21-15

 ol:Zzfrt :-

DEVELOPMENT OF LOAD-DEPENDENT RITZ VECTOR METHOD

FOR STRUCTURAL DYNAMIC ANALYSIS OF LARGE SPACE STRUCTURES

Final Report

NASA/ASEE Summer Faculty Fellowship Program - 1990

Johnson Space Center

Prepared By:

Academic Rank:

University & Department:

James M. Ricles, Ph.D.

Assistant Professor

University of California,

San Diego

Department of AMES, R-011

La Jolla, California 92093

NASA/JSC

Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Engineering

Structures and Mechanics

Loads and Structural

Dynamics

David A. Hamilton

August i0, 1990

NGT-44-005-803

22-I
ABSTRACT

In the analysis of large space structures, such as the Space Shuttle and

Space Station Freedom, analytical models are dealt with which have a large

number of degrees of freedom (DOF). It is important that the method used

to analyze these structure be as accurate and efficient as possible in order

that structural margins and areas requiring redesign can be identified and

subsequent analysis be performed. This study involved the development and

preliminary assessment of a method for dynamic structural analysis based on

load-dependent Ritz vectors. The vector basis is orthogonalized with respect

to the mass and structural stiffness in order that the equations of motion

can be uncoupled and efficient analysis of large space structure performed.

A series of computer programs was developed based on the algorithm for

generating the orthogonal load-dependent Ritz vectors. Transient dynamic

analysis performed on the Space Station Freedom using the software was

found to provide solutions that required a smaller number of vectors than

the modal analysis method. Error norms based on the participation of the

mass distribution of the structure and spatial distribution of structural loading, respectively, were developed in order to provide an indication of vector

truncation. These norms are computed before the transient analysis is performed. An assessment of these norms through a convergence study of the

structural response was performed. The results from this assessment indicate

that the error norms can provide a means of judging the quality of the vector

basis and accuracy of the transient dynamic solution.

22-2
INTRODUCTION

The finite element method is the standard method for developing models of space structures for structural analysis. The finite element models of

present and future space structures, such as the Space Shuttle and planned

Space Station Freedom, possess a large number of degrees of freedom (DOF).

To calculate the response of these structures for dynamic loading conditions

involves using the finite element models in a transient dynamic or random

structural vibration analysis. The standard procedure for doing this utilizes

the modal analysis method. For structural models with a large number of

DOF the modal vector basis is generally truncated because of the enormously

large amount of computation effort and time required to calculate all eigenvectors and eigenvalues of the finite element model. The constraint of having

to use a truncated modal basis and the fact that the computational effort to

calculate vibration characteristics is costly compared to Ritz based methods

[Bathe, 1982] gives motivation and justification for considering other procedures for generating an orthogonal vector basis.

This study is concerned with the development and preliminary assessment

of a load-dependent Ritz vector method for use in structural dynamic analysis

of large space structures. The scope of work in this study included developing

an algorithm which was efficient in terms of numerical operations and has the

ability to be used in a production environment. The algorithm was coded on

the CRAY X-MP/EA 464 Supercomputer using the FORTRAN computer

language. A transient dynamics analysis of the Space Station Freedom was

performed using the computer program in order to assess the algorithm. This

assessment involved comparing these results to those from a modal analysis.

A summary of this assessment is presented in this report. Conclusions based

on the finding of the analysis performed are noted and topics for future research are suggested. Also included in this report is a description of the

series of computer programs developed which allow one to perform a transient dynamic load-dependent Ritz vector based analysis using NASTRAN

[CSA/NASTRAN, 1988] finite models and to post process the results.

LOAD-DEPENDENT RITZ VECTOR ALGORITHM

Block Algorithm and Error Norms

The algorithm for generating an orthogonal load-dependent vector basis

22-3
is based on using the static amplitudes of the dynamic loads as selected

times. The vector basis generated aligns itself with respect to the loading,

consequently the vector basis has the potential of a high participation with

respect to the response to the dynamic loading. A summary of the algorithm

is given in Fig. 1. The Ritz vectors axe generated in blocks, each block having

several vectors, in order to deal with closely spaced vibration frequencies and

a spatial varying loading case that can often exist in space structures. The

algorithm begins with the shifting of the structural stiffness matrix K using

the shift constant a, followed by the calculation of the set of displacements U0

reflecting the response to the block loading F(s). Shifting of K is necessary

in order to remove the rigid body modes and permit K to be factorized. Any

structural vibration frequencies near the shift point are well represented in

the Ritz vector basis. The vectors X which are generated are orthogonalized

with respect to the mass matrix M and contain a static residual to reduce

the effects of truncation of higher frequencies. A final step of orthogonalizing

the vectors X with respect to K is necessary to produce the vector basis °X

having the frequencies & in order to uncouple the equations of motion.

The concept of a block form was motivated by it use in previous work

by the investigator [Ricles et. al., 1990] and that of other researchers [NourOmid and Clough, 1985].

During the generation of vectors Xi the norm e,,, is computed, and is intended to provide an indication of the participation of the mass distribution.

This norm is computed using the displacements Ui at each cycle and the

initial displacement set U0, where at cycle i:

IIMU II 

= IIMUoll , (1)

For the analysis performed the behavior of _. was studied to determine

whether it could be used as criteria to judge when to terminate the generation of vectors. A second norm e_,, intended to represent the degree of

participation of the spatial load distribution, was also calculated and studied

to determine whether it was reliable for use in judging the quality of the

vector basis °X in responding with high participation to the loading. This

norm is computed by the following formula, where for k vectors in °X:

IP(t)eL [

EL= pT(t)P(t ) (2)

22-4
1. Dynamic Equilibrium Equations:

MZ(t) -4- CT,(t) + KZ(t) = F(s)er(t)

2. Initial Calculations:

(a) Shift and Factorize Stiffness Matrix

K* =K+trM

K* = LDL T

(b) Solve for Static Response to Block Loading

K'Uo = F(s)

3. Calculate Rigid Body Modes:

(a) Use Geometric Description and DOF Relationship to Describe Rigid Body Motions X_

(b) Generate First Block(s) of Orthonormalized Ritz Vectors

Xl = #Xl

T -1

(c) Remove Rigid Body Modes From Static Block Uo (Gram-Schmidt Orthogonalization)

Us = Uo - Xl (XTMUo)

4. Generate Additional Ritz Vectors Xi, i = 2,... ,n - h

(a) Solve for Xi

K* Xl = MUI- l

(b) M-Orthogonalize _[! against previous blocks (Gram-Schmidt)

i--1

X; =Xi- ZXjXj rMxi l_<m<_i-2

j=m

(c) M-Orthogonalize Vectors in Block X_ by Modified Gram-Schmidt to obtain Xi

(d) Remove New Ritz Block Xl Prom Static Block Ui-1 (Gram-Schmidt)

Ui = Ui-_ - Xi (XITMUI-1)

5. Add Static Block Residual U--1 as Static Correction Terms Xn.

6. Make Ritz Vectors X Stiffness Orthogonal (Optional - Uncouples Equations of Motion):

(a) Form and Solve the Reduced n x n eigenvalue Problem

K** = XTK'X

[K** -&2I] _I, = 0

(b) Compute Final Ritz Vectors °X = X@

Fig. 1,-. Block Form Load-Dependent Ritz Vector Algorithm for Semipositive Definite Systems.

22-5
where P(t) is the load vector at time t and eL the error in the representation

of the load in Ritz coordinates, with

k

eL = P(t)- _-'_ °xjpT(t)M°Xj

j=l

(3)

Software Development

The computer program named RITZ was developed to generate °X, &,

and e_. RITZ interfaces with the bulk NASTRAN data set and NASTRAN

OUTPUT4 [CSA/NASTRAN, 1988] files Containing the structural mass and:

stiffness matrices to define the structural model. To maximize the efficiency,

the symmetry and any sparseness of these matrices are exploited in their

incore storage and factorization. A second computer program named NASDYN was developed which gives theuser the choice of calculating either the

norm eL or the transient dynamic response of the structure. Either Ritz vectors or NASTRAN modal vectors can be used. The results of the transient

analysis can be post processed by a third newly developed program POSTRN

for producing plot files which are compatible with the JSC Structures and

Mechanics Division PLOT2D graphics software.

SPACE STATION ANALYSIS

Program of Investigation

The Space Station Freedom was analyzed for a simulated docking with

the Space Shuttle. A view of the Station is given in Fig. 2. In the analyses

performed the NASTRAN MB15YZ model of the Station with 2803 DOF

was used along with docking load case 915L, consisting of the set of transient

forces shown in Fig. 3 applied to the end of the docking arm. The position of

the PV arrays in this model had an orientation in the Y-Z plane as opposed

to the X-Y plane as shown in Fig. 2. RITZ was used to generate vector

bases with 30, 60, and 90 vectors and DYNTRN used to compute the elastic

displacements and total accelerations of selected DOF based on a constant

damping ratio of 0.01. The vector bases were generated using a block size

of 6 vectors, with U0 consisting of six displacement vectors corresponding

to the response of the six individual docking loads acting on the structure

at time-l.93 seconds. The response at the tip of an outboard PV array

(node 8022 in Fig. 2) is discussed in this report. Neither a complete solution

22-6
.i

;JEW LOOKINC AFT

I

I

- Point of _ PV ArrayF

i

! x

i view LOGNINC rO_wA_O _ 11 Y _,

Fig. 2.-Space Station Freedom with PV Arrays in The Xoy Plane.

22-7
(a)

Gee.O

49e.e

2Q0.8

.0

-ZBO.8

-400.e

SHUTTL[/MB15, SCALED DOCKING MAST, CASE 915L

PITC_ SLAP DYNAMICS ,MAX Y LOAD, MISS-.25AT÷45

NASA/ISC SCHLIE51NG 04/g4/89 %1:44:47 l KSCE[PYlee

OlU_:_ STar_ee l_w;_ w L_D,LI

al_ IYnTI_ E*0ClllvL • LN0,LII

• I_ STmTlee I_I_ Z Leel, L1

I

J) -

1E.E 15. 28.0

TIME,SECONDS

.G ' 5.e 2S.q

J SHUTTLE/MBIS, SCALED DOCKING MAST, CASE 915L |

_NASA/,ISC

PITCH" SLAP DYNAMICS

SCHL I{SING

,MAX Y LOAD,

B4,'e4/89

MISS-.25AT÷45

| i : 44 : 47 I

Li*IVrLK_ |Tmr|(Iq |NYlmr_ TII4U[S • TTI_IS . 114it ,wI , . ;_14 ,_

(b)

6N. |

41_.8

21NI. •

.8

-2_.e

-4_.0

li,

_JL

I,

IS

I _ ii

• 8 5.8 t0.0 15.0 20.0

TIME, SFCOHDS

s -9

25.0

Fig° 3 Dockin9 Load Case 915L (a) Translational,

and (b) Rotational Transient Forces.

22-8

O_G;?JAL PAGE" IS

OF POOR OUALITY
based on a numerical integration of the coupled equations of motion nor a

complete modal vector basis was available to compare with the Ritz solution. For purposes of this preliminary study a comparison was made with

solutions obtained from a truncated modal basis consisting of 210 modes.

The frequency range of the modes ranged from 0 to 6.0 Hz. Fig. 4 shows

a comparison of the frequency range of the modal vectors and selected Ritz

vector bases. The 60 Ritz vector basis with a static residual (identified as SR

in the legend of Fig. 4) has approximately the same frequency range as the

modal basis. It can be seen that a larger Ritz vector basis has a greater span

and that this span is extended for those basis containing the static residual as

opposed to not having it (identified as NSR in the figure's legend). All Ritz

vector basis presented in this report were generated with a shift constant of

a = 1.0, except for the 60 vector basis with a = 200.0 identified in Fig. 4

as SHI. The effect of using a larger value for a is that the basis has more

distantly spaced frequencies and a greater span.

Presentation of Transient Response

A time history plot of the translational displacements and accelerations

in the x-direction are shown in Figs. 5 and 6 for the 90 Ritz vector and

modal solution. The Ritz based displacements are shown to agree closely

with the modal solution, whereas there exists a greater discrepancy between

the accelerations computed by the two methods. The maximum discrepancy

in the transient translational displacement and acceleration along the X-, Y-,

and Z-axes based on the results from the modal vectors and Ritz vectors with

a 90, 60, 30 vector basis are shown in Fig. 7. These results correspond to

the node at the end of the outboard PV array. It is apparent in Fig. 7 that

the discrepancy between the 210 modal vector and Ritz solutions decreases

as the number of vectors in the Ritz basis is increased from 30 to 90 vectors.

The Ritz solution with a 60 vector basis is able to achieve the same result

as the 210 modal vector solution for displacements. The Ritz based solution

for accelerations shows greater discrepancies with the modal solution, and

requires a larger number of vectors in the basis to achieve the same level as

accuracy as that found in the displacements at 60 Ritz vectors.

Analysis of Results

The norm e,, is plotted against the number of blocks of Ritz vectors in

22-9
0

0

C_

%

m- o

c_J

l,i

EK_NVALUE VS. VECTOR NUMBER _LATIONSX_

go RiTZ VECTOR BASIS. SR

3 60 R_TZ VECTOR BA_S, 60 RtTZ BA_S.

5 - 60 R+Tz VECTOR BA,T:JS,S_i

/ / /

0 40 80 120 160 200 240

Vector Number

Fig. 4.- Span of Eigenvalue (Frequecy) Range of Modal and Ritz Vector Bases.

_AC( STATION OOCE'IN_LOAD 915_ ANALYgS, MBtSYZ 2803 OOF

e--4

r"

v

I

O

r"

aJ

E

QJ

e5 I

u')

-l,--

C_

! ('4J

X I

I

/

/,

'L,_.

, • II

- go210mTZ MODAL VECTORS. VICTORSe/BLOCK I

0 4 8 12 16 20

Time - (sec.)

Fig. 5.- Displacement Response at End of Outboard Pv Array.

22-10
m m m i v

c c c

040 _ N

GI

0"I S'O

a3

.o c

o

.C' _J

O

88 [ _,

O<_O _

i

_'I 0"I _'0

r'_ (_._,3_S/U.L) - _, JOJJ_

.mOC

o

C:_ U

",,0_

Z

&

OJ £_,-

•'o E

_ L) 0

r.-- i-_ "1_

0"I-"

3= : 0

(_ C_ -_

0 t'- ,..-

L_.I 4-_ _

I

e 0 ..-._

•r- ,i_ c-" _..

