NASA/TM-2009-215771

Design of Quiet Rotorcraft Approach

Trajectories

Sharon L. Padula, Casey L. Burley, D. Douglas Boyd, Jr., and Michael A. Marcolini

Langley Research Center, Hampton, Virginia

June 2009
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NASA/TM-2009-215771

Design of Quiet Rotorcraft Approach

Trajectories

Sharon L. Padula, Casey L. Bin-ley, D. Douglas Boyd, Jr., and Michael A. Marcolini

Langley Research Center •, Hampton, Virginia

National Aeronautics and

Space Administration

Langley Research Center

Hampton, Virginia 23681-2199

June 2009
Trade names and trademarks are used in this report for identification only. Their usage does not

constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space

Administration.

Available from:

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076-1320

443-757-5802
Abstract

A optimization procedure for identifying quiet rotorcraft approach

trajectories is proposed and demonstrated. The procedure employs a

rmrlti-objective genetic algorithm in order° to reduce noise and create

approach paths that will be acceptable to pilots and passengers. The

concept is demonstrated by application to tn,o different helicopters. The

optimized paths are compared with one another and to a standard 6-deg

approach path. The N,o demonstration cases validate the optimization

procedure but highlight the need for improved noise prediction

techniques and for additional rotorcraft acoustic data sets.

Introduction

Background

The Subsonic Rotary Wing (SRW) project under the NASA Fundamental Aeronautics Program seeks to

develop multidisciplinary design optimization (MDO) tools that enable the design and operation of safe

and environmentally compatible rotorcraft. One segment of the SRW project emphasizes vehicle

aeroacoustics and noise propagation tools. For example, the present study develops traiectory

optimization techniques for reducing community noise near helipads. The present study builds upon past

theoretical and experimental research sponsored by industry, government, and academia.

The rotorcraft industry sponsors MDO research that involves topics such as structural dynamics,

aerodynamics, noise mechanisms, noise propagation, and modified takeoff and landing procedures. For

example, Orr and Narducci provide a summary of current MDO research at Boeing Helicopters (ref. 1).

In addition to industry internal research and development efforts, a consortium of industry partners

sponsors noise measurement activities and supports the Helicopter Association International (HAI) Fly

Neighborly Program to educate pilots about low-noise operating procedures (refs. 2 and 3).

The U.S. Government military and civilian laboratories perform fundamental research in noise prediction

methods and noise reduction technologies, and sponsor rotorcraft flight and wind-tunnel testing (refs. 4

and 5). This fundamental research has produced a number of software tools, such as the Rotorcraft Noise

Model (RNM) that is described in reference 6. The RNM code uses a database of measured or predicted

noise hemispheres as input to predict the noise footprint for a given rotorcraft flight path (see fig. 1). The

RNM code can provide a wide variety of noise metrics, such as time histories of overall sound pressure

level (OASPL) and sound exposure level (SEL) noise footprints. The calculations can include the effects

of uneven terrain, layered atmosphere with attenuation, ground attenuation, and ground reflections.
Speed

a

Q

Altitude

ML

Noise footprint

(a) Approach diagram (b) Noise hemisphere

Figure 1. RNM approach path diagram and representative noise hemisphere.

Scope

The present research uses the RNM code to predict the community noise that is generated by a helicopter;

a segmented approach with linearly varying parameters on each segment is assumed. A standard

atmosphere is also assumed and ground effects (reflection and attenuation) are included. This research

uses the iSight framework* (a product of Engineous Software, Inc.) to automate the detennination of

optimized flight paths. Two different helicopters are considered in this paper, the Bell CH-146 and

McDonnell Douglas MD-900. The CH-146 optimization effort uses measured noise hemispheres, and

the MD-900 effort uses predicted noise hemispheres. The best approach trajectories that were identified

for each helicopter are compared with one another and with a standard approach that uses a 6-deg descent

angle. The acceptability of the optimized approach is assessed by comparing it with the guidelines that are

discussed in references 2 through 4.

The proposed optimization strategy employs a multi-objective genetic algorithm (MOGA) to

simultaneously reduce average community noise and average rate of descent. The iSight framework

provides software tools to wrap the RNM code; to define and calculate optimization design variables,

objective functions and constraint functions; and to iterate between the RNM and MOGA codes. The

MOGA generates a large number of possible approach trajectories and uses an optimization strategy to

converge to the best one.

This paper describes the optimization problem, provides sample results, and makes recommendations for

future experimental testing and validation of the results. Section 2 discusses the optimization problem,

and proposes design variables, objective functions, and constraints for the optimization problem. Section

3 discusses results for the CH-146 helicopter and identifies some issues that are associated with the

RNM, the MOGA, and the CH-146 experimental data set. Section 4 discusses results for the MD-900

helicopter and demonstrates an improved version of the MOGA. Section 5 provides concluding remarks

and suggestions for future research.

* The use of trademarks or names of manufacturers in this report is for accurate reporting and does not constitute an

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics

and Space Administration.
Optimization Strategy

MOGA

Multi-objective genetic algorithms (MOGA) are a popular class of combinatorial optimization methods.

