Dennis L. Huff

Lewis Research Center, Cleveland, Ohio

Fan Noise Prediction: Status and Needs

NASA/TM—97-206533

December 1997

AIAA–98–0177

https://ntrs.nasa.gov/search.jsp?R=19980017289 2018-05-11T16:44:45+00:00Z
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Dennis L. Huff

Lewis Research Center, Cleveland, Ohio

Fan Noise Prediction: Status and Needs

NASA/TM—97-206533

December 1997

National Aeronautics and

Space Administration

Lewis Research Center

Prepared for the

36th Aerospace Sciences Meeting & Exhibit

sponsored by the American Institute of Aeronautics and Astronautics

Reno, Nevada, January 12–15, 1998

AIAA–98–0177
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“Copyright © 1998 by the American Institute of Aeronautics and

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Abstract

The prediction of fan noise is an important part to the

prediction of overall turbofan engine noise. Advances in

computers and better understanding of the flow physics

have allowed researchers to compute sound generation

from first principles and rely less on empirical correlations.

While progress has been made, there are still many aspects

of the problem that need to be explored. This paper presents

some recent advances in fan noise prediction and suggests

areas that still need further development. Fan noise

predictions that support the recommendations are taken

from existing publications.

Introduction

This paper is not meant to be a thorough review of all

work related to fan noise prediction. It contains

recommendations for future research in fan noise for

subsonic aircraft engines based on the current status of fan

noise prediction, mostly within NASA’s research

programs.

Dominant noise sources for turbofan engines are

identified in Fig. 1 (Ref. 1). As the bypass ratio of engines

increase beyond ten, jet noise is being reduced to the point

where fan noise becomes the primary noise source (Ref. 2).

It is generally accepted that fan noise is produced by the

following sources:

1. Inlet boundary layer or inflow distortions

 interacting with the fan

2. Self noise from the fan

3. Fan wakes interacting with stators or struts

There are a variety of predictions methods being

developed that either try to model all of these sources

simultaneously, or consider them as components. Key

aspects to either approach include accurate prediction of

the steady and unsteady flow field through the fan stage,

analysis of the fluctuating pressures on the aerodynamic

surfaces, the generation of sound from the fluctuating

pressures, and the propagation/radiation of the sound to

the far field.

The ultimate goal for fan noise prediction is to be able

to accurately predict the absolute levels for sound as it

propagates away from the engine. This includes properly

modeling the effects of changes to geometric features and

flow conditions. An alternate goal is to be able to predict

the correct trends of sound as a function of geometry and

flow field changes. Neither of these objectives have been

fully achieved, although there has been considerable

progress.

This paper is divided into two categories: tone noise

and broadband noise. In each category, noise resulting

from both interaction and self noise sources are discussed.

Finally, the status of Computational AeroAcoustics (CAA)

for fan noise prediction is presented.

Tone Noise

Fan tone noise is generated by either rotor-alone or

rotor/stator/strut interaction with flow distortions. Fan

wakes interacting with stators is one of the principle noise

sources for fans with subsonic tip speeds. As a result, there

has been a lot of work done to predict the unsteady

aerodynamics associated with gusts (prescribed fan wakes)

interacting with airfoils. Most of this work has been done

for two-dimensional flows, but this has changed in recent

years with better computing technology and a greater

understanding of the three-dimensional flow physics.

Interaction Noise

A common approach for predicting interaction tone

noise is to use modal analysis in conjunction with the

Tyler-Sofrin theory (Ref. 3). Fan wakes or inlet distortions

are specified as vortical gusts based on empirical

correlations from model or full scale engine tests. The

gusts interact with the rotor or stator, generally represented

as strips of airfoils or flat plates in a cascade. A “source”

model is used to determine the unsteady surface pressures

on the blades responding to the interaction with the vortical

gusts. The sound generated in the nacelle is determined by

coupling the unsteady surface pressures to the propagating

FAN NOISE PREDICTION: STATUS AND NEEDS

Dennis L. Huff *

National Aeronautics and Space Administration

Lewis Research Center

Cleveland, Ohio 44135

*Senior Member AIAA
NASA/TM—97-206533 2

American Institute of Aeronautics and Astronautics

duct modes for both the inlet and exhaust. A fan noise

prediction code originally developed in the early 1980’s

(Ref. 4), and revised in the 1990’s (Ref. 5), uses this

approach. Radiation codes have been developed (Refs. 24

to 29) to predict the propagation of these modes to the far

field. This procedure is repeated for each frequency where

interaction tones exist, i.e., the Blade Passing Frequency

(BPF) and higher harmonics. Figure 2 shows a schematic

of the process.

Source Models. Many of the current source models

use a “strip” approximation for the unsteady aerodynamics.

The blades are represented as two-dimensional, flat plate

cascades stacked in the spanwise direction. These models

were developed in the early 1970’s by Smith (Ref. 6),

Whitehead (Ref. 7) and Kaji (Ref. 8). A disturbance

corresponding to either an inflow distortion into the fan or

the fan wake interacting with the stators/struts are

prescribed as vortical gusts that convect with the mean flow

(the “frozen gust” assumption). The unsteady surface

pressures on the blades are determined by satisfying a no

flow boundary condition normal to the plate and can be

represented as a distribution of dipoles in the chordwise

direction. Applications and extensions of these models

have led to the understanding that chordwise source noncompactness effects are important for tone predictions

(Refs. 9 to 12). The unsteady surface pressures are

integrated and coupled to the propagating duct modes inside

the nacelle to determine the inlet and exhaust sound power

levels.

