Kenol Jules

Glenn Research Center, Cleveland, Ohio

Paul P. Lin

Cleveland State University, Cleveland, Ohio

Artificial Neural Networks Applications:

From Aircraft Design Optimization to Orbiting

Spacecraft On–Board Environment Monitoring

NASA/TM—2002-211811

August 2002
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Kenol Jules

Glenn Research Center, Cleveland, Ohio

Paul P. Lin

Cleveland State University, Cleveland, Ohio

Artificial Neural Networks Applications:

From Aircraft Design Optimization to Orbiting

Spacecraft On–Board Environment Monitoring

NASA/TM—2002-211811

August 2002

National Aeronautics and

Space Administration

Glenn Research Center

Prepared for the

2001 Advanced Study Institute on Neural Networks for Instrumentation, Measurement,

and Related Industrial Applications

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Crema, Italy, October 9–20, 2001
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Available electronically at http://gltrs.grc.nasa.gov
NASA/TM2002-211811 1 

Artificial Neural Networks Applications: From Aircraft Design Optimization to 

Orbiting Spacecraft On-board Environment Monitoring 

Kenol Jules 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Paul P. Lin 

Cleveland State University 

Cleveland, Ohio 44115 

Abstract -- This paper reviews some of the recent applications of 

artificial neural networks taken from various works performed by 

the authors over the last four years at the NASA Glenn Research 

Center. This paper focuses mainly on two areas. 1) Artificial neural 

networks application in design and optimization of aircraft/engine 

propulsion systems to shorten the overall design cycle. Out of that 

specific application, a generic design tool was developed, which can 

be used for most design optimization process. 2) Artificial neural 

networks application in monitoring the microgravity quality onboard the International Space Station, using on-board 

accelerometers for data acquisition. These two different 

applications are reviewed in this paper to show the broad 

applicability of artificial intelligence in various disciplines. The 

intent of this paper is not to give in-depth details of these two 

applications, but to show the need to combine different artificial 

intelligence techniques or algorithms in order to design an 

optimized or versatile system. 

I. INTRODUCTION 

Over the last ten years, multidisciplinary design 

optimization has developed into a field of its own. The 

primary objective of Multidisciplinary Design Optimization 

(MDO) is to improve design tools in order to rapidly and 

efficiently explore high-order design spaces with the aim of 

increasing significantly system performance and reducing 

end-product cost substantially by cutting system design cycle 

time by half. With that in mind, a few years ago the NASA 

Glenn Propulsion System Analysis Office (PSAO) undertook 

a review of all its system analysis tools to assess how some of 

them can be integrated into one unit and at the same time cut 

drastically the time required to perform the daily aircraft 

propulsion analysis task by taking advantages of emerging 

analysis techniques and faster CPUs. The motivation for that 

review was to minimize the time required to assess 

technology impact on current and future aircraft / engine 

design by both analysts and managers. Based on the outcome 

of that review, the decision was made to develop a 

simulation-based analysis tool using soft computing 

techniques. Section II of this paper reviews and discusses the 

pertinent results obtained using such system. 

The second area, though drastically different from the 

design optimization one, is the field of data acquisition and 

system monitoring. In this specific case, artificial neural 

networks were used to monitor the International Space 

Station (ISS) microgravity environment quality using onboard accelerometers for data acquisition. The objective of 

this work was to monitor in near real time the microgravity 

environment of the station so that Principal Investigators (PI) 

performing research on the station can know at any moment 

what the environment is. Artificial neural networks were used 

to design such a system. The system is used to monitor the 

operating machinery as well as crew activities. Operating 

machines and mechanical systems generate vibration. 

Measurement of these signals (vibration) yields information, 

which is used for monitoring. For example, data about how a 

signal varies with time can be used to assess defective and 

worn parts, design attributes and other problems. Operation 

of pumps, fans, compressor, generator, engines and most 

mechanical systems involves periodic motion due to rotation 

of interacting components. The motion of fans, coils, 

bearings and shafts generates vibration, which can be related 

to the machine running speed. Based on that knowledge, a 

robust monitoring system using artificial neural networks can 

be developed for use in many fields. Section V of this paper 

reviews and discusses such a monitoring system for the case 

of acceleration data acquisition for environment monitoring. 

