February 2011 

NASA/CR–2011-217057 

Development and Validation of a 

Multidisciplinary Tool for Accurate and 

Efficient Rotorcraft Noise Prediction (MUTE) 

Yi Liu, Phuriwat Anusonti-Inthra, and Boris Diskin 

National Institute of Aerospace, Hampton, Virginia 
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February 2011 

NASA/CR–2011-217057 

Development and Validation of a 

Multidisciplinary Tool for Accurate and 

Efficient Rotorcraft Noise Prediction (MUTE) 

Yi Liu, Phuriwat Anusonti-Inthra, and Boris Diskin 

National Institute of Aerospace, Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 
iii

Abstract

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft 

noise was developed and validated with a wide range of flight configurations and conditions. 

MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently 

model the physics of the source of rotorcraft noise, and predict the noise at far-field observer 

locations. It uses systematic coupling approaches among multiple disciplines including 

Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and highfidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade 

to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive 

noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by 

using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details 

required for blade/wake and fuselage/wake interactions. The accuracy of the source noise 

prediction is further improved by utilizing a coupling approach between CFD and CSD, so that 

the effects of key structural dynamics, elastic blade deformations, and trim solutions are 

correctly represented in the analysis. The blade loading information and/or the flow field 

parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to 

predict the acoustic signatures at far-field observer locations with a high-fidelity noise 

propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade 

aerodynamic loading and far-field acoustic signatures are compared and validated with a 

variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data. 
iv

Table of Contents

Lists of Figures.................................................................................................................... IV

Abbreviations...................................................................................................................... VIII

Acknowledgements ............................................................................................................. IX

Chapter I Introduction and Background ......................................................................... 1

Chapter II CFD Coupled with CSD for Accurate Noise Prediction .............................. 4

2.1 CFD and CSD Coupling Procedure .................................................................... 4

2.2 Rotorcraft Wake Capturing/Modeling Method Studies...................................... 6

2.3 High-order Near-Blade CFD Algorithm Studies................................................ 21

2.4 CFD/CSD Coupled Method for DNW High-speed Noise Predictions............... 26

2.5 Simulations for HART Baseline Low-speed BVI Case...................................... 28

Chapter III Particle Vortex Transport Method for Accurate Wake Modeling............ 31

3.1 UH60 Simulations Using Coupled CII/TURNS/PVTM Method ....................... 32

3.2 HART II Simulations Using Coupled CII/TURNS/PVTM Method .................. 39

Chapter IV Improved and Efficient Interfaces with Acoustic Propagation Codes...... 45

4.1 DNW High-speed Noise Prediction.................................................................... 46

4.2 HART II BVI Noise Prediction .......................................................................... 54

Chapter V Concluding Remarks....................................................................................... 58

References............................................................................................................................ 60
v

List of Figures

Figure 1.1: MUTE Tool Structure ........................................................................................ 1

Figure 2.1: CFD/CSD Coupling Procedures between TURNS/Free-Wake and CII ............ 5

Figure 2.2: Overset Grid (near body grid + background grid).............................................. 7

Figure 2.3: CFD+Overset Grid Method: Convergence History (c8534).............................. 8

Figure 2.4: CFD+Overset Grid Method:

Uh60-A High-speed case (c8534) Total Normal Force Comparisons...................... 9

Figure 2.5: CFD+Overset Grid Method:

Uh60-A High-speed case (c8534) Pitch Moment Comparisons............................... 9

Figure 2.6: CFD+Overset Grid Method:

Uh60-A Low-speed case (c8513) Convergence History .......................................... 10

Figure 2.7: CFD+Overset Grid Method:

Uh60-A Low-speed case (c8513) Total Normal Force Comparison ........................ 11

Figure 2.8: CFD+Overset Grid Method:

Uh60-A Low-speed case (c8513) Pitch Moment Comparisons ............................... 11

Figure 2.9: CFD+Overset Grid Method:

Uh60-A Dynamic Stall Case (c9017) Convergence History .................................... 12

Figure 2.10: CFD+Overset Grid Method:

Uh60-A Dynamic Stall Case (c9017) Total Normal Force Comparisons ................ 13

Figure 2.11: CFD+Overset Grid Method:

Uh60-A Dynamic Stall Case (c9017) Pitch Moment Comparisons ......................... 13
vi

Figure 2.12: CFD+Free-Wake Method: Convergence History (c8534)............................... 16

Figure 2.13: CFD+Free-Wake Method:

Normal Force and Pitch Moment Comparison at r/R = 55% (c8534)...................... 16

Figure 2.14: CFD+Free-Wake Method:

Normal Force and Pitch Moment Comparison at r/R = 96.5% (c8534)................... 17

Figure 2.15: CFD+Free-Wake Method: Convergence History (c8513)............................... 18

Figure 2.16: CFD+Free-Wake Method:

Normal Force and Pitch Moment Comparison at r/R = 55% (c8513)...................... 18