Using strip theory for the source model always raises

questions of validity. Namba (Ref. 10), who developed a

three-dimensional lifting surface analysis, reported that

the three-dimensional effects become less important for a

given spanwise gust wave number as the acoustic response

frequency increases. This was also verified by Kobayashi

(Ref. 11) who assessed the importance of including threedimensional effects by comparing strip theory to a threedimensional lifting surface code for several fans. He

concludes that “two-dimensional calculations of the

unsteady forces are reasonably adequate for fan noise

prediction.” The acoustic power of the duct modes agreed

to within ±2 dB. At this time, most of the applications were

concerned with inflow distortions from either inlet guide

vanes, inlet boundary layers, or engine angle-of-attack.

Today, inlet guide vanes are less common in turbofan

engines and inlet design technology has evolved to where

inflow distortions are not as severe. The emphasis has now

changed to fan wakes interacting with stators and struts. It

should also be pointed out that all of this work was done

for subsonic flows. While this may be adequate for fan/

stator interaction where the mean flow is subsonic,

disturbances interacting with the rotor typically involve

supersonic relative flows near the tip of the fan. Even

transonic flow can significantly distort the vortical gusts

and change the local propagation characteristics of the

acoustic energy (Ref. 15).

Concerns about “real” blade effects for both

aeroacoustic and aeroelastic applications have led unsteady

aerodynamicists to develop computational methods for

the gust response problem. Frequency domain approaches

typically use perturbation methods from a nonlinear, twodimensional mean flow (Refs. 13 to 16). There have been

a few applications of these class of codes to aeroacoustic

problems. Preliminary results indicate that gusts convecting

through a cascade of airfoils with a transonic mean flow

can generate significantly different sound. Figure 3 was

taken from Atassi, Fang and Hardy (Ref. 15) and shows

how the upstream and downstream acoustic pressure can

be significantly different for an airfoil in transonic flow

when compared to a flat plate calculation. They

conclude that the magnitude of the acoustic modes can be

significantly larger for a loaded cascade. Lorence and Hall

(Ref. 16) applied a similar analysis to study the sensitivity

of sound to design variables like blade thickness, camber,

and stagger angle. They found that it is possible to

redistribute the inlet and aft sound levels by varying these

parameters, but the overall levels were not significantly

changed.

Three-dimensional versions of these methods are

now being developed. An analysis based on the linearized

Euler equations has been developed by Montgomery and

Verdon (Ref. 17) for turbomachinery applications. The

unsteady flow field is assumed to be a small perturbation

from the nonlinear steady flow. This method uses modal

analysis and the solutions are determined for a single blade

passage, which helps reduce computational requirements.

Since this approach analyzes the fan and stator as separate

components, the unsteady boundary conditions in the

interstage region where swirling flow exists have presented

a major computational technology hurdle (Refs. 18 and

19). One way to circumvent this difficulty is to model both

the fan and the stator simultaneously, which eliminates the

computational boundary in the swirl region. This has been

done by Rangwalla and Rai (Ref. 20) for acoustic

applications. This approach relies on the analysis to predict

the fan wake properties (as opposed to specifying the

wake from data or other analyses) and requires solutions

from multiple blade passages to account for uneven fan/

stator blade counts. Since the grid representing the fan

blades rotates relative to the stator grid, sufficiently small

time steps are needed to accurately transmit the acoustic

waves across the interface boundary (Ref. 21). All of these

issues make time-marching solutions for interaction tone

predictions difficult, requiring hundreds of hours of CPU
NASA/TM—97-206533 3

American Institute of Aeronautics and Astronautics

time to obtain accurate three-dimensional solutions for

even one fan speed/configuration.

Very little has been done yet to apply the threedimensional codes to aeroacoustic fan problems.

Parametric studies are needed to determine when the twodimensional flat plate models can be used and when more

complex codes are needed, particularly for transonic flows.

It is unlikely that the three-dimensional codes that solve

the Euler or Navier-Stokes equations will be used in a

design environment in the near future, but they can be used

to sort out what is important for improving fan noise

prediction methods.

An outstanding issue for any of the approaches

mentioned so far is how to deal with near sonic flows on

the fan. All discretized approaches have problems when

the local Mach number is nearly one and the acoustic

wavelengths become small for upstream propagating

waves. Many of the current computational methods require

10 to 20 points/wavelength to accurately model the

acoustics. Can these regions near the blade be ignored?

Studies are needed that identify how severe this problem

is for fan applications.

When the source model is used for a single blade row,

specification of the fan wake is usually done through

empirical correlations. Important parameters for tone

predictions include wake width and depth as a function of

axial distance between the fan and stators. Wake models

based on measured data have been developed (Ref. 22)

and updated as more fans are tested. The correlations relate

the wakes to local characteristics of the fan, like blade

section drag and loading. A disadvantage of this approach

is that correlations may not be representative of a particular

fan. Sutliff, et al. (Ref. 23) compared acoustic predictions

using measured fan wakes versus empirical wake

correlations. They found that the differences between

measured and correlated wake characteristics can influence

the duct power by a few decibels (Fig. 4), but the trends are

similar as a function of fan speed (for a subsonic rotor).

Ultimately, CFD predictions may be used to provide wake

properties for a specific fan design. Since the acoustic

source models need this information near the stators, CFD

codes need to be able to accurately convect the wakes over

a few fan chord lengths.

