NASA Contractor Report 181792 

Experimental Assessment of Helicopter Rotor 

Turbulence Ingestion Noise in Hover 

J. C. Simonich. R. H. Schlinker, and R. K. Amiet 

U n it ed Tech n olog ies Resea rc h Center 

East Hartford, CT 

Contract NAS1-17763 

June 1989 

tUlSA-CR- 18 17 92) BIPEBXUEUT1L ASSESSMBOT OF 

HELTCOPTBR WTOB TUBBULBYCE xiiewnol NOISE 

18 HOIBB Fi-1 Beport maited Technologies Research Center) 68 p csa 20a 

63/71 

NASA 

National Aeronautics and 

Space Ad mi nist r at ion 

1189-27468 

flnclas 

0224571 

Langley Research Center 

Hampton, Virginia 23665-5225 
CONTENTS 

SUMMARY 1 

INTRODUCTION 2 

LIST OF SYMBOLS AND NOMENCLATURE 2 

PREVIOUS INVESTIGATIONS 3 

PRESENT INVESTIGATION 3 

Objectives.. ...................................... 3 

Approach ........................................ 4 

DESCRIPTION OF THE EXPERIMENT 5 

Acoustic Research Tunnel ............................... 5 

Experimental Arrangement .............................. 5 

Instrumentation .................................... 9 

TEST PROGRAM 16 

FLOW VISUALIZATION 16 

ROTOR INFLOW AERODYNAMIC MEASUREMENTS 19 

Data Acquisition and Reduction Procedure ..................... 19 

Mean Velocity ..................................... 20 

RMS Turbulence .................................... 23 

i 
Autocorrelation and Streamwise Integral Length Scale ............... 26 

Cross Correlation and Transverse Length Scales ................... 29 

Power Spectral Density ................................ 33 

Cross Spectra ...................................... 33 

TURBULENCE INGESTION NOISE MEASUREMENTS 35 

Basic Characteristics .................................. 35 

Polar Directivity .................................... 37 

Blade Pitch Angle ................................... 42 

Tip Speed ........................................ 42 

TURBULENCE INGESTION NOISE PREDICTION PROGRAM 44 

Approach ........................................ 44 

Assumptions ...................................... 44 

Formulation of Inputs ................................. 45 

Parameter Sensitivity ................................. 46 

Theory-Experiment Comparison ........................... 50 

CONCLUSIONS 56 

APPENDIX 58 

Definitions of Time Series Formula .......................... 58 

. 

REFERENCES 60 

ii 
LIST OF FIGURES 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

Turbulence Ingestion Noise Mechanism ..................... 4 

Rotor Drive Rig in Anechoic Chamber ..................... 6 

Rotor Drive Rig and Microphones in Anechoic Chamber ........... 7 

Closeup of Rotor Showing Hama Trips ..................... 8 

Overhead View of Microphone and Hot Film Arrangement in Anechoic 

Chamber ..................................... 9 

Geometry and Coordinate System for Experiment .............. 10 

Acoustic Instrumentation System ........................ 11 

Background Noise Level ............................. 12 

Data Acquisition System ............................. 14 

Sequential Channel Digit iaing Effect ...................... 15 

Effect of Lowpass Filtering on Hot Film Data ................. 16 

Smoke Flow Visualization of Rotor Inflow ................... 18 

Location of Fixed and Traversing Hot Film Probes .............. 19 

Axial Mean Velocity Profiles ........................... 21 

Radial Mean Velocity Profiles .......................... 22 

Azimuthal Mean Velocity Profiles ........................ 23 

Velocity Vector Above Rotor .......................... 24 

Root Mean Square Axid Turbulence Intensity ...... .......... 25 

19 Typical Axial Turbulence Autocorrelation Coefficient ............. 26 

20 Axial Turbulence Autocorrelation Coefficient Across the Rotor ....... 27 

21 Contours of Autocorrelation in Delay Time and Radial Displacement Space 27 

... 

111 
22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

38 

39 

40 

41 

42 

43 

44 

Axial Integral Length Scale as a Function of Position . . . . . . . , . . . . . 29 

Typical Axial Turbulence Velocity Cross Correlation Coefficient . . . . . . . 30 

Axial Turbulence Velocity Cross Correlation Coefficient as a Function of 

Position.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

Contours of Cross Correlation Coefficient in Delay Time and Displacement 

Space ....................................... 32 

Zero Delay Time Cross Correlation Coefficient as a Function of Radial Position 33 

Typical Axial Turbulence Power Spectrum . . . . . . . . . . . . . . . . . . . 34 

Axial Turbulence Power Spectrum as a Function of Radial Position . . . . . 34 

Contours of Power Spectrum as a Function of Frequency and Displacement 35 

Typical Axial Turbulence Cross Spectrum . . . . . . . . . . . . . . . . . . . 36 

Axial Turbulence Cross Spectrum as a Function of Radial Position . . . . . 36 

Contours of Cross Spectrum as a Function of Frequency and Displacement. 37 

Turbulence Ingestion Noise Spectra - Vtip = 144 m/s, 5" pitch, 0 " polar 

directivity angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 

Polar Directivity Characteristics of Noise Spectra - Vtip = 144 m/s, 5" pitch 39 

Polar Directivity for Several Harmonics - Vtip = 144 m/s, 5" pitch. . . . . . 42 

Effect of Pitch Angle on 10th Harmonic Levels . . . . . . . . . . . . . . . . 43 

Effect of Blade Tip Speed on 10th Harmonic Levels - Vtip = 144 m/s, 5" pitch 43 

Sensitivity of Mean Axial Velocity on Predicted Sound Pressure Levels . . . 47 

Sensitivity of Mean Radial Velocity on Predicted Sound Pressure Levels . . 48 

Sensitivity of Axial Integral Length Scale on Predicted Sound Pressure Levels 48 

Sensitivity of Radial Integral Length Scale on Predicted Sound Pressure Levels 49 

Sensitivity of Input Turbulence Intensity on Predicted Sound Pressure Level 49 

Predicted Peak Levels vs Measured Spectra . . . . . . . . . . . . . . . . . . 51 

Predicted vs Measured Blade Passage Frequency Levels . . . . . . . . . , . 54 

iv 
45 Predicted VB Measured 5th Harmonic Levels .................. 54 

46 Predicted VI Measured 10th Harmonic Levels ................. 55 

47 Predicted vs Measured 14th Harmonic Levels ................. 55 

V 
LIST OF TABLES 

1 Test Conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 

vi 
SUMMARY 

A model helicopter rotor was tested in an anechoic chamber in hover in a closed chamber 

so that turbulence generated by the rotor was reingested by the recirculating flow into the 

rotor. An analytical procedure was developed to use measured aerodynamic properties at 

the rotor to predict the noise due to turbulence ingestion. The noise generation model is 

based on an analysis and computer code previously developed by Amiet for non-isotropic 

turbulence. 

The experiment confirmed that the turbulence at the rotor was non-isotropic. Integral 

length scales computed from measured auto and cross correlations determined that typical 

eddies were 30 times longer than they were wide. Measured power spectra were similar to 

isotropic von Karman spectra but offset in amplitude, indicating a degree of non-isotropy. 

The measured noise spectrum showed quasi-tonal noise out to at least 20 harmonics, 

before changing to broadband in character. The polar directivity of the measured noise 

spectrum peaks at about 20" off the rotor axis and falls to a minimum in the plane of 

the rotor for the 5th through the 14th harmonics. The noise level at the blade passage 

frequency increases as the polar angle approaches the rotor plane. Increasing blade pitch 

angle and blade tip speed increases the measured sound pressure level for all measured tip 

speeds. 

The prediction procedure which was devised can be used to reasonably calculate absolute sound pressure levels for the quasi-tonal and broadband spectrum generated by 

non-isotropic turbulence ingestion. Five rotor aerodynamic input parameters are needed: 

axial inflow velocity, radial velocity, axial turbulence integral length scale, radial integral 

length scale, and turbulence intensity. 