,n

m

s

w

O'Z 0"I o o'I-

(Z,_,_OeSlpe._) - UO.L:T_JBLB3DV-X

22-11

oi

i ii

0 _-

0

-m

0

q0

Ld

O0 E C_.

m_

0

t.>

I

&

.r=,
Fig. 8 to establish whether it can give a good indication of when to terminate

the generation of vectors. An examination the data reveals that for 30 Ritz

vectors (5 blocks x 6 vectors per block = 30) the value of e_ is approximately

50 times greater than that for 60 Ritz vectors (10 blocks x 6 vectors per

block = 60), at which the displacements were in very close agreement with

the modal solution. The fact that e_ has become quite small at 60 Ritz

vectors suggest that there is not much to be gained by 30 additional vectors

to achieve a 90 vector basis. As discussed above this was found to be true

only for the displacements. Thus, e_ could be considered more reliable for

displacements than accelerations.

The behavior of e_ in Fig. 8 shows that a fluctuation in the curve can

exist where a local maximum develops. This phenomenon is quite noticeable

for the case of 5 blocks with 6 vectors per block. This behavior is associated

the structure responding more to the forces related to the displacements Ui

as opposed to U0. Numerical experimentation indicated that apparently

this occurs when the block loads used to calculate Uo are not uniformly

displacing the mass, leading to displacement patterns UI in some of the

later cycles which 'accelerate' more the mass. This same experimentation

indicated that the fluctuation tends to occur in the initial cycles of vector

generation and that as the number of vectors is increased the phenomenon

appears to decay in a similar manner as shown in Fig. 8. This phenomenon

is not as pronounced in the vector basis generated using fewer vectors per

block, as indicated by the results shown in Fig. 8 for a block size of I vector.

Asymptotic convergence of e_ to zero can thereby be expected as the number

of vectors is increased. A threshold value for Q to terminate the process of

generation of vectors can not be assigned at this time, for its sensitivity to

models with a more uniformly distributed mass is not known as well as the

fact that the results presented are from only one loading condition.

A presentation of the norm eL associated with the translational components of the docking loading at time=l.93 seconds is given in Figs. 9 to

11. These results include the 60 and 90 Ritz vector basis (cases already

discussed above, which contain the static residual identified as SR in the

legends of Figs. 9 to 11) in addition to the 210 modal basis and a 60 Ritz

vector basis without a static residual (identified as NSR in the legends). It

can be observed that the Ritz vectors' basis can better represent the load

than the modal vectors. This representation is improved as a static residual

is added and further improved as the number of Ritz vectors in the basis is

22-12
L_

C_

J

E

o

z

"t3

it-.

OJ (_J

U

.r--

4-}

C_

Fig. 8.-

STATIC RE,DUAL NORM VS VECTOR NUMBER RELATIONSHIP

'2

f

i

i

i

!

?

0 10 20

I _ - ?V{CTOR/BLOCK VECTORS/SLOC_

30 " 40

Number of Blocks of Vectors

eu-Block Number Relationship, Block Sizes I and 6 Vectors.

E

O

Z

tO

,i.--

°l.J

r-,

.p

S.

(,'1

,p

nD

.p

.,i.J

tO

CL

(.Z')

tlD

O

.-J

Fig.

C_

O

O

OO

O

_D

C_

c,,J

O

qEPR[._NTATION OF X-T_C.NSLATW_L IrORCE BY VI_CTOR BASIE$(TI_ 93)

:.,_

'1

i - k4ODAL VECTORS I

32 - 90 R1TZ VECTOR BAS_. SR I

- 60 RITZ VECTOR BASIS.

4 - 60 RITZ VECTOR BA$_, NSR.

0 40 80 120 160 200

Vector Number

24O

9.- _L Norm for X-Translational Docking Force at Time 1.93 sec.

22-13
I_PI_,SENTAT'ION or Y-TRANSLATL_4AL Ir0ACE BY VECTOR Bk,_S (T-l.g3}

O

EE

.TO

Z C_.

00

co

e_ C_.

4-)

C3

,.- ,_-

e_

.l.a

ro

r-_

C,9 C_

04"

"0

rO

0

=.J

-..°

i "

2 - 90 RITZ VECTOR BAS_J, _ |

3 - 60 RITZ V_CTOR BASIS, _ I

(_ A - S _T V CTOR _ _ 'r

0 40 80 120 160 260 240

Vector Number

Fig. 10.- e L Norm for Y-Translational Docking Force at Time 1.93 sec.

o

E

IL

o

z c3

cO

c

o

4-)

c3

L

to

,r,,

to

ca9 c3

o

..J

c3

RI_PRE_NTATION OF Z-TRAMSLATIONAL FOFK_ BY VECTOR BASES (T-1.g3}

L4 L., , __

!

I

3

2

- _OOALRITZ VECTORS VECTOR BA,T_IS. I

3 Go R_TZVECTORBA_S,

4 - _0 RITZ VE,CTOR BA$_, NSR (

0 40 80 120 160 200 240

Vector Number

Fig. 11.- _L Norm for Z-Translational Docking Force at Time 1.93 sec.

22-14
increased. It can also be observed that representation of the loading by the

90 Ritz vector basis is as good (X- and Y-translation loadings) or better (Ztranslational loading) compared to that of the 210 modal vector basis. The

fact that fewer Ritz vectors were found to be required than modal vectors

to achieve comparable results for eL gives an indication that the Ritz vector

bases align more with the loading than the modal vector basis, such that the

product of the Ritz vectors with the load vector is larger than the product

using modal and the load vector. The participation of the Ritz modes °X

in the dynamic response can therefore be expected to occur, as opposed to

the possibility of having the vectors orthogonal to the loading as is sometimes the case in modal analysis. A close examination of Figs. 9 to 11 shows

that most of the modal vectors' contribution to the load representation by

its basis occurs in the mid frequency range (the vector number in Figs. 9 to

11 correspond to the ordering of the frequencies). Also, the modal vectors

which are orthogonal to the loading are spread throughout the spectrum.

This orthogonality is identified by the parts of the curve joining the values

of eL where the slope is zero.

A comparison of the behavior of _ with that of eL indicates that although

e= may become small, eL may not. Since convergence for accelerations was

observed to require more Ritz vectors, more emphasis should be placed on

eL for analysis in which the acceleration response is of prime interest when

attempting to judge the quality of the vector basis for the selected loading

conditions. This could perhaps be extended to situations where the Ritz

vectors were generated based on a particular load case and there is the desire

to use them for another load condition.

CONCLUSIONS

Based on the results of the preliminary analysis of the Space Station

Freedom using the load-dependent Ritz vector algorithm presented herein,

the following conclusions are noted:

1. Load-dependent Ritz vectors appear to provide accurate displacement

solutions using fewer number of vectors than modal based vectors. The

use of e_ for criteria to stop the generation of the vector process appears

to be promising.

22-15
. More load-dependent Ritz vectors are required when computing accelerations. Criteria for judging the quality of the vector basis using eL

needs to be more strict when acceleration response is of interest as

opposed to displacements.

. Load-dependent Ritz vectors have a broader frequency range than the

equivalent number of modal vectors and are able to better represent

the loading function. These features of the Ritz vectors are enhanced

by including the static residual in the basis.

Areas which are suggested for future study include:

1. A study of the basic behavior and calibration of e_ and e L for different

load cases and structures, respectively.

. Assessments of the reliability of the vector basis generated from a different load case than that used in the transient dynamic analysis and

whether eL can provide a means of judging the vector basis quality in

such cases.

3. The optimization of the algorithm to take advantage of the vectorization procedure by the CRAY X-MP/EA 464.

REFERENCES

1. Bathe, K., Finite Element Procedures in Engineering Analysis, PrenticeHall, 1982.

2. Computerized Structure Analysis and Research Corporation, CSA/

NASTRAN Users Manual, Vol. 1, 1988.

3. Nour-Omid, B. and R.W. Clough, Block LanCzos Method for Dynamic

Analysis of Structures, Earthquake Engineering and Structural Dynamics, Vol. 13, pp.271-275, 1985.

4. Ricles, J., Leger, P., and L. Robayo, Reducing Modal Truncation Error

in the Wave Response Analysis of Offshore Structures, Communications in Applied Numerical Methods, Vol. 6, pp 7-16, 1990.

22-16
Mechanics, Impact Loads and EMG on the Space Shuttle Treadmill e p._ _

Final Report

NASA/ASEE Summer Faculty Fellowship Program--1990

Johnson Space Center

Prepared by:

Academic Rank:

University & Department:

NASA/JSC

Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

William G. Squires Ph.D.

Professor

Contract Number:

Dept of Biology Texas Lutheran College

Space and Life Sciences

Medical Sciences

SBRI

Mike Greensien, Ph. D.

August 17, 1990

NGT-44-005-803

23-1
_S_A_

The ability of astronauts to egress the Shuttle, particularly

during emergency conditions, is likely to be reduced following

physiological adaptations in space.

It is well established that effective application of exercise counter

measures requires the exercise be applied specifically. The problem

is that objective scientific evidence is not available to validate the

space shuttle treadmill with respect to in its role in diminishing the

deleterious effects of a prolonged exposure to the microgravity

environment.

INTRODUCTION

The purpose of this study is to analyze locomotion and resulting

impact loads during treadmill walking and running on orbit during

STS missions This data will be acquired with video motion tracking,

force measurement instrumentation and electromyography (EMG)

The data will be used to analyze the gait, to determine impact forces

and to determine skeletal muscle responses, during extended O g

conditions. These results will be used to determine appropriate

exercise countermeasures prescriptions to counteract space

adaptations in general and to prepare for the contingency of

emergency egress in specific.

This study requires video recording astronaut performance during

locomotion in zero g on the space shuttle treadmill mated to a six

degrees of freedom force plate while wearing EMG telemetered

electrodes

A total of twenty assigned astronauts (n=20) will be requested, five

pilots and fifteen mission specialists, to participate in this study.

Repeated measures on the same individual are necessary to avoid

the large variance associated with comparisons between individuals.

23 -2
This study will be conducted in three phases.

Phase 1. In the full fuselage trainer , crewmembers will be video

recorded while performing locomotion tasks on the instrumented

treadmill while wearing EMG telemetered electrodes.

Phase 2. During parabolic flight on the KC 135, crewmembers will be

video recorded performing locomotion tasks on the instrumented

treadmill while wearing EMG telemetered electrodes.

Phase 3. On orbit, crewmembers will be video recorded while

performing locomotion tasks on the instrumented treadmill while

wearing EMG telemetered electrodes.

An accurate measurement of the gait patterning, locomotion impact

loads and EMG data can provide real data into the system for further

exercise countermeasure prescriptions.

23 -3

N91-27113

SOLUTIONS TO PROBLEMS OF WEATHERING IN ANTARCTIC EUCRITES

Final Report

NASA/ASEE Summer Faculty Fellowship Program--1990

Johnson Space Center _,,/_j 5._1_

Prepared By:

Academic Rank:

University & Department:

Melissa M. Strait, Ph.D.

Assistant Professor

Alma College

Department of Chemistry

Alma, Michigan 48801

NASA/JSC

Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Space and Life Sciences

Solar System Exploration

Planetary Science

Marilyn M. Lindstrom, Ph.D.

August 10, 1990

NGT-44-005-803

24-1
ABSTRACT

The number of samples available for studies of asteroidal differentiation and early Solar System

history has been dramatically increased with the discovery of meteorites in Antarctica. However, it has

been recently shown that significant differences in minor and trace element abundances between

Antarctic and non-Antarctic eucrites are probably due to weathering (Mittlefehldt and Lindstrom,

1990). This alteration is thought to occur in interstitial material leaving primary silicate phases intact

and suitable for petrologic modelling.

This study performed neutron activation analysis for major and trace elements on a suite of eucrites

from both Antarctic and non-antarctic sources. The chemistry was examined to see if there was an

easy way to distinguish Antarctic eucrites that had been disturbed in their trace element systematics

from those that had normal abundances relative to non-Antarctic eucrites. There was no simple

correlation found, and identifying the disturbed meteorites still remains a problem.

In addition, a set of mineral separates from an eucrite studied that had been studied earlier were

analyzed. The separates were acid-washed to remove any interstitial material or contamination before

they were analyzed. The results showed no abnormalities in the chemistry and provides a possible way

to use Antarctic eucrites that have been disturbed in modelling of the eucrite parent body.

24-2
INTRODUCTION

The eucrite association (euerites, howardites, diogenites and mesosiderites) represents an

occurrence of planetary volcanism from the smaller bodies of the Solar System. This meteorite

association is studied to understand chemical and genetic relationships among the meteorite groups

and to look at processes that occurred 4.5 billion years ago in the Solar System.

The euerites are pyroxene-plagioclase achondrites, the diogenites are hypersthene achondrites, the

howardites are polymict breccias of eucritic and diogenitic material and the mesosiderites are stony

irons with silicates of a composition similar to the eucrites. A spectrum of chemical and mineralogical

compositions tie together the eucrites, howardites and diogenites (Figure 1). At one time all four

classes of meteorites were considered to come from one parent body, possibly even the Moon (Duke

and Silver, 1967). The return of lunar samples eliminated the Moon as a parent body, and recent work

on chemistry and mineralogy of the mesosiderites has shown that while they may form in a similar

process to the howardite-eucrite-diogenite grouping, they come from a separate parent body

(Mittlefehldt, 1990).

This study will look specifically at the eucrites to examine the chemical effects of weathering and to

further ongoing studies of asteroidal differentiation and early Solar System history. Euerites have

textures that resemble terrestrial basalts and are modelled in an analogous manner. They consist of

calcium-poor pyroxenes and calcium-rich plagioclases, with the pyroxene generally dominant over

plagioclase. In addition, the euerites contain accessory amounts of olivine, tridymite, quartz,

cristobalite, chromite, magnetite, ilmenite and phosphates, such as apatite and whitlockite, as well as

minor amounts of metal and troilite (Duke and Silver, 1967).

The euerites can be divided into several classes based on structural and chemical composition.

Structurally, the meteorites are divided into monomict and polymict breeciated meteorites. Classically,

euerites were monomict and howardites were polymict. The new influx of meteorites from Antarctica

has necessitated the addition of a mineralogical-chemical criteria which adds a group called polymict

eucrites. These have a polymict structure, but eucritic compositions (Delaney, et al., 1983). The

polymict eucrites contain clasts that represent a wide range of igneous lithologies, including eucrites,

two-pyroxene mafic rocks, pigeonite cumulate eucrites, alkali-enriched mafic rocks, diogenites, fayalite

rich clasts and feldspar cumulate euerites. (Delaney, et al., 1984).

There are two models for the formation of the euerites. Both models depend upon different

interpretations of the same set of samples and analyses. Mason (1962) proposed that the eucrites

formed by the complete melting of an asteroid and that the meteorites might be related as successive

members of a fractional crystallization sequence from a melt of approximately chondritic composition.