Figure 2.17: CFD+Free-Wake Method:

Normal Force and Pitch Moment Comparison at r/R = 96.5% (c8513)................... 19

Figure 2.18: CFD+Free-Wake Method: Convergence History (c9017)............................... 20

Figure 2.19: CFD+Free-Wake Method:

Normal Force and Pitch Moment Comparison at r/R = 55% (c9017)...................... 20

Figure 2.20: CFD+Free-Wake Method:

Normal Force and Pitch Moment Comparison at r/R = 96.5% (c9017)................... 21

Figure 2.21: High-Order CFD Scheme provides sufficient spatial and temporal accuracy . 21

Figure 2.22: Normal Force and Pitch Moment Comparison (High-order scheme 55%)...... 24

Figure 2.23: Normal Force and Pitch Moment Comparison (High-order scheme 96.5%)... 25

Figure 2.24: The Overset Grid for DNW High-Speed Simulations...................................... 26

Figure 2.25: The Convergence History for DNW High-speed Case .................................... 27

Figure 2.26: Normal Force and Pitch Moment Comparison at r/R = 55% (DNW).............. 27

Figure 2.27: Normal Force and Pitch Moment Comparison at r/R = 96.5% (DNW)........... 28

Figure 2.28: The Convergence History for HART II Low-Speed Case ............................... 29
vii

Figure 2.29: The Blade Loading Comparison at 87% Span for HART II Case ................... 29

Figure 2.30: The CFD+Free-wake Method Grid .................................................................. 30

Figure 2.31: The CFD+Free-wake Blade Loading Predictions for HART II Case .............. 30

Figure 3.1: Gridless PVTM prescribes and preserves evolution of wake from rotor system31

Figure 3.2: UH60 Blade Grid for Near Body CFD Calculation ........................................... 33

Figure 3.3: (a) UH60 Blade Profiles and (b) Blade Twist Distribution................................ 33

Figure 3.4: Schematic of PVTM Grid with Multi-resolution Zones for c8534.................... 34

Figure 3.5: Convergence History for Rotor Trim Parameters (UH60A c8534) ................... 34

Figure 3.6: Convergence History for Rotor Thrust (UH60A c8534).................................... 34

Figure 3.7: Normal Force Comparison (UH60A c8534)...................................................... 35

Figure 3.8: Converged Vorticity Field (UH60A c8534)....................................................... 36

Figure 3.9: Convergence History of Thrust and Trim Parameters (UH60A c8513) ............ 36

Figure 3.10: Comparison of Normal Force (UH60A c8513)................................................ 37

Figure 3.11: Comparisons of Normal Forces (UH60A c8513)............................................. 38

Figure 3.12: Converged Vorticity Field (Uh60A c8513)...................................................... 38

Figure 3.13: HART II Blade Grid......................................................................................... 40

Figure 3.14: HART II Blade Twist Profile ........................................................................... 40

Figure 3.15: Trim Convergence History (HART II: BL, low resolution) ............................ 40

Figure 3.16: Comparison of Blade Normal Force (HART II: BL, low Res.)....................... 41

Figure 3.17: Comparison of Blade Normal Force at r/R = 0.87 (HART II: BL, low res).... 41

Figure 3.18: Converged Vorticity Field (HART II: BL, low res)......................................... 42

Figure 3.19: Schematic of PVTM grid with Multi-resolution zones ................................... 42

Figure 3.20: Trim Convergence History (HART II: BL, high resolution) ........................... 42
viii

Figure 3.21: Comparison of Blade Normal Force (HART II: BL, high res.) ....................... 43

Figure 3.22: Comparison of Blade Normal Force at r/R = 0.87 ........................................... 43

Figure 3.23: Comparison of Blade Normal Force ................................................................ 44

Figure 3.24: Comparison of Blade Normal Force ................................................................ 44

Figure 4.1: Noise Propagation from the Rotorcraft to Observer Locations.......................... 45

Figure 4.2: The Acoustic Experimental Set-up for DNW High-speed Case ........................ 46

Figure 4.3: The Sound Pressure of Microphone 1 ................................................................ 47

Figure 4.4: The Sound Pressure of Microphone 7 ................................................................ 47

Figure 4.5: The Sound Pressure Comparison for Microphone 1 (Impermeable Surface) .... 49

Figure 4.6: The Sound Pressure Comparison for Microphone 7 (Impermeable Surface) .... 49

Figure 4.7: The Sound Pressure Comparison for Microphone 1 (Pressure Surface)............ 50

Figure 4.8: The Sound Pressure Comparison for Microphone 7 (Pressure Surface)............ 50

Figure 4.9: Acoustic Data Surface (BL surface vs. Grid 1).................................................. 51

Figure 4.10: Acoustic Data Surface (BL surface vs. Grid 2)................................................ 51

Figure 4.11: Acoustic Data Surface (BL surface vs. Grid 3)................................................ 52