The predictive procedure waa found to be primarily sensitive to three inputs: turbulence 

intensity, and the two length scales. The sensitivity of the length scales was confined mainly 

to high frequencies, while changes to the turbulence intensity affected the entire spectrum. 

A change in turbulence intensity from 2.25% to 10% increased the predicted sound pressure 

level by 12 dB. 

The general agreement between the noise prediction theory and the experiment was 

good, although the theory overpredicts the quasi-tonal noise in the low to mid range 

frequencies and underpredicts the higher frequepcy broadband signals. The measured polar 

directivity trends were matched by the predictions for low to mid harmonics, except in the 

plane of the rotor. At this angle, other noise generation mechanisms are more important, 

such as unsteady thickness noise. Since the analysis was developed for unsteady inflow 

noise generation, the steady loading blade passage frequency was not predicted. 

1 
INTRODUCTION 

Turbulence ingestion is a quasi-tonal and broadband noise mechanism which can contribute to the rotor noise of a helicopter in hover. It occurs when a turbulent eddy is 

ingested into a rotor blade and chopped. Due to the high contraction ratio during hover, 

the turbulent eddies are typically stretched into an elongated shape, resulting in nonisotropic turbulence interactions with the blades. When the eddies are non-isotropic, they 

tend to be much longer than they are wide. The large, elongated eddies are chopped 

a multiple number of times and contribute the quasi-harmonic tones in the low to mid 

frequencies. Eddies of smaller scale are only chopped by a single blade and lead to high 

frequency broadband noise. 

The turbulence ingestion noise problem has been investigated experimentally and analytically at UTRC for over a decade. First, interactions of isotropic turbulence with an 

isolated airfoil were examined. Later, the case of a rotating blade with isotropic turbulence was treated, including a comparison between theory and experiment. More analytical 

studies at UTRC performed rigorous calculations of the sound produced by a stretched 

eddy, as well as modeled the details of the stretching process. The current study provides 

a benchmark experiment with a rotating blade interacting with non-isotropic turbulence 

to compare with the theory. 

LIST OF SYMBOLS AND NOMENCLATURE 

Longitudinal or streamwise integral length scale 

Lateral or cross stream integral length scale 

Transverse integral length scale 

Displacement in x direction 

Velocity correlation function 

Correlation coefficient 

Time 

Integral time scale 

Turbulence velocity fluctuation in the x direction 

Local time mean velocity 

Upstream Cartesian coordinate system, i=1,2,3 

Downstream Cartesian coordinate system, i= 1,2,3 

Time delay 

Subscripts 

ij,k Specify either a vector or a component of a vector 

2 
PREVIOUS INVESTIGATIONS 

The concept of turbulence ingestion as a possible noise mechanism is a recent development. A study by Sofrin and McCann[l] in 1966 indicated that ingestion of turbulence 

might be a possible source of rotating blade harmonic noise. The first conclusive experimental evidence was obtained by HansonI21 in 1974. He determined that spectral peaks 

from propellers, helicopter rotors and fans were probably due to ingestion of atmospheric 

turbulence and not due to fixed inflow distortion as had previously been assumed. 

Paterson and Amiet(31 performed an experiment in 1979 to study the effect of turbulence ingestion on the noise generated by a model helicopter rotor. They used both grid 

generated turbulence in a wind tunnel as well as naturally occuring atmospheric turbulence. The grid generated turbulence was nearly isotropic a short distance downstream of 

the grid. Test conditions included forward flight and vertical ascent. They were unable 

to generate turbulence from grids which would remain non-isotropic long enough downstream so that it could interact with a rotor. They therefore used naturally occuring 

non-isotropic turbulence from the atmospheric boundary layer by running their rotor rig 

outdoors in hover. Both far-field noise spectra and directivity were measured in addition 

to incident turbulence intensities, length scales and spectra. A theory capable of absolute 

level prediction of rotor turbulence ingestion noise spectra and directivity was presented. 

Low frequency narrowband random noise was overpredicted and high frequency broadband noise was underpredicted. However, the existence of this noise source was definitely 

verified. 

PRESENT INVESTIGATION 

0 bj ec t ives 

The overall objective of the present study was to provide an experimental assessment 

uf the accuracy of a turbulence ingestion noise qheory for a helicopter rotor developed by 

Amiet[4]. An exact test of the theory which is dapable of handling non-isotropic turbulence would require the complete description of "the flow into the rotors. This experiment 

provided data to validate the theory for non-isotropic turbulence. Representative measurements of the flow into the rotor were acquired in the present study. Simultaneous far-field 

noise and rotor inflow turbulence statistics were acquired. 

The particular nature of the turbulence ingestion mechanism is illustrated in Figure 1. 

In this test, a hovering rotor in a closed chamber was used so that turbulence generated 

3 
EDDY 

STRETCHED n 

\ 

‘L 

Figure 1: Turbulence Ingestion Noise Mechanism 

by the rotor was reingested by the recirculating flow into the rotor. In this manner, a 

non-isotropic turbulent eddy generated by the blades is allowed to relax to more nearly 

isotropic conditions before being contracted by the rotor inflow and stretched into a nonisotropic eddy. It is this non-isotropic eddy that interacts with the blade and generates 

the noise. Although ingestion of isotropic turbulence also causes noise (see reference [4]), 

it is more common for helicopter in hover to interact with non-isotropic turbulence. A 

non-isotropic eddy which is stretched into an elongated shape by the contraction effect of 

a hovering rotor will be chopped a multiple number of times, which results in harmonics 

appearing at much higher frequencies than steady loading noise would create. 

Approach 

A model helicopter main rotor was tested in an anechoic chamber. Non-isotropic turbulence was ingested by the rotor by recirculating the rotor-generated turbulence. The 

high contraction ratio of the rotor in hover accounted for the large stretching of the turbulence at the rotor face. Inflow turbulence self and cross product statistics were acquired 

at a number of locations a small distance above the rotor simultaneously with noise data. 

Rotor tip Mach number and pitch were varied to see the effect on noise. 

4 
DESCRIPTION OF THE EXPERIMENT 

Acoustic Research Tunnel 

This study was conducted at the UTRC Acoustic Research Tunnel. Although the tunnel 

has forward flight capability, this capability was not utilized in the current experiment since 

the rotor was only operated in hover inside the anechoic chamber. (The facility is described 

in detail by Paterson[5].) The chamber is 4.9 m (16 ft) high, 5.5 m (18 ft) long (in the 

rotor axis direction) and 6.7 m (22 ft) wide. The chamber walls are lined with 0.5 m (1.5 

ft) high fiberglass acoustic wedges which provide an anechoic environment above 175 Hz. 

The inlet to the chamber and the collector were sealed and covered with acoustic foam to 

prevent air drafts and eliminate any reflecting surfaces. 

Experimental Arrangement 

Figures 2, 3, and 4 show photographs of the anechoic chamber experimental arrangement. A four bladed rotor of 0.76 m (28 in) dia with untwisted NACA 0012 blades of 

5.1 cm (2 in) chord with rectangular planform was used. The blade pitch angle was set 

with a blade profile template and an inclinometer. Rotor rotational speed was set by the 

use of a variable frequency control. The speed was monitored using a once- per-revolution 

pulse generated by an optical sensor. This pip signal was also recorded on analog tape. 

A Hama[6] trip strip of the type used by Schlinker and Amiet(71 was used on the blades 

to trip the boundary layer. This was done to force the boundary layer on the blades to 

become turbulent and eliminate the laminar boundary layer vortex shedding noise mechanism. The technique used consisted of cutting a strip of adhesive backed aluminum tape 

with pinking shears to produce a uniformly jagged edge and then attaching the tape to 

both the pressure and the suction surfaces of the blades. 