The eucrites were derived from a thin surface ernst, while the core consisted of pallasitic material

(another type of stony-iron meteorite) surrounded by a diogenitic mantle (Mason, 1967). Stolper

(1977) proposed partial melting of the euerite parent body in successive stages that produced the

different types of basaltic achondrites. The different groupings of meteorites were produced when

melting occurred in the source region from which earlier melts had been removed.

In 1969 a important event happened in the world of meteoritic.s: meteorites were discovered in

abundance on the ice in Antarctica. Prior to this about 2000 meteorites had been identified in the 250

years since meteorites were recognized to have come from outside the Earth. Since 1969,

approximately 12000 meteorite samples have been recovered from the ice in Antarctica by teams of

scientists sent from the US and Japan. Samples from Antarctica are individual rocks that have been

picked up from the ice. Some of these were members of the same meteorite that shattered before or

upon impact with the ice. Chemical and petrographic analysis allows these to be paired. This means

that the number of samples from Antarctica is much larger actual number of meteorites represented.

Meteorites from Antarctica have extended the amount of material available on which to do

meteorite research, as well as filled in gaps in existing suites of meteorites. In addition, new types of

24-3
L_

'I"--

[] ,11 X 4,

-I-

| I I

I I I I ! I

6/6w _0

i |

24-4
extraterrestrial material have been discovered, including meteorites that originate from a known source

- the Moon.

About 4300 meteorite samples have been returned from Antarctica by US scientists, of which "80

have been classified as basaltic achondrites (Score and Lindstrom, 1990). This includes 56 samples of

eucrites which represent approximately 28 meteorites, as well as 17 howardite samples and 8 diogenite

samples.

Meteorites are traditionally divided into two types - falls and finds. "Falls" are meteorites that have

been recovered from observed meteorite falls. Therefore their residence time upon the earth is well

documented. Finds are stones that have been found and subsequently recognized as meteoritic. Their

residence time upon the earth is unknown, although there are ways that this can be approximated

• (Schultz, 1990). Most prior meteorite work has been done on material from falls. This reduces the

chance that observations are the result of interactions that occurred upon the earth rather than the

processes that are of interest - those that occurred during the formation and evolution of the solar

system.

With the wealth of material coming from Antarctica, this strategy has changed. Meteorites that are

recovered from the ice have spent from an average of about 230,000 years up to as long as 1,000,000

years immersed in the Antarctic icesheet (Schultz, 1990). Despite the low temperatures, it has come to

be recognized that chemical reactions are occurring that alter the meteorites from their pristine state

(Gooding, 1990). These meteorites must be carefully examined to look for the effects of weathering

prior to using them in any studies of their origin.

Prior work with the Antarctic eucrites has led to the definition of two groupings based on the

abundances of the REE: those with normal REE patterns, and those with abnormal patterns, mostly

apparent enrichments in Ce and Eu (Mittlefehldt and Lindstrom, 1990). This is an important

observation. The REE are used extensively to model igneous materials and abnormal abundances due

to causes other than igneous processes must be examined.

The REE are incompatible in most minerals that crystallize in a melt. They tend to act as a group

and not be fractionated from one another easily. There are slight differences in their size, however,

and this leads to variable incorporation into mineral phases and consequent known fractionations. The

fractionations are smooth functions of atomic number. An analysis of the REE contents of a suite of

related geologic materials provides evidence for the geochemical history of these samples. The only

common exception to the smooth fractionation is Eu because under certain oxidizing-reducing (redox)

conditions, the element will exist in the + 2 oxidation state rather than the more normal + 3 state

exhibited by the other REE when the conditions are reducing enough. In the + 2 state, it has a

different ionic radius, and is incorporated into different mineral phases than the other REE.

In all cases, the REE are considered incompatible, and do not readily enter into the crystallizing

minerals until the late stages when they are higher in abundance in the melt. As the melt continues to

crystallize, the solids will be progressively more concentrated in REE as the REE are concentrated in

the melt. Therefore, early and late materials from a single melt body can be determined by their REE

abundances. The size of the Eu anomaly gives dues as to how much plagloclase has been previously

crystallized from the melt.

Abnormalities in the REE pattern, one or more elements breaking the smooth pattern, are not

readily explained in igneous materials. In chondritic materials, which are presumed to condense from

the solar nebula, abnormalities are sometimes observed which can be attributed to localized redox

conditions in the solar nebula. In an achondritic material, however, these abnormalities will have been

reset by melting and reerystallization.

It has recently been proposed (Mittlefehldt and Lindstrom, 1990) that the abnormalities observed in

some Antarctic eucrites are caused by terrestrial weathering that occurs in the Antarctic ice. The

primary REE minerals in the eucrites are the calcium phosphates. The phosphates are readily

susceptible to dissolution in slightly acidic solutions. It was proposed that the phosphates dissolved

24-5
during the residence of the meteorite in Antarctica. When the phosphates dissolve and are removed

from the meteorite, they carry with them a large part of the REE budget of the meteorite. Because

Eu has been preferentially crystallized in the plagioclase, which is much less susceptible to alteration,

Eu is retained in the meteorite. It is proposed that the Ce anomaly arises because it is oxidized to the

+ 4 state by the acidic conditions that leach the apatite. Ce + 4 forms an insoluble Ce oxide which

precipitates and remains behind in the meteorite. Therefore, the abnormalities are not enrichments of

Ce and Eu, but depletions of the ot_er REE.

This doesn't appear to happen in all cases. There are Antarcticeucrites with normal REE patterns

that fall within the range of patterns exhibited by non-Antarctic eucrites. However, the presence of

these abnormal Antarctic eucrites presents problems for workers who model the origins and evolution

of the eucrites using REE data. Using abnormal eucrites will lead to erroneous results in models. This

study had a two-fold purpose: first to enable abnormal eucrites to be readily recognized and second to

look at ways to use abnormal eucrites in petrogenetic models.

SAMPLES

In the first case a suite of eucrites from a variety of locales was analyzed. Included in the set were

five non-Antarctic meteorites to enable comparisons to be made with stones that have not undergone

Antarctic exposure. All five non-Antarctic meteorites were falls. Antarctic meteorites were sampled in

a variety of ways. Several groupings of dast and matrix samples were taken, as well as a paired

interior/exterior samples. This latter was done with the assumption that weathering is more extensive

on the exposed outer surface of a rock than in the enclosed interior of a rock. The latter assumption is

not always true, as cracks and fissures in the stone allow water to enter the supposedly pristine interior

of the rock. Care must be taken to examine all samples for evidence of weathering, regardless of

whether it is an interior or an exterior sample. The easiest indication of weathering is ferrous rust,

which can result from the oxidation of metal and troilite in the sample. Unfortunately, in the case of

eucrites, these minerals are rare and the ubiquitous weathering is the alteration of silicates to produce

a sialic rust (Gooding, 1990), which does not have the characteristic "rusty"color of weathered iron.

The second part of the study attempted to find a way to use the abnormal Antarctic eucrites. An

abnormal sample, LEW85300,57, examined in an earlier study (Mittlefehdlt and Lindstrom, 1990) was

utilized. The sample was separated into its constituent minerals. If the weathering truly affected

primarily the phosphates, the primary pyroxene and plagioclase should be minimally affected. Minerals

separates were performed in two different ways: magnetic separation and heavy liquid density

separations.

The separates were washed with a series of different acids to remove phosphates, cerium oxide, and

other interstitial material from the pyroxene and plag/oclase. This should remove the phases that

caused the abnormal REE patterns in the whole rocks.

EXPERIMENTAL

All samples were described, obvious weathered material was removed (this was primarily rusty

appearing material), the samples crushed in a diatomite mortar and pestle and packaged in silica tubes

for irradiation. In addition to the meteorite samples, four standard samples were included to

determine concentrations, as well as three standard materials included as system monitors. Irradiation

was performed at the University of Missouri Research Reactor at a thermal neutron flux of "5 x 1013

n-cm-2-sec-1. The samples were counted three times in the JSC neutron activation facility to obtain

data on a number of nuclides with varying hail-lives. The spectra were processed in an updated version

of the TEABAGS program (Lindstrom and Korotev, 1982) and further processed using undocumented

24-6
software to perform various corrections and determine concentrations. The final results are available

from the author at the listed address.

RESULTS

The results were examined in a variety of ways. Antarctic samples were compared to the nonAntarctic meteorites analyzed, clasts were compared to matrix and interior samples were compared to

exterior samples. Observations of the sample S-e-/sai-e Summarized below and followed by a discussion

of the entire suite of samples.

LEW88005 - Three splits of this sample were analyzed, two clasts (,13 and ,16) and one matrix (,22)

sample. Split ,16 showed little weathering, while split ,13 had obvious weathering, mostly non-ferrous.

The matrix had slight amounts of weathering. The two clasts were different in their chemistries,

especially when compared to the matrix. ,16 tended to track the matrix more closely than did the more

obviously weathered ,13. All of the splits exhibited Ce anomalies; the two clast samples exhibited Eu

anomalies, but the matrix sample did not.Barium was enriched in the clast relative to the matrix, and

Cr and Co depleted; in all cases variations in split ,16 were more pronounced than split ,13. Split ,13

was depleted in the REE relative to the matrix, while split ,16 was not.

EET87542 - Three splits were also analyzed from EET87542. The two matrix (,11 and ,16) samples

and one clast (,16) sample were all remarkably consistent in their chemistries. The only exceptions

were U, which was enhanced and K and Na which were slightly depleted in the clast relative to the

matrix. None of the samples exhibited Ce anomalies. All of the samples were described as having

small rusty patches pervasive over the surface, larger patches of which were removed.

HOW88401 - Five samples were taken from HOW88401, both clast - matrix pairs and an interior -

exterior pair. Splits ,13 and ,15 were compared as an interior/exterior pair, while clasts splits ,9,13 and

,21 were compared to matrix split ,25. This meteorite showed more variety in its chemistry than the

other clast - matrix pairings. There was a wide spread of REE abundances, as well as a variety of

patterns. Splits ,13 and ,25 showed the Ce anomaly indicative of abnormal eucrites. With the exception

of split ,15, the exterior sample, none of the samples were originally described as having any obvious

weathering. Split ,21 is depleted in all of the measured elements relative to the matrix. Split ,9 was

enriched in all elements except Fe, Sc, Cr, Co and Eu, which are the same as the matrix. Split ,13 was

depleted in all elements relative to the matrix except Ca, Fe, Sc, Cr and Co. Split ,13 had a large piece

of chromite, which is shown by its enriched Cr. The matrix pattern was fiat at 7 times the cosmic

abundance of the REE with no Eu anomaly, but a positive Ce anomaly.

The interior/exterior comparison leads to some interesting observations. First, the interior is

depleted in all elements relative to the exterior except for Fe, Cr and So. Fe and Cr are enriched in the

interior, while Sc is not changed. The REE in the exterior sample are the most enriched of all the

splits from this sample, has no Ce anomaly, but does have a pronounced negative Eu anomaly.

Other Eucrites - Of the remaining five samples, three exhibited Ce anomalies and two did not. One

of the latter samples (EET85548,12) is apparently a plagioclase cumulate with low REE abundances.

The Ce abundance was below the detection limit, so it was not possible to tell if there was a Ce

anomaly. This sample was observed to have slight weathering. Of the other four samples, two were

described as having little to no weathering (LEW85353,6 and EET87520,14), while the other two

exhibited mild weathering (LEW87026,11 and RKPAS0204,16). The samples were all whole rock, and

the abundances of elements other than the REE tended to fall within ranges established by nonAntarctic eucrites with the exception of EET85548,12 the plagioclase cumulate.

LEW85300,57 - In this study only mineral separates were analyzed of LEW85300,57. In a prior study

(Mittlefehldt and Lindstrom, 1990), a suite of whole rock samples from the_LEW8530x suite were

analyzed. The ,57 split exhibited extensively disturbed REE systematics. It was chosen for this study

because of this. Four mineral separates were analyzed, of which data was obtained for three. The two

24-7
pyroxene samples were almost identical, except for Eu, which was more depleted in the heavy density

pyroxene separate (3.55 < d < 3.7) than in the pyroxene obtained by magnetic separation. The pattern

obtained was a typical pyroxene pattern with depleted LREE relative to HREE and a negative Eu

anomaly. The plagioclase pattern obtained from the magnetic separation was also a typical pattern for

plagioclase: LREE enriched and a strong positive Eu anomaly. None of these samples exhibited a Ce

anomaly.

DISCUSSION

In general, the Antarctic meteorites fall in the same range of abundances that the non-Antarctic

eucrites do. The exceptions were generally readily explained, such as being extensively weathered

(HOW88401,15) or a different lithology (the cumulate eucrite EET87548,12).

The suite of samples as a whole was examined to determine if there was a simple way to distinguish

abnormal from normal Antarctic eucrites. Abnormal eucrites have been defmed as samples which

have more than a 10% fractionation of La from Ce. Seven of the sixteen samples exhibited Ce

anomalies, however only two of them show greater than 20% fractionation. This data set, therefore is

not a particularly good one for looking at differentiating normal from abnormal eucrites (Figure 2).

Easy, obvious ways of differentiating the two groups are not particularly useful. Weathering,

identified by naked eye or using a binocular microscope, whether ferrous or sialic, shows no apparent

correlation between the amount of weathering observed on the meteorite and the presence of a Ce

anomaly. Of the seven samples which had a Ce anomaly, four of them had no obvious weathering.

The most heavily weathered sample, the exterior one, had no Ce anomaly.

Even when this data set is combined with earlier data collected to look at the Ce anomaly problem,

no simple result comes out that unequlvocably sets apart the abnormal eucrites. Previous work has

shown that chondrite normalized Ce/La and Eu/Sm deviate from a chondritic ratio of one more

extensively in the Antarctic meteorites. This is not clearly demonstrated in this limited data set, but the

one sample in which Eu/Sm deviates from chondritic is one of the abnormal samples. The other

samples fall within a typical range as established by non-Antarctic eucrites. It was attempted to use Hf,

another incompatible element in the same fashion, but similar results were obtained (Figure 3).

The interior - exterior pair in this study showed depletions for all elements except iron and

chromium in the interior of the sample relative to the exterior (Figure 4). Apparently the materials

being removed contain Ca, Na, and most of the trace elements. This would correspond to phosphates

and other interstitial material. Pyroxene, which contains most of the iron, and chromite, which

contains the chromium, are less susceptible to weathering and are not removed.