Duct Propagation and Radiation Models. There are a

variety of approaches available for propagating the sound

through the nacelle and predicting far field levels. Input is

required that specifies source levels from either predictions

or experimental measurements. Early methods based on

ray tracing and Wiener-Hopf methods (Ref. 24) were

developed that are quick, but typically do not include flow

effects with realistic geometry. There were also methods

available that analyze the modal characteristics of the

source (based on cut-off ratio) to project far field directivity

and sound levels (Ref. 25). These methods include

convection effects assuming a uniform mean flow through

the duct. Numerical methods have been developed that

account for mean flow variations and acoustic wave

interactions with curved center bodies and ducts. This was

initially done for inlet radiation problems (Refs. 26 to 28),

and later extended to the nozzle radiation problem (Ref. 29).

An important aspect for aft radiation is including the

refraction effects from the nozzle shear layer. These

methods use a two-dimensional grid that cuts the engine in

an x-r plane (Fig. 2) and uses duct mode information as

input at a specific axial plane inside the nacelle.

Applications of the numerical methods are usually

restricted to frequencies around 3 BPF and below for a

typical fan. This is constrained by grid size requirements

and practical CPU times on current computers.

Results from predictions have been encouraging for

axisymmetric nacelles. Improvements in measurement

techniques (Ref. 30) that determine duct mode source

strengths have allowed researchers to validate propagation

codes. Heidelberg, et al. (Ref. 31) have shown how these

codes can do an excellent job of predicting far field

directivity when the source levels are known (Fig. 5).

Improvements have been made (Ref. 32) in the midangle

regions by properly accounting for the inlet and aft mode

phase relationships. These results suggest that work should

concentrate on source models that can accurately predict

the duct modes in the nacelle.

The radiation code cited in Refs. 28 and 29, uses finite

element analysis in the near field and a wave envelope

method to obtain far field information. This method gives

good overall results for sound pressure amplitudes, but

falls short if both amplitude and phase information are

needed. Spence (Ref. 33) recently showed how phase

accuracy can be improved by using a Kirchhoff analysis in

place of the wave envelope method.

The methods presented above assume axisymmetric

nacelle geometries. In reality, turbofan engines have

asymmetric nacelles, struts and pylons, requiring threedimensional analyses. Advances in computing technology

and development of highly efficient methods offer ways to

meet these needs (Refs. 34 and 35).

System Predictions. In recent years, the effects of

mode trapping between the fan and stator due to swirling

flows, transmission effects through the fan, frequency

scattering and reflections from the inlet/nozzle have also

been included in fan noise prediction (Refs. 36 to 40).
NASA/TM—97-206533 4

American Institute of Aeronautics and Astronautics

Again, the approach is to use components consisting of the

inlet, fan, stator and nozzle. Influence coefficients are

used at the interface boundaries to model interactions

from all components. Topol (Ref. 39) recently presented

sample predictions for a subsonic fan and compared them

with experimental data from a model scale test (Fig. 6).

The results show that including coupling and swirl effects

can significantly change the predicted directivity of the

interaction tones. Overall, it looks like the predictions are

in better agreement with the data, but more applications are

needed. Sijtsma, Rademaker, and Schulten (Ref. 41) also

reported that reflections from the inlet and nozzle can be

significant over a wide range of speeds and should be

included in fan noise predictions. Schulten’s method

(Ref. 42) uses a three-dimensional lifting surface code for

the source model.

System predictions that model multiple components

of an engine have been needed for many years and are

finally being developed. The next step is to determine when

system predictions are needed over simpler approaches.

System predictions are considerably more complex and

require expert users, at least for now.

Multiple Pure Tones

Predictions for Multiple Pure Tone (MPT or “buzzsaw”) noise are typically not robust. This noise source is

caused by variations in the fan blade geometry (like

stagger angle) that results from either manufacturing or

installation. Unless careful inspection is done for each fan

after production, it is difficult to know what the MPT

characteristics will be in advance. The spectra for MPT

noise is experimentally distinguished by observing shaftorder tones in the near field inlet microphone measurements

as the fan speed becomes supersonic. Prediction methods

need to input blade-to-blade variations in order to predict

the orders of the shaft frequency where the tones will be a

problem.

There has been work done recently to predict the fan

speeds for the onset of MPT noise using CFD (Ref. 43).

This approach only requires knowledge of the steady flow

field near the fan since MPT noise can be related to the

formation of shock waves. Sufficient grid resolution is

needed to capture the bow shock from a single blade and

its interaction with adjacent blades.

Broadband Noise

Methods for predicting fan broadband noise have

been mostly empirical in nature. They usually rely on

correlations from engine or model data that relate the

measured fan broadband noise with fan loading and tip

speed. Since the tone levels have been significantly reduced

from modern turbofan engines with higher bypass ratios

(Ref. 44), methods that can predict broadband noise with

less empiricism are needed. Analyses that calculate the

unsteady aerodynamics associated with broadband noise

can use, in principle, the same gust response methods cited

for tone noise prediction. However, many more frequency

bands must be considered compared to tone prediction

methods. This significantly increases the computational

time. Time marching methods are also difficult to use

since they need accurate resolution of the temporal and

spatial flow properties. One of the underlying requirements

for any pure prediction method for broadband noise is

accurate representation of the flow turbulence. Another

challenge to fan broadband noise is a general lack of

understanding of important source mechanisms as the

operating conditions vary. It is likely that the total

broadband noise levels result from a number of different

sources within the engine.