The test rig was powered by a 150 HP varisble frequency drive motor. The rotor was 

rotated in a vertical plane so that ground mounted microphones could be utilized. The 

rig was mounted on a frame so that the centerline of the rotor was half way between the 

ceiling and the floor of the chamber. The rig was positioned 3 m (9 ft) from the acoustic 

wedges on the thrust direction wall and 2 m (6 ft) from the wedges on the downwash wall 

so that microphones could be positioned in the acoustic far-field. 

Ten microphones in a fixed array on a 1.83 m (6 ft) radius arc were centered on the 

rotor hub. The microphones were located in a horizontal plane at the rotor hub height at 

10" increments between 0" and 90" from the rotor axis. A schematic of the microphone 

arrangement is shown in Figure 5. 

5 
ONG1NAL PAGE 

5BCK AND WHITE PHOTOGRAPH 

Figure 2: Rotor Drive Rig in Anechoic Chamber 

6 
Figure 3: Rotor Drive Rig and Microphones in Anechoic Chamber 

7 
Figure 4: Closeup of Rotor Showing Hama Trips 

a 
FAR FIELD I 

MICROPHONE 100 oo 

STATIONS p-i 

40° 

60° K 

Figure 5: Overhead View of Microphone and Hot Film Arrangement in Anechoic Chamber 

The rotor inflow was measured with two pairs of cross hot film probes, one fixed and 

one remotely movable. In order to avoid altering the flow characteristics, the two axis 

traverse stand was left in the same location regardless of the measurement location and 

was covered with acoustic foam to avoid reflections. The geometry used in the experiment 

is shown in Figure 6. 

The inflow into the rotor was visualized using a smoke probe technique. This was 

used for diognostic purposes to search for asymmetry in the flow as well as to trace inflow 

particle lines. 

Instrument at ion 

- A sketch of the acoustic instrumentation system is shown in 

Figure 7. Measurements of the far field noise were made with 0.635 em (0.25 in) diameter 

condenser microphones at grazing incidence. (Grazing was chosen over normal incidence 

since it provides a flatter frequency response out to high frequency.) The frequency response of these microphones was flat for the range of interest in this study (120 to 20,000 

Hz). The microphone preamplifier signals were amplified and recorded on magnetic tape, 

9 
top virn 

SO" 

- -4 - -> I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 0" 

6, 3 

Ix I x_ (vortical) 

ac 

1 

lrr 

a 

x 

1 

angl-) 

(horizontal ) 

iront virn 

di~oation OS rotrtion 

Figure 6: Geometry and Coordinate System for Experiment 

10 
I 

v 

I 

AMPLIFIERS/ 

ATIENUATORS 

U HIGH PASS FILTERS 

EQUALIZATION 

AMPLIFIERS 

.v. 

15 TRACK FM 

TAPE RECORDER 

.v/ 

800 LINE 

I 

I I 

Figure 7: Acoustic Instrumentation System 

11 
TYPICAL SPECTRUM 

4360 

I 

n 

cn 

40 

BACKGROUND NOISE (NO FLOW) 

20 

t 

0' I 

2K 4K 6K 8K 10K12K 14K16K18K 20K 

FREQUENCY - HZ 

Figure 8: Background Noise Level 

using a 14 track, FM tape recorder system in wide band group I1 at 30 ips, giving a maximum frequency response of 125 kHz. Data were typically averaged 200 times. Acoustic 

data were reduced to narrow band spectra on an 800 line spectrum analyzer. The effective 

bandwidth was 25 Hz for the 0-20 kHz range typically used, and other ranges can be 

determined by direct proportion. Hardcopy plots were produced. Since no direct digital 

transfer method between the analyzer and the laboratory computer was available, these 

plots were later digitized for analysis and comparison to predicted noise levels. 

Signal attenuation due to atmospheric attenuation was not accounted for in the data 

reduction due to the low frequencies (maximum of 20 kHz) and small sound propagation 

distances in the current experiment. 

A comparison between background noise levels and a typical turbulence noise spectrum 

is shown in Figure 8. The background noise data were recorded without the rig running 

and represents ambient chamber noise levels. The measured signal-to-noise ratio is seen 

to be better than 20 dB for all but the lowest frequencies. 

Turbulence Measur- - Turbulence data were acquired using two cross hot film 

probes. One probe was placed at a fixed location and the other was positioned remotely 

under computer control. A two axis traversing system was used to move the probe using 

stepping motors. Traverse increments of 0.0127 mm (0.0005 in) in a closed loop system 

12 
were used. The traverse system was moved under computer control and the probe was 

allowed to stabilize for 10 seconds before taking the measurement. The probe support arm 

was circular with a flat section welded to the back half to prevent vortex shedding and 

hence probe vibration. The traversing probe was located 6.4 cm (2.5 in) upstream of the 

rotor hub. The fixed probe was positioned downstream of the traversing probe by 1.4 cm 

(9/16 in). Turbulence statistics were assumed to be frozen over the 1.4 cm axial separation 

distance. 

A four channel, linearized, constant temperature anemometer system was used for 

turbulence measurement. The outputs of the four anemometers were digitized directly by 

the data acquisition system (discussed in detail in the subsection entitled Data Acquisition 

System) analog to digital converter (A/D) as well as recorded on FM tape at 7.5 ips. Both 

the DC coupled signals as well as amplified AC coupled signals from all four hot films were 

recorded on tape. The encoder position was also stored on the data acquisition system and 

recorded on FM tape. The raw hot film voltages were then converted to velocity, corrected 

for temperature changes between run time and calibration and the data were digitally 

summed and differenced to obtain two orthogonal velocity components. Depending on 

hot film orientation, these were either axial and radial components or axial and azimuthal 

components. 

Data Acquisition System - Hot film data were acquired in the present study by a 

computer controlled system. A schematic of the system is shown in Figure 9. 

Data acquisition was performed in the following manner. After the rotor operating 

condition was set and the chamber allowed to come to equilibrium, the data acquisition 

system was activated. The computer controlled the stepping motor traverse which moved 

the probe to a preprogrammed point from an array of desired locations. Once the probe 

reached the requested position (as indicated by an optical encoder) a settling period was 

taken for the probe to come to equilibrium with its environment. All four hot film sensors 

were then digitized in sequence as fast as the built in multiplexer could switch channels. 

This was necessary since a four channel simultaneous sample and hold A/D was unavailable. 

Some error is associated with this sequential data acquisition, but it can be shown to 

be negligible for the sampling rate used in this experiment. A calibration of the error 

ww done. A signal from a sine wave generator was input into channels 1 and 4 (i.e. the 

channels farthest apart in slew time). The frequency of the generator was varied from 3 

Hz to 1000 Hz. The cross correlation coefficient was then calculated for the two channels. 

The results are shown in Figure 10. As can be aeen from the graph, a significant error can 

be incurred when trying to digitize a signal above 250 Hz. This was not a problem in the 

current study since the desired sampling frequency was below 30 Hz. 

Due to computer memory limitations, only 4,000 points for each hot film channel were 

13 
I I 

HOT WIRE 

- 

* 

no1 WIRE 

TIME CODE DIGITAL 

GENERATOR INPUT 

ANEMOMETER 

HOT WIRE 

1 ANEMOMETER 

14 

STEPPINQ POSWTON 

ENCODERS 

15 TRACK 

1 

& 

- 

I1 

AC COUPLED AMPLIFIER 

MICROPHONES [-I 

Figure 9: Data Acquisition System 

14 
10 100 

FREQUENCY - HZ 

Figure 10: Sequential Channel Digitizing Effect 

digitized continuously. Four blocks of 4,000 points taken with a few seconds between them 

(disk access time) were typically acquired. 