Leached materials appear to be removed from the interior and deposited on the outside of

HOW88401. However, this is not always the case. Other samples from the prior study show variable

results for interior/exterior couples. The materials leached from the interior are sometimes deposited

on the exterior of the sample, but in other cases, are completely removed from the sample, it would be

interesting to analyze the ice in contact with an abnormal eucrite and see if the leached elements end

up in the ice,

Finally, the attempt to see if abnormal eucrites potentially could be used for geochemical modelling

seems to have been successful. The mineral separates from LEW85300,57, a decidedly disturbed

eucrite, show normal REE patterns with no evidence of the disturbance present in the whole rock.

Comparisons between the results obtained in this study and earlier studies on mineral separates from

non-Antarctic eucrites show good agreement (Duke and Silver, 1967; Schnetzler and Philpotts, 1969).

According to our results, the Ce anomaly does arise in the interstitial material as hypothesized

(Mittlefehldt and Lindstrom, 1990) and not in the major minerals. This result does not agree with an

earlier study by Heavilon and Crozaz, 1990 using in situ secondary ion mass spectrometry. They looked

24-8
5O

50

20

10

2

0.5

a,

| Stannem

X Sioux County

A Moore County

x Paaamonte

| I I I I _ I I I I I I I I l

La Ce Sm Eu Tb Yb Lu

20'

10,

2

b*

LEW88005

O ,13 clast

,16 clast

X ,22 matrix

0,5 i i i i i i i i i l i ! t t i

La Ce Sm Eu Tb Yb Lu

5O

2O

10,

5

2

0,_=

5O

II m

EET87542

O ,11 matrix

! ,16 rnatdx

X ,16 clast

I ! I I I I I 1 I I I I I I I

La Ce Sm Eu 1"o Yb Lu

el

2O

!s

1

0,5 I i i I I [ I I I --I i i I I i

La Ca) Sm Eu Tb Yb Lu

5O

20'

10

2

ut

x ,25 mabix

0,5 I I I I I I I I J I ! I I I !

La Ce Sm Eu Tb Yb Lu

50

20'

10

i5

2

1

0.5

f.

LEW85300,57

o plag I/

, pyx (mag) I/

La Ce $m Eu Tb Yb Lu

Figure 2.- The REE patterns for the eucrltes analyzed in this study.

a) The non-Antarctic eucrites, b) LEW88005, c) EET87542, d) HOW88401,

e) a set of whole rock samples, t) mineral separates.

24-9
b=

_

• .D.

3

2

=

..... "- 1

0

0

te

1 2 3 4 5

La

al

L L i = ,

io

0 1 2 3 4 5

La

Figure 3.- Ratios of trace elements used to

differences between Antarctic and

meteorites, a. Ce/La b. Ce/Hf.

demonstrate

non-Antarctic

0

g_

(o

fe

eq}

E

m

_J

Interior/Exterior Comparison

_

0

BBB BBB

i •

Na Ca Fe Sc Or Co La CeSmEu Tb Yb Lu HI TaTh

Element

Figure 4.- Demonstration of the migration of elements

between the interior and exterior of HOW88401.

24-10
at thin sections of the same meteorite, LEW85300, and found Ce anomalies present in pyroxene. Both

positive and negative Ce anomalies were present, including adjacent spots with opposite anomalies or

even no anomaly. The anomaly was randomly distributed. If this is real, the small scale variations in

individual grains may be averaged out by the larger amounts of material analyzed using instrumental

neutron activation analysis, as in this study. Or, the variable results may be due to interstitial material

smeared on the surface of the thin section during preparation. In our study we removed these types of

potential contaminants with the acid leaches. Further work needs to be done to resolve this problem.

Potentially, however, it appears that if a sample could be readily identified as abnormal, it could still

be used for doing geochemical modelling if care is taken in handling the materials, i.e., using minerals

separates.

CONCLUSION

In conclusion, this study did not establish an easy way to distinguish abnormal from normal Antarctic

eucrites. However, if the sample is identified as abnormal, it can be used for modelling if mineral

separates of the pyroxenes and plagioclases are used rather than whole rock data. This will require

extra work on the part of the investigator, but will allow meteorites from Antarctica to be used in

investigations of the evolution of the Solar System.

24-11
ACKNOWLEDGEMENT

I would like to acknowledge and thank David W. Mittlefehldt of Lockheed ESC for his help and

support in this project. He re-introduced me to the neutron activation lab, and he provided access to

his vast knowledge of the eucrites and other basaltic achondrites.

24-12
REFERENCES

Delaney, J.S., Prinz, M. and Takeda, H.: The Polymict Eucrites. Jour. Geophys. Res., vol. 89, 1984,

pp. c251-c288.

Delaney, J.S., Takeda, H., Prinz, M., Nehru, C.E. and Harlow, G.E.: The Nomenclature of Polymict

Basaltic Achondrites. Meteoritics, vol. 18, 1983, pp. 103-111.

Duke, M.B. and Silver, L.T.: Petrology of eucrites, howardites and mesosiderites. Geochim.

Cosmochim. Acta, vol. 31, 1967, pp. 1637-1665.

Gooding, J.L.: Clay-mineraloid weathering in Antarctic meteorites. Geochirn. Cosrnochim. Acta, vol.

50, 1986, pp. 2215-2223.

Heavilon, C.F. and Crozaz G.: Ce anomalies in the Antarctic eucrite LEW85300. Lunar Planet. Sci.,

vol. XXI, 1990,pp. 487-488.

Lindstrom, D.J. and Korotev, R.L.: TEABAGS: computer programs for instrumental neutron

activation analysis. J. Radioanal. Chem., vol. 70, 1982, pp. 439-458.

Mason, B.: Meteorites. John Wiley and Sons, 1962.

Mason, B.: Meteorites. Amer. Scient., vol 51, 1967, pp.429-455.

Mittlefehldt, D.W.: Petrographic and chemical characterization of igneous fithic clasts from

mesosiderites and howardites and comparison with eucrites and diogenites. Geochim. Cosmochim.

Acta, vol. 43, 1979, pp. 1917-1935.

Mittlefehldt, D.W. and Lindstrom, M.M.: Generation of abnormal trace element abundances in

Antarctic eucrites by weathering processes. Geochim. Cosrnochim. Acta, 1990, in press.

Schnetzler, C.C. and Philpotts, J.A.: Genesis of the calcium-rich achondrites in fight of rare-earth and

barium concentration. In Meteorite Research, ed. P. M. Millman, Reidel, pp. 206-216.

Schultz, L.: Terrestrial ages and weathering of Antarctic meteorites. LPI Tech. Rpt. 90-03, 1990,

pp. 56-59.

Score, R. and Lindstrom, M.M.: Guide to the U.S. Collection of Antarctic Meteorites 1976-1988. Ant.

Met. Newslet., vol. 13, 1990.

Stolper, E.: Experimental petrology of eucritic meteorites. Geochim. Cosmochim. Acta, vo141, 1977,

pp.587-611.

24-13

N91-27114

ASSESSMENT OF THE PHARMACODYNAMICS OF INTRANASAL, INTRAVENOUS

AND ORAL SCOPOLAMINE

Final Report

NASA/ASEE Summer Faculty Fellowship Program

Johnson Space Center

i 1990

Prepared By:

Academic Rank:

University & Department:

Karen J. Tietze, Pharm. D.

Associate Professor

Philadelphia College of Pharmacy

and Science

Department of Pharmacy Practice/

Pharmacy Administration

Philadelphia, PA 19104-4495

NASA/JSC

Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Space and Life Sciences

Medical Sciences

Biomedical Operations and Research

Lakshmi Putcha, Ph.D.

August i0, 1990

NGT-44-005-803

25- !
ABSTRACT

Space motion sickness is an important issue in the space medical

sciences program. Although scopolamine has been documented to be

an effective antimotion sickness agent, limited information is

available regarding the pharmacodynamics of scopolamine and the

relationship between the pharmacodynamics and the

pharmacokinetics of the drug. One of the objectives of the

ongoing clinical experimental protocol "Pharmacokinetics of

Intranasal Scopolamine in Normal Subjects" is to evaluate the

pharmacodynamics of scopolamine using salivary flow rate and pH

profiles and cognitive performance tests as pharmacodynamic

parameters.

Normal volunteers collected saliva and performed the NTI

Multiresource Performance Battery test at designated time

intervals to establish control salivary flow rates, salivary pH

profiles, and the characteristics of the learning curve for the

performance program under normal conditions. Salivary flow

ranged between 50 to 60 ml/hr with pH ranging between 6.5 to 7

over a 2 hour period. Preliminary assessment of the performance

data from the first few volunteers suggests that the duration of

the learning curve is short reaching a plateau after two test

sessions for task one and five to eight test sessions for task

tWO.

In the clinical part of the study, saliva samples and performance

test scores are collected from healthy nonsmoking subjects after

receiving a single 0.4 mg dose of either intranasal, intravenous,

or oral scopolamine. Both salivary flow rate and pH decreased as

a function of time after administration of all the three dosage

forms. The pharmacodynamic effect as indicated by salivary flow

rate and pH was the greatest after intravenous administration and

the least after oral administration. Preliminary analysis of the

performance parameters for a limited number of subjects indicates

that there is no significant change in task one or task two

scores following the administration of any of the three dosage

forms. However, the mean correct response times for tasks

following the transition from one task to another appear to be

slightly longer during the first few hours following scopolamine

administration.

Preliminary evaluation of this limited data suggests that

salivary flow rate and pH are good pharmacodynamic indicators for

scopolamine, and that intranasal scopolamine produces a reliable

and consistant pharmacodynamlc effect. Limited performance data

collected so far suggest that scopolamine does not decrease

cognitive function as measured with the NTI Multiresource

Performance Battery. Results from the study may be useful in

identifying and selecting effective dosage forms for the

management of SMS.

25- 2
INTRODUCTION

Space motion sickness (SMS) is a major concern in the space

medical sciences program. Approximately 50-60% of astronauts

experience SMS, which usually affects astronauts during the first

three days of flight and may be severe enough to compromise the

operational performance of the astronaut (i). Use of antimotion

sickness drugs in flight has been limited by drug ineffectiveness

or by undesirable side effects that impair the operational

performance of the astronaut. Scopolamine, an anticholinergic

drug, has been documented to be an effective antimotion sickness

agent (2,3). It has been suggested that scopolamine interupts

the acetylcholine-dependent neurotransmission of information from

the vestibular organs to the vomiting centers of the brainstem

(4,5). At present, scopolamine (0.4 mg) in combination with

dextroamphetamine (5 mg) is administered orally as a capsule for

the prophylaxis and treatment of SMS. An important side effect

of operational significance induced by scopolamine is the

impairment of cognitive performance. Another side effect that is

not of clinical importance but can be used as a pharmacodynamic

indicator is inhibition of salivary secretion.

Although a veteran drug, the pharmacodynamics of scopolamine and

the relationship between the pharmacokinetics and the

pharmacodynamics of the drug have not been well characterized.

Several investigators have documented that oral scopolamine

impairs cognitive performance; the techniques used are complex

and time-consuming (6-12). Identification of a sensitive and

convenient method for the assessment of drug-induced performance

changes would be helpful in establishing the selection criteria

for drugs used for the prophylaxis and treatment of SMS and also

for the pharmacodynamic assessment of these drugs during space

flight. The project therefore was designed to i) establish

salivary parameters in control subjects using the Sarstedt

Salivette system, 2) to determine cognitive performance in

control subjects using the NTI Multiresource Performance Battery,

and 3) to compare the effect of a 0.4 mg dose of intranasal,

intravenous, and oral scopolamine on these parameters.

MATERIALS AND METHODS

Scopolamine Study

A conventional clinial IND protocol was designed to evaluate the

pharmacokinetics and bioavailability of the three dosage forms.

Healthy nonsmoking male subjects ranging between 18 to 40 years

of age are participating in the study. All subjects passed an

Air Force Class III physical examination, including hematology,

electrocardiogram, urinalysis, and biochemistry tests before

admission to the study. The study protocol was approved by the

human research review committees of NASA-JSC and the St. John

Hospital. Informed consent was obtained from all the subjects

prior to the study.

25-3
The study consists of an intranasal, an intravenous, and an oral

treatment phase. A 0.4 mg dose is used in all three treatment

phases. Each 24 hour treatment phase is separated by at least a

two week period. The treatments are administered in a

predetermined randomized crossover design. Oral scopolamine was

customed manufactured by AC Engle & Co, Houston, Texas,

intravenous scopolamine dosage form was procured from a pharmacy,

and intranasal scopolamine was customed-manufactured by the

Pharmaceutics Department of the University of Houston, Houston,

Texas. Subjects are admitted to the hospital the night before

each study period. Each subject is given written and oral

instructions for the Multiresource Performance Battery computer

program and trained until they are comfortable with the computer

keyboard and the program. Mixed saliva is collected over a two

minute interval using the Sarstedt Salivette system (a cotton

swab collection system) before and at 5, I0, 15, 20, 30, 45, 60,

90 minutes and 2, 3, 4, 5, 6, 8, i0, and 12 hours after dosing.

The Multiresource Performance Battery test is taken pre-dose and

at I, 2, 3, 4, 5, 6, 8, i0, and 12 hours post-dose.

The investigational Multiresource Performance Battery computer

program was obtained from NTI, Inc. The program runs on a

portable personal computer and is designed to test the higher

cognitive functions most likely to be affected by centrally

active drugs. The program consists of two timed tasks presented

pseudosimultaneously with an attention allocation indicator.

Both tasks appear on the screen simultaneously with two bars of

differing height at the bottom center of the screen. The active

task is identified as the task on the side of the screen with the

highest bar. The math task appears on the left of the screen and

consists of a two step (addition, subtraction, or combination)

single digit math problem. The key "z" is pressed if the answer

is less than five and the key "v" is pressed if the answer is

greater than five. The memory task appears on the right of the

screen and consists of two single digit numbers separated by a

line. The task is to determine if the numerator of the current

active task matches the denominator of the prior active task.

The key "m" is pressed if the numbers match and the key "/" is

pressed if the numbers do not match. The test battery consists

of i00 randomly generated tasks with an approximately equal

distribution between the two tasks. Each i00 task test battery

is preceded by a randomly generated 20 task warm-up battery that

may be repeated until the subject no longer feels his performance

is improving. The program allows for a maximum of five seconds

for a response before randomly changing to a new test in the same

task or to the alternate task. Performance parameters include

the percent correct, incorrect and timed out and the mean correct

response times for both tasks as well as the percent correct,

incorrect, and timed out and the mean correct response times for

the transitions to each task and for the total transitions.

25-4
Control Data Collection

Because the NTI Multiresource Performance Battery is a new

investigational program, no data exists on the learning curve

with the program. Preliminary analysis of data from the first

few subjects suggested a learning curve interval which may mask

the drug effect. Another contributing factor of variability may

be the time of day (i.e. morning vs. afternoon). To address

these issues, ten normal volunteers were selected for twice daily

testing for ten days. Each subject received verbal and written

instructions for the Multiresource Performance Battery and was

then tested twice daily (in the morning and afternoon) for a

total of 20 tests over a three week period. The performance

parameters (percent correct, incorrect and timed out; mean

correct response times; and transition percents and response

times) for each task will be analyzed and the learning curve will

be characterized.