II. NEURAL NETWORKS APPLICATION FOR DESIGN 

AND OPTIMIZATION 

The main objective of this work was to develop a fast 

optimizing multidisciplinary system design, which could 

perform both analysis/design and optimization of aircraft and 

their propulsion systems, such as engine cycle analysis, 

aircraft sizing, aircraft / engine mission analysis, aircraft 

noise and emission, engine economics analysis and system 

weight prediction (Fig. 1). To design such system [1–2], 

Taguchi techniques, fuzzy logic and neural networks were 

used. Neural Networks [3–6] were used in this work to 

provide mappings for fast analysis and design. The use of 

neural networks was very important in this work because it 

added both capability and flexibility to analyze the impact of 

new technologies on engine and aircraft systems and greatly 

reduces the turnaround time for analysis and design. The 

result obtained using such approach showed that it yields 

significant performance improvement in optimizing the 

design objectives as well as in predicting the system output in 
NASA/TM2002-211811 2 

near real time for both single and multiple design objectives 

even when conflict exists among the design objectives (which 

is usually the case in design optimization). The use of Neural 

Networks enables the design analysis tool to simulate a very 

complex system in near real time (since it is simulation-based 

instead of being an iterative search) within acceptable 

accuracy (less than 5% error). 

III. DISCUSSION 

The optimum solutions obtained using the combined 

Taguchi Neural Networks and fuzzy logic techniques to 

maximize thrust, minimize fuel consumption, emission and 

jet velocity are listed in Tables I- III. As a design constraint, 

every case starts with the same Mach number and altitude. 

All other input parameters were free to vary within their prespecified ranges. The results show that the optimum design 

solutions can be easily found using the combined techniques; 

especially in resolving the conflict among design objectives. To test the generalization of the trained network, 26 

unseen cases were used. Each case contains 14 input 

variables and 4 (out of 11) output variables. Table IV shows 

the generalization test results in terms of root-mean-square 

(RMS) error percentage. The error percentages listed in that 

table are less than 5 %, and are generally acceptable. But, 

we feel that emission could be further improved by carefully 

adding more trained input patterns cases. The main reason 

why emission shows a higher error percentage is because the 

ratio of the maximum to the minimum input value of the 

trained patterns is very high, approximately 6.7. 

From the result obtained, one can infer that the 

combination of Taguchi, Fuzzy logic and Neural Networks 

shows excellent prospect in the MDO field. 

IV. DEVELOPMENT OF A GENERIC DESIGN TOOL 

The successful result obtained using the neural networks 

for aircraft and propulsion systems design/analysis and 

optimization, led to the development of a generic 

multidisciplinary design optimization tool using the same 

approach [7]. The objective of the generic design tool was to 

automate the multidisciplinary system design optimization so 

that any user can mount his/her application program on top of 

the generic tool to optimize the design objectives without 

having to do all the tedious work required in performing 

MDO. As was the case in the previous work discussed 

above, neural networks were used to generate the instant 

input-output mapping. That allows the user to instantly 

evaluate the optimization performance; to place weights on 

different objectives or fine-tune the optimum solution. In 

summary, the use of artificial neural networks helps greatly in 

accomplishing the following: 

1. Greatly reduces turnaround time for technology 

impact assessment 

2. Quick data availability for project advocacy 

3. Identification of high payoff investment for less 

R&D funding 

4. Save time, money and resources 

5. Keep and increase corporate knowledge 

6. Incremental and/or global analysis capability 

7. Excellent learning and training tool for new 

employees 

The generic design tool presented here (figs. 2 and 3) 

combines Taguchi techniques, fuzzy logic and neural 

networks for system optimization. Figure 2 shows the 

scheme for the case when the user does not have a database, 

while fig. 3 shows the scheme when such a database is 

available to the user. More specifically, 

1. Taguchi techniques are used to generate the parameter 

significance index 

2. Fuzzy logic is used to resolve the conflicts among 

different objectives 

3. Neural networks are used to generate instant input-output 

mapping. Such a capability allows the user to instantly 

evaluate the optimization performance, particularly in 

placing the preference weights and fine-tuning the 

optimum solution. 

4. Combination of Taguchi techniques and fuzzy logic 

proved to be a powerful tool for performing 

multidisciplinary system optimization 

The combination of the three techniques is better than 

most traditional search-based optimization techniques in three 

aspects: 

1. In seeking the optimum solution, this approach never 

diverges 

2. The optimum solution is obtained in near real time 

because this approach is simulation-based, which does 

not rely on an iterative search 

3. As a result of Taguchi analysis, the parameter 

significance index gives the user a good guidance to finetune the optimum solution. 