The hot film signals were low pass filtered. This was done to prevent the particle 

velocity of the rotor acoustic near field at shaft and blade passage frequencies from contaminating the inflow turbulence velocity signal. The reason for this was also described by 

Paterson and Amiet 131. They showed that inclusion of the blade passage tones resulted 

in an autocorrelation function which was periodic, making determination of length scales 

impossible. 

The effect of filtering on the power spectrum of the turbulence signal is shown in 

Figure 11. The signal is virtually unchanged by the filter below the cutoff frequency, but 

shove the cutoff frequency the shaft frequency in the filtered data were reduced by 20 dB 

and the blade passage frequency were reduced. by more than 50 dB. 

15 
% 

I 

n 

n 

v) r" 

I 30 I I I I 

1 10 100 500 

FREQUENCY - HZ 

Figure 11: Effect of Lowpass Filtering on Hot Film Data 

TEST PROGRAM 

The test program is summarized in Table 1. Most of the aerodynamic data was obtained for the 5" pitch angle and 144 m/s tip speed case. It was decided to obtain a 

comprehensive turbulence statistics measurement set for one operating condition instead 

of a limited measurement set for many operating conditions, thereby assuring an adequate 

measurement set for acoustic verification. 

FLOW VISUALIZATION 

In order to visualize the inflow to the rotor, a smoke probe technique was employed. 

Smoke was generated at the tip of probe by electrically heating mineral oil. A pumping 

system feeds oil to the tip and the rate is controllable so that the injection rate matches 

the local flow velocity. Local heating of the oil is done to avoid condensation of oil if 

vaporization is carried out further from the point of injection. Figure 12 shows typical 

results from the flow visualization. This figure shows four particle paths for the rotor at 

the same operating condition. The turbulent nature of large scale disturbances are clearly 

16 
Pitch 

Angle 

Tip Acoustic Turbulence 

Speed Data Data 

(degrees) 

5 

P - partial 

(m/s) 

71.8 a 

Table 1: Test Conditions 

5 

5 

5 

10 

17 

108 a 

144 a a 

71.8 a 

192 a P 
Figure 12: Smoke Flow Visualization of Rotor Inflow 

18 
Figure 13: Location of Fixed and Traversing Hot Film Probes 

visible in the photos. Near the rotor tip, the streamlines are highly curved, indicating 

skewing of the turbulent eddies. 

ROTOR INFLOW AERODYNAMIC 

MEASUREMENTS 

Data Acquisition and Reduction Procedure 

The rotor inflow velocity field was measured using hot film anemometry. Two sets of 

cross film probes were used, one for a fixed probe and the other for a traversing probe. 

Hot film data were acquired over both horizontal and vertical traverses. The location of 

the fixed probe and the traversing probe for each survey is shown in Figure 13. 

The data acquisition procedure was as follows: The blade angle was set manually with a 

blade profile template and an inclinometer. The rig was speed was then set at the required 

operating condition. The FM tape recorder was then started. Hot film data were then 

acquired by moving the traversing probe to the desired location, waiting for the probe to 

19 
come to equilibrium, and then rapidly filling memory with A/D voltage readings from the 

four hot film sensors. Once memory was filled, this data set was written in binary format 

to disk (to increase data transfer rate and conserve disk space), and another block of data 

was acquired. All individual data points were stored digitally so that the data could be 

post processed using time series analysis. After several blocks were acquired (typically 

four blocks of 4000 points), the traversing probe was moved to a new location using the 

stepping motor driven traverse under computer control. The acquisition process was then 

repeated until all locations in the scan had been acquired. 

Once the 5 Mbyte hard disk was filled, the data set was transferred electronically to 

a multi-user, 32 bit supermini computer for analysis and reduction. Data reduction was 

accomplished using a combination of commercially acquired signal analysis software and 

custom written analysis routines. 

Mean Velocity 

The mean velocity was obtained by averaging all the data acquired at a given traverse 

location (typically 16,000 points) for each film sensor. The raw voltages were converted to 

velocity and corrected for temperature differences between run time and calibration. The 

velocities for a hot film pair were then summed and differenced digitally to obtain either 

the axial and radial or axial and tangential data respectively. 

The mean, axial velocity profile in front of the rotor plane for the vertical and horizontal 

traverses respectively is shown in Figure 14. The data obtained in different traverses were 

consistent and repeatable. However, the flow was slightly different between the horizontal 

and the vertical traverses and the flow was nonsymmetric. This latter observation was 

particularly observable in the horizontal data. This asymmetry may be caused by the fact 

that the room was not perfectly symmetric. The two openings which allow flow to enter 

and leave the chamber were covered with acoustic foam to eliminate unwanted reflections. 

Also, the rig was not exactly centered in the room and there was only one hot film traverse 

stand in the chamber. 

Figure 15 shows the mean radial velocity profile measured in front of the rotor plane 

for vertical and horizontal traverse respectively. The data from several runs were again 

consistent and repeatable, but again showed nonsymmetry. The radial velocity was seen 

to reach a negative peak at 95% of the rotor radius. 

As a test of the measurement accuracy, several traverses were made to measure the 

azimuthal component of velocity upstream of the rotor. This should be zero upstream of 

the rotor since swirl is not introduced into the flow until after the rotor. This plot is shown 

in Figure 16. As can be seen from the figure, the azimuthal component was found to be 

20 
0.050 o*07s F 

0.025 

om 

s 4.025 

4.050 

3 4.075 

> E 

c 

W 

> 

J 

; 

: 

; 

; 

; 

4.125*100 0.6 rn 4.4 4.2 0.0 02 

VERllCAL DISPLACEMENT - NAMETERS 

J 

0.6 

J 

0.6 

b) Horizontal 

Figure 14: Axial Mean Velocity Profiles 

21 
4.1001. . . I.. . I.. . I.. . I 

4.125 

4.6 4.4 4.2 0.0 02 

0.075 

0.050 

E 0.025 

i 

a > 

o.Oo0 

3 s 0.025 

2 U 4.050 

w 

w 

E 

a 4.075 

4.100 

4.125 

U 

0.4 

c 

; 

; 

; 

; 

; 

A mwlu 

0 -ff 

0 -l# ttmEum 

; 0 lmMr 

e- 

; 

- * * ' - - - '. - * l * * * '. -. 

VERTICAL DISPLACEMENT - DIAMETERS 

b) Horizontal 

Figure 15: Radial Mean Velocity Profiles 

22 
0.075 

C 

4*’oo 4.125 

4.6 

I 

4.4 0.2 0.0 02 

0 lyulu 

0.4 0.6 

HORIZONTAL DISPLACEMENT = DIAMETERS 

Figure 16: Azimuthal Mean Velocity Profiles 

negligible across the rotor. 

The variation in the flow magnitude and direction for the vertical and horizontal traverses is shown in Figure 17. In the horizontal survey, the flow does not approach the hub 

in an axial direction. Rather the flow is skewed as it enters as a result of the asymmetry 

noted earlier. The results of several surveys are shown on this figure and only a slight 

variation in magnitude and direction from points taken at nearly the same position is 

apparent. 

RMS Turbulence 

The axial component of the rms turbulence intensity normalized by the axial mean 

velocity as a function of position is shown in Figure 18 for the vertical and horizontal 

traverses respectively. There is more scatter in these plots than in the mean velocity plots, 

but the trends are clear. The turbulence intensity is a maximum slightly beyond the rotor 

tips and dips to a minimum at the 25% radius position. The intensity then increases to 

a local maximum at the hub. This effect is probably caused by the locally lower axial 

velocities at the hub. 