MOGA algorithms randomly create a large population of candidate designs and then generate improved

designs by merging the best traits of each of the candidate designs. MOGA are not computationally

efficient but are easy to apply to a wide variety of optimization problems. MOGA do not calculate

gradients; therefore, they can tolerate analysis codes that return no result or an anomalous result for

certain values of the design variables. MOGA generate random combinations of design variables;

therefore they can converge to one or more excellent solutions that are quite different from the bestknown solution. MOGA are especially appropriate to trajectory optimization problems because these

problems tend to have a few excellent solutions that differ from one another. Many variants of the MOGA

optimization method exist. The MOGA software that is provided by the iSight framework is based on the

Nondominated Sorting Genetic Algorithm II that is described in reference 7.

Design Variables

Noise caused by an aircraft flyover is known to depend on vehicle speed and altitude as a function of time

(e.g., see ref. 8). The angle between the vehicle and the observer is another important factor. As pictured

in figure 1, helicopter source noise on a hemisphere of constant radius is usually louder (i.e., more red) in

the right forward quadrant. Thus, finding the quietest approach path requires a tradeoff between landing

quickly, approaching steeply, and maintaining an approach angle that exposes the community to the

lowest source noise, while still remaining within the safe operating envelope of the vehicle.

Selecting the appropriate design variables to parameterize the approach path is essential. The

computational cost of the MOGA increases with the number of design variables. Therefore, the number of

design variables must be reduced if possible. In addition, the design variables must be scaled so that all

have the same order of magnitude; otherwise, one design variable can dominate the optimization results.

Finally, each possible set of design variable values must create a reasonable approach path; otherwise,

most of the computer time will be spent evaluating and rejecting poor candidate paths.

Unfortunately, the approach path parametrization that is used by the RNM does not meet the needs of the

MOGA. The RNM input describes a flight path in terms of altitude and speed at a set of fixed distances

from the helipad. The present research defines the flight path by using 35 linear path segments. This

suggests a parameterization with 105 variables (i.e., 35 distances, 35 altitudes, and 35 speeds). The

parameterization is unfortunate because of the large number of variables and because the value of speed is

two orders of magnitude smaller than the value of distance. Moreover, random changes to any of the 105

variable values will typically produce approach paths with both acceleration and deceleration and with

abrupt changes in altitude. This sort of roller coaster approach path is not acceptable to passengers.

An alternate parameterization of the approach path has only 14 design variables and produces a wide

variety of acceptable candidate approach paths. The new design variables have small integer values and

are used to select the initial altitude, the initial speed, and the percentage of change in speed and descent

rate for each of the next 11 approach path segments. The CH-146 optimization effort uses fixed distances

for each of the flight path segments. The improved MD-900 optimization effort uses variable distances.

In the MD-900 effort, the initial distance is fixed, but the distance at which descent begins and ends

varies. In both optimization efforts, the values of integer design variables are converted into the physical

distances, altitudes, and speeds by using simple arithmetic equations which are given below. The

advantages of this new parameterization are that the integer design variables all have similar magnitudes

3
and they automatically create flight paths that are smooth and monotonically descending. The calculation

of physical units and the creation of the RNM input file are easily accomplished in the iSight framework.

Metrics

Previous studies have compared candidate quiet approach procedures with an approach path that has a

constant 6-deg descent , which is herein referred to as a standard approach. Reference 8 demonstrates that

the 6-deg approach path that is specified by FAR-36 for rotorcraft certification is not a low-noise

approach path for the XV-15 tiltrotor. Reference 9 proposes several quiet approach procedures for the

XV-15 and notes that flight along a 6-deg flight path produces increased noise at approach speeds from

60 to 100 knots. References 3 and 4 contain acoustic flight test results for a wide variety of helicopters

and both conclude that the 6-deg approach path that is specified for certification is rarely the best choice

for day-to-day operations. The present paper extends the research begun in references 3 through 9 and

develops an optimization-based strategy for identifying quiet approach paths for any type of rotorcraft.

The success of the optimization process is evaluated by comparing the optimized approach path with the

standard 6-deg approach path. For this purpose, the standard approach path is defined as having a level

flight segment at an altitude of 1400 ft and a 6-deg descent segment from an altitude of 1400 ft to 300 ft.

The vehicle decelerates from 100 to 63 kt along this approach path. The speed and altitude as a function

of distance from the helipad are pictured in figure 2. This standard approach is appropriate for both the

MD-900 and the CH-146 helicopter and is similar to the decelerating 6-deg approach that is discussed in

reference 4.

140

120 2000

Speed 

_ _ —7 100

1500 — -

._,_,_,_ ._,_,_,_,_• 80 Y

_0 Altitude

70

Q 1000 y 60 C)

40

500

20

Noise assessment locations

0 -30000 -20000 -10000 0 0

Distance from the helipad, ft

Figure 2. Standard approach path and noise assessment locations defined.

4
The most important objective of the optimization process is to reduce community noise. Normally, this

would mean reducing the noise at every location surrounding the helipad (recall the noise footprint in

figure 1). However, for the current demonstration, noise is reduced at five locations that are directly under

the flight track and located 4000, 8000, 12000, 16000, and 20000 ft from the helipad. These locations are

shown as black dots in figure 2.