This tool is generic in the sense that it can take virtually 

any type of data set, such as ASCII file or spreadsheet. It is 

flexible enough to allow the user to easily switch between 

single and multiple objective optimizations, and between 

local and global optimizations, at any time. Furthermore, the 

user can place preference weights on each objective, and also 

has a chance to fine-tune the optimum solution. Most 

importantly, the system optimum solution can be obtained in 

near real time. 

V. NEURAL NETWORKS APPLICATION FOR SYSTEM 

MONITORING 

 

The residual acceleration environment of an orbiting 

spacecraft in a low earth orbit is a very complex 

phenomenon. It is subject to quasi-steady acceleration, higher 

frequency acceleration, and transient disturbances. Many 

factors [8], such as experiment operation, life-support 

systems, equipment operation, crew activities, aerodynamic 

drag, gravity gradient, rotational effects as well as the vehicle 

structural resonance frequencies (structural modes) contribute 
NASA/TM2002-211811 3 

to form the overall reduced gravity environment. 

Weightlessness is an ideal state, which cannot be achieved in 

practice because of the various sources of acceleration 

present in an orbiting spacecraft. As a result, the environment 

in which experiments are conducted is not zero gravity; 

therefore, experiments can be affected by the residual 

acceleration because of their dependency on acceleration 

magnitude, frequency, orientation and duration. Therefore, 

experimenters must know what the environment was when 

their experiments were performed in order to analyze and 

correctly interpret the result of their experimental data. In a 

terrestrial laboratory, researchers are expected to know and 

record certain parameters such as pressure, temperature, 

humidity level and so on in their laboratory prior to, and 

possibly throughout their experiment. The same holds true in 

space, except that acceleration effects emerge as an important 

consideration. In order to aid the researchers assessing the 

impact of the space environment on their experiments 

remotely, a monitoring system tool development was 

developed using artificial neural networks. A brief 

description of the work is given below. 

The main focus of this work [9–11], using soft computing 

techniques to monitor the microgravity environment on-board 

the ISS, was to develop an artificial intelligence monitoring 

system to help Principal Investigator (PI) teams identify the 

primary vibratory disturbance sources that are active, at any 

moment of time, on-board the ISS, which might impact the 

microgravity environment their experiments are exposed to. 

Such information is made available to PIs via the world wide 

web (WWW) in near real time, where they can graphically 

see which event(s) is/are active, such as crew activities, 

pumps, fans, centrifuges, compressor, crew exercise, etc... in 

order to decide whether or not to run their experiments based 

on the magnitude of the acceleration associated with a 

specific event. This monitoring system detects primarily the 

vibratory disturbance sources. The system has built-in 

capability to detect both known and unknown vibratory 

disturbance sources. A known pattern is defined as the 

nominal values of the previously measured (learned data set) 

frequency and acceleration of an existing disturbance source. 

To design such a complex system, the following artificial 

neural networks techniques were used: Kohonen’s SelfOrganizing Feature Map (SOFM) [12], Learning Vector 

Quantization (LVQ) [13–15], Backpropagation Neural 

Networks (BPNN) [16]. In addition, Fuzzy logic [17–18] was 

used to deal with some of the decision making process. 

For the acceleration data acquisition, two accelerometers 

were flown to ISS, April 19, 2001, on the Space Shuttle flight 

STS-100. One accelerometer, Microgravity Acceleration 

Measurement System (MAMS), is a low frequency 

acceleration measurement system, which measures 

acceleration levels up to 100 Hz. The other, Space 

Acceleration Measurement System (SAMS), is a high 

frequency measurement system, which measures acceleration 

from 0.01 to 300 Hz on the ISS. Both of these accelerometers 

are tri-axial sensors. Acceleration data are currently being 

received in real time at our Telescience Center (TSC) at 

NASA Glenn for the on-board microgravity environment 

monitoring of the ISS. 