23 
a) Vertical 

b) Horizontal 

Figure 17: Velocity Vector Above Rotor 

24 
"F 

b) Horizontal 

Figure 18: Root Mean Square Axial Turbulence Intensity 

25 
1.4 

12 

1 .o 

0.8 

0.6 

0.4 

0.2 

0.0 

0.2 

0 1 2 3 4 5 0.4t - I * I - * I - I * - * ’ 

nme-seconds 

Figure 19: Typical Axial Turbulence Autocorrelation Coefficient 

Autocorrelation and Streamwise Integral Length Scale 

In order to determine the longitudinal or streamwise integral length scales, the autocorrelation of the signal must first be determined. A typical normalized autocorrelation 

for the streamwise velocity component is shown in Figure 19. The autocorrelation is unity 

for a zero time delay and falls off to near zero as the time approaches 3 seconds. The 

autocorrelations for a typical 20 point survey taken in front of the rotor are shown in an 

offset plot fashion in Figure 20. As can be seen from the graph, the autocorrelation has the 

same general shape for all points in the survey. Figure 21 shows another representation of 

the same data plotted in a contour format. Here, contours of normalized,autocorrelation 

coefficient are plotted versus position across the rotor face and delay time. For large values 

,f autocorrelation coefficient, there is little variation across the rotor. Lower coefficient 

contours show some variation across the rotor span, including a decrease near the rotor 

axis. 

The autocorrelation can also be described in terms of a velocity correlation function. 

A complete set of possible correlations is given by a second order tensor of the form: 

26 
I .o 

3 0.7 

2 0.4 a 

0 0.1 

s 4.t 

- 

4 

L 

L 

e 

0 

d 

4 

"*%.o 1.6 3.0 4.6 6.0 7.6 

tlar - rreondr 

Figure 20: Axial Turbulence Autocorrelation Coefficient Across the Rotor 

I 8.000 

6.000 

I 

3.000 t 

yz 

2.000 

1 .ooo 

0.000 

-0.300 -0.200 -0.100 0.000 0.100 0.200 

ROTOR DIAMETERS 

Figure 21: Contours of Autocorrelation in Delay Time and Radial Displacement Space 

27 
For the mean, axial velocity, autocorrelation, the particular function of interest is: 

The correlation functions are generally normalized by using rms values to yield correlation coefficients: 

An integral time scale of the turbulence can be defined by integrating the correlation 

coefficient: 

The axial integral length scale was calculated from the integral time scale by the use 

of Taylor’s frozen flow hypothesis. This hypothesis assumes that the turbulence field 

is “frozen” as it convects into the measurement probe. This will strictly only hold for 

homogeneous, isotropic turbulence, but it has been found to be reasonable for other nonisotropic flows. The basic assumption is that: 

rz = UT 

Therefore, the integral length scale is related to the integral time scale by: 

(5) 

In practice, this integral must be evaluated over a finite range. Three different techr,!ques were used to determine the time integral: integration over the complete range of 

the computed autocorrelation, integration over the autocorrelation until the first non-zero 

point was reached, or by using the value where the autocorrelation was l/e of its zero 

time delay value. The axial integral length scale is shown as a function of measurement 

location relative to the rotor hub in Figure 22 for the three techniques. There is scatter 

both between the integration techniques as well as over the range of the traverse. The 

effect of the variability of the data on the predicted noise output is accessed below. 

28 
'i 25 

A 

4 

15 I A 

lot 5 

0 

oo A 

A OA 

00 

B 

A A 

0 

4.6 

1 

4.4 4.2 0.0 02 0.4 0.6 

DiyJacement*tnameten 

Figure 22: Axial Integral Length Scale as a Function of Position 

Cross Correlation and Transverse Length Scales 

The cross correlation function for the axial mean velocity displaced by a radial distance 

is described by: 

The normalized correlation coefficient is: 

A typical cross correlation is shown in Figure 23. It is similar in form to the autocorrelation shown in Figure 19, but the coefficient does not approach unity for a zero time 

delay, unless the probe separation distance is very small. The coefficient is a maximum for 

a zero time delay, i.e. there is no phasing in the problem which increases the similarity of 

the two hot film signals. 

A typical survey of cross correlation measured across the face of the rotor is shown in 

29 
1.0 

0.4 -0.2 0 * 1 2 3 4 5 

Time - wmls 

Figure 23: Typical Axial Turbulence Velocity Cross Correlation Coefficient 

Figure 24. The correlation is seen to be highest when the probes are close together and 

decreases with both separation distance and delay time. The same data were presented 

in contour form in Figure 25. In this figure, contours of cross correlation coefficient are 

plotted in delay time and probe separation distance space. The maximum correlation is 

again seen to be for close separation distances and delay times. 

The transverse length scale is determined from cross correlation measurements: 

where is evaluated at zero time delay. 

A plot of the zero time delay cross correlation coefficient as a function of separation 

distance is shown in Figure 26. This same data can be seen in the zero time delay data in 

Figure 24. This data wa8 integrated vs distance to obtain the transverse length scale. 

Length scales in the axial direction were typically 30 times longer than the radial length 

scales, indicating that the turbulence into the rotor is highly non-isotropic. 

30 
- 3 

4 

a 

0 

L 

L 

0 

V 

I 

I .o 

0.7 

0.4 

0.1 

.O 1.5 3.0 4.8 6.0 7.8 

TLmr - roooadr 

Figure 24: Axial Turbulence Velocity Cross Correlation Coefficient as a Function of Posit ion 

31 
I 6.000 

ri 

"2 

Q) j 4.000 

Q 

8 

-0.300 -0.200 -0.100 0.000 0.100 0.200 

ROTOR DIAMETERS 

Figure 25: Contours of Cross Correlation Coefficient in Delay Time and Displacement 

Space 

32 
1.00 

OX 0.50 I 

4.75 

-1 .00 

4.6 0.4 4.2 0.0 02 0.4 0.6 

Displacement - diameters 

Figure 26: Zero Delay Time Cross Correlation Coefficient as a Function of Radial Position 

Power Spectral Density 

Figure 27 shows a plot of the power spectral density for a typical axial turbulent velocity 

signal. These data have been nondimensionalized by multiplying the spectral density by the 

mean velocity, dividing by the axial integral length scale and by the turbulence intensity. 

The frequency has been normalized by muitiplying it by the axial integral length scale and 

dividing by the mean axial velocity. The normalizations were based on standard procedures 

used for turbulent flows. Also shown plotted on the graph is the von Karman spectrum, 

an isotropic spectrum. Since the measured turbulence is non-isotropic, the two spectra 

should not be identical and the degree of non-isotropy can be described by the difference 

in the two spectra. 

Figure 28 show the power spectra for a typical 20 point survey taken in front of the 

rotor. The spectra all appear similar. The same data displayed in a contour plot in 

frequency and displacement space in Figure 29 also showed that the spectra are similar. 

Cross Spectra 

The cross spectrum is a similar quantity to the power spectrum. For an homogeneous, 

33 
0.01 0.1 1 10 100 

FvUenCy-1 LI U 

Figure 27: Typical Axial Turbulence Power Spectrum 

P 

0 

Q 

1 0 

2 0.001 

t 0.0001 

Figure 28: Axial Turbulence Power Spectrum as a Function of Radial Position 

34 
+ / 4 

-0.46 30 - 

- 

20 25 - 5&?---- 

N 

I 7- - 

0 3- 

0.6 - 

0.2 -0.10 

-0.300 -0.200 -0.100 0.000 0.100 0.200 

ROTOR DIAMETERS 

Figure 29: Contours of Power Spectrum as a Function of Frequency and Displacement 

isotropic field, they are the same. A typical cross spectra plot for the axial component 

velocity of two probes separated in space in front of the rotor is shown in Figure 30. It is 

similar in shape compared to the power spectrum. Figure 31 shows the cross spectra for a 

typical 20 point survey in front of the rotor. There is considerably more scatter in the data 

at the far separation distances compared to close separation distances. A contour plot of 

the cross spectra on frequency and separation distance space is shown in Figure 32. The 

data presented in this figure have been smoothed using a tensioned spline in both cross 

spectra vs frequency and cross spectra vs separation distance. The cross spectra falls off 

with increasing frequency and separation distance. 