At each location, the SEL is computed. Reference 10 explains that the SEL is based on the maximum Aweighted sound level with a duration correction for the length of time that the sound level is at or near this

maximum value. This measure is sometimes referred to as noise exposure level and can be represented by

acronyms or symbols such as SEL, NEL, Lx, or L S . In this paper, the acronym SELA is used to denote

the value of the SEL that is computed by the RNM code. The SELA is deemed appropriate for

commmunity noise because annoyance is affected by sound level, sound duration, and source frequency.

The average noise reduction is determined by comparing the SELA levels for the optimized approach

path with the levels that were estimated for the standard 6-deg approach. Thus, the average change in

noise is computed as

n

Save = n^i-1 4SELAi (1)

where n = 5 and ASELA, is the difference at location i between the SELA that is estimated for the current

approach path and the SELA that is estimated for the standard approach path. Hence, the goal is to

minimize S,,e and to obtain negative values for each ASELA; . Experience has shown that reducing noise

at several locations under the flight track tends to reduce community noise (e.g., see ref. 8). However,

equation (1) can be applied in other ways. The Save can be calculated with the large number of locations

required to create a noise footprint. Alternately, because the area that surrounds the helipad may include

both industrial and residential zones, the noise assessment locations could be placed only in the most

noise-sensitive areas.

Another important objective of the optimization process is to create an approach path that is acceptable to

pilots and passengers. Reference 9 discusses detailed procedures for evaluating handling qualities and

pilot acceptance. For this study, a simplified criterion based on the rate of descent as a function of time is

used to decide whether a given flight path is likely to be an acceptable choice. The RNM can produce a

detailed time history of flight conditions, including interpolated values of speed and flight path angle. At

each time step, the following parameters are calculated from the RNM output:

V _ 

S * 0.51444 

rd = v * sin(y * 7c/180) R = —rd /z (2)

0.00508

Here, s is the speed in kt, v is speed in ft/min, y is the flight path angle in deg, rd is the rate of descent in

ft/min, z is the altitude in ft, and R is the rate-of-descent ratio. Acceptable values of rd are roughly —1000

ft/min at 1000 ft of altitude. At lower altitudes, pilots prefer to descend more gradually. A rule of thumb

for determining acceptable descent rates is "at an altitude of x ft, the descent rate should not exceed x

ft/min" (e.g., see ref. 9). In other words, a rate-of-descent ratio R that is close to unity is desirable.

Special safe landing rules apply when the helicopter is operated at very low altitude and slow speed. The

famous dead man's curve defines a set of low-speed flight conditions for which handling qualities

degrade and pilot response is too slow to recover from the loss of engine power.

For this study, an acceptable flight path is one where the average rate-of-descent ratio Rave is less than 1.0

and the maximum rate-of-descent ratio R1 ax is less than 2.0. This constraint on R111aX matches the highest

descent rate for the standard 6-deg approach pictured in figure 2. All flight paths, including the standard

one, will start at or above an altitude of 1000 ft and end at an altitude of approximately 300 ft. The

5
landing segment of the flight path from 300 ft to the ground is assumed to be the same for all flight paths.

The optimizer has two objective functions: minimize the average change in SELA (S Tv,) and minimize

the average rate of descent ratio (R, v,). The first objective addresses community noise, and the second

objective addresses passenger safety and comfort. Note that Save can be an order of magnitude larger than

R,v, and, thus, will have a bigger influence on the optimized results.

Lori--Noise Approach Path

Reference 2 suggests that pilots should choose an approach path (i.e., glide slope) that avoids main rotor

blade-vortex interaction (BVI) noise. BVI noise occurs when the rotor blades intersect the tip vortices

from previous blade passages as the vehicle descends. Reference 2 suggests that pilots avoid certain

combinations of speed (s) and rate of descent (rd), which are indicated by a white ovoid in figure 3.

Reference 2 explains that the best glide slope for each rotorcraft will differ but, that the basic strategy for

avoiding BVI noise will be the same. This explanation is supported by the flight test results given in

references 3 and 4.

Figure 3 is a notional chart copied from the HAI Fly Neighborly Guide (ref. 2). This chart indicates two

generic flight strategies that are acceptable to pilots and that avoid the flight conditions that are associated

with BVI noise. Both strategies decrease the forward speed from the cruise speed to the landing speed and

then flare and land. The first strategy is to use a low rate of descent until the speed is decreased to about

60 kt and then increase the rate of descent. The second strategy is to increase the rate of descent while

still at high speed. These strategies are generic because the conditions that cause BVI differ for each

rotorcraft. But, these strategies indicate speeds and rates of descent that are acceptable to pilots.

The goal of the optimization routine is to select from all pilot-acceptable flight paths the one that has the

least noise for a given rotorcraft. Figure 4 shows how the notional HAI Fly Neighborly chart can be

discretized into a set of 11 possible approach strategies. The MOGA will select one of these 11 strategies,

plus one initial altitude and one initial speed. The MOGA will also select a percentage of speed reduction

along each of the next 11 segments of the approach. These 14 pieces of information (1 strategy, 1 initial

altitude, 1 initial speed, and 11 speed reduction percentages) uniquely describe an acceptable approach

path. The best approach path depends not only on the values of the 14 design variables but also on the set

of noise hemispheres that describe the noise that is generated by the rotorcraft.

6
500

t

Speed

R/C 20 40 60 80 100 120 140 (knots)

a s o

20 40 60 a0 100 120 140 (mph)

FPM 0

flare and landing \

R/D /

optional conversions to

approach glidescope

-500

K

c

approach main rotor impulsive

noise boundary

-1,000

Figure 3. HAI Fly Neighborly chart.