Interaction Noise

 Broadband noise can result from either inflow

turbulence interacting with the fan or fan turbulent wakes

impinging on stators and struts. There is a version of the

tone noise prediction code cited in Ref. 4 that also predicts

broadband interaction noise. It uses a two-dimensional,

flat plate gust response model for specific turbulence

intensity levels and distributions. A sample prediction

was done for a generic cascade subjected to vortical

excitations that simulate the fan wake turbulence interacting

with a stator. The turbulence was assumed to be isotropic

at the stator leading edge with an intensity of 1 percent.

The inlet sound power levels (taken from Ref. 4) are

plotted in Fig. 7 for four different integral length scales

and frequencies ranging from BPF to 3 BPF. As the

integral length scale becomes sufficiently large,

haystacking occurs near the harmonic frequencies. This

work was one of the first attempts to predict fan broadband

noise by first principle estimates of the source.

More recent work for interaction noise has been done

by Mani, et al. (Ref. 45) and Martinez (Ref. 46). Mani uses

a flat plate source model that includes both dipole and

quadrupole distributions using a strip approach. The steady

loading effects are estimated to assess quadrapole

contributions. He shows how the quadrapole effects can

be significant for higher frequencies when compared to

the dipole contributions. Martinez has also investigated

blade loading on fan broadband noise by applying a

two-dimensional linearized Euler code (Ref. 47) to model

“real” blade effects. This work is relatively new and has

not yet been rigorously applied.
NASA/TM—97-206533 5

American Institute of Aeronautics and Astronautics

Fan-Alone Sources

Rotor blade self noise has been investigated for

isolated airfoils by a number of researchers (see Refs. 48

and 49 for listings). Sources mechanisms for isolated

airfoils include turbulent boundary layer/trailing edge

noise, separation stall noise, laminar boundary layer vortex

shedding noise, tip vortex formation noise, and trailing

edge bluntness vortex shedding noise (Ref. 50). For ducted

fan applications, these sources are influenced by the

presence of the casing and hub. Glegg (Refs. 48 and 49)

has extended this work to ducted fans by using measured

spectra from the isolated airfoil tests to evaluate blade

surface unsteady pressure distributions. The surface

pressures were input to a strip approach for rotating blades

(similar to methods described earlier in this paper) to

determine mode amplitudes for a ducted fan. Results show

that the duct sound power scales with the fifth power of the

fan speed for low fan speeds, and the sixth power for high

fan speeds. The effect of rotor incidence was determined

through the experimental correlations for airfoil angle of

attack. Glegg reports that blade incidence increases the

self noise as 2.4 dB/degree for an unstalled fan. This is

consistent with Gliebe (Ref. 51), who found that forward

radiated fan broadband noise increases 2.5 dB/degree of

incidence.

While this approach looks promising, it still relies on

empirical relationships for the sound sources. Methods

are needed that can accurately predict the source spectra,

which ultimately depends on improved turbulence models.

Computational Aeroacoustics

Computational AeroAcoustics (CAA) has gained

considerable attention over the past few years. If successful,

it offers a way to reduce the number of component analyses

needed to predict the total noise and provide a more

realistic and accurate prediction tool. For example, fan

tone and broadband noise can be computed together with

the mean flow and unsteady pressures solved

simultaneously. All nonlinearities and complexities

associated with three-dimensional geometries in highspeed flows can be computed. An immediate challenge for

CAA is to demonstrate accurate noise predictions in a

reasonable amount of CPU time. For internal flows,

unsteady boundary conditions are still an issue.

There have been various workshops and conference

sessions dedicated to CAA and its applications to selected

problems. The Second Computational Aeroacoustics

Workshop on Benchmark Problems (Ref. 52) concentrated

on applications to various model problems. One of the

problems designed for turbomachinery noise applications

uses the same two-dimensional gust response model

utilized in the source model from Ref. 4 to benchmark

state-of-the-art CAA codes. It is imperative that CAA

methods demonstrate accurate predictions of linear flows

where analytical solutions can be used for comparisons,

before attempting more difficult problems. There is no

reason to believe that a nonlinear code is producing

accurate predictions if it cannot match a linear subset of

the governing equations! The turbomachinery benchmark

problem (designated “Category 3”) calls for the solution

to a gust convected with a mean flow interacting with an

unstaggered cascade of flat plates (Fig. 8). Four passages

are used with the gust phase offset by 90° for each plate

(interblade phase angle). The amplitude of the gust is

small so comparisons can be made with linear theory. Gust

frequencies corresponding to reduced frequencies of about

8 and 20 (based on plate length) are prescribed and the

inflow Mach number is 0.5. The resulting unsteady surface

pressure distributions on the plate and the upstream/

downstream sound intensity distributions are requested

from each participant in the workshop.

Four predictions for this problem were submitted to

the workshop and the results vary significantly.

Comparisons for “Problem 2,” which asks for solutions

for a convected gust that is introduced upstream, are

shown in Fig. 9 (low frequency case) and Fig. 10 (high

frequency case). The pressure distributions show the first

harmonic of the real and imaginary parts of the pressure

differential distribution across the plate after a Fourier

transform is applied to the unsteady pressures. The mean

square pressure distributions (sound intensity) are shown

along a line at x = –2 upstream of the cascade and along a

line at x = 3 downstream of the cascade.