TURBULENCE INGESTION NOISE 

MEASUREMENTS 

Basic Characteristics 

As summarized in Table 1, acoustic data were acquired over a range of blade pitch angles 

and tip Mach numbers. A typical turbulence ingestion spectrum is shown in Figure 33. The 

35 
Y s! 

v) i 

Figure 30: Typical Axial Turbulence Cross Spectrum 

0. I 9 

2 a.01 

t 0.001 

: 0.0001 

u la+ 

O 104 - 

0 

30 

0 

L. 

c 

I, 

i 1a-TV 

0.01 0.1 I 10 IO0 

Iroqmamoy - Or 

Figure 31: Axial Turbulence Cross Spectrum its a Function of Radial Position 

36 
Figure 32: Contl 

-0.60 

20 

1s 

10 

7 

6 

4 

3 

2 

1 

0.7 

0.6 

0.4 

0.3 

0.2 

-0.300 -0.200 -0.100 0.000 0.100 0.200 

ROTOR DIAMETERS 

trs of Cross Spectrum as a Function of Frequency and Displa ement 

spectra shows harmonics of blade passage frequency evident out to at least 20 harmonics 

before changing to a broadband character. 

Figure 34 shows ten spectra obtained from the polar microphone arc for one operating 

condition. The signatures appear similar in characteristic except for two features. Near 

the plane of rotation where turbulence ingestion noise is expected to approach zero, the 

level of the blade paasage frequency increases above the other harmonics. This increase 

is thought not to be caused by turbulence ingestion noise, but is the result of other noise 

mechanisms, such as unsteady thickness noise. Another feature of the signatures appears 

near the rotor axis. Here, a slight waviness of the spectrum is apparent in the broadband 

portion of the spectrum. 

Polar Directivity 

The polar directivity of the far field turbulence ingestion noise is shown in Figure 35. 

Here, the blade passage frequency (BPF), the 5th harmonic, the loth harmonic and the 

14th harmonic are shown for a 5" pitch angle and a tip speed of 144 m/s. Except for 

the BPF, a common trend is apparent with polar directivity. The directivity reaches a 

maximum slightly off the axis of rotation, and drops off towards the plane of rotation. 

37 
Figure 33: Turbulence Ingestion Noise Spectra - Vtip = 144 m/s, 5" pitch, 0 O polar directivity angle 

38 
POLAR ANGLE = 0 DEGREES 

%f 

POLAR ANGLE = 10 DEGREES 

90F 

POUR ANGLE : 20 DEGREES 

*f 

POUR ANGLE : 30 DEGREES 

Figure 34: Polar Directivity Characteristics of Noise Spectra - Kip = 144 m/s, 5" pitch 

39 
POUR ANOLE I 40 DEGREES 

#F 

80 E 

POUR ANOLE: 50 DEGREES 

9or 

POUR ANGLE : 60 DEGREES 

9or 

POUR ANGLE : 70 DEGREES 

*F 

Figure 34: Continued 

40 
POUR ANGLE : 80 DEGREES 

*f 

60 

50 

40 

POUR ANGLE : 90 DEGREES 

Y 

860 

50 

40 

30 

0 

. 

5 10 15 20 25 

Fmlucncy-~ 

Figure 34: Concluded 

41 
m 

U 

a 

70- 

60- 

lllhHrwlc I 

Polar Angle degrees 

Figure 35: Polar Directivity for Several Harmonics - Vt,* = 144 m/s, 5" pitch 

Blade Pitch Angle 

Increasing blade pitch angle increases the sound pressure level, as shown in Figure 36. 

This plot shows the effect of pitch angle change on the loth harmonic for four different 

blade tip speeds. The 10th harmonic was chosen as a representative harmonic. 

Tip Speed 

Figure 37 shows the effect of changing tip speed on the loth harmonic of blade passage 

frequency for the three blade pitch angle settings used in the test. Increasing tip speed 

increases the measured sound pressure level. 

42 
8 

8 

Figure 36: Effect of Pitch Angle on 10th Harmonic Levels 

Figure 37: Effect of Blade Tip Speed on 10th Harmonic Levels - V,, = 144 m/s, 5" pitch 

43 
TURBULENCE INGESTION NOISE 

PREDICTION PROGRAM 

Approach 

The purpose of this study was to experimentally assess a non-isotropic, non-homogeneous 

turbulence ingestion noise theory of Amiet. This theory has been used as part of a larger 

effort to develop a prediction methodology for helicopter rotor noise due to the ingestion of 

atmospheric turbulence. The focus of this work is a computer code, developed according 

to ANOPP standards, which has been transferred to NASA for incorporation into ROTONET. With the prediction methodology completed (References [8] - [ll]) the present 

experimental measurement was conducted to determine the applicability of the analysis. 

The theory for the atmospheric turbulence prediction and mean flow and turbulence 

contraction is described in Reference [8]. The noise calculation theory is described in 

Reference [9]. Reference [lo] is a users manual for the atmospheric turbulence and the 

mean flow and turbulence contraction programs. A users manual for the noise calculation 

program is described in Reference [ 111. 

The prediction method incorporates an atmospheric turbulence model, a rotor mean 

flow contraction model and a rapid distortion turbulence model which together determine 

the statistics of the non-isotropic turbulence at the rotor plane. Critical to the turbulence 

contraction model is the inclusion of a rotor mean flow model which predicts the turbulence 

vorticity distortion during the rotor in-flow process. 

A generalized acoustic source model was used to predict the far field noise generated 

by the non-isotropic flow incident on the rotor. Absolute levels for acoustic spectra and 

directivity patterns are calculated without the use of empirical or adjustable constants. 

This procedure was extended from a previous prediction procedure (4) to include nonhomogeneous turbulent inflow to the rotor. 

Assumptions 

The purpose of the present study was to devise an experiment which would allow comparison of measured non-isotropic, non-homogeneous turbulence ingestion noise against 

Amiet’s theory. The analysis for atmospheric turbulence ingestion was written such that 

turbulence properties of the atmosphere are specified at infinity and the properties at the 

rotor face are calculated using a rapid distortion approach. In the current experiment, 

44 
properties were measured directly at the rotor face. A method had to be devised to rearrange the measured data into a form acceptable to the acoustic prediction program. 

While the noise prediction theory is capable of dealing with nonhomogeneous, nonisotropic turbulence, it would require measurements over a large number of points on the 

rotor. While in concept it is possible to do this, it is not practical. Therefore, it was 

decided to compare only the homogeneous theory against the experiment. 

Two approaches are possible depending on the amount of approximation acceptable and 

the type and quantity of data available. The more exact approach involves specifying the 

cross spectra at numerous locations above the rotor face. Measurements would be required 

over a large number of different locations since the flow into the rotor is nonhomogeneous, 

even for the current, simple, recirculating, hover operating condition. 

Since in the current experiment, the cross spectra were obtained over a limited number 

of points, the cross spectra data must be surface fit as a function of location, since the 

program requests the amplitude of the cross spectra at any frequency and location. Also, 

the program may request the cross spectra at a frequency that is higher than the measured 

data, in which case an extrapolation procedure would be required. The turbulence ingestion noise computer program presented in reference (41 would also have to be modified in 

order to accept the measured cross spectrum. 

This approach was pursued initially until the above implications of using it became 

clear. In leu of the difficulties associated with this approach and the limited amount of 

time available for data reduction, an alternative approach was executed. 

The alternative approach used the turbulence ingestion noise prediction program in its 

“production” version, the one supplied to Langley as part of this contract effort which will 

be incorporated into the ROTONET noise prediction program. 