500

c

0

c

U

070

w

0

0 -500 M

ry

-10000

tnrfex

- 1

2

3

4

5

6

- 7

8

9

10

11

20 40 60 80 100 120 140 160

Speed, kt

Figure 4. Discrete glide slope options available to MOGA.

7
Formulation of the Optimization Problem

The candidate approach paths are completely defined by the 14 integer design variables k,. The first 11

design variables determine the speed and altitude at the end of the first 11 flight path segments, and k12

determines the index of the discrete glide slope that is shown in figure 4. Finally, k13 and k14 determine

the initial speed and altitude:

sl = sj_, (1.0 — 0.02kj) 1 < k, < 9, j = 1, ... ,11 (3)

so=145-10k13

1Ck13<4

Yo = 100k,4 10<k,4<15

Nvhere yo is the initial altitude and so is the initial speed. For any flight-path segment, the reduced speed sj

is determined from the speed in the previous segment by using equation (3). For example, if kj equals 2,

then the new speed is 96 percent of the previous speed. Given speed sj and index k12 i the appropriate rate

of descent rd can be estimated from figure 4. Given r d and altitude y; _ 1 , the next altitude y can be

calculated. The calculated values of speed and altitude are used until the terminal conditions of 63 kt and

300 ft are reached. This procedure creates smoothly varying descent flight segments with monotonically

decreasing speed and altitude.

The optimization problem can be stated as

Choose k,

Minimize Ra„e + S,,„e

Subject to R,,,,X < 2.0 (4)

where S,,,, is the average change in SELA and R,ve, and R.x are the average and maximum R over all time

steps.

Results: CH-146

Data Available

References 5 and 6 describe the data collection and data reduction processes that are used to create noise

hemispheres for the RNM. Reference 6 indicates good agreement between the RNM SELA calculations

and the measured SEL noise footprints whenever a sufficient number of high-quality measured noise

hemispheres are available. One of the most complete data sets is for the CH-146 Griffon. (Note: This is

the Canadian designation for the Bell 412SP.) Figure 5 shows the speed and the rate of descent for the 26

hemispheres used to test the optimization strategy. Color contour plots of each of the 26 hemispheres

were inspected and informally rated for their ability to cause high community noise. The shape and color

of the symbols in figure 5 indicate flight conditions that will cause the highest and lowest noise levels.
20 40 60 80 100 120 140 160

-2000 0

E

4-

c

U

Q)

70

0

a^

i^

0!

-500

-1000

-1500

500

C

Speed, kt

Figure 5. Rate of descent and speed for CH-146 data (symbol color and shape indicate relative noise levels).

Sample Results

With the application of the optimization strategy described earlier, the MOGA successfully identified a

flight path that reduced the SELA under the flight track. Figures 6 and 7 compare the SELA noise

footprints that are produced by the standard 6-deg approach and the optimized approach. The color scale

for these noise footprints was chosen to allow a comparison between the CH-146 results and the

MD-900 results. The black dots on figures 6 and 7 indicate noise assessment locations. The SELA is

reduced by 1 to 4 dB in most parts of the footprint. This amount of reduction is significant, although not

as dramatic as was anticipated; it is consistent with earlier low-noise operations studied by Yoshikami and

Cox (ref. 3).
L7 -1000

-2000

-3000

3000

2000

1000

a^U

C

f0

U

0

a)C

-20000 -10000 0

SELA

88

86

84

82

80

78

76

74

72

70

68

66

64

62

60

2000

1000

U

C

ca

U

0

C

a)

-1000

-2000

-3000

3000

-20000 -10000 0

SELA

88

86

84

82

80

78

76

74

72

70

68

66

64

62

60

Distance from helipad, ft

Figure 6. Noise footprint for CHI 46 on a standard approach.

Distance from helipad, ft

Figure 7. Noise footprint for CH146 on the optimized approach.

10
0)

a)

aS

ca

isQ

0)

LL

0

-5

-10

140

Speed - optimized

120

2000

Speed-standard

F — 100

1500 7

^ ` ^. ^ 80 Y 

Altitude - standard

Q 1000 60 c)

Altitude - optimized 40

500 ^

20

^— Noise assessment locations

0 -30000 -20000 10000 0 •0

Distance from the helipad, ft

Figure 8. Altitude and speed as a function of distance for CH-146 approach paths.

L_^­ Optimized

— • — ^ Standard

-30000 -20000 -10000 0

Distance from helipad, ft

Figure 9. Flight path angle as a function of distance.

11
Optimized Approach Path

Figure 8 compares the standard and optimized approach paths. The standard path is a 6-deg approach that

starts at an altitude of 1400 ft and at a speed of 100 kt (see solid symbols in fig. 8). The optimized path

starts at an altitude of 1400 ft and has a higher speed during the level-flight segment (see the open

symbols in fig. 8). Both paths are supposed to end at the same speed and altitude (i.e., 63 kt at 300 ft);

however, the optimized path includes a few extra segments below 300 ft, as shown in figure 8. These

extra segments are excluded from the calculation of R.,, and R,v,j but may have a slight effect on the

value of S,,,,e.