To evaluate whether the repeated collection of saliva with the

Salivette system during the first hour of the study would alter

salivary flow dynamics, four volunteers repeated the saliva

sampling protocol for the clinical study. Salivary volume and pH

were measured and flow rate calculated for each time period.

PRELIMINARY RESULTS

Control Data

Salivary Data. Sample collection and data analysis for the

salivary data was completed. Salivary flow rate and pH appears

to be relatively constant over the twelve hour sampling period

using the Salivette system (Figure 1). Mean flow rate ranged

between 50 to 60 ml/hr and mean salivary pH ranged between 6.5

and 7.0. No significant changes in flow rate or pH were noticed

as a function of either the frequency of samples or time of day.

Performance Data. Data collection for this parameter is still in

progress. Preliminary assessment of the data from the first few

trials suggests that there is a subject dependent learning

pattern with the program. In some individuals there was no

improvement which suggests that these subjects did not exhibit a

learning curve, but in others there was an apparent learning

curve. There was a considerable intersubject variability in

scores, but plateau scores were relatively constant for each

individual. The learning curve appears to plateau after two test

sessions with the math task and after five to eight test sessions

with the memory task. There does not appear to be any difference

between scores achieved in the morning versus scores achieved in

the afternoon.

Scopolamine Study

Salivary Data. A limited number of subjects (intranasal n=8;

intravenous n=7; oral n=8) have completed the study so far.

25-5
Preliminary results from these subjects were evaluated and

reported here. Although all three dosage forms of scopolamine

appear to decrease salivary flow rate and salivary pH,

differences are apparent between the dosage forms (Figure 2).

Intravenous scopolamine markedly inhibited salivary flow rate and

lowered salivary pH. Flow was nearly totally inhibited in the

first 15 minutes following administration of the drug, with a

slow recovery to pre-dose flow in four to six hours. Salivary pH

fell from the pre-dose mean of 6.6 to 5 in the first 90 minutes

following intravenous administration of the drug, with slow

recovery to pre-dose value by five hours. Intranasal scopolamine

lowered salivary flow to about 5 ml/hr in about one hour with a

slow recovery at nearly the same rate as with intravenous

scopolamine. Salivary pH fell from the pre-dose mean of 6.7 to

4.8 in the first 90 minutes following the intranasal

administration of the drug, which was a slightly larger decline

than with the intravenous dosage form. Oral scopolamine caused a

small decrease in flow in about one hour, with recovery to predose flow by two hours. Salivary pH fell slightly from the predose mean of 6.7 to 6.3 in the first three hours following oral

administration.

Performance Data. Preliminary analysis of the performance

parameters for the first few trials suggests that there is no

change in task one or task two scores (percent correct, percent

incorrect, percent timed out) following the administration of

intranasal, intravenous, or oral scopolamine. None of the three

dosage formulations adversely affected the mean correct response

times for task one or task two. However, the mean correct

response times for performance of task one following transition

from task two and for performance of task two following

transition from task one appear to be slightly longer in the

first few hours following drug administration (Figure 3).

DIBCUSSION

The flow rate of mixed saliva following the administration of

anticholinergic drugs has been reported but was not well

characterized. Grundhofer and Gibaldi found that oral

administration of the anticholinergic drugs propantheline and

hexocyclium lowered salivary flow rate by approximately 75% in

two hours, with a gradual recovery to pre-dose rate by four to

six hours (13,14). Brand e t al reported that the administration

of scopolamine at oral doses of 0.42 and 0.7 mg lowered salivary

flow rate to approximately 50% of baseline by two to three hours

(15). Gordon et al found that transdermal scopolamine lowered

salivary flow rate by about 50% after 12 to 18 hours of

transdermal administration (16). The present investigation is

the first of its kind to utilize salivary characteristics for

pharmacodynamic evaluation of dosage forms of scopolamine.

Limited data collected so far indicate that there are differences

in the pharmacodynamic effect as suggested by salivary flow rate

after intranasal, intravenous, and oral administration of

scopolamine, with a fast and pronounced effect from the

25-6
intravenous dosage form and a relatively slow and minimal effect

from the oral dosage form. This appears to reflect the

differences in pharmacokinetics and bioavailability of the three

dosage forms.

Salivary pH depends on salivary flow (17). Therefore, it is

anticipated that salivary pH decreased with the decreased flow

rate. However, the lowest mean salivary pH following the

intravenous and intranasal administration of scopolamine is lower

than the 5.8 reported by Kreusser for unstimulated salivary

glands. Salivary pH appears to be a good indicator of the

differences in pharmacodynamic effect of different dosage forms,

which may be a function of the pharmacokinetic properties of the

three dosage forms.

Although psychometric testing has been used to assess the

pharmacodynamic effects and side effects of centrally active

drugs, limited information is available regarding the shapes and

duration of learning curves or their interference in repeated

measures testing. Schulz suggested that subjects should be given

a period of familiarization with the testing procedures followed

by repeated testing until the subjects achieve a predetermined

coeffiecient of variation (18). Our results suggest that there

is a learning curve with both tasks in our study, and that

although there is some intersubject variability in individual

plateaus for efficiency, the subjects require several tests to

reach peak efficiency with the memory task. This data suggests

that subjects require more experience with the program than is

currently being provided prior to testing after administration of

the drug.

Traditional psychometric testing has documented that oral

scopolamine impairs cognitive performance (6-12). Acetylcholine

appears to be important for the processes of sustained attention

and vigilance (7,19,20) as well as stimulus processing and

memory storage (21-26). Although early studies indicated that

scopolamine impaired longterm recall (23,27), more recent studies

indicate that scopolamine impairs tasks involving sustained

continuous attention or the storage of new information into

memory rather than recall of information memorized before drug

administration (8,11,12). In an investigation using a different

investigational NTI performance battery, it was reported that

scopolamine did not affect performance when using reaction time

as a parameter for either spatial or verbal resources, but that

scopolamine did impair the attention allocation system (28).

Although preliminary, present data appear to support the findings

that scopolamine impairs tasks requiring sustained continuous

attention and processing of newly memorized information.

CONCLUSION

Limited information regarding the pharmacodynamics of scopolamine

has been reported. This study was designed to quantitate the

differences in pharmacodynamics between the intranasal,

25-7
intravenous, and oral dosage formulations of scopolamine. Data

analysis for the completed studies will be performed and the

relationship between the pharmacodynamics and the

pharmacokinetics of the drug will be determined. It is

anticipated that the results from these studies will provide new

information that will be useful in the selection of appropriate

drugs and dosage forms for the management of space motion

sickness.

25- 8
-r"

Q.

)-

IZ

<

>

m

-I

<

09

7.50

7.00

6.50

6.00

5.50

5.00

4.50

_ T

±

I 1 I ! ] I

0 2 4 6 8 10 12

TIME (hours)

BASELINE SALIVARY FLOW

rE

O

.J

I,I.

70-

-

5O

4O

>- 30

IZ

<

>

"i 20

o9

10

0 I I ] I I I

0 2 4 6 8 10 12

TIME (hours)

FIGURE 1.-BASELINE SALIVARY FLOW RATE AND pH.

25-9
_al_var y pm

I

U)

6.50

Time (hours)

--I-- IN

n'=B

--@-- w

11=7

-- A- - Oral

hi8

Salivary Flow

--I!--- IN --@-- IV --A-- Oral

n=8 n=7 n=8

5060 _Ti T 4o

0

u.

L

>

m

AT &, , ",.. :t:: .... -Z> "_4'

;_ ,, ,, ,,' ,....... _ ..- /

I I

3O

2O

10

0

0

I I

2 4 6 8 10 12

Time (hours)

FIGURE 2.- SALIVARY FLOW RATE AND pH AFTER SCOPOLAMINE

ADMINISTRATION.

25- 10
Mean Transitions to Task 1 MCRT

o

In

E

v

in"

O

tO

09

tO

,f

0o

C

t=.

t5000

4000

3000

2000

1000

E i

:E

0 2 4 6 8 10 12

Time (hours)

--I-- tN

n=.B

--e-- tV

n,=7

--'A--- Oral

n,,B

Mean Transitions to Task 2 MCRT

O

¢ 5000

Ev

n" 4000 0

3000

0

2000

0

c 1000

C

0

0

L 1 I I I I

2 4 6 8 10 12

Time (hours)

FIGURE 3.- MEAN CORRECT RESPONSE TIMES FOR TRANSITIONS TO TASK

ONE AND TASK TWO AFTER SCOPOLAMINE ADMINISTRATION.

25- ]1
.

,

•

•

•

.

•

•

•

i0.

II.

12.

13.

Homick JL, VanderploegJM. The Neurovestibular System.

a_Sp_hysioiQq7 S and Medicine, Nicogossian AE, Hunt.on CL,

Pool SL, eds, Lea & Febiger, Philadelphia, 2nd ed,. 1989,

p. 159.

Brand JJ, Perry WLM. Drugs used in motion sickness•

Pharmacol Rev 1966; 18:895-24.

Wood CD, Mann. JE, Mann. BR, Redetzki HM, Wood M, Vekovius

WA. Side effects of antimotion sickness drugs. Aviat Space

Environ Med 1984; 58:113-16.

Kohl RL, Homick JL. Motion sickness:

the central cholinergic nervous system•

Rev 1983; 7:73-85.

a modulatory role for

Neurosci Biobehav

Wood CD, Graybiel A. Theory of antimotion sickness drug

mechanisms. Aerospace Med 1972; 43:249-52.

Schmedtje JF Jr., Oman CM, Letz R, Baker EL. Effects of

scopolamine and dextroamphetamine on human performance.

Aviat Space Environ Med 1988; 59:407-10.

Poulton EC, Edwards RS. Interactions, range effects, and

comparisons between tasks in experiments measuring

performance with pairs of stresses: mild heat and 1 mg of

L-hyoscine hydrobromide. Aerospace Med 1974; 45:75-41.

Parrott AC. The effects of transdermal scopolamine and four

dose levels of oral scopolamine (0•15, 0.3, 0.5, and 1.2 mg)

upon psychological performance. Psych.pharmacology 1986;

89:347-54•

Parrott AC. Transdermal scopolamine: effects of single and

repeated patches Upon psychologicl task performance.

Neuropsychobiology_1987; 17:53-9.

Rusted JM, Warburton DM. The effects of scopolamine on

working memory in healthy young volunteers•

Psych.pharmacology 1988; 96:145-52.

Rusted JM. Dissociative effects of scopolamine on working

memory in healthy young volunteers. Psych.pharmacology

1988; 96:487-92.

Flicker C, Serby M, Ferris SH. Scopolamine effects on

memory, language, visuospatial praxis and psychomotor speed•

Psych.pharmacology 1990; 100:243-50.

Grundhofer B, Gibaldi M. Biopharmaceutic factors that

influence effects of anticholinergic drugs: comparison of

propantheline, hexocyclium, and isopropamide. J Pharm Sci

1977; 66:1433-5.

25- 12
14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

Gibaldi M, Grundhofer B. Biopharmaceutic influences on the

anticholinergic effects of propantheline. Clin Pharmacol

Ther 1975; 17:457-61.

Brand JJ, Colquhoun WP, Perry WLM. Side effects of

L-hyoscine and cyclizine studied by objective tests.

Aerospace Med 1968; 39:999-1002.

Gordon C, Ben-Aryeh H, Attias J, Szargel R, Gutman D.

Effect of transdermal scopolamine on salivation. J Clin

Pharmacol 1985; 25:407-12.

Kreusser W, Heidland A, Hennemann H, Wigand ME, Knauf H.

Mono- and divalent electrolyte patterns, pCO2 and pH in

relation to flow rate in normal human parotid saliva. Eur J

Clin Invest 1972; 2:398-406.

Schulz R, Reimann IW. Practice effect of volunteers in

repeated psychometric testing. How to handle this

intervening variable in clinical pharmacology studies?

and Find Exptl Clin Pharmacol 1988; 10:657-661.

Meth

Colquhoun WP. Effects of hyoscine and meclizine on

vigilance and short-term memory. Brit J Industr Med 1962;

19:287-96.

Wesnes K, Warburton DM. Effects of scopolamine and nicotine

on human rapid information processing performance.

Psychopharmacology 1984; 82:147-50.

Crow TJ, Grove-White IG. An analysis of the learning

deficit following hyoscine administration to man. Br J

Pharmac 1973; 49:322-7.

Drachman DA. Memory and cognitive function in man: does

the cholinergic system have a specific role? Neurology

1977; 27:783-90.

Ghoneim MM, Mewaldt SP. Effects of diazepam and scopolamine

on storage, retrieval and organizational processes in

memory. Psychopharmacologia 1975; 44:257-62.

Jones DM, Jones MEH, Lewis MJ, Spriggs TLB. Drugs and human

memory: effects of low doses of nitrazepam and hyoscine on

retention. Br J Clin Pharmac 1979; 7:479-83.

Deutsch JA. The cholinergic synapse and the site of memory.

Science 1971; 174; 788-94.

Warburton DM, Wesnes K. Drugs as research tools in

psychology: cholinergic drugs and information processing.

Neurospychobiology 1984; 11:121-32.

25-|3
27.

28.

Peterson RC. Scopolamine induced learning failures in man.

Psychopharmacology 1977; 52:283-9.

Botha, CJ: The effects of low-dose scopolamine on selective

cognitive functions. M.S. Thesis, Wright State University,

1990.

25- 14
N 9 1 - 2_ __i7

HIGH ACCURACY OPTICAL RATE SENSOR _-_.... I_

Final Report

NASA/ASEE Summer Faculty Fellowship Program - 1990 f"

Johnson Space Center _, __ L_ _j(_

Prepared By: J. Uhde-Lacovara, Ph.D.

Academic Rank: Assistant Professor

University & Department Stevens Institute of Technology

Dept. of Electrical Engineering

and Computer Science

NASA/JSC

Directorate: Engineering

Division: Navigation, Control and Aeronautics

Branch: Navigation and Guidance Systems

JSC Colleague

Date Submitted

Indulis Saulietis

August 17, 1990

Contract Number NGT-44-005-803

26-1
ABSTRACT

Optical rate sensors, in particular CCD arrays, will be used on Space

Station Freedom to track stars in order to provide inertial attitude reference.