The following assumptions are made for this calculation procedure: 

0 isotropic turbulence upstream 

0 upstream von Karman spectra 

0 rotor flow is homogeneous 

0 rotor flow is non-isotropic 

0 noise is produced near the blade tip 

Formulation of Inputs 

This program assumes that the turbulence far upstream of the rotor is isotropic and 

45 
is deformed into non-isotropic turbulence as it is convected into the rotor. The upstream 

turbulence spectrum is described by the von Karman model. The transformation of the 

turbulence from isotropic to non-isotropic turbulence is described by a deformation tensor. In the ANOPP version, this deformation tensor is calculated by a streamline tracing 

technique. Since the streamline coordinates were not known in the current experiment, a 

simplified, but still non-isotropic, turbulence description model was utilized. For a pure 

contraction with no skewing or turning of the streamlines, a non-isotropic, diagonal, distortion tensor can be described by: 

[z] = [ 0 

1/e2 0 

lP1 0 

0 O l/t, O 1 

where el is the longitudinal or streamwise integral length scale and l2 is the lateral or 

cross stream integral length scale. The longitudinal length scale describes the length of 

a typical eddy in the streamwise direction whereas the lateral length scale describes the 

width of a typical eddy entering the rotor. This type of deformation was considered by 

Ribner and Tucker[l2]. 

In addition to the length scales, the axial and radial mean velocities, and the axial 

turbulence intensity at the rotor are also required inputs to the noise prediction theory. 

Since a homogeneous assumption was made, only a single value of each parameter can be 

input. Either a single representative value can be chosen, or values averaged over the rotor 

face can be used. Since higher noise levels are produced in high speed regions near the 

blade tip and less noise is produced near the hub where both inflow velocities and blade 

speeds are lower, it was decided to use representative measurements from near the blade 

tip for input into the noise prediction program. 

Parameter Sensitivity 

There are five aerodynamic measurements which influence the predicted noise: 

0 axial mean velocity 

0 radial mean velocity 

0 axial integral length scale 

0 radial integral length scale 

0 streamwise turbulence intensity 

A study was undertaken to determine the sensitivity of each parameter. An average 

value was chosen as inputs for the five experimental parameters and then each parameter 

46 
100 

90 

80 

70 

60 

50 

40 

30 

20 

10 

t?! 

I I I 

0.1 1 10 

Frequency = kHz 

Figure 38: Sensitivity of Mean Axial Velocity on Predicted Sound Pressure Levels 

was varied from a minimum value from any position on the rotor from the experimental 

data to a maximum value, holding the other parameters at their average value. 

Figure 38 shows the sensitivity of the predicted sound pressure level spectra to the mean 

axial velocity. In order to avoid confusion by overplotting, only the peaks and troughs of 

the harmonics are shown for the three cases. Varying the axial velocity has a large effect on 

the troughs of the harmonics. There is little sensitivity for axial velocity variation for the 

peaks of the harmonics, especially at the lower frequencies. The sensitivity of the predicted 

noise output to variations in radial velocity is insignificant over the range of values tested, 

as shown in Figure 39. 

The sensitivity of the predicted sound pressure level spectra for axial and radial integral 

length scales is shown in Figures 40 and 41. Very little effect is noted for either length 

scale for frequencies below 3 kHz. Little effect is noted for the troughs for the radial length 

scales, and only a small effect is noticed for the troughs on the axial length scale. There is 

a rather large effect on the spectrum peaks for frequencies above 3 kHz for both the axial 

and radial length scales. 

The most sensitive parameter over the entire frequency range is the turbulence intensity 

shown in Figure 42. A 12 dB variation in sound pressure level is noted for a change in 

turbulence intensity from 2.25% to 10%. 

47 
8 

100. 

90- 

80- 

70- 

a50- 

40- 

30- 

20- 

lo a 

10 0 

0.1 1 

FWUenCy - kHZ 

Figure 39: Sensitivity of Mean Radial Velocity on Predicted Sound Pressure Levels 

80 

70 

60 

50 

40 

30 

20 

10 

% 

E 

U 1 I I I 

1 10 0' 

0.1 

hequency=m 

Figure 40: Sensitivity of Axial Integral Length Scale on Predicted Sound Pressure Levels 

48 
80- 

70- 

60- 

So40- 

30- 

20- 

10 " - - I I 

Figure 41: Sensitivity of Radial Integral Length Scale on Predicted Sound Pressure Levels 

100 

90 

80 

70 

m 60 

U 

I 50 

40 

E 

30 

20 

10 

0 - 

0.1 

1 I 1 

0 

A 

0 

0 

A 

0 1 I 

1 10 

Frequency - kHz 

Figure 42: Sensitivity of Input Turbulence Intensity on Predicted Sound Pressure Level 

49 
Theory-Experiment Comparison 

Predictions were made using Amiet's theory, using input values taken from measurements near the rotor blade tip, where most turbulence ingestion noise is assumed to be 

produced. The comparison between the measured and predicted peak spectra is shown 

in Figure 43. Ten directivity angles are shown. In general, the theory overpredicts the 

lower harmonics and under predicts the higher harmonics, although the agreement is quite 

good. The theory predicts much lower peaks and carries harmonics to much higher frequency than the experiment showed. The only directivity angle where the agreement is not 

good is at go", in the plane of the rotor. The reason for this is that other noise mechanisms 

are more important than turbulence ingestion noise for this directivity. 

The predicted sound pressure level as a function of polar angle for the blade passage 

frequency, the 5th harmonics, the 10th harmonic and the 14th harmonic is shown in 

Figures 44, 45, 46 and 47 respectively. 

Except for the blade passage frequency plot, the theory is in excellent agreement with 

the measurements in both trends and level. The most discrepancy occurs in the rotor plane 

where other noise mechanisms such as unsteady thickness noise may dominate. The best 

agreement between theory and experiment occurs for the 5th harmonic. The theory does 

not predict the trend of the experimental data for the blade passage frequency although 

the absolute levels are not far wrong. 

50 
20- 

0 

10 --I 

100 loo0 lww 

-=k 

Figure 43: Predicted Peak Levels vs Measured Spectra 

51 
WURANOLLsaDWREES 1 1 

i 

n I' I 

Figure 43: Continued 

52 
0 " 

100 1Qoo loo00 

I. . . . __. I 

Figure 43: Concluded 

53 
8 

k 

m 

0 ). 

f 

Figure 44: Predicted vs Measured Blade Passage Frequency Levels 

Figure 45: Predicted vs Measured 5th Harmonic Levels 

54 
20 .i

=20 0 20 40 60 80 100 

Polar Angle - degrees 

80- 

70- 

60- 

50- 

40- 

Figure 46: Predicted vs Measured loth Harmonic Levels 

m 

U 

c 

s 

.c 

0 

20 

-20 0 20 40 60 80 100 

Polar Angle - de(pees 

Figure 47: Predicted vs Measured 14th Harmonic Levels 

55 
CONCLUSIONS 

A model helicopter rotor was tested in an anechoic chamber in hover in a closed chamber 

so that non-isotropic turbulence generated by the rotor was reingested by the recirculating 

flow into the rotor. An analytical procedure was developed to use measured non-isotropic 

aerodynamic properties at the rotor to predict the noise due to turbulence ingestion. The 

noise generation model is based on a simplification of an analysis and computer code 

previously developed by Amiet for non-isotropic turbulence. The following conclusions 

were drawn from this study: 

0 The turbulence at the rotor was non-isotropic. Integral length scales computed from 

measured auto and cross correlations determined that typical eddies were 90 times longer 

than they were wide. Measured power spectra were similar to isotropic von Karman spectra 

but ofset, indicating a degree of non-isotropy. 

e Turbulence ingestion noise was the dominant noise mechanism in hover. Although 

this test was carried out on a madel rotor, the results should apply equally well to a full 

scale helicopter in hover providing there is no interaction between the main and the tail 

rotors. 

e The measured noise spectrum showed quasi-tonal noise out to at least 20 harmonics, 

before changing to broadband in character. The polar directivity of the measured noise 

spectrum peaks at about 20" of the rotor axis and falls to a minimum in the plane of the 

rotor for the 5th through the 14th harmonics. The noise level at the blade passage frequency 

increases as the polar angle approaches the rotor plane. I 

l 

e Increasing blade pitch angle increases the measured sound pressure level for all measured tip speeds. Increasing blade tip speed increases the measured sound pressure level for 

all measured blade pitch angles. I 

, 

0 The simplified non-isotropic prediction procedure which has been developed can be used 

to reasonably predict absolute sound pressure levels for turbulence ingestion noise in hover, 

given 5 input parameters: axial velocity, radial velocity, asial integral length scale, radial 

integral length scale, and turbulence intensity. 