Figures 8 and 9 show that the optimized path begins to descend sooner and more gradually as compared

with the path of the 6-deg approach. Intuitively, this result makes sense because a shallow flight path

angle typically avoids the maximum BVI operating conditions, while the higher speed allows the

rotorcraft to pass the noise assessment locations more quickly. Passing a location quickly is advantageous

because it affects the duration correction in the SELA evaluation. This optimization result is consistent

with the experimental evidence that is given in references 3 and 4.

Analysis of Results

The test of the optimization strategy using the CH-146 data set was successful. That is, a flight path was

identified that had reduced noise and that met the criteria for pilot acceptance. During this initial study, a

number of areas were identified where improvements could be made to the RNM code and to the

optimization strategy.

First, understanding why the optimized flight path is an improvement over the standard 6-deg approach is

necessary. Figures 8 through 10 clearly show that the optimized approach allowed for passing most of the

noise assessment locations with a higher speed but at a lower flight path angle than for the standard

approach. And, figure 10 clearly shows that, at a given noise measurement location along the flight track,

the optimized approach produced lower maximum sound levels and reduced the amount of time that the

sound was near the maximum level. Figure 10 plots altitude versus time rather that altitude versus

distance. Notice that the altitude was generally lower for the optimized approach path than for the

standard 6-deg approach path. Increasing the distance from the vehicle to the noise-measuring location is

important, but optimizing the rate of descent and the speed at each time step appears to be the most

effective way to reduce sound levels on the ground.

12
100 150

2000

1500

-i

Q 1000

500

00

OASPL

50

90

85

80

75 m

70 oQ

65

60

55

200 0

Optimized

....... Standard

o Optimized

• Standard

Altitude

Time, s

Figure 10. Altitude and OASPL as a function of time at a selected location.

Examination of the convergence behavior of the optimization process revealed that many seemingly good

approach paths were rejected as infeasible. Diagnosis of this behavior suggests that the constraint R,,,x < 2

was too restrictive if we consider that all candidate approach paths began their descent at a fixed distance

(i.e., 16,000 ft) from the helipad. The only way to meet the R,mx constraint was to have a high initial

altitude or to begin the descent further than 16,000 feet from the helipad. Therefore, the optimization

procedure was modified to allow the descent start distance to vary. This modification was tested with the

MD-900 data set.

Another issue of concern was the reliability of the measured flight test data. Each of the CH-146 noise

hemispheres was created from a limited amount of flight test data. During these flight tests, the pilot tried

to maintain a constant speed and a constant flight path angle while flying over an array of microphones.

Reference 4 reports that the mean altitude errors in the 1995 acoustic test flights were smaller than the

EL30 ft window that was specified for noise certification. However, the rate-of-descent tracking

performance was less than desirable (e.g., EL250 ft/min in run number 14). In other words, to stay on the

specified glide slope, the pilots were constantly making small adjustments to speed and flight path angle,

which resulted in accelerations and decelerations throughout the measurement process. Chen et al. (ref. 4)

explain how these factors increase the uncertainty in the measured acoustic data. The CH-146 data set is

expected to contain similar levels of uncertainty. More recent acoustic test flights have reduced the

measurement uncertainties significantly with improvements in testing procedures, piloting techniques,

and pilot aids such as "heads-up displays" (e.g., see ref. 9).

In addition to the above issues, the interpolation methods in the RNM code also need to be unproved. At

any given time step on a flight path, the code uses the provided noise hemispheres to predict the OASPL

at a given set of locations. However, the flight condition at that time step typically does not exactly match

13
95

>dified

70 90

85

a^L

Q

7

80

Q

^L, W

O> 75

70

40 60 80 100 120 140

any noise hemisphere in the data set. To perform this calculation, the RNM internally chooses up to four

hemispheres that are at flight conditions that closely approximate the current flight condition and

interpolates between these hemispheres. If no flight conditions are close enough, then no interpolation is

performed, and the hemisphere at the closest flight condition is chosen. Unless the noise hemisphere

database contains an adequate set of flight conditions, this simple interpolation procedure can create

discontinuties in the predicted OASPL time history (see fig. 11). The abrupt jump that can be seen in

figure 11 (near the point at which time equals 55 s) is caused by switching from one hemisphere to the

next in a discontinuous manner. When the RNM uses a scheme that forces interpolation between (up to

four) noise hemispheres, the jump in the OASPL is eliminated. This can be accomplished by modifying

the RNM parameters that determine when a flight condition is close enough to allow interpolation

between hemispheres. For general cases, even the modified interpolation scheme produces questionable

results unless the noise hemisphere data set contains a large array of closely spaced flight conditions.

Unfortunately, this large array of flight conditions is difficult to obtain because of the high cost of flight

testing and the requirement for highly reliable measurements.

To minimize the above effects, the optimization procedure was modified. For each approach path that

was generated by MOGA, two RNM predictions such as the two in figure 11 were made. If the two

predictions differed dramatically, then the MOGA penalized this candidate path by adding another term in

the objective function (eq. 4). With this modification, all candidate paths with questionable noise

estimates were avoided, and the results shown in figures 6 through 10 were produced. However, a large

number of attractive candidate paths have been avoided as a result of this interpolation issue.

Time, s

Figure 11. Effect of interpolation parameters on RNA/1 noise prediction.