Reasons for discrepancies are likely to be due to

either poor grid resolution, or problems with the unsteady

boundary conditions at the inflow and outflow. It is

possible that local nonlinearities may exist at the leading

or trailing edges of the flat plate, but this can be assessed

by simply reducing the gust amplitude. The CPU times for

these runs vary from just a few minutes for the frequency

domain solutions to many hours for the time marching

solutions. Predictions for fan noise require extensions to

three dimensional flows, including real blade effects for

staggered cascades, running the entire rotor and stator

blade passages instead of only the four used in this

problem (time domain approaches), accurate prediction of

the rotor wakes if including both the fan and stators, and

accurate propagation of the acoustic waves to the far field

(unless another code is used for duct propagation and

radiation). For the time domain solvers, this makes the

problem extremely complex and well beyond today’s

capabilities if the codes are to be used for design and

analysis. However, progress in both the algorithm
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American Institute of Aeronautics and Astronautics

development for CAA codes and computer speeds make

these efforts worth pursuing. For example, one of the

solutions (Ref. 52, Lockard & Morris) was done using an

algorithm for parallel computing and showed significant

reductions in CPU time over traditional time-marching

methods.

There have been other algorithms developed that

have demonstrated potential for significant savings in

CPU time. One approach (Ref. 34), called “Green’s

Function Discretation (GFD)” reduces the number of grid

points per acoustic wavelength to numbers near the Nyquist

criterion. This technique shows promise for being able to

use CAA for three-dimensional applications.

Codes using advanced CAA algorithms need to be

developed far enough to do noise predictions for fan

designs where acoustic data is available. The best way to

generate interest in CAA is to demonstrate that it can do a

better job of predicting fan noise than current methods,

even if this requires significantly more CPU time and user

experience. The turbomachinery benchmark problem is a

first step towards demonstrating what CAA algorithms can

offer as an eventual substitute for simpler source models.

Model problems for three-dimensional fan flows where

analytical comparisons can be made do not yet exist. This

is a problem that faces many applications of CAA and at

some point, comparisons to experimental data will be the

only available benchmark. However, there is a need for the

community to define three-dimensional problems that

researchers can use to compare the many prediction

methods that are becoming available. Meetings, like the

First and Second Computational Aeroacoustics

Workshops, need to continue and lead the CAA community

towards relevant applications that can eventually be used

by industry.

Summary and Recommendations

Considerable progress has been made in recent years

towards improving fan noise prediction. Portions of the

work have been highlighted in this paper. Better knowledge

and prediction of noise sources is a vital requirement for

improving prediction methods. Duct propagation and

radiation models do a good job of predicting far field levels

if the source distribution/amplitude inside the nacelle can

be accurately defined. As tone levels are being reduced in

modern turbofan engines, emphasis is being shifted to the

prediction of fan broadband noise. Efforts are needed to

characterize turbulence spectra of the flow near the fan

and stators, including intensity and integral length scale

distributions. Research is needed that identifies the level

of analysis required to accurately predict duct sound

power levels.

The following recommendations are made regarding

future work:

1. Apply three-dimensional source models to identify

important flow physics from an acoustic perspective.

Compare the results with two-dimensional source models

for both tone and broadband noise predictions.

2. Apply source models that include “real” blade

effects and compare them to flat plate source models for

transonic flows. Study high Mach number flow effects on

acoustic wave propagation through a fan.

3. Assess CFD capabilities to predict both steady and

unsteady (turbulence spectra) for fan wakes near stator

leading edges. Need to determine if descriptions of

turbulence can be predicted, or should wake/boundary layer

models be developed/updated from experimental

measurements.

4. Continue development of highly efficient and

accurate methods (through CAA or theory) that can solve

three-dimensional problems like asymmetric nacelles.

Work needs to continue in developing three-dimensional,

unsteady boundary conditions for internal flows.

5. Run parametric studies for various engine

configurations to identify when system predictions are

needed (that include inlet, fan, stators, struts, and nozzles),

as opposed to uncoupled methods that may neglect some

components.

6. A study should be done to determine whether

time-marching or frequency-domain methods are best

suited for broadband noise predictions.

7. Need to define benchmark problems that address

the three-dimensional aspects of fan noise prediction. The

aeroacoustics community should work together to define

these model problems. More code-to-code and code-todata comparisons are needed to identify which sources

should be included for providing accurate fan noise

predictions.

References

1. Groeneweg, J.F., Sofrin, T.G., Rice, E.J., and

Gliebe, P.R., “Aeroacoustics of Flight Vehicles:

Theory and Practice,” Turbomachinery Noise, edited

by Hubbard, NASA RP–1258, Vol. 1, August 1991,

pp. 151–209.

2. Low, J.K.C., “Ultra-High Bypass Ratio Jet Noise,”

NASA CR–195394, October 1994.

3. Tyler, J.M., and Sofrin, T.G., “Axial Flow Compressor

Noise Studies,” SAE Trans., Vol. 70, pp. 309–332,

1962.

4. Ventres, C.S., Theobald, M.A., and Mark, W.D.,

“Turbofan Noise Generation,” NASA CR–167952,

Vols. 1 and 2, July 1982.
NASA/TM—97-206533 7

American Institute of Aeronautics and Astronautics

5. Meyer, H.D. and Envia, E., “Aeroacoustic Analysis

of Turbofan Noise Generation,” NASA CR–4715,

March 1996.

6. Smith, S.N., “Discrete Frequency Sound Generation

in Axial Flow Turbomachines,” R&M 3709, British

Aeronautical Research Council, London, England,

UK, March 1972.

