AD0A193 428 FEDERAL A VIATION ADMINISTRATION WASHINGTON DC OFFICE -ETC F/6 1/3

C ORRELATION OF HELICOPTER NOISE LEVELS WITH PHYSICAL AND PERFOR -ETC(U)Sep 80 J S NE WMAN

UNCLASSIFIED FAA EE - M A22 N

liii4 1.8 ~ III~III -11111N i IIIlt2.

111111L25 .lI4 111111.6

MICROCOPY RESOLUTION I ES CHARI

WOW N fqA f I l A l'Nf

-IMFel Correlations of He lcopterNoise Levels withUSf Deoprtmeno.,wd ,o,,o Physical and PerformanceMminist.fo. CharacteristicsOffice of EnvironmentAnd Energy A Preliminary ReportWashington, D.C. 20591

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FAA-EE-8042 September 1960 Document is available tothe U. S. public through

By J. Steven Newman the National Technical

tL.J Springfield, Virginia 22161

.H.

ACKNOWLEDGMENT

The author wishes to thank

Mr. Paul Hamilton

of Information Planning Associates

for his invaluable assistance in researching

input parameters and providing analytical

and software support.

iiN

_ _ _ _ Technical keport Documentation Peg

Correlation of Helicopter Noise Levels WithJ / eievb 08'___Physical and Performance Ciaracteristics u . errung, g , -o °.... ... . ... ..... 'DOT/FAA

8. Performing Oganization Report No.

,S e DOT-FAA-EE-80-42- anrforming 57ganizgtio rn e and Address 10. Work Unit No. (TRAIS)

Department of Transportation, Federal Aviation Admin.Office of Environment and Energy, Noise Div., Tech. Bra lckionreto, GrantNo800 Independence Avenue, S.W.Washington, D.C. 20591

12. Sponsoring Agency Name arnd Address Prlm9rPrel iminary

I ~rig gency ode4 FAA

I. Supplementary Notes

i16. Abstract

his report investigates the correlation between physical and performancecharacteristics of helicopters and the noise levels which they generate invarious operational modes. The analysis is generally empirical althoughseveral theoretical functions described in the literature have been examined.The EPNL is the acoustical metric employed in this study. One, two, andthree-step multiple regression analyses are conducted for takeoff, approach,and level flyover operations. Plots are provided for the three best singlevdriable regression models for each mode of flight

I,

17. Key Words I sl. Olw flebutIe Sttememt

Helicopter, Noise, This document is available toNoise Prediction, Correlation, the public through the NationalRegression, Effective Perceived Technical Information Services,Noise Level (EPNL) Springfield, Virginia 22161

19. Sec mr-ty lessie. (of *is revort) 30. Security Clessilf. (of thia pee) 21. N. of ergs 22. Priee

Unclasifid)*geutn Unclassifiled 1 _

Fem DOT F 1700.7 (8-72) Itepreduttle" ef 0*mpeted page oeuthoaod

TABLE OF CONTENTS

Page

ACKNOWLEDGMENT ...................... * ..................... ..

1.0 INTRODUCTION .................. . ................ 1

1.1 Input Data Files ....................................... 1

2.0 CROSS CORRELATION OF ANALYSIS PARAMETERS .................... 3

2.1 Cross Correlation Matrix ............................... 3

2.2 Summary of Best Correlates ............................. 11

2.3 Plots of Best Single Variable Regression ............... 12

3.0 STEPWISE REGRESSION ANALYSES ................................ 12

4.0 DISCUSSION ..................... ... .................. 15

I'g

)Accession. tor

?NTIS GR&rTICTAno e

Djst~%t-n

ID

LIST OF FIGURES

Page

2.3.1 TAKEOFF: REGRESSION OF EPNL VERSUS LOGTB... ................ 16

2.3.2 TAKEOFF: REGRESSION OF EPNL VERSUS LOGW .................... 17

2.3.3 TAKEOFF: REGRESSION OF EPNL VERSUS F3 ...................... 18

2.3.4 LEVEL FLYOVER: REGRESSION OF EPNL VERSUS F2 ................ 19

2.3.5 LEVEL FLYOVER: REGRESSION OF EPNL VERSUS LOGW .............. 20 j2.3.6 LEVEL FLYOVER: REGRESSION OF EPNL VERSUS LOGMD ............. 21

2.3.7 APPROACH: REGRESSION OF EPNL VERSUS LOGTB .................. 22

2.3.8 APPROACH: REGRESSION OF EPNL VERSUS LOGW ............. ..... 23i

2.3.9 APPROACH: REGRESSION OF EPNL VERSUS LOGMD .................. 24

LIST OF TABLES

1.1 INPUT DATA FILES ................. ..................... 4

2.1 CROSS CORRELATION MATRIX .......................... . .* .. 7

- - .-- -- -- '-. ," - .1- ..*.. ....