An algorithm to provide attitude rate information by directly manipulating the

sensor pixel intensity output is presented. The star image produced by a sensor

in the laboratory is modeled. Simulated, moving star images are generated, and

the algorithm is applied to this data for a star moving at a constant rate. The

algorithm produces accurate derived rate for the above data. A step rate change

requires two frames for the output of the algorithm to accurately reflect the

new rate. When zero mean Gaussian noise with a standard deviation of 5 is added

to the simulated data of a star image moving at a constant rate, the algorithm

derives the rate with an error of 1.9 per cent at a rate of 1.28 pixels per

frame.

26-2
INTRODUCTION

Optical rate sensors will be of great use on Space Station Freedom as part

of the onboard guidance, navigation, and control system. These sensors can be

used to track stars to provide inertial attitude reference. The information may

also be used by astronomical experiments to allow reference to a known star

catalog to provide precise pointing of instruments. Optical sensors can be used

on Freedom to keep track of other nearby objects such as incoming orbiters.

Co-orbitting platforms may also use optical sensors for their GN&C needs.

Because optical sensors will provide attitude information to Space Station,

it would be useful to derive attitude rate could from these sensors. An optical

rate sensor is currently being developed in the Navigation, Control, and Aeronautics Division at Johnson Space Center. The sensor will look at stars to

obtain both attitude information and to derive attitude rate. A Videk Megapixel

camera is supported by a Compaq Deskpro 386 for data capture and processing.

The Videk camera uses a Kodak charge coupled device (CCD) array. This array is

made up of 1320 horizontal by 1035 vertical pixels. The pixels are 6.8 microns

square and the array is full fill. The camera is capable of 7 still frames per

second; it has a grey scale of 256 levels.

In previous work on the rate sensor [1,2], two different approaches were

taken. The first approach was to differentiate the centroid of the star image

to obtain derived rate. A discrete-time, nonrecursive, filtering algorithm was

used. The Space Station accuracy and bandwidth requirements could not simultaneously be met using this technique. The second approach was to directly manipulate the image sensor data to obtain derived rate. This technique was successfully simulated for a highly idealized star image in the presence of additive

Gaussian noise.

Source stars were simulated in the laboratory by a fiber-optic strand illuminated by a light source. This laboratory setup is shown in Figure 1. The

fiber-optic strand is mounted in a metal plate for stability. A filter plate

with various apertures and colored filters is placed in front of the fiber

optic. This assembly is attached to a micrometer drive to allow precise control

of the movement of the optical fiber. The simulated star light was then passed

through a collimator to make the light appear to emanate from a point source at

infinity. After the light passes through the collimator, it reaches the camera.

26-3
The camera data is captured using a frame grabber.

STAR IMAGE MODELING

A three dimensional plot of a star image obtained from the above laboratory

experiment is shown in Figure 2. The integration time, that is the time for

which the camera shutter was open and the array was exposed to the star light.

was 100 milliseconds. The data was captured for an array of 68 pixels in the

x-direction by 32 pixels in the y-direction. The plot presented is actually for

three frames of the same star image averaged for noise reduction.

The problem was first reduced from a two-dimensional problem to a

one-dimensional problem. Column sums were performed on the array to produce the

x-direction pixel sums, and row sums were performed to produce the y-direction

pixei sums. This gave a one-dimensional image which was fit by a Gaussian Curve

of the form;

I(x) = be

The original star image was thresholded to remove noise generated in the

unilluminated pixels. All pixels with intensities less than or equal to 3.0 had

their intensities set equal to zero for the thresholded star. This produced an

image in which all pixels with non-zero intensity were contiguous. The center

of mass and moment of inertia of the x-direction column sums were found for the

thresholdecl star image as follows:

X_ = 31.1 pixels

o- = 3.03 pixels

Figure 3 shows a plot of the x-direction pixel sums versus a generated Gaussian

with the same and , and with a peak intensity of 1.1533.

In general, a moving star image can be simulated by generating a set of

Gaussian curves in which the center of mass of the image changes as a function

of time. The equation for the intensity seen by a given pixel at a position x,

and time, t is given below:

I(x,t) = be

264
A set of Gaussian curves was generated for a star image with a constant rate of

motion in the x-direction. A Gaussian with a peak intensity of 1200, and moment

of inertia of 2.5 pixels was used to attempt to fit the central portion of the

thresholded x-direction column sums. A rate of 1.28 pixels per frame for 30

frames was used for the generated images to simulate data obtained in the laboratory.

RATE SENSOR ALGORITHM

Derivation of Algorithm

Centroiding a star image to derive rate is essentially a curve-fitting

problem in which the peak intensity and spread (moment of inertia) of the image

are unknown. Stars of different magnitudes will have different peak intensities

for the same integration time. If different integration times are used for the

same star, the spread of the image may change. Knowledge of the Gaussian function, and the type of rate to be sensed is exploited to eliminate the need to

determine the above parameters directly.

The algorithm is derived for the one-dimensional case, and is then extended

to two-dimensions. It is important to remember that the star data to be processed is discrete in time, space, and amplitude. The algorithm is first derived by assuming the rate is constant. This implies that the position of the

center of mass is the product of the rate in pixels per frame times the frame

number plus an offset to account for the original position at the first frame.

That is

X4 (0 = rf + 'q0

If the offset is made zero, the intensity for a given pixel, n, at a given

frame, f, for the constant rate is shown below:

-{ r÷

I(n,f) = be

26-5
Taking the ratio

frames gives

of the intensity for a given pixel, n, for two

_(,_- r (_o_I'_')_/7_._ =

be

be-( i,, - r_ ") _'/Z. d"2"

-(,r_-+ _.r=# - 2__'_/_.O -_-

e

successive

The natural logarithm of the ratio yields

In II(n'f+l)ll(n,f)

= (2nr - r2(2f + 1))/2 0"2

This is a linear function of f with slope proportional to r2 and 0 "2.

Similarly, the natural logarithm of the intensities for a given frame, f,

for two adjacent pixels is given by ....

In /l(n_+l'O / = -(2n + 1 -2rf)/2 O"7..

L_l(n't) ._

This is also a linear function of f with slope proportional to r and 0"2.

Taking the negative of the slope found previously, and dividing by the

slope found above, yields the rate, r. The logarithm of the ratio is equivalent

to subtracting the logarithms of the intensities. R should be noted that the

peak intensity, b, and the spread of the image, 0", need never be found.

Rate Algorithm

1) Find the natural logarithms of the pixel intensities

2) Find the frame to frame differences of the log intensities for a given

pixel; do this for several pixels and pairs of frames.

3) Find the frame to frame differences of the above; let the result of these

26-6
manipulations be represented by the constant, Kf = -r2/d" 2.

4) Find the pixel to pixel differences of the log intensities for a given

frame; do this for several frames and pairs of pixels.

5) Find the frame to frame differences of the above; let the result of these

manipulations be represented by the constant, Kp = r/d" 2.

6) Divide -Kf from 3) by Kp from 5) to obtain the rate.

RESULTS

Constant Rate Without Noise

The algorithm was applied to the simulated moving star data previously

described which had a rate of 1.28 pixels per frame for 30 frames. The resulting derived values are as follows:

Kf = -0.2621;

Kp = 0.02048;

rderive d -- 1.2800; (carried out to machine precision)

Step Rate Change Without Noise

Fifteen frames of data were simulated as before with a rate r1=1.2800

pixels per frame. At the sixteenth frame, the rate was increased by ten per

cent to r2-1.4080. Fifteen additional frame were generated at the new rate

for a total of 30 frames. The constant Kf settled to its new value by the

eighteenth frame; the constant Kp settled to its new value by the seventeenth

frame. The correct new rate could, therefore, be derived by the eighteenth

frame. Frames 16 and 17 produced outputs which could not be used to correctly

derive the rate. This region of indeterminacy will be dealt with in the same

way as noisy images. This method will be described in the next section.

Constant Rate Change With Noise

The algorithm was tested in the presence of noise. Gaussian noise with zero

mean and standard deviation of 5 was generated. The absolute value of the noise

26-7
was added to the 30 frames of generated star images moving at a constant rate

of 1.2800 pixels per frame. Figure 4 shows a log plot of the generated data. As

seen from Figure 4, the low intensity values were significantly distorted by

the noise. Thresholding was performed on the data; all log intensities less

than 4.5 were replaced with 0. The first five steps of the algorithm were performed on the noisy, thresholded data. Figure 5 shows a plot of the output of

the algorithm at step 3. The spikes and numerous zero values which occur are

artifacts of the thresholding. The desired data is the horizontal line just

below the zero line. This data needs to be extracted from the output. A similar

plot is seen for the output of step 5.

A histogramming technique similar to that employed in previous work was

applied to this data [2]. Histograms of the frame differences and of the pixel

differences were made. The peak frequency of zero was excluded because this is

an artifact of the thresholding. Taking the second highest peak frequencies,

the following values are obtained:

Kf = -0.2531;

Kp - 0.1940;

rderive d = 1.3044;

This represents an error of 1.9 per cent in the derived rate.

Improvements in this error performance might be obtained by increased

thresholding, preprocessing to reduce the noise in the image and more sophisticated histogramming techniques, it should also be noted that histogramming

techniques will handle a step rate change; the false rates obtained during the

transition phase will have a low probability of occurrence, and will therefore

not be included in the rate calculations. The rate is constantly updated by

removing stale estimates as fresh data points are added to the histogram.

Laboratory Data

Figure 6 shows the total intensity versus the column sums for the

x-direction pixels. This is 30 frames of data for a star moving at approximately four thirds of a pixel per frame. It can be seen from this plot that there

are noticeable dips in the intensity at regularly spaced intervals of 16 pixels. This phenomenon was n_ced toward 1heend of the summer program. An explanation of it was not obtained in time to compensate fully for the effect in

26-8
applying the algorithm. It may be stated that, for this corrupted data, an

approximation to the rate may be obtained from applying the algorithm, but the

results are inaccurate at this point. The sixteen pixel periodicity in the

sensor response is related to the physical process used to manufacture the CCD

array.

CONCLUSIONS

The rate algorithm described works well for simulated data for the optical

rate sensor. The open issues which will be addressed immediately are those of

refining the model of the star image which the sensor produces, and compensating for the systematic intensity errors introduced by the CCD array. A parametric study of the algorithm, in conjunction with real sensors, needs to be performed in order to determine such quantities as allowable noise, frame rate,

pixel size, number of grey levels, number of pixels and frames to be processed

at one time, etc.

The algorithm is easily extended to two-dimensions by taking both the

x-direction column sums to obtain the x-direction rate, and the y-direction row

sums to obtain the y-direction rate in field coordinates, the field coordinates

can then be transformed to the body coordinates of the vehicle.

26-9
REFERENCES

[1] Uhde-Lacovara, J., "Analysis of the Continuous Stellar Tracking Attitude

Reference (CSTAR) Attitude Rate Processor", NASA/ASEE Summer Faculty Fellowship

Program, 1986, Volume 2 (N87-25884 19-85).

[2] Uhde-Lacovara, J., "Optical Rate Sensor Algor_ms", NASA/ASEE Summer Faculty Fellowship Program, 1989, Volume 2 (N90-24985 18-80).

26-10
FIGURE 1

Rate Sensor Optical Bench

Star plate

Fiber Optic Filters Collimator Camera

FIGURE 2

;>,

STAR IMAGE: 100 mscc integradon tirnc

X-DIRECTION PIXELS

26-11
FIGURE 3

1200

I000

8OO

20O

0

0

THRESHOLDED STAR IMAGE vs GENERATED GAUSSIAN

i

I i

I0 20 30 - 40 50

X-DIRECTION PIXELS: column sums

60 7O

FIGURE 4

104

103

102

i 10 I

10 0

10-I

10-2

NOISY MOVING GAUSSIAN IMAGES: 30 frames generated

i

10"_

0

! |

-i'o _o 30 4o s'o _o 7O

PI'XELS

26-12
FIGURE 5

6

4

2

i°

-2

..4

-6

0

DIFFERENCE OF FRAME DIFFERENCES: thresholdcd; noisy

, i

" 'I 'A A

. ", _

v t: _!

FRAMES: 2nd difference

1200

1000,-

i 800t600t2OO

0

0

FIGURE 6

• •

MOVING STAR: 100 msec integradon;

t : • •

i : , i

,_., ,

r i , ,

.:Ill "

",:_::

"_1: ,;

iiii vv,'., q :: j,,

li!, ,,'o

10, _

':W{ _..... " ,,,_....

i' ::

:,,, ;" ;:;:U,:' ..

,, v ;' _ T;

i;i:/7.!";"--"" .....

........ 7,.7

30 40

3O

,°,

,,i ,t

. i

t

i ':h ,

i

I:i_ I

7t_it,,

i • , t

--' ,.r---' ');'-';'- "

50 6O

X-DIRECTION PIXELS: COLUMN SUMS

70

26-13

PLANETARY SPACECRAFT COST MODELING UTILIZING

LABOR ESTIMATING RELATIONSHIPS

Final Report

NASA/ASEE Summer Johnson Faculty SpaceFellowship Center Program--1990_/_ _ _/_ _ _._/"67-/

I"

Prepared by:

Academic Rank:

University & Department:

Raymond Williams, Ph.D.

Associate Professor

Mississippi Valley State

University

Computer Science and

Mathematics Department

Itta Bona, Mississippi 38941

NASA/JSC

Directorate:

Division:

JSC Colleague:

Date Submitted:

Contract:

New Initiatives Office

Project Planning & External

Affairs

Jared R. Woodfill

August I0, 1990

NGT 44-005-803

27-1
ABSTRACT

A basic computerized methodology is presented for

estimating labor hours and cost of unmanned planetary and lunar

programs. The user friendly methodology designated Labor

Estimating Relationship/Cost Estimating Relationship (LERCER)

organizes the forecasting process according to vehicle

subsystem levels. The level of input variables required by the

model in predicting cost is consistent with pre-Phase A type

mission analysis. Twenty-one program categories were used in

the modeling: sixteen spacecraft subsystem categories

(structures and devices, thermal control, cabling and

pyrotechnics, propulsion, attitudes and articulation control,

telecommunication, antennas, command and data handling, RTG,

solar batteries, aerodeceleration, radar, line-scan, vidicon,

particle and field, remote sensing, and direct sensing and

sampling); and five support categories (program management,

system support and ground equipment, launch +30 days operation

and ground software, image data development, and science data

development). To develop this model, numerous LER and CER

studies were surveyed and modified where required. The most

recent models that estimated cost of historical data to within

20% of the actual cost were selected. The result of the

research along with components of the LERCER program are

reported. On the basis of the analysis the following

conclusions were established: (I) spacecraft cost is highly

correlated with man (labor) hours; (2) no user friendly program

was found that linked LERs with CERs to produce spacecraft cost

as computer output; (3) the cost of a spacecraft is best

estimated by summing cost of individual subsystems; (4) the

group accumulation labor estimation method proved to be a

useful method for finding total manpower labor hours and cost

of a spacecraft; (5) the cost of 1 kg mass across subsystems

can vary from $152K (structure and devices) to $6.6M (thermal

control, cabling and pyrotechnics); (6) program labor cost

constitutes approximately 30 percent of the total program cost.