7. Whitehead, D.S., “Vibration and Sound Generation

in a Cascade of Flat Plates in Subsonic Flows,”

A.R.C., R&M 3685, 1972.

8. Kaji, S. And Okazaki, T., “Generation of Sound by

Rotor-Stator Interaction,” Journal of Sound and

Vibration, Vol. 13, No. 3, pp. 281–307, 1970.

9. Kaji, S., “Noncompact Source Effect on the Prediction

of Tone Noise from a Fan Rotor,” AIAA-75-446,

March 1975.

10. Namba, M., “Three-Dimensional Analysis of Blade

Force and Sound Generation for an Annular Cascade

in Distorted Flows,” Journal of Sound and Vibration,

Vol. 50, No. 4, 1977, pp. 479–508.

11. Kobayashi, H., “Three Dimensional Effects on Pure

Tone Fan Noise Due to Inflow Distortion,”

AIAA-78-1120, July 1978.

12. Atassi, H. and Hamad, G., “Sound Generated in a

Cascade by Three-Dimensional Disturbances

Convected in a Subsonic Flow,” AIAA-81-2046,

October 1981.

13. Fang, J., and Atassi, H.M., “Numerical Solutions for

Unsteady Subsonic Vortical Flows Around Loaded

Cascades,” ASME Paper 92-GT-173, June 1992.

14. Verdon, J.M., “Unsteady Aerodynamic Methods for

Turbomachinery Aeroelastic and Aeroacoustic

Applications,” AIAA Journal, 31(2):235–250,

February 1993.

15. Atassi, H., Fang, J. And Hardy, B., “Sound Radiated

From a Loaded Cascade in Nonuniform Transonic

Flow,” AIAA-96-1756, May 1996.

16. Lorence, C.B., and Hall, K.C., “Sensitivity Analysis

of the Aeroacoustic Response of Turbomachinery

Blade Rows,” AIAA-95-0166, January 1995.

17. Montgomery, M.D., and Verdon, J.M., “A ThreeDimensional Linearized Unsteady Euler Analysis

for Turbomachinery Blade Rows,” NASA CR–4770,

March 1997.

18. Kousen, K.A., “Eigenmode Analysis of Ducted Flows

With Radially Dependent Axial and Swirl Components,” CEAS/AIAA-95-160, pp. 1085–1094,

June 1995.

19. Golubev, V.V, and Atassi, H.M., “Acoustic-Vorticity

Modes In An Annular Duct With Mean Vortical

Swirling Flow,” AIAA-97-1695, May 1997.

20. Rangwalla, A.A., and Rai, M.M., “A Numerical

Analysis of Tonal Acoustics In Rotor-Stator

Interactions,” Journal of Fluids and Structures,

Vol. 7, pp. 611–637, 1993.

21. Rumsey, C.L., “Computation of Acoustic Waves

Through Sliding-Zone Interfaces Using An Euler/

Navier-Stokes Code,” AIAA/CEAS-96-1752,

May 1996.

22. Majjigi, R.K., and Gliebe, P.R., “Development of a

Rotor Wake/Vortex Model,” Volume I–Final

Technical Report, NASA CR–174849, 1984.

23. Sutliff, D.L., Bridges, J., and Envia, E., “Comparison

of Predicted Low Speed Fan Rotor/Stator Interaction

Modes to Measured,” AIAA-97-1609, May 1997.

24. Savkar, S.D., and Edelfelt, I.H., “Radiation of

Cylindrical Duct Acoustic Modes With Flow

Mismatch,” NASA CR–132613, 1975.

25. Rice, E.J., “Multimodal Far-Field Acoustic Radiation

Pattern Using Mode Cutoff Ratios,” AIAA Journal,

Vol. 16, No. 9, Sept. 1978, pp. 906–911.

26. Horowitz, S.J., Sigman, R.K., and Zinn, B.T., “An

Iterative Finite Element-Integral Technique for

Predicting Sound Radiation From Turbofan Inlets in

Steady Flight,” AIAA-82-0124, January 1982.

27. Baumeister, K.J., and Horowitz, S.J., “Finite ElementIntegral Acoustic Simulation of JT15D Turbofan

Engine,” ASME J. Vibration, Acoust., Stress, &

Reliab.,Vol. 106, No. 3, July 1984, pp. 405–413.

28. Eversman, W., and Danda, R.L, “Ducted Fan

Acoustic Radiation Including the Effects of NonUniform Mean Flow and Acoustic Treatment,”

AIAA-93-4424, October 1993.

29. Eversman, W., “Aft Fan Duct Acoustic Radiation,”

CEAS/AIAA-95-155, June 1995.

30. Heidelberg, L.J., and Hall, D.G., “Inlet Acoustic

Mode Measurements Using a Continuously Rotating

Rake,” Journal of Aircraft, Vol. 32, No. 4,

pp. 761–767, July-August 1995.

31. Heidelberg, L., Hall, D.G., Bridges, J.E., and

Nallasamy, M., “A Unique Ducted Fan Test Bed for

Active Noise Control and Aeroacoustics Research,”

AIAA-96-1740, May 1996.

32. Heidelberg, L.J., Sutliff, D.L., and Nallasamy, M.,

“Azimuthal Directivity of Fan Tones Containing

Multiple Modes,” AIAA-97-1587, May 1997.

33. Spence, P.L., “Ducted Fan Noise Prediction Using

Wave Envelope Analysis and the Kirchhoff

Formula,” AIAA-97-1651, May 1997.