I 

I 

e The predictive procedure was found to be sensitive to three inputs: turbulence intensity, 

and the two length scales. The sensitivity of the length scales was confined mainly to high 

frequencies, while changes to the turbulence intensity afected the entire spectrum. A change 

in turbulence intensity from 2.25% to 10% increased the predicted sound pressure level by 

12 dB. 

e The general agreement between the non-isotropic noise prediction theory and the experiment was good, although the theory generally overprcdicts the quasi-tonal low to mid 

56 
range frequencies and underpredicts the higher broadband signals. The measured polar directivity trends were matched by the prediction for low to mid harmonics, except in the 

plane of the rotor. At this angle, other noise generation mechanisms are more important, 

such as unsteady thickness noise. The agreement was not good for blade passage frequencies, where steady loading noise is probably the primary noise source. The predicted sound 

pressure levels are slightly overpredicted compared to the measured values, but the trends 

are the same. 

57 
APPENDIX 

Definitions of Time Series Formula 

Mean Value - The mean value or average is defined by: 

T 

p, = lim 1 z(t)dt T-rm T 

The typical units are volts. 

Mean Square Value - The mean square value is defined by: 

The typical units are (volts)2. 

Variance - The variance is defined by: 

**. * 

(J; 42 - p2 

The typical units are (volts)2. 

(3) 

Autocorrelation - The autocorrelation relates how similar a signal is to itself when 

it is compared at different delay times. It is defined by: 

= z(t)z(t + 7) (4) 

The typical units are (volts)2. The autocorrelation for a zero time delay is equal to the 

mean square, R,,(O) = e:. 

Cross Correlation - The cross correlation relates how similar two signals are. It 

expresses that relationship at both the same point in time as well as at different delay 

times. It is defined by: 

I 58 
The typical units are (volts)*. The cross correlation is equal to the autocorrelation 

when both signals are the same. 

Puwer Spectral Density - The power spectral density describes how the energy in 

the signal is distributed in the frequency domain. It is defined by: 

00 

= 4 Rzz(T) COS(27rfT)dT 

The typical units are (volts)2 sec. The integral of the power spectrum over any given 

frequency interval is equal to the mean square in that same integral: 

Cross Spectral Density - The cross spectral density describes how the energy in 

the cross product of two signals is distributed in the frequency domain. It is defined by: 

%...a * ;, 

The typical units are (volts)2 sec. 

59 
REFERENCES 

(11 Sofrin, T. G. and McCann, J. C.: Pratt and Whitney Ezperience in Compressor Noise 

I Reduction, Paper presented at the 72nd Meeting of the Acoustical Society of America, 

Los Angles, CA., Nov. 2-5, 1966. I 

[2] Hanson, D. B.: Spectrum of Rotor Noise Caused by Atmospheric Turbulence, J. Acoustical SOC. Amer., Vol. 56, No. 1, July 1974, pp. 110-126. 

131 Paterson, R. W. and Amiet R. K.: Noise of a Model Helicopter Rotor Due to Ingestion 

of Turbulence, NASA CR-3213, November 1979. 

141 Simonich, J. C., Amiet, R. K., Schlinker, R. H., and Greitzer, E. M.: Helicopter Rotor 

Noise Due to Ingestion of Atmospheric Turbulence, NASA CR-3973, May 1986. 

(51 Paterson, R. W., Vogt, P. G. and Foley, W. M.: Design and Deueloprnent of the United 

Aircraft Research Laboratories Acoustic Research Tunnel, J. Aircraft, Vol. 10, No. 7, 

1973, pp. 427-433. 

I 

[6] Hama, F. R.: An Eficient Tripping Deuice, J. of Aeronautical Sciences, Vol. 24, pp. 

236-237, March 1957. i 

17) Schlinker, R. H. and Amiet, R. K.: Rotor-Vortez Interaction Noise, NASA Clt-3744, 

October 1983. 

[8] Simonich, J. C.: Noise Produced by Turbulent Flow into a Rotor: Theory Manual 

for Atmospheric Turbulence Prediction and Mean Flow and Turbulence Contraction 

[9r Aaiet, R. K.: Noise Produced by Turbulent Flow Into a Rotor: Theory Manual for 

I Prediction, NASA CR-181789, June 1989. 

Noise Calculation, NASA CR-181788, June 1989. I 

[lo! Simonich, J. C. and Caplin, B.: Noise Produced by Turbulent Flow into a Rotor: 

Users Manual for Atmospheric Turbulence Prediction and Mean Flow and Turbulence 

Contraction Prediction, NASA CR-181791, June 1989. I 

[Ill Amiet, R. K., Egolf C. G. and Simonich, J. C.: Noise Produccd by Turbulent Flow 

Into a Rotor: User Manual for Noise Calculation, NASA CR-181790, June1989. 

112) Ribner, H. S. and M. Tucker, Spectrum of Turbulence in a Contracting Stream, NACA I Technical Report 1113, 1953. 

60 
Report Documentation Page 

17. Key Words (Suggested by Author(s1l 

helicopter noise. 

turbulence ingestion noise. rotor noise 

1. Report No. I 2. Government Accession No. 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21 No. of pages 22. Price 

NASA CR-181792 I 

Unclassified 

4. Title and Subtitle 

Experimental Assessment of Helicopter Rotor 

Turbulence Ingestion Noise in Hover 

Unclassified 67 A0 4 

7. AuthorlsJ 

J. C. Simonich, R. H. Schlinker, and R.K. Amiet 

9. Performing Organization Name and Address 

United Technologies Research Center 

East Hartford, CT 06108 

12. Sponsoring Agency Name and Address 

National Aeronautics and Space Administration 

Langley Research Center 

Hampton, VA 23665-5225 

3. Recipient's Catalog No. 

5. Report Date 

June 1989 

6. Performing Organization Code 

8. Performing Organization Report No. 

10. Work Unit No. 

505-63-51 

11. Contract or Grant No. 

NASI-17763 

13. Type of Report and Period Covered 

Contractor Report 

14. Sponsoring bgency Code 

15. Supplementary Notes 

Langley Technical Monitor: Thomas F. Brooks 

Final Report 

6. Abstract 

An experiment was conducted to assess the accuracy of a theory for non-isotropic turbulence ingestion. In order to generate non-isotropic turbulence in a controlled environment, a scale model rotor in a closed chamber was used so that the turbulence 

generated by the rotor was reingested by the recirculating flow. 

surements of turbulence inflow properties and far field acoustics were acquired. 

Measurements confirmed that the inflow turbulence was highly non-isotropic. 

sured aerodynamic properties were used as inputs for the noise prediction procedure. 

The general agreement between the non-isotropic noise prediction procedure and the 

experiment was good, although the procedure generally overpredicts the quasi-tonal 

low to mid range frequencies and underpredicts the higher broadband signals. The predicted sound power level as a function of polar angle was in close agreement with measurements, except near the rotor plane, which is not modeled by the present analysis. 

noise was the turbulence intensity. 

Simultaneous meaThe meaIt was determined that the most sensitive parameter influencing the predicted 

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18. Distribution Statement 

Unclassified - Unlimited 

Subject Category 71