27-2
INTRODUCTION

Space program cost estimation in pre-Phase A stage of

development remains a challenge for cost forecasters. A

central idea presented in this study is that cost forecasting

can be improved by selecting manhours as the primary cost unit.

One major advantage of manhours as the basic cost unit over

forecasting total program dollar amount is the separation of

inflationary factors. Two programs costed at different times

can only be compared if some inflationary factor is applied to

the older program. The ideal inflation factors are often

difficult to formulate for total program cost and often fail to

be good representative of the actual financial conditions

within industry. Industry still has not been able to mass

reproduce spacecraft and consequently the total cost of each

subsystem is not significantly decreased through additional

production.

The purpose of this study was (i) to model spacecraft

cost as a function of manhours; (2) to interconnect manhour

equations and cost equations; (3) to develop a computer program

that uses LER output as input into CERs producing the cost of a

spacecraft as final output; and (4) to extract from the

literature those labor estimating relations (LERs) that

determine the total cost of a spacecraft by summing the cost of

its individual subsystems.

LABOR ESTIMATING RELATIONSHIPS (LERs)

Early and adequate estimation of program cost

requirements should be of major concern to both the government

and government contractors. Experience indicates that final

cost of advanced technology activities has been extremely

higher than that originally predicted. Somehow, prediction

procedures have resulted in large errors in costing spacecraft

programs. Thus, there is a rapidly growing need for a credible

spacecraft cost model. It should be noted that a cost model

does not necessarily represent factual information, but rather

it is based on experimental approximations. Costing spacecraft

by using dollar amounts as the basic cost unit is the method

reported by most cost forecasting studies. This paper uses,

instead of dollar expenditure, manhours (labor hours) as the

basic cost unit. Forecasting labor hours has several

advantages over directly forecasting total program dollar

amount. One such advantage,

27-3
and most important, is that manhours separate inflationary

factors from the model. Inflationary factors are very difficult

to formulate for total program cost and often time are not

consistent with inflation rates within industry. Additional

information reported by industry reveals that space industry is

still unable to mass produce spacecraft. That is, the cost to

make additional copies of the same flight unit is not

significantly reduced from that of the original unit. Thus, it

appears that project hardware cost is directly related to the

manhours involved in the development, fabrication and testing.

Hence, the manpower approach to cost forecasting should provide

management with a tool to evaluate the cost of new projects in

light of formulated manpower estimating relationships.

COSTESTIMATING RELATIONSHIPS (CERs)

Historically, early estimates of spacecraft project cost

have been considerably less than the final actual cost.

Spacecraft have been very expensive to produce because of

stringent weight and performance requirements, heavy emphasis

on reliability, and small production quantities. Various

parametric cost estimating models have been developed from

experience of the past 30 years, and those models reproduce the

cost of the traditional spacecraft with acceptable accuracy.

Due to the limited data base of manned space vehicles, a

spacecraft cost estimating procedure based on historical data

often contains errors. Mathematical relationship errors will

exist in any estimating procedure which attempts simplification

of actual cause and effect by empirical approximation. A good

cost model, of course, is one that strives to minimize these

inevitable errors. Cost regression equations for spacecraft

subsystems are typical of the type

Y -- AX b or y = A + BX c

Where Y _ cost and X = weight or some other subsystem

characterist!9_ ....... Those cost models that were show n to predict

the cost of historical vehicles to within 20 percent of the

actual cost were selected or developed for this study.

27-4
LERCER PROGRAM APPLICATION/OPERATION

All spacecraft information for use with the LERCER

computer program is found within the LERCER folder on diskette.

To operate the program open the LERCER folder and all files

therein. Input data description is found at the top of each

column. Row 5 of column A gives instructions for handling

spacecraft flight units.

The LERCER program strives to use features resident in

EXCEL and the Macintosh system in a user friendly fashion.

Among these features is the "GOTO" instruction. Selecting the

"GOTO" from the "FORMULA" column on the menu bar opens a

listing of all 21 subsystems and other pertinent categories

listed in alphabetical order. Scroll through this list and

make your selection. Control will then be transferred to this

selection in the program. You may at this point make

modifications in the input data if you so desire.

Each file with the surname TOTAL represents an

accumulation of all spacecraft calculations for the total of a

particular spacecraft program. Sample calculations for two

planetary programs are included in LERCER: one of which is the

Mars Rover Sample Return (MRSR) program developed by Martin

Marietta Corporation and the second calculation is for the Mass

Matrix (MM) program developed in-house at JSC.

The LERCER program has a fundamental feature: to use

pre-Phase A weight estimates for each subsystem to estimate

manpower hours; to input the forecasted manhours into a

corresponding cost equation for forecasting cost of planetary

subsystems; and finally, to calculate the total forecasted

cost.

The LERCER program features facilitate this fundamental

operation. Initial input data is entered in ledger fashion in

a single column of the EXCEL spreadsheet matrix. The input

data consists of listed constants selected by the user,

adjustment factors, and the subsystem mass. Each LERCER

equation is listed adjacent to the entry data column to assist

the user. Again, a designated ledger column serves for listing

of all LERs and CERs.

27-5
Total cost includes two categories: non-recurring

(design, development, test, and evaluation (DDTE)) cost and

recurring flight hardware (FH) cost. Each subsystem has two

cost forecasting equations listed on separate LERCER lines.

The total cost for each spacecraft is found by summing the

total cost of individual subsystems. Similarly, total

recurring cost of each spacecraft is found by summing the

recurring cost of individual subsystems. Non-recurring cost

may be obtained by subtracting recurring cost from the total

cost.

SUMMARY AND CONCLUSION

Early estimates of planetary spacecraft programs have

been significantly less than the final actual cost. The ratio

of actual cost to forecasted cost has been reported as high as

4:1. Manhour expenditures as a major costing unit was found to

substantially lower this ratio. The manhours needed to build a

planetary spacecraft may be adequately estimated and costed for

initial pre-Phase A design utilizing credible historical data

and regression analysis within a programmed methodology. The

LERCER is such a system developed by this study. This program

was developed for the New Initiatives Office (NIO) at JSC.

Content of the methodology includes a slightly modified

version of Science Applications Incorporated (SAI) manhour

model and cost estimating relationships developed by the study.

Dry mass is expressed in kilograms and cost in 1990 milliondollar units.

27-6
N91-27

STRUCTURAL CONCEPT STUDIES FOR

A HORIZONTAL CYLINDRICAL LUNAR HABITAT

AND

A LUNAR GUYED TOWER

Final Report

NASA/ASEE Summer Faculty Fellowship Program - 1990

Prepared by:

Academic Rank:

University and Department:

NASA/JSC

Directorate:

Division:

Branch:

JSC Colleague:

Date Submitted:

Contract Number:

Johnson Space Center

Paul K. Yin, Ph.D.,P.E.

Associate Professor

U.S. Coast Guard Academy

Engineering Department

New London, Conn. 06320

Engineering

Systems Engineering

Systems Definition

Clarence Bell, Ph.D.

August i0, 1990

NGT-44-005-803

11-_'3 _ __

fl5

28-i
ABSTRACT

A conceptual structural design of a horizontal

cylindrical lunar habitat is presented. The design includes

the interior floor framing, the exterior support structure,

the foundation mat, and the radiation shielding. Particular

attention has been given on its efficiency in shipping and

field erection, and on selection of structural materials.

Presented also is a conceptual design of a 2000-foot

lunar guyed tower. A special field erection scheme is

implemented in the design. In order to analyze the over-all

column buckling of the mast, where its axial compression

includes its own body weight, a simple numerical procedure is

formulated in a form ready for coding in FORTRAN. Selection of

structural materials, effect of temperature variations,

dynamic response of the tower to moonquake, and guy anchoring

system are discussed.

Proposed field erection concepts for the habitat and for

the guyed tower are described.

INTRODUCTION

The Systems Engineering Division of the NASA/JSC is

studying the development of permanent human presence on the

moon. The development includes a relatively large habitat to

provide long term human habitation, antenna towers for

communication, among others.

The author designed the internal framing and the external

foundation for a spherical inflatable lunar habitat during the

summer of 1989 (A concept developed within NASA/JSC) [1,2].

With the experience gained on the first design, the Systems

Engineering Division is considering a horizontal cylindrical

habitat in addition to a spherical one. A conceptual

structural design of this second concept (excluding the

inflatable air enclosure) is presented in this report. The

structure was analyzed by Ed Robertson of Systems Engineering

Division, Systems Definition Branch using SDRC's IDEAS program

[3]. An order-of-magnitude estimate of total structural dead

weight is presented. The estimate is based on the structural

members of Aluminum Lithium 8090-T8771 for the inside of the

habitat and Magnesium alloy ZCM 711 [4] for the outside of the

habitat, all within the AISC stress limitations [6] under the

specified design live loads. The architectural and functional

requirements including design live loads were provided by K.

J. Kennedy of the Systems Engineering Division. The interior

and exterior structural framings are to be pre-assembled,

packaged for shipping, and automatically deployed into its

final installed configuration with minimum effort required for

28-2
field assemblage.

The development of permanent human presence on the moon

also includes systems which may require installations of tower

structures. Such systems may be for the lunar surface

communication, power transmission, and area illumination.

Given the radius of the lunar sphere the height of the towers

is dictated by the distance between two adjacent towers. The

longer the distance between towers, the higher the towers must

be. While the communication area covered by a tower increases

with the square of the tower's height the structural dead

weight increases, generally speaking, with the cubic of the

tower's height. This implies that the optimum height of the

tower for lunar surface communication is zero. Further tradeoff study on the use of towers for communication to the far

side of the moon is needed including other factors and other

possible alternatives.

For a surface area on the moon covered by a 30-mile

radius (a limitation on an un-pressurized lunar rover) the

height of the tower is approximately 2000 feet. A conceptual

design of a 2000-foot lunar guyed tower, its materials, and

field erection scheme are presented.

The installation of a 2000-foot guyed tower on the

surface of the moon represents some unique design

considerations. These considerations were studied and are

discussed in this report.

THE HABITAT

General Concept

The basic concept of the horizontal cylindrical lunar

habitat is similar to that of the spherical inflatable lunar

habitat studied in the summer of 1989 [1,2] except that the

over-all shape of the inflatable air enclosure is horizontal

cylindrical, the regolith shielding is self supporting, the

site preparation involves the removal of only the first onefoot layer of the lunar surface soil, and the framing is more

efficient for shipping and field erection (Figure I).

pesign Requirements

The requirements, objectives and constraints are:

i. A minimized total structural dead weight.

2. Satisfaction of the Specification for the Desian.

Fabrication. and Erection of Structural Steel for

(AISC) [6].

3. Efficient for shipping and field erection.

28-3
4. A design live load of 50 earth pound per square foot

(2.40 kPa) for the upper floor and I00 earth pound per

square foot (4.79 kPa) for the lower floor (from K. J.

Kennedy, Systems Engineering Division, JSC).

5. Architectural requirements as given by K.J. Kennedy of

the Systems Engineering Division (Figure I).

Interior Structural Framing

A highly efficient flat space truss (a pallet, developed

by the Structural Engineering Division of JSC [12]) was

determined to be suitable for both the two interior floors.

The truss can collapse into a highly compact bundle for

shipping and be automatically deployed into its final

configuration at the site. The interior floor trusses for

both the two levels (Figure I) are about one-foot deep and are

composed of 0.394-inch (l-cm) diameter round bars of Aluminum

Lithium 8090-T8771 all of equal length for the upper and the

lower cord planes and the 0.315-inch (0.8-cm) web members.

Each floor truss is supported at ten points through which the

area live loading is transferred across the fabric to the

exterior support framing (Figure I).

Exterior S_pDort Structure

The support structure outside the habitat is designed so

that the structure can be retracted into a bundle for shipping

with its length slightly greater than the diameter of the

cylindrical air enclosure (Figure I).

The interior floor trusses and the exterior support

structure were analyzed by Ed Robertson of the Systems

Engineering Division, JSC using SDRC's IDEAS [3]. Under the

design live loads, the maximum stresses and deflections were

all found within AISC's limitations [6].

The foundation mat provides enough bearing area for a

factor of safety of 4 against ultimate bearing capacity of the

lunar soil at about one-foot depth based on the lunar soil

properties given by Mitchell, et.al. [7] using an empirical

formula by Vesic [8]. The mat is designed so that it can be

rolled into a bundle for shipping (Figure I). The site

preparation requires removal of the first one-foot layer of

the lunar surface soil.

Radiation Shieldina

The radiation shielding is essentially made of flat space

trusses similar to the floor trusses inside the habitat,

28-4
filled with lunar regolith, and each is wrapped with fabric to

contain the regolith (Figure i).

Aluminum Lithium 8090-T8771 is proposed for the interior

floor trusses for its superior strength-to-weight ratio. Its

relevant properties at room temperature are: [4,5]

Ultimate Strength:

Yield Strength:

Percent Elongation:

Mass Density:

Modulus of Elasticity:

Thermal Expansion Coeff.:

64 ksi (441.28 MPa)

50 ksi (344.75 MPa)

.5 to 2 %

0 091 lbm/in 3 (2,519 kg/m 3)

l{.7x106 psi (80,672 MPa)

13x10-6/OF (23xi0-6/oC)

Magnesium alloy ZCM 711, high in its strength-to-weight

ratio, is selected for the exterior support structure and the

foundation mat. This material has a relatively low combustion

temperature and therefore is unsafe to use inside the habitat

where oxygen is present. The relevant properties of this

material are: [4,5]

Ultimate Strength:

Yield Strength:

Percent Elongation:

Mass Density:

Modulus of Elasticity:

Thermal Expansion Coeff.:

40 ksi (275 MPa)

27 ksi (185 MPa)

12 %

0.065 ibm/in 3 (1,795 kg/m 3)

6,500 ksi (45 GPa)

15x10-6/OF (27xi0-6/oc)

Structural Dead Weight

The following weight estimate is based on a very basic

conceptual design and therefore gives only a rough idea of the

amount of materials to be shipped to the moon:

Upper Floor Truss: 1,320 ib m (

Lower Floor Truss:

Support Structure:

Foundation Mat:

Total

Field

600 kg) of AI-Li

950 lbm ( 430 kg) of AI-Li

560 ibm ( 254 kg) of ZCM

4,400 ibm (1,995 kg) of ZCM

7,230 ibm (3,280 kg)

The following is a general outline of the field erection

procedure with much further refinement yet to be developed.

i. A flat level surface is prepared at the site after the

removal of the first one-foot layer of the lunar soil.