In the course of developing the monitoring system many 

technical challenges were encountered. Chief among them 

was unknown patterns recognition. SOFM has no ability to 

do so, nor can LVQ. Unknown patterns recognition was a 

crucial feature of the monitoring system if it were to succeed 

at doing what it was supposed to because many of the 

fundamental frequencies of the events on ISS are not yet 

known, and since the ISS is being built in increments, they 

will, without doubt, change to some degrees from increment 

to increment until assembly complete. Therefore, to 

overcome that problem we had to come up with a modified 

approach to pattern classification and recognition. The model 

we proposed and used throughout this work combines SOFM, 

BPNN and LVQ, which is referred to as Adaptive Pattern 

Recognition and Classification (APRC) (Fig. 4). In the 

proposed model, BPNN is inserted in between SOFM and 

LVQ to help recognize the unknown patterns, while SOFM 

and LVQ are used to classify the known patterns. Also, in the 

proposed model, BPNN is used to keep SOFM in check in 

order to prevent it from misclassifying patterns since one of 

the shortcomings of SOFM is the potential for misclassifying 

patterns that fall on the boundary between two clusters due to 

the fact that SOFM relies solely on Euclidean distance to 

assign patterns to respective clusters. 

In summary, the proposed APRC model used throughout 

this work is very innovative in the following aspects: 

1. Can recognize unknown patterns 

2. Can avoid patterns misclassification 

3. Take into account multi-dimensional ranges of 

neighboring clusters 

VI. DISCUSSION 

This section presents some preliminary results obtained 

using the artificial neural networks, which assess the ISS 

microgravity environment, in order to share with the 

microgravity scientific community what is currently being 

seen in terms of disturbances aboard the ISS. It must be 

pointed out that the ISS microgravity environment 

characterization is at an early stage, therefore, not too much is 

known with certainty. The results presented here are, 

therefore, VERY preliminary since we are in the early stage 

of receiving, processing, analyzing, digesting and comparing 

the data. There are more questions, at this stage, than 

answers. With that in mind, let’s discuss the content of 

table 4. 

Table 4 shows both acceleration levels and frequencies 

(after Fourier Transform was performed) for different 

activities recorded by MAMS-HiRAP aboard the ISS over a 

10-day period. The table shows activities, which are present 

only on the X and Z axes of the MAMS-HiRAP, respectively, 

and then, activities present only on two axes. This is very 

important information for experiments, which have 
NASA/TM2002-211811 4 

directional and/or frequency sensitivities. It is very important 

to note that these data are presented in the MAMS-HiRAP 

on-orbit coordinate systems. The table provides information 

such as the nature of the disturbance detected (narrow or 

broadband). In the case of the broadband, the frequency range 

(low and high end) is provided; also, the median acceleration 

level is provided along with the lowest and the highest level 

of acceleration detected for that specific activity over the 10-

day period. Finally, a column, which lists some comments, 

provides some speculations on what that specific activity 

might be. We have reasons to believe that most of them are 

correct, but since this is a preliminary look at the data, we 

will call them “speculation” at this point, until more in-depth 

analysis is performed. The result obtained, thus far, using 

actual data showed that the monitoring system correctly 

detected the disturbance sources activation. The result 

showed a high success rate in classifying and recognizing the 

detected events correctly. The total elapsed time from 

detection of an event on the ISS to pattern classification and 

recognition and actual display of the events on the world 

wide web (WWW) for PIs viewing takes approximately 12 

seconds for all three axes using a PC with a 500 MHz clock 

speed. 

This monitoring system has demonstrated its capability to 

automatically detect the vibratory disturbance sources and to 

correctly classify and recognize them. The Adaptive Pattern 

Recognition and Classification (APRC) approach presented 

here has the ability to classify the known patterns, prevent 

possible misclassification, and also recognize the unknown 

patterns. Fuzzy logic was used to exploit the tolerance for 

imprecision, uncertainty and partial truth, along with the 

experience of the human experts (by means of fuzzy logic 

rules), to make intelligent decisions as to what vibrating 

sources are more relevant to a specific sensor. 

VII. OVERALL CONCLUSION 

In this paper two major applications of artificial neural 

networks were presented: a non-linear input-output mapping 

for system optimization, and pattern classification and 

recognition for system monitoring. In the former, 

backpropagation networks, a supervised learning algorithm, 

were used, while in the latter, Kohonen’s Self-Organizing 

Feature Map, unsupervised learning, was used in conjunction 

with Learning Vector Quantization (supervised learning). It 

was shown that supervised learning and unsupervised 

learning compliment each other very well. 