34. Caruthers, J.E., French, J.C., and Raviprakash, G.K.,

“Recent Developments Concerning a New

Discretization Method for the Helmholtz Equation,”

CEAS/AIAA-95-117, June 1995.
NASA/TM—97-206533 8

American Institute of Aeronautics and Astronautics

35. Dougherty, R.P., “A Wave-Splitting Technique for

Nacelle Acoustic Propagation,” AIAA/CEAS

97-1652, May 1997.

36. Philbrick, D.A. and Topol, D.A., “Development of a

Fan Noise Design System Part 1: System Design and

Source Modeling,” AIAA-93-4415, October 1993.

37. Topol, D.A., “Development of A Fan Noise Design

System Part 2: Far-Field Radiation and System

Evaluation,” AIAA-93-4416, October 1993.

38. Meyer, H.D., “Effect of Inlet Reflections on Fan

Noise Radiation,” AIAA-93-4427, October 1993.

39. Topol, D.A., “Development and Evaluation of a

Coupled Fan Noise Design System,” AIAA97-1611, May, 1997.

40. Hanson, D.B., “Acoustic Reflection and Transmission

of Rotors and Stators Including Mode and Frequency

Scattering,” AIAA/CEAS 97-1610, May 1997.

41. Sijtsma, P., Rademaker, E.R., and Shulten, J.B.H.M.,

“Experimental Validation of a Lifting Surface Theory

for Rotor-Stator Interaction Noise Generation,”

AIAA-96-1744, May 1996.

42. Shulten, J.B.H.M., “Sound Generated by Ducted Fans

and Propellers as a Lifting Surface Problem,” Thesis,

University of Twente, 1993.

43. Weir, D., and Lieber, L., “Estimation of the Onset of

Multiple Pure Tone Noise of a Fan,” AIAA97-0383, January 1997.

44. Gliebe, P.R., Ho, P.Y., and Mani, R., “UHB Engine

Fan Broadband Noise Reduction Study,” NASA

CR–198357, June 1995.

45. Mani, R., Gliebe, P.R., and Ho, P.Y., “Fan Broadband

Noise Model Development,” NASA CR–198457.

46. Martinez, R., and Rudzinsky, J., “Predictions of Diffraction Of Broadband Noise By An Engine Nacelle;

Part I: Calculation of a Stator Cascade’s Near- And

Far-Field Radiations From The Ingestion of Rapidly

Distorting Turbulence,” AIAA-97-1693, May 1997.

47. Fang, J., and McCoy, C.M., “CASGUST - A Code to

Calculate the Aerodynamic and Aeroacoustic

Response of a Cascade in Unsteady Flow,” University

of Notre Dame Aerospace Engineering Dept. Report

95-5, May 1995.

48. Glegg, S.A.L., “Airfoil Self Noise Generated in a

Cascade,” AIAA-96-1739, May 1996.

49. Glegg, S.A.L., and Jochault, C., “Broadband Self

Noise from a Ducted Fan,” AIAA-97-1612, May,

1997.

50. Brooks, T.F., Pope, D.S., and Marcolini, M.A., “Airfoil

Self-Noise and Prediction,” NASA RP–1218, July,

1989.

51. Gliebe, P.R., “The Effect of Throttling on Forward

Radiated Fan Noise,” AIAA-79-0640, March 1979.

52. Tam, C.K.W., and Hardin, J.C., “Second Computational Aeroacoustics (CAA) Workshop on

Benchmark Problems,” NASA CP–3352, June 1997.
NASA/TM—97-206533 9

American Institute of Aeronautics and Astronautics

zzy zzyy ||{{

zz||y{zz||yy{{

Inflow disturbances

Inlet boundary layer

Strut potential field

Rotor

Struts

R

S

Compressor inlet

Turbine exit Rotor leading

edge shocks

Rotor Stator

Wakes

Vortices

Turbulence

Figure 1.—Dominant noise sources for turbofan engines.

Radiation of sound

r

x

Fan wakes Unsteady surface

pressure on stators

Figure 2.—Prediction of fan wake/stator interaction noise.
NASA/TM—97-206533 10

American Institute of Aeronautics and Astronautics

Upstream pressure amplitude, zpz 0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

2 4 6 8 10 12 14 16

k1, reduced frequency (based on semi-chord)

10th S.C. (airfoils)

Flat plates

10th S.C. (airfoils)

Flat plates

(a)

Downstream pressure amplitude, zpz 0.00

0.05

0.10

0.15

0.20

0.25

6 7 8 9 10 11 12 13 14 15 16

k1, reduced frequency (based on semi-chord)

(b)

Figure 3.—Magnitude of downstream pressure for 

 second acoustic mode, airfoil vs. flat plate 

 response to gust, M∞ = 0.8. (a) Upstream.

 (b) Downstream.