14
Results: MD-900

Data Available

The second test of the optimization procedure used noise hemispheres at 36 flight conditions for the

MD-900 rotoreraft. These noise hemispheres were generated with the Comprehensive Analytical Model

of Rotoreraft Aerodynamics and Dynamics II (CAMRAD-II) and the Ffowes Williams Hawkings solver,

PSU-WOPWOP, as discussed in reference 11. Given a model of the rotorcraft, CAMRAD-11 determines

the trim state (i.e., aerodynamics, blade motion, vehicle attitude, etc.) that is required to fly along a

specified quasi-steady flight path. The aerodynamics and the blade motion of the vehicle are used in PSUWOPWOP to compute the acoustic pressure time histories at a set of observers (i.e., microphone

locations). These acoustic pressure time histories are analyzed to compute the discrete frequency noise

content at each location. These frequency data are then converted to one-third octave band frequency

data. The observers for all of these cases were on a hemisphere below the vehicle at a distance of 10 rotor

radii. This procedure was repeated for all 36 flight conditions to generate a noise hemisphere database to

be used with the RNM.

These 36 flight conditions (see fig. 12) were chosen for this study to provide improved coverage of the

flight speeds and descent rates of interest. This strategy provided the RNM with enough data to avoid

rejection of any flight path by the optimization scheme based on the interpolation penalty that was

discussed in the previous section.

Low

500 Medium

High

Highest

0

C - - n n

-500 V

C_: a) U

a)

p -1000 ^,>

is

-1500

-2000................................

0 20 40 60 80 100 120 140 160

Speed, kt

Figure 12. Rate of descent and speed combinations of predicted MD-900 data set.

15
-30000 -20000 -10000 0

2000

500

1500

ai

Q 1000

With the previous flight-path restrictions removed, the MD-900 optimization strategy had a larger

number of feasible flight paths from which to choose. For example, figures 13 and 14 shoe- a wide variety

of acceptable low-noise flight paths that were generated by the MOGA (i.e., Rm, < 2 and Sake < 0 for

each path). Figure 13 shows altitude versus distance to the helipad; figure 14 shows the corresponding

speed versus distance. These plots show only the descending portion of the approach. Each approach

started at the selected initial altitude yo and the initial speed so and at a distance of 35,000 ft from the

helipad. Each flight path was level and maintained a constant speed until the descent portion of the path

was reached.

Distance from helipad, ft

Figure 13. Possible approach paths for AID-900 helicopter.

16
140

120

100

80 Y

70

60 c)

40

20

L.L30000 L.-20000 10000 0 •0

Distance from helipad, ft

Figure 14. Possible speed schedules for AID-900 helicopter.

Sample Results

Three sets of optimization results are presented. First, the optimization problem that was stated in

equation (4) was repeated for the MD-900 rotorcraft. Recall that the constraint R.,, < 2.0 was selected

because it matched the standard 6-deg approach case. Next, the optimization problem was repeated with

the constraint set to R,vax < 1.2. This constraint was selected to produce a flight path that was more

acceptable to passengers. Finally, the same problem (R,,Ix < 1.2) was repeated with a larger number of

candidate flight paths. Figure 15 compares the noise footprints for the standard 6-deg approach with those

optimized approaches. Figure 16 compares the speed and altitude for the three approaches. Clearly, both

optimized approaches reduce community noise. Figure 17 shows how the first two optimized approaches

compare with the glide slopes that were recommended by the HAI Fly Neighborly document (fig. 3).

17
2000

1000

U

C

f0

0

a)

C

3000

-20000 -10000 0

-1000

-2000

-3000

3000

2000

1000

U

C

fa

U

0

C

a)

-1000

-2000

-3000 -20000 -10000 0

SELA

88

86

84

82

80

78

76

74

72

70

68

66

64

62

60

Distance from helipad, ft

(a) Standard approach.

Distance from helipad, ft

(b) R^ < 2.0 using 300 RNXI runs.

18
2000

1000 a^U

C

f6

0

a)

3000

-20000 -10000 0

-1000

-2000

-3000

2000

"- 1000

m

U

C

ca

U)

0

a)

a)

-1000

-2000

-3000

3000

-20000 -10000 0

Distance from helipad, ft

(c) R,,,A , < 1.2 using 300 RNNI runs.

Distance from helipad, ft

(d) R,.A , < 1.2 using 1000 RNM runs.

Figure 15. Noise footprints for MD-900 on standard and optimized approaches.

SELA

88

86

84

82

8o

78

76

74

72

70

68

66

64

62

60

SELA

88

86

84

82

8o

78

76

74

72

70

68

66

64

62

60

19
2000

1500

Q 1000

500

120

100

40

20

80

Y

70

60

140

120

100

80 Y

70

60 c)

40

20

0 -30000 -20000 10000 0 0

Distance from helipad, ft

(a) RmaS < 2.0

2000

1500

Q)

Q 1000

500

• 1000 Runs

-30000 -20000 -10000 0

Distance from helipad, ft

(b) R,., < 1.2

Figure 16. Speed and altitude as a function of distance for optimized approach paths.

20
20 40 60 80 100 120 140

Speed, kt

E

2000 0

500

-500

a) U

a)

o -1000

a)

M

ry

-1500

Figure 17. Optimized glide slopes compared to HAI recommendations.