2. The top horizontal truss for the radiation shielding

(roof) is fully stretched and is resting on the vertical

28-5
trusses (walls) which are not yet fully stretched.

3. The top horizontal truss is raised to its final

elevation by jacking with the vertical trusses (walls)

fully stretched to their final configuration

simultaneously.

4. All fabric bags over the trusses were pre-attached and

are now in their fully stretched shapes.

5. All trusses are filled with lunar regolith from top by

a bucket elevator.

6. The foundation mat is laid.

7. The habitat module is placed on the mat.

8. The cylindrical air enclosure is inflated to its final

volume while the pre-attached interior floors and the

exterior support frame are also fully stretched to their

final configuration.

AluminumrLithium AI-Li 8090 is selected for the interior

floor trusses for its superior strength-to-weight ratio to

minimize shipping weight. The strength-to-weight ratio of

Magnesium ZCM 711 is higher than that of AI-Li 8090 but ZCM

711 is unsafe to use inside the habitat due to its low

combustion temperature. ZCM 711 is therefore selected for only

the exterior support frame and the mat. Most Magnesium alloys

including ZCM 711 are known to be weak in corrosion

resistance. This presents no problem on the moon for there is

no oxygen nor moisture outside the habitat.

While the Aluminum alloy is known to have superior

resistance to embrittlement at low temperature [ii] the

Magnesium alloy ZCM 711 looses its ductility at low

temperature and is therefore unsuited for load carrying in

tension. It is therefore chosen for the support structure and

the mat only where materials are primarily under compression.

The thickness of the first layer of the lunar surface

soil to be removed was arbitrary in the design. Generally

speaking, the soil bearing capacity increases with increase in

depth but at the expense of increased site preparation.

THE GUYED TOWER

General Concept

The general concept of the proposed lunar guyed tower is

essentially the same as that of typical guyed towers found on

Earth (Figure 2). Unlike the design of tower structures on

Earth, a lunar guyed tower requires no resistance to lateral

wind loads for there is no wind on the moon. The only

significant environmental lateral loading is perhaps due to a

28-6
moonquake. It is possible, however, that significant stress

and deformation occur due to severe daily (lunar) temperature

variations. A structural analysis under this thermal loading

(with the gravity loading properly combined) should be

performed after the structure is better defined and a cyclic

heat transfer and temperature-time history throughout the

structure is assessed. In an effort to mitigate this

temperature effect two different materials were selected,

i.e., Magnesium alloy for the mast and Aluminum-Lithium for

the guys. This temperature effect is further discussed in a

later part of the report.

Design Requirements

Given below is a list of a very preliminary requirements/

constraints for the design of the lunar guyed tower.

I. A payload of I,I00 ib m (500 kg) at approximately 2,000

feet above ground (given by M.L. Roberts, Systems

Engineering Division, JSC).

2. A minimized total structuraldead weight.

3. Satisfaction of the _pecification for the Desian.

Fabrication. and Erection of Structural Steel for

(AISC) [6].

4. Efficient for shipping and field erection.

5. Resistant to moonquake.

6. Resistant to daily (lunar) temperature variations.

7. An elevator to the top of the tower.

The Mast

The mast is composed of identical 9-foot truss sections

made of standard tubular members of Magnesium alloy CZM 711.

The three cords and the braces make the cross sectional shape

of the mast an equilateral triangle (Figure 2). The bottom 9-

foot truss section is different from the rest (Figure 3) where

larger openings are provided to facilitate traffic to the

interior of the mast.

The use of wire ropes made of Aluminum Lithium 8090 is

proposed for the guy cables. A simple calculation (assuming

uniform temperature variation throughout the mast and the

guys) shows that, for a guy cable making an angle of

approximately 58 degrees with horizontal and having its

thermal expansion coefficient 23/27 of that of the mast [5],

stresses induced in the guys and the mast are minimal due to

the diurnal temperature variations. Using the formulas

provided in [16] the maximum and minimum temperatures are

approximately 240OF (388°K) and -248°F (II8°K) respectively

28-7
(depending also on the location on the lunar surface). These

temperature extremes corresponds to a temperature range of

487°F (270°K) [16]. Since the metal tower is more reflective

than the lunar surface the above temperature variation of the

lunar surface can be used as the upper and lower bounds of

temperature variation of the tower structure.

Since there is no lateral wind load on the lunar surface

vertical locations of upper guy connections are determined

solely by the buckling consideration of the mast. Thus the

vertical spacings for the upper guy connections are determined

by the Euler buckling formula [9] with a factor of safety of

2, i.e., L = n (EI/2P) I/2 where P is conservatively taken as

the axial compression at the lower end of the unbraced length

L (the structural dead weight makes the axial compression at

the lower end greater than at the upper end). To include the

effect of the mast's own body weight on its critical buckling

load a numerical procedure was formulated but is not presented

in this report for lack of verification. Published information

on column buckling including the column's own body weight can

not be found at time of this writing [15]. This may be

attributed to its relatively small effect in design (on Earth)

as compared to that due to wind load.

The guys are to be sized to provide a lateral support

capacity at their upper connections equal to 10% of the axial

compression in the mast (to be considered fully effective).

According to a recommended practice [10], the guys are to be

pretensioned to 1/8 of their breaking strength. These

pretensions in guys contribute to the axial compression in the

mast (Figure 2).

An equilateral triangular mat is sized to provide a

bearing area at the base of the mast with a factor of safety

of 4 against the ultimate bearing capacity of the lunar soil

at approximately one foot below grade [7,8]. Immediately above

the mat is a ball joint to effect a hinged support so that

there will be no over-turning moment to be resisted by the

foundation during a moonquake [14] (Figure 3).

All anchors of the guys should provide an ultimate uplift capacity based on the breaking strength of the guys. A

safety device is to be installed in series at the lower

connection of each guy. The device should disengage when

tension in the guy reaches a certain fraction of the guy's

breaking strength. Experience in up-lift and lateral load

capacities of piled or coil anchors on lunar surface and their

installation is not available at time of this design study.

Estimates of these capacities can be made using some existing

28-8
numerical methods generally available within today's civil

engineering practice. A less efficient alternative for the

anchors would be the use of the so-called "deadman" concrete

blocks. This anchoring alternative may become more attractive

when cast-in-place lunar concrete becomes feasible.

The same Aluminum Lithium 8090-T8771 and Magnesium alloy

ZCM 711 described earlier in this report for the lunar habitat

are proposed for the guys and the mast respectively.

Structural Dead Weight

The total structural mass is estimated at about 9,000 ib m

with the following breakdowns: (excluding payload and anchors)

Mast: 8,620

Guys: 250

Pedestal: 160

ib m (3,910 kg) of ZCM

lbm ( 113 kg) of AI-Li

lbm ( 73 kg) of ZCM

Total: 9,030 Ibm (4,096 kg)

Field

The following field erection scheme for the guyed tower

is proposed (Figure 4).

i. Prepare a flat level surface at the site after removal of

the first one-foot layer of the lunar soil.

2. Set the foundation mat and the hinged-support

assemblage (pedestal) on the prepared surface.

3. Position the top 9-foot section of the mast onto the

pedestal.

4. Connect the three top-level guy cables to the top 9-

foot section of the mast and to their anchors with some

reels with a controlled tensioning device.

5. Install the hydraulic jack inside the mast.

6. Jack up the top 9-foot section of the mast until its

bottom is clear for the next 9-foot section to be

assembled around the hydraulic jack immediately below.

7. Assemble the next 9-foot section.

8. Repeat steps 6 and 7 and connect guy cables as

designed until the last section.

9. Assemble the last bottom 9-foot section and secure to

the support pedestal.

10. Remove the hydraulic jack.

ii. Install the elevator.

12. Remove the three temporary members which were holding

the support pedestal to the foundation mat.

13. Adjust tension in guys for mast's verticality.

28-9
Due to the loss of ductility of the Magnesium alloy ZCM

711 at low temperature, ZCM 711 is selected only for the mast

since the mast is primarily under compression. AluminumLithium is selected for the guys (in tension) for their known

ductility, modulus of elasticity, and strength preservation at

both high and low temperatures [11,13]. Possibility of

creeping of the mast and guys at the upper temperature extreme

should be studied.

Numerical predictions of capacity and driveability of

piled anchors are well developed for design on Earth.

Applicability of these numerical techniques to lunar surfaces

needs further investigation.

Dynamic amplification and response of the tower to

moonquake should be analyzed and the structural integrity be

verified at the two temperature extremes [14,16].

The in-situ guy cables are pretensioned to 1/8 of the

breaking strength of the cable as generally practiced for

guyed towers on Earth [I0]. This pretensioning should be

studied and possibly revised for guy cables on the moon.

Each 9-foot section of the mast can be made collapsible

for shipping, stretched and assembled around the hydraulic

jack at the site.

The daily (lunar) temperature variations of the structure

can be reduced to a certain extent by coating the structure

with some reflective material. The reflective coating reduces

the net solar heat influx and thus lowers the upper extreme of

the temperature variation.

It may be necessary to avoid direct contact between the

mast (of Magnesium alloy) and the guys (of Aluminum-Lithium)

at the upper guy connections for possible galvanic corrosion

due to the two dissimilar metals.

A constant angle (approx. 60 ° ) from the horizontal is

proposed for all the guys to mitigate the stresses due to the

temperature variations (in contrast to a single anchor for

each vertical plane of guys, Figure 2).

2.8-10
REFERENCES

i. Yin, P.K., "A Preliminary Design of Interior Structure and

Foundation of an Inflatable Lunar Habitat," Final Report,

NASA/ASEE Summer Faculty Fellowship Program, Johnson Space

Center, 1989.

2. Yin, P.K., "Structural Design of an Inflatable Lunar

Habitat," Enaineerinao Construction. and 0oerations in

____/j_ Proceedings of Space 90, ASCE, 1990.

3. Structural Dynamics Research Corporation (SDRC),

User's Guide. I-DEAS Level 4. Supertab ._ngine_ri_ig

_, 1988.

4. Charles, J.A.,Crane, F.A.A., "Candidate Materials for

Aircraft Structures," Selection and Use of Engineering

_, 2nd Ed., Section 15.4, Butterworths, 1989.

5. Gere, J.M., Timoshenko, S.P., "Mechanical Properties

of Materials," Mechanics of M_terials, 2nd Ed., Appendix

H,

Brooks/Cole Engineering, 1984.

6. American Institute of Steel Construction, Inc. (AISC),

Manual of Steel Construction, 8th Edition, 1980.

7. Mitchell, J.K. et.al., "Mechanical Properties of Lunar

Soil: Density, Porosity, Cohesion, and Angle of Internal

Friction," Proceedinas of the Third Lunar _¢ience

_[_L_, Vol. 3, pp 3235-3253, The M.I.T. Press, 1972.

8. Vesic, A.S., "Bearing Capacity of Shallow

Foundations," Foundation Enaineering Handbook, Van

Nostrand Reinhold Co., 1975.

9. Gere, J.M., Timoshenko, S.P., "Chapter ii: Columns,"

_ of _, 2nd Ed., Brooks/Cole Engineering,

1984.

i0. Zar, Max, "Section 24: Towers," Structural Engineering

_, McGraw-Hill Book Company, 1968.

Ii. Glazer, J., et al, "Cryogenic Mechanical Properties of

AI-Cu-Li-Zr Alloy 2090," Presented at the International

Cryogenic Materials Conference, Cambridge, MA, August 12-

16, 1985.

12. Wesselski, C.J., "Expandable Pallet for Space Station

Interface Attachments," U.S. Patent number 4,805,368, Feb.

21, 1989.

13. American Society of Metals (ASM), Metals Handbook Desk

_, 1985.

14. McCaffrey, R.J., Hartmann, A.J., "Dynamics of Guyed

Towers," J. of the Structural Division, Proceeding8 of the

ASCE, Vol. 98, No. ST6, June, 1972.

15. Goldberg, J.E., Gaunt, J.T., "Stability of Guyed

Towers," J. of Structural Division, Proceedings of the

ASCE, Vol. 99, No. ST4, April, 1973.

16. Binder, Allen, Lunar Data Base, Lockheed Engineering &

Sciences Co. Inc., April, 1990.

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_j_ REPORT DOCUMENTATION PAGE

National Aeronautics and

Space Administration

1. Report No.

NASA CR 185637

I 2 Government Accession No

3 Recipient's Catalog No

4. Title and Subtitle

NASA/ASEE Summer Faculty Felllowship Program--1990

Volume 2

7. Author(s)

Richard B. Bannerot and Stanley H. Goldstein, Editors

5. Repo_ Date

December 1990

6. Performing Organization Code

8 Performing Organization Report No.

9. Performing Organization Name and Address

University of Houston-University Park

Houston, TX 77204

12 Sponsoring Agency Name and Address

National Aeronautics and Space Administration

Washington, D.C. 20546

10 Work Unit No.

11. Contra_ or Grant No.

NGT 44-005-803

13 Type of Repo_ and Period Covered

Contractor Report

14. Sponsoring Agency Code

15. Supplementary Notes

"7

!

iE ; Z

16_ Abstra_

The 1990 Johnson Space Center (JSC) National Aeronautics and Space Administration

(NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program

was conducted by the University of Houston-University Park and JSC. The lO-week program

was operated under the auspices of the ASEE. The program at JSC, as well as the programs

at other NASA Centers, was funded by the Office of University Affairs, NASA Headquarters,

Washington, D.C. The objectives of the program, which began nationally in 1964 and at JSC

in 1965, are (1) to further the professional knowledge of qualified engineering and

science faculty members; (2) to stimulate an exchange of ideas between participants and

NASA; (3) to enrich and refresh the research and teaching activities of participants'

institutions; (4) to contribute to the research objective of the NASA Centers.

Each faculty fellow spent at least 10 weeks at JSC engaged in a research project

commensurate with his/her interests and. b_ckground and worked In collaboration with a

NASA/JSC colleague. This document i_ _ c_mpi]atton of the final reports on the research

projects performed by the faculty leT low during the summer of 1990. Volume 1 contains

reports 1 through 14, and Volume 2 contains reports 15 through 28.

17. Key Words (Suggested by Author(s))

"_. D=_ribution Statement

Unclassified - Unlimited

19 Security Classification (of this repot)

Unclassified

20. Security Classification (of this page)

Unclassified

21. NO of pages I 22 Price

For sale by the National Technical Information Service, Springfield, VA 22161-2171

JSC Form 1424 (Rev Aug 89) (Ethernet Jan 88)