Although neural networks alone can provide very accurate 

input-output mapping, it cannot deal with data imprecision, 

fuzziness and uncertainty. In contrast, fuzzy logic is well 

suited for that. Therefore, the combination of these two 

techniques, known as soft computing techniques, is a 

powerful tool for design, analysis and systems monitoring, 

where neural networks provide precision, while fuzzy logic 

provides intelligence to the system. The works discussed in 

this paper clearly demonstrated that. 

REFERENCES 

1. Lin, P.P. and Jules, K., “Optimized Multidisciplinary System 

Design for Aircraft and Propulsion Systems,” AIAA 98–3265, 

34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & 

Exhibit, 1998. 

2. Jules, K., and Lin, P.P., “Multidisciplinary System Design 

Optimization Using Taguchi Techniques, Fuzzy Logic and 

Neural Networks,” AIAA–2000–0688, 38th Aerospace Sciences 

Meeting & Exhibit, 2000. 

3. Hagan, M.T., Demuth H.B., and Beale, M., Neural Network 

Design, PWS Publishing Company, 1996. 

4. Lin, C.T., and Lee, G.C.S., Neural Fuzzy Systems, Prentice 

Hall, Inc., 1996. 

5. Freeman, J.A., and Skapura, D.M., “Neural Network: 

Algorithms, Applications, and Programming Techniques, 

Addison-Wesley Publishing Company, Inc., 1991. 

6. Haykin, Simon., Neural Networks:A Comprehensive 

Foundation, Prentice Hall, Inc., 1994. 

7. Lin, P.P. and Jules, K., “A Simulation-Based Generic Tool for 

Multidisciplinary Design Optimization,” 8th AIAA/NASA/ 

USAF/ISSMO Symposium on Multidisciplinary Analysis and 

Optimization, September 2000, Long Beach, CA. 

8. Hamacher, H., “Low-Frequency Residual Acceleration,”

Journal of Spacecraft and Rockets, Vol. 32, No. 2, pp. 324–

327, 1995. 

9. Jules, K. and Lin, P.P., “Monitoring the Microgravity 

Environment Quality On-board the International Space Station 

Using Soft Computing TechniquesPart I: System Design,”

51st International Astronautical Congress, October 2000, Rio de 

Janeiro, Brazil. 

10. Jules, K. and Lin, P.P., “Monitoring the Microgravity 

Environment Quality On-board the International Space Station 

Using Soft Computing TechniquesPart II: Preliminary 

System Performance Results,” 52nd International Astronautical 

Congress, October 2001, Toulouse, France. 

11. Lin, P.P. and Jules, K., “An Intelligent System for Monitoring 

the Microgravity Environment Quality On-board the 

International Space Station,” IEEE Instrumentation and 

Measurement Technology Conference, May 2001, Budapest. 

12. Kohonen, T., Self-Organization and Associative Memory, 3rd 

Edition, New York, Berlin: Springer-Verlag, 1988. 

13. Kohonen, T., “Learning Vector Quantization for Pattern 

Recognition,” Technical Report TKK-F-A601, Helsinki 

University of technology, Finland, 1986. 

14. Kohonen, T., “Improved Versions of Learning Vector 

Quantization,” International Joint Conference on Neural 

Networks, V. 1, pp. 545–550, San Diego, CA, 1990. 
NASA/TM2002-211811 5 

15. Kohonen, T., “Learning Vector Quantization for Pattern 

Recognition,” Technical Report TKK-F-A601, Helsinki 

University of technology, Finland, 1986. 

16. Lippmann, R.P., “Pattern Classification Using Neural 

Networks,” IEEE Communication Magazine, 27, pp. 47–64, 

1989. 

17. Jang, J.S., and Gulley, N., Application of Fuzzy Logic in 

Control Design, Technical Brief, Mathworks Inc., 1996. 