Prediction with wake model

Prediction with measured wakes

Data rotating rake

PWL, dB

100

102

1600 1700

rpm, corr

1800 1900 2000

104

106

108

110

112

114

116

118

120 Inlet

PWL, dB

100

102

1600 1700

rpm, corr

1800 1900 2000

104

106

108

110

112

114

116

118

120 Exhaust

(a)PWL, dB

90

92

1600 1700

rpm, corr

1800 1900 2000

94

96

98

100

102

104

106

108

110 Inlet

PWL, dB

90

92

1600 1700

rpm, corr

1800 1900 2000

94

96

98

100

102

104

106

108

110 Exhaust

(b)

Figure 4.—Comparisons of duct mode power levels 

 from NASA low-speed fan (48-in. diam). (a) BPF,

 16 blades, 13 stator vanes, (3,0) mode. (b) 2 BPF,

 16 blades, 13 stator vanes, sum of (6,0) and (–7,0) 

 modes.
NASA/TM—97-206533 11

American Institute of Aeronautics and Astronautics

Measurement

Prediction (using measured duct modes) 

Sound pressure level, dB

50

60

70

80

90

100

110

0 20 40 60 80 100 120 140 160 180

Far-field angle, deg

(a)

Sound pressure level, dB

50

60

70

80

90

100

110

0 20 40 60 80 100 120 140 160 180

Far-field angle, deg

(b)

Inlet Aft

Inlet Aft

Figure 5.—Measured and predicted far-field directivity,

 2 BPF, NASA low-speed fan (48-in. diam).

 (a) 1886 rpm. (b) 1700 rpm.

Sound pressure level, dB

20 40 60 80 100 120 140 160

Directivity angle, deg

(a)

10 dB

Sound pressure level, dB

20 40 60 80 100 120 140 160

Directivity angle, deg

(b)

Fully coupled prediction with swirl

Coupled prediction with no rotor, no swirl

Data

Figure 6.—System prediction of interaction tones for a 

 subsonic fan. (a) 2 BPF. (b) 3 BPF.
NASA/TM—97-206533 12

American Institute of Aeronautics and Astronautics

Computational

domain

30

40

50

60

70

80

90

1BPF 2BPF 3BPF

Frequency

Total sound power level, dB

Upstream

1% turbulence intensity

Wake width = 0.072 rduct

Lx = Lr = Lu = 0.1 ~~~

Lx = Lr = Lu = 0.2 ~~~

Lx = Lr = Lu = 0.3 ~~~

Lx = Lr = Lu = 0.5 ~~~

Figure 7.—Predictions of total duct sound power for various integral length 

 scales of isotropic turbulence.

y

x

U∞

Periodic

boundary

Periodic

boundary

Reference plate

with length c

Gust

1

2

3

4

–2 –1 0 1 2 3

Figure 8.—Computational domain for turbomachinery noise benchmark problem, 

 cascade of unstaggered flat plates interacting with a gust.
NASA/TM—97-206533 13

American Institute of Aeronautics and Astronautics

–2.0

–1.0

0.0

1.0

2.0

3.0

Nondimensional pressure difference

0.0 0.2 0.4 0.6 0.8 1.0

x

(a)

–2.0

–1.0

0.0

1.0

2.0

3.0

Nondimensional pressure difference

0.0 0.2 0.4 0.6 0.8 1.0

x

(b)

0.00

0.01

0.02

0.04

0.03

Pressure squared

0.0 0.2 0.4 0.6 0.8 1.0

y

0.00

0.01

0.02

0.04

0.03

(c)

Pressure squared

0.0 0.2 0.4 0.6 0.8 1.0

y

(d)

Figure 9.—Comparison of predictions for turbomachinery benchmark problem, low frequency 

 case. (a) Unsteady pressure distribution, real. (b) Unsteady pressure distribution, imaginary.

 (c) Upstream mean square pressure, x = –2. (d) Downstream mean square pressure, x = 3.

Smith (theory)

Fang Hu

Hall

Lockard and Morris

Tam
NASA/TM—97-206533 14

American Institute of Aeronautics and Astronautics

–1.0

–0.5

0.0

0.5

1.0

1.5

Nondimensional pressure difference

0.0 0.2 0.4 0.6 0.8 1.0

x

(a)

–1.0

–0.5

0.0

0.5

1.0

1.5

Nondimensional pressure difference

0.0 0.2 0.4 0.6 0.8 1.0

x

(b)

0.00

0.01

0.02

0.04

0.05

0.03

Pressure squared

0.0 0.2 0.4 0.6 0.8 1.0

y

0.00

0.01

0.02

0.04

0.05

0.03

(c)

Pressure squared

0.0 0.2 0.4 0.6 0.8 1.0

y

(d)

Figure 10.—Comparison of predictions for turbomachinery benchmark problem, high frequency 

 case. (a) Unsteady pressure distribution, real. (b) Unsteady pressure distribution, imaginary.

 (c) Upstream mean square pressure, x = –2. (d) Downstream mean square pressure, x = 3.

Smith (theory)

Fang Hu

Hall

Lockard and Morris
December 1997

NASA TM—97-206533

AIAA–98–0177

E–11022

WU–538–03–11–00

This publication is available from the NASA Center for AeroSpace Information, (301) 621–0390.

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Fan Noise Prediction: Status and Needs

Dennis L. Huff

Aeroacoustics; Fan noise; Computational aeroacoustics

Unclassified -Unlimited

Subject Categories: 07 and 71 Distribution: Nonstandard

Prepared for the 36th Aerospace Sciences Meeting & Exhibit sponsored by the American Institute of Aeronautics and

Astronautics, Reno, Nevada, January 12–15, 1998. Responsible person, Dennis L. Huff, organization code 2200,

(216) 433–3913.

The prediction of fan noise is an important part to the prediction of overall turbofan engine noise. Advances in computers

and better understanding of the flow physics have allowed researchers to compute sound generation from first principles

and rely less on empirical correlations. While progress has been made, there are still many aspects of the problem that

need to be explored. This paper presents some recent advances in fan noise prediction and suggests areas that still need

further development. Fan noise predictions that support the recommendations are taken from existing publications.