Analysis of Results

The noise footprints in figures 6 and 15 demonstrate definitively that the five-bladed MD-900, which has

a maximum gross takeoff weight (GTW) of 6,500 lb, generates less noise than the four-bladed 12,000 lb

GTW CH-146. This can be partly explained by the fact that the measured CH-146 data contains

contributions from all noise sources, while the predicted MD-900 data contains rotor blade noise only.

But, these are also very different vehicles. The MD-900 is a smaller vehicle that has no tail rotor

(NOTAR) and was designed for reduced noise. Thus, these two data sets provide a good test of the

approach trajectory optimization procedure.

Fortunately, the optimization procedure can be successful whether or not the MD-900 noise hemispheres

are underpredicted. As long as the trends are correct, the absolute levels that are produced by the

candidate approaches are inconsequential because the MOGA algorithm is not a gradient-based method.

As long as the RNM correctly identifies which of two candidate approaches generates less noise, the

MOGA will function as expected. Based on comparisons between the MD-900 predicted data and a

limited amount of measured data, the authors believe that the trends are representative of this class of

vehicle.

For the optimization strategy to be considered successful, we must be able to demonstrate that the best

approach path has been found. One way to demonstrate this is to make additional runs with the MOGA.

The original results were found by using only 300 RNM predictions. If the MOGA is allowed to run the

RNM 1000 times, then a similar but improved approach path can be found (see figure 16b). The improved

approach path that was identified with 1000 RNM runs is smoother and reduces S,,,, by an additional 0.5

dB.

21
Another way to assess the results is to use a design of experiments (DOE), as suggested in reference 12.

One DOE is used to examine other candidate paths that are near the optimized approach path by

perturbing one design variable at a time. A second DOE (e.g., Latin hypercube sampling) is used to

examine candidate paths from other parts of the design space. These DOE studies found several other

good approach paths that were quite sinular to the optimized approach paths in figure 16. This is further

evidence that the MOGA is operating correctly.

The same DOE assessment can be used to test our hypothesis that reducing the average SELA will also

reduce the noise footprint. For example, given a noise footprint of SELA values on a uniform grid, define

a new objective called Aave:

Aave = h2 f f SELA,, di dj (5)

,;

where h is the grid cell length in ft. Note that a uniform grid is used for convenience; any sufficiently

dense grid likely would give similar results. Equation (5) represents the volume under the SELA noise

footprint surface and can be approximated by using multidimensional numerical integration. We define

Aave as the volume for any candidate approach path, and A *ave as the volume for the optimum approach

path found with 1000 RNM runs.

Figures 18 and 19 reveal the correlation between Sake and Aave for a Latin hypercube DOE centered on the

optimum design. The optimum design has R,,, aX < 1.2 and Sake < —3.2 dB, indicated by a black diamond in

each graph. First, notice in figure 18 that no acceptable design has a lower Save than the optimum design.

Next, notice in figu re 19 that a high correlation exists between Sage and A,,,e . The correlation between

these two noise metrics suggests that the MOGA would converge to a similar optimized approach path if

Aave were used in the objective function instead of Sake . This hypothesis has not been tested because of the

computational expense of calculating 1000 noise footprints.

22
8

7

6

c

5

L

0U 4

X

(6

3

tY

2 Unacceptable

•

1 Acceptable •Is Owe0 6

5 4 3 2 1

0

1

2

Average SELA reduction, dB

Figure 18. Plot of S.,. e versus RI..., for designs near the optimum.

1.06

0••

1.04 ,•

0

0

>

1.02 •

E •

Z • •

0.98 6 1 51 41 31 21 1 0 2

Average SELA reduction, dB

Figure 19. Plot of S„,. e versus A,,., /A "A , a for designs near the optimum.

23
Concluding Remarks

Successful Optimization Procedure

Application of the MOGA procedure to two rotorcraft vehicles and their associated acoustic data sets

demonstrates the capability of the optimization-based procedure to design quiet approach trajectories.

The multi-objective optimization is able to reduce noise at several locations and design an approach path

that is likely to be acceptable to the pilot and passengers. The optimized approaches compare favorably

with HAI Fly Neighborly recommendations and are consistent with previous low-noise flight test results.

The optimization results are validated by repeating the MOGA procedure with 300 and then 1000

objective function evaluations. The resulting optimized approach paths are quite similar. The results are

also validated by using a Latin hypercube DOE to generate alternate approach paths. This DOE study

indicates that the MOGA functions correctly. The DOE study further suggests that by intentionally

reducing noise at five individual locations the MOGA is also reducing total noise in the area represented

by the noise footprint.

Recommended Improvements for Prediction and Optimization

The present research effort points to the need for additional high-quality measured data. The CH-146

data set is the largest set available, yet it does not contain enough information to meet the needs of the

MOGA optimization procedure. Without a set of noise hemispheres that completely and adequately

covers all of the approach angles and speeds of interest, the current RNM interpolation scheme introduces

errors that impact the optimization procedure.

On the other hand, flight testing is expensive, and some approach paths are difficult or even dangerous to

fly. One attractive possibility is to acquire a limited set of high-quality measurements, including

characterization of the measurement uncertainty. Once high-quality data are available, the dense set of

hemispheres that is required by the RNM can be created as a preprocessing step by careful interpolation

between measured hemispheres. DOE and optimization procedures such as the ones discussed in this

paper can be used to assess the value of this preprocessing step.