18. Matlab Fuzzy Logic Toolbox User’s Guide, Version 2, Math 

Inc., 1998 

FLOPS ANOPP +

FOOTPR. COMPEAT WATE CSPAN TURBAN MAPGEN FUTURE

MODULE

DESIGN OBJECTIVES

DISCIPLINARY APPLICATION MODULES

Taguchi

Techniques

Neural Net

Training/

Module

Module

Optimizer

(Taguchi & Fuzzy)

Module

Compromiser

(Fuzzy Logic)

Multidisciplinary

Compromiser (Fuzzy Logic)

NEPP:

WATE:

FLOPS:

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FOOTPR:

COMPEAT:

CSPAN.:

TURBAN:

MAPGEN:

FUTURE MODULE:

Engine Cycle Analysis Program

Engine Weight Prediction Program

Mission Analysis Code

Aircraft Noise Prediction Program

Engine Noise Prediction Program

Engine Cost Prediction Program

Axial Compressor Design Program

Turbine Design Program

Components Map Design Program

Socket for Future Programs

NEPP 

Cost

Thrust

Fuel Weight

Engine Weight

System Weight

Aircraft Noise

Emission

DESIGN METHODOLOGY

Identification of

significant parameters

Generation of maps

for each module

Optimum solution for

design objective per

module

Compromised solution

for conflicting objectives per module

Compromised solution

for conflicting objectives

for all modules used

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(1a) Maximizing Thrust 

Thrust Fuel Emission Jet vel. 

35,000 ft 12.57 % 0.15 % 19.52 % –7.62 % 

Sea level 6.24 % –0.63 % –10.09 % –4.93 % 

(1b) Minimizing Fuel Consumption 

 Thrust Fuel Emission Jet vel. 

35,000 ft 0.95 % 14.97 % –8.93 % 1.02 % 

Sea level –2.53 % 9.38 % –4.84 % 3.55 % 

(1c) Minimizing Emission 

 Thrust Fuel Emission Jet vel. 

35,000 ft –6.40 % 3.44 % 27.38 % 4.34 % 

Sea level –4.44 % 0.73 % 41.60 % 4.02 % 

(1d) Minimizing Jet Velocity 

 Thrust Fuel Emission Jet vel. 

35,000 ft –6.04 % –4.72 % 13.24 % 4.33 % 

Sea level –9.01 % 2.27 % 25.80 % 8.35 % 

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(2a) Maximizing Thrust, Minimizing Fuel Consumption 

and Emission

Thrust Fuel Emission Jet vel. 

35,000 ft 1.87 % 3.57 % 5.80 % –1.44 % 

Sea level –0.19 % 3.53 % 16.91 % 0.46 % 

(2b) Maximizing Thrust, Minimizing Fuel Consumption 

and Jet Velocity

Thrust Fuel Emission Jet vel. 

35,000 ft 1.53 % 3.69 % 0.41 % –1.29 % 

Sea level –2.57 % 5.75 % 9.16 % 2.73 % 

(2c) Maximizing Thrust, Minimizing Emission 

and Jet Velocity

Thrust Fuel Emission Jet vel. 

35,000 ft 2.34 % 1.85 % 5.08 % –1.89 % 

Sea level –2.38 % 3.72 % 17.29 % 2.33 % 

(2d) Minimizing Fuel Consumption, Emission and 

Jet Velocity

 Thrust Fuel Emission Jet vel. 

35,000 ft –2.30 % 8.23 % 10.12 % 1.76 % 

Sea level –3.96 % 6.31 % 21.58 % 4.00 % 
NASA/TM2002-211811 7 

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Thrust Fuel Emission Jet vel. 

35,000 ft 1.28 % 3.48 % 5.43 % –1.14 % 

Sea level –1.87 % 4.12 % 15.60 % 1.96 % 

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Design Objective / Output RMS Error

Output 1: Thrust 1.719 % 

Output 2: Fuel Consumption 1.617 % 

Output 3: Emission 3.727 % 

Output 4: Jet Velocity 1.242 % 

• Number of Parameters

• Range of Parameters

An L81 Taguchi Table is 

generated

A Table of 81 cases to

run the defined

parameters above

User generates objective 

function outputs for the 

81 cases

• Baseline design

• Selection of objective

 function(s)

user software specific

USER

provides

obtains

provides the

objective

functions data

defines

Taguchi global analysis

is performed

Taguchi global analysis

is transformed to local

analysis

Optimum solution set is

obtained

Optimized set using

his/her software

Optimized parameter set

is delivered to user

Fuzzy logic is used for 

conflict handling for

multiple objectives

Parameters setting &

normalization are

performed

obtains

runs

obtains

optimized set

user software specific

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NASA/TM2002-211811 8 

• Number of Parameters

• Range of Parameters

• A database based on the

 defined parameters

Neural networks map is

generated using user's

database

The neural networks map

is used to find the

objective functions output

user software specific or default

USER

provides

Taguchi global 

analysis is performed

w/inner & outer arrays

Neural networks map is

used to obtain user

optimum solution set

• Baseline design

• selection of objective

 function(s)