1.0 INTRODUCTION

This report investigates the correlation between physical and

performance characteristics of helicopters and the noise levels which the

helicopters generate in various operational modes. The analysis is

generally empirical although several theoretical functions described in

the technical literature have been examined. The Effective Perceived

Noise Level (EPNL) is the acoustical metric employed in this study. It

is anticipated that subsequent analyses will examine trends for other

metrics, in particular the Noise Exposure Level (single event, integrated

A-Weighted Sound Level). This report has been limited to presenting

statistical analyses. Parameters are tested for correlation with EPNL, asingle event cumulative enerqy noise metric. The units of most analysis

parameters are not enerqy. However, a limited number of variables

dimensionally consistent with power, intensity or energy are tested.

1.1 Input Data Files

This study utilizes a data file assembled for analyses conducted in

FAA-AEE-79-3, "Noise Levels and Flight Profiles of Eight Helicopters

Using Proposed International Certification Procedures (Newman, J. S.,

Rickley, E. J.). This data file (Table 1.1) has been expanded to include

a variety of physical parameters which may be expected to Influence

resulting noise levels. The legend for Table 1 Is presented below:

NOTE: VH is the speed at maximum continuous power.VNE is the never exceed speed.

----

I • . . . .. "

LEGEND FOR CROSS CORRELATION MATRIX

TYPE - Helicopter designation

EPNL - Effective Perceived Noise Level, (level flyover) expressedin decibels

WEIGHT - Test weight, lbs.

AREA - Total main rotor blade area (square feet)

MACH - Mach number of advancing blade; sum .9 (lesser of VH or

VNE) level flyover forward speed qrd rotational tip speed

SHP - Maximum engine shaft horsepower

M-DISC - Main disc area, square feet

BRC - Best rate of climb, feet/minute

M-FREQ - Main rotor blade frequency. Using main rotor rpm (Jane's)and number of blades (Jane's) units in Blade Passages/Sec.

* T-BLADE - Total tail rotor blade area (square feet)

EPNLA - Effective Perceived Noise Level (approach), decibels

LOGW - Common logarithm of weight

LOGA - Common logarithm of area

LOGS - Common logarithm of shaft horsepower

LOGMD - Common logarithm of main disc area

DISPLO - Main disc loading, lbs/square feet

LOGTB - Common logarithm of total tail blade area

MACH6 - Mach number to sixth power

TSPEEI) - Rotational tip speed of tail rotor (feet/second)

MSPEED - Main rotor rotational tip speed (feet/second)

T-MACH - Tail rotor, rotational tip mach number

F1 - Loglo ((T-MACH x Weight)2/T-Blade)

-2

F2 - Loglo ((MACH x Weight)2 /AREA)

F3 - Loglo (SHP x M-DISC/MACH)

F4 - LoglO (MACH6 x T-BLADE)

FS - LoglO (T-MACH6 x AREA)

2.0 CROSS CORRELATION OF ANALYSIS PARAMETERS

This section examines and discusses the interdependence between the

parameters used in the regression analyses.

2.1 Cross Correlation Matrix

The matrix displayed as Table 2.1 (four pages) provides insight Into

the interrelationships between test variables and the relationship of

each variable to EPNL for takeoff, approach and level flyover. Each

entry in the matrix includes the correlation coefficient "R," the

probability that the observed correlation is due merely to chance, and

the number of observations. Many of the variables correlate equally well

with the acoustical measures for all operational modes. Therefore, it Is

possible to develop a large number of single variable regression models

which predict noise with similar accuracy.

The "good predictor" family of parameters includes:

'.I Weight: Helicopter Weight

- Area: Main Rotor Area

- SHP: Shaft Horsepower

- MDISC: Main Rotor Disc Area

- LOGW: Log Weight

- LOGA: Log Area

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- LOGTB: Log Tail Blade Area

- LOGS: Log Shaft Horsepower

- LOGMD: Log Main Rotor Disc Area

- DISLO: Main Rotor Disc Loading

Other quantities which are linearly independent of these parameters

can be expected to play the roles of second and third variable in the

multiple regression analyses. However, it is possible to see more than

one of these variables appear in a multiple regression if the two

variables are largely independent of each other.

2.2 Summary of Best Correlates

The table provided below identifies those single parameters which

best correlate with EPNL for the various operational modes.

Level Flyover Takeoff Approach

Parameter R2 Parameter R2 Parameter R2

LOG TB .889

'I F2 .774 LOG TB .910 LOG W .867, LOG W .769 LOG W .891 LOG MD .863

LOG MD .764 F3 .852 F3 .856

F4 .765 LOG A .850 LOG A .828

F3 .739 LOG S .826 F2 .821

LOG TB .727 LOG MD .826 M DISC .778

LOG A .727 T BLADE .819 LOG S .778

AREA .774

T BLADE .774

11-

il I Il l I I " - . . . . . . . . . . .. . ... .