To improve the prediction of safe, low-noise optimized flight paths, a set of measured or predicted

hemispheres that adequately cover the range of angles and flight speeds should be acquired for any

rotorcraft of interest. The following recommendations are made to improve the functionality and

useability of the current RNM: (1) the RNM should output warnings if the requested flight conditions are

outside the range of flight conditions in the noise hemisphere data, (2) inputs to the RNM should be

adjusted so that safe and comfortable flight paths are easy to specify, and (3) RNM calculations should

include some indication or estimate of the uncertainty in the predicted noise levels. These

recommendations have been implemented in a modified version of the RNM and in the modified

Optimization procedure that is described in this paper and have been tested with the CH-146 data set. The

results show optimized flight paths that meet our criteria for pilot acceptability and that have reduced

noise in comparison with the standard approach.

The successful MD-900 helicopter optimization results suggest a need to rethink the manner in which

flight test matrices are determined and the manner in which noise hernispheres are processed. The results

show that, when the MOGA procedure uses a data set that adequately covers the range of speeds and rates

of descent of interest, the optimization converges to a quiet approach trajectory. These results also suggest

the need to rethink and improve the interpolation algorithms that are used by the RNM. The quality of the

optimized approach path can be improved by allowing an increased number of RNM function

24
evaluations. However, even with 300 function evaluations, the optimized path is shown to be an excellent

flight path that meets the given criteria and constraints for the MD-900 helicopter.

References

1. Orr, S. and Narducci, R.: "Framework for Multidisciplinary Analysis, Design, and Optimization with High

Fidelity Analysis Tools, " NASA CR-2009-215563, February 2009.

2. Helicopter Association International: Fly Neighborly Guide, Second Edition, 1993- URL:

http://www.rotor.com/ (accessed May 21, 2009).

3. Yoshikami, S. A. and Cox, C. R.: " FAA/HAI Helicopter Flight Operations Noise Tests and Initial Results,

Presented at American Helicopter Society 41st Annual Forum, Fort Worth. TX, May 15-17, 1985.

4. Chen, R. N.; Hindson, W. S., and Mueller, A. W.: "Acoustic Flight Tests of Rotorcraft Noise-Abatement

Approaches Usin g Local Differential GPS Guidance, " NASA TM-110370, September 199.5.

5. Conner, D. A.; Burley, C. L.; and Smith, C. D.: "Flight Acoustic Testing and Data Acquisition for the

Rotor Noise Model (RNM), " Presented at American Helicopter Society 62nd Annual Forum, Phoenix, AZ,

May 9-11, 2006.

6. Conner, D. A. and Page, J. A.: "A Tool for Low Noise Procedures Design and Community Noise Impact

Assessment: The Rotorcraft Noise Model (RNM), " Presented at Hell Japan 2002, Tochigi, Japan,

November 11-13, 2002.

7. Deb, K.; Pratap, A.; Agarwal, S.; and Meyarivan, T.: "A Fast and Elitist Multiobjective Genetic Algorithm:

NSGA-II," IEEE Transactions on Evohrtionaiy Compntation, Vol. 6, No. 2, April 2002.

8. Conner, D. A., Marcolim, M. A.; Decker, W. A.; Cline, J. H.; and Edwards, B. D.: "XV-15 Tiltrotor Low

Noise Approach Operations, " Presented at the American Helicopter Society 55 th Annual Form, Montreal;

Quebec, Canada, May 25-27, 1999.

9. Decker, W. A: "Handling Qualities Evaluation of XV-15 Noise Abatement Landing Approaches Using a

Flight Simulator, " Presented at the American Helicopter Society 57 `h Annual Forum, Washington, DC,

May 9-11, 2001.

10. Bennett, R. L. and Pearsons; K. S.: "Handbook of Aircraft Noise Metrics," NASA-CR-3406, March 1981-

11. Boyd, D. D.; Burley, C. L.; and Conner, D. A.: "Acoustic Predictions of Manned and Unmanned Rotorcraft

Using the Comprehensive Analytical Rotorcraft Model for Acoustics (CARMA) Code System, " Presented

at the American Helicopter Society International Specialists' Meeting on Unmanned Rotorcraft, Phoenix,

Arizona; Jan. 18-20, 2005.

12. Santner, T. J.; Williams, B. J.; and Notz, W. I: The Design and Analysis of Computer Experiments,

Springer-Verlag, New York, NY, 2003-

25
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Design of Quiet Rotorcraft Approach Trajectories

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Padula, Sharon L.; Burley, Casey L.; Boyd, David D., Jr.; Marcolim,

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14.ABSTRACT

A optimization procedure for identiA ing quiet rotorcraft approach trajectories is proposed and demonstrated. The procedure

employs a multi-objective genetic algorithm in order to reduce noise and create approach paths that will be acceptable to pilots

and passengers. The concept is demonstrated by application to two different helicopters. The optimized paths are compared

with one another and to a standard 6-deg approach path. The two demonstration cases validate the optimization procedure but

highlight the need for improved noise prediction techniques and for additional rotorcraft acoustic data sets_

15. SUBJECT TERMS

Genetic algorithm; Noise reduction: Rotary wing aircraft; Trajectories

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