Optimized parameter set

is obtained for user's

specified baseline design

Fuzzy logic is used for 

conflict handling for

multiple objectives

Parameters setting &

normalization are

performed

obtains

user software specific or default

An L81 Taguchi Table is 

generated

81 cases are chosen

from database for the

defined parameters

user software specific or default

defines

Taguchi global analysis

is transformed to local

analysis

provides

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NASA/TM2002-211811 9 

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MAMS-HiRAP: X-axis only 

Nominal Frequency (Hz) Low end (Hz) High end (Hz) Band type Median Level (μg) Low end (μg) High end (μg) Excitation level (μg) Comment 

1.8311 Narrow 27 8.4121 88.892 Structural 

3.7842 Narrow 65 6.9859 121.09 188.93 Structural 

4.6387 Narrow 50 21.582 111.43 Structural 

11.597 Narrow 20 7.1607 48.046 

13.672 Narrow 30 5.0939 69.260 

14.404 Narrow 51 17.729 84.30 

15.137 Narrow 59 16.879 84.536 

15.991 Narrow 28 15.855 72.29 162.14 

16.602 Narrow 30 5.9014 62.211 

18.677 18.818 19.531 Broadband 21 8.8645 37.908 

MAMS-HiRAP: Z-axis only 

Nominal Frequency (Hz) Low end (Hz) High end (Hz) Band type Median Level (μg) Low end (μg) High end (μg) Excitation level (μg) Comment 

5.9814 5.8594 6.2256 Broadband 16 10.398 17.775 185.04 Structural 

7.5684 Narrow 8 5.2704 12.047 171.64 Structural 

MAMS-HiRAP: X and Z-axes 

Nominal Frequency (Hz) Low end (Hz) High end (Hz) Band type Median Level (μg) Low end (μg) High end (μg) Excitation level (μg) Comment 

0.97656 (x) 

0.97656 (z) 

 Narrow 

Narrow 

14.8 

33 

4.8229 

8.0831 

43.306 

65.824 

 Structural 

Structural 

39.185 (x) 

39.185 (z) 38.82 41.75 

Narrow 

Broadband 

9.0 

5.0 

5.7623 

3.2659 

15.071 

10.555 

 

MAMS-HiRAP: Y and Z-axes 

Nominal Frequency (Hz) Low end (Hz) High end (Hz) Band type Median Level (μg) Low end (μg) High end (μg) Excitation level (μg) Comment 

9.5215 (y) 

9.5215 (z) 

9.1553 

9.2773 

9.887 

9.644 

Broadband 

Broadband 

17 

30 

3.2905 

4.6486 

54.391 

87.878 

 

15.625 (y) 

15.625 (z) 

15.259 

15.015 

15.99 

15.99 

Broadband 

Broadband 

19 

36 

7.3364 

8.9874 

46.698 

74.023 

 

83.984 (y) 

83.984 (z) 

 Narrow 

Narrow 

30 

25 

7.5534 

21.413 

34.195 

34.03 

 EXPPCS 

EXPPCS 
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15

Artificial Neural Networks Applications: From Aircraft Design Optimization to

Orbiting Spacecraft On–Board Environment Monitoring

Kenol Jules and Paul P. Lin

Multidisciplinary design optimization; Taguchi techniques; Fuzzy logic

Unclassified -Unlimited

Subject Categories: 19 and 35 Distribution: Nonstandard

Prepared for the 2001 Advanced Study Institute on Neural Networks for Instrumentation, Measurement, and Related

Industrial Applications sponsored by the North Atlantic Treaty Organization (NATO), Crema, Italy, October 9–20, 2001.

Kenol Jules, NASA Glenn Research Center, and Paul P. Lin, Cleveland State University, Cleveland, Ohio 44115–2425.

Responsible person, Kenol Jules, organization code 6727, 216–977–7016.

This paper reviews some of the recent applications of artificial neural networks taken from various works performed by

the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First,

artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the

overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most

design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent

of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial

intelligence techniques or algorithms in order to design an optimized or versatile system.