The most apparent trend one observes is the much higher correlation

for takeoff and approach EPNL values as compared with level flyover

EPNL. One plausible reason for this is that the level flyovers are

conducted at a higher speed than the approaches or takeoffs. It is

reasonable to assume that forces generating noise in higher speed

operation are subject to more variant and anomolous aerodynamic

influences.

Another observation is the decline of LOG TB to "fifth place" for the

higher speed level flyover. This can be attributed to the diminished

tail rotor counter-torque load as the speed increased. This occurs as

unbalanced airframe slip stream forces tend to counter the main rotor

torque.

2.3 Plots of Best Single Variable Regression

The three best single variable regression models are plotted in

Figures 2.3.1 through 2.3.3 for takeoffs, in Figures 2.3.4 through 2.3.6

for approach, and in Figures 2.3.7 through 2.3.9 for level flyover. Each

plot includes identification of helicopter type and the line of

regression.

3.U STEPWISE RE(WRESSION ANALYSES

This analysis investigates the improvement In prediction accuracy

associated with adding a second and third variable to the single

parameter regression equation. For the correlation coefficient to

12

... . .. -

improve the added variables (or steps) must be largely independent of

previous step(s) but still related to the level of noise. Three-step

models are presented below for takeoff, approach, and level flyover.

Approach

1 Step

EPNL a 10.2 log (TB) + 85.9

R2 . .89

2 Step

EPNL = 7.1 log (TB) + .0007 (M DISC) + 86.9

R2 = .93

3 Step

EPNL = 7.9 log (TB) + .005 (T SPEED) + .0006 (N DISC)

R2 = .94

Level Flyover

1 Step

'I EPNL - 9.4 (F2) + 37.4R2 = .76

2 Step

EPNL - 10.4 log (ND) + 3.6 (F5) + 59.5

R2 = .84

3 Step

EPNL a 10.7 log (MD) + 17 (MACH) + 3 (FS) + 44.9

R2 .85

13

* _ _ _ _ _ _ _ _ _ _

Takeoff

1 Step

EPNL = 9.6 log (TB) + 83.87

R2 = .91

2 Step

EPNL = 9.55 log (TB) - 7.3 (MACH)6 + 86.1

R2 = .93

S3 Step

EPNL = 6.3 log (TB) + 5.5 log (MD) -9.6 (MACH)6 + 71.97

R2 = .95

The following table provides a summary of parameters and correlation

coefficients associated with each step of the multiple regression

analyses for the various operational modes:

Takeoff

Step R2 Parameter (s

1 .91 Log TB

2 .93 Log TB, (MACHi)6

3 .95 Log 1B, (MACHI6 , Log (MB)

Approach

Step R2 Parameter(s)

1.89 Log TB

2 .93 Log TB, M DISC

3 .94 Log TB, M DISC, T SPEED

-14-

-

e~iI ew. I F lyover-,. Parameter c

.84 F5, Log MO

3 .85 Log MD, MACH, F5

4.0 UISCUSSION

The empirical noise prediction techniques presented above provide a means

to estimate single event cumulative noise exposure for helicopters whose

technology and operational characteristics are similar to the helicopters used

in this analysis. The obvious advantage of the strictly empirical approach

(using the single event cumulative energy metric EPNL) is the averaging out of

source directionality, ground interference effects, anomolous air in-flow

characteristics, and speed effects. All of these considerations pose

significant difficulties in the theoretical approach. On the other hand, the

theoretical approach can be more successful in predicting the change in noise

level associated with a design change for a specific helicopter.

The parameters appearing (or not appearing) as significant correlates to

noise in this study raise some interesting questions. For example, the strength

of LOG TB was not anticipated nor is "Tail Blade Area" a particularly good)I

acoustical design parameter. On the other hand, the main rotor advancing tip

mach number to the sixth power (considered an important correlate to noise)

appears as a weak correlate by itself, although it does strengthen the takeoff

correlation in the multiple regression analysis. Interpretation of these

results and 0iscussion of application in predicting noise of new design

helicopters or reducing levels of existent maclirles is left for subsequent study.

-15-

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REFERENCES

1. Newman, J. S., and Rickley, E. J., Noise Levels and Flight Profilesof Eight Helicopters Using Proposed International CertificationProcedures," FAA-EE-79-03, March 1979.

2. True, H. C., and Rckley, E. J., "Noise Characteristics of EightHelicopters," FAA-RD-94, July 1977.

3. Jane's All the World's Aircraft, 1964-1965.

4. Jane's All the World's Aircraft, 1975-1976.

5. Jane's All the World's Aircraft, 1977-1978.

6. Defense Intelligence Agency DIA DT-2 (personal communication) RussianHelicopter Data.

7. Pat Lang, FAA Eastern Region (personal communication) SikorskyHelicopter Data.

8. Mr. Arland Doe, Westland Helicopters, Yeovil, U.K., (personalcommunication) Westland Helicopter Data.

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