Figure 4.12: WOPWOP3 Predictions for Different Acoustic Data Surface Grids............... 52

Figure 4.13: The Sound Pressure Comparison for Microphone 1 ........................................ 53

Figure 4.14: The Sound Pressure Comparison for Microphone 7 ........................................ 53

Figure 4.15: Experiment BVI SPL Contour (HART II BL Case) ........................................ 55

Figure 4.16: BVI SPL Contour with CFD+Overset Grid Method........................................ 55

Figure 4.17: BVI SPL Contour with CFD+Free-wake Method............................................ 56

Figure 4.18: BVI SPL Contour with CFD+PVTM Low Resolution Wake Method ............ 56

Figure 4.19: BVI SPL Contour Comparisons with PVTM Method ..................................... 57
ix

Abbreviations

2D Two Dimensional

3D Three Dimensional

ADS Acoustic Data Surface

ARL Army Research Laboratory

BVI Blade Vortex Interaction

CAMRAD Comprehensive Analytical Model of Rotorcraft Aerodynamics 

and Dynamics

CII CAMRAD II

CFD Computational Fluid Dynamics

CSD Computational Structure Dynamics

DLR German Aerospace Center

HART Higher Harmonic Control Aero-acoustic Rotor Test

NASA National Aeronautics and Space Administration

NIA National Institute of Aerospace

PVTM Particle Vortex Transport Method

RANS Reynolds Averaged Navier-Stokes

VTM Vortex Transport Method
x

Acknowledgements

The financial support for this research from the NASA Fundamental Aeronautic Program 

Subsonic Rotary Wing Project under Contract NNL07AA31C, with Dr. Doug Nark as a 

technical monitor, is gratefully acknowledged. The authors are also grateful for the financial 

support for the initial investigation prior to this research at NIA from another NASA contract 

NAS1-2117, Task Order NNL04AA96T (Rotorcraft Aeromechanics), with Dr. Mark Nixon 

(ARL) as a technical monitor.

The authors would like thank Dr. Doug Nark, Dr. Ken Brentner, Ms. Casey Burley, Dr. Doug 

Boyd, Dr. Jayanarayanan Sitaraman, and Dr. Wayne Johnson for their valuable technical 

guidance, support and suggestions.
1

Chapter I

Introduction & Background

MUTE is a systematic combination of multidisciplinary analysis tools that accurately and 

efficiently models the physics of the source of the rotorcraft noise, and predicts the noise at farfield observer locations. MUTE uses a systematic coupling approach among multiple disciplines 

including Computational Fluid Dynamics (CFD) with high-fidelity wake modeling, 

Computational Structural Dynamics (CSD), and high-fidelity acoustics. Figure 1.1 illustrates 

MUTE with its multidisciplinary analysis tools.

The objective of developing the MUTE tool is to accurately model the physics of the 

noise source, and to understand the basis of the noise generation from rotorcraft in a wide range 

of flight regimes. Within MUTE, advanced high-order CFD tools are used around the rotor 

blade to predict the transonic flow (shock wave) effects, which generate the high-speed 

impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also 

Figure 1.1: MUTE uses a systematic coupling approach among multiple disciplines, to 

accurately and efficiently predict rotorcraft noise.

High-fidelity Wake Modeling

(Particle-VTM)

Computational 

Structure Dynamics 

(CSD)

High-order 

Computational Fluid 

Dynamics (CFD)

Shock Wave 

Boundary Layer

Noise Propagation 

(WopWop, RNM)

CAMRAD II, DYMORE

Multi-body, Nonlinear 

FUN3D, OVERFLOW

NASA RANS Flow Solver

Blade-Vortex

Interaction
2

improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake 

flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source 

noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so 

that the effects of key structural dynamics, elastic blade deformations, and trim solutions are 

correctly represented in the analysis.

To efficiently and successfully develop the MUTE tool, the project was divided into 3

major tasks. Within each of them, an essential element of the MUTE tool was developed and 

validated during the first two years. The integration of these elements and the validation of the 

whole MUTE tool were undertaken in the third year of the project. 

The purpose of the first task (task 1) was to 

develop high order near blade CFD algorithms

coupled with CSD for accurate source noise 

prediction. In this task, the effect of high-order 

CFD algorithms on the prediction of rotor blade 

loading was investigated. The effect of 

conventional wake modeling/capturing methods, such as free-wake and CFD with overset-grid 

methods, on the prediction of rotor blade loading were also studied and compared with the new 

PVTM method. 

The second task (task 2) was to apply 

and validate the Particle Vortex 

Transport Method (PVTM) for 

accurate wake modeling. In this task, predictions from the PVTM method developed by the Co-I 

were compared with the UH60-a blade loading test data, and the BVI loading data from the

HART II test.

The purpose of the final task

(task 3) was to develop an 

improved and efficient interface 

with the acoustic propagation code (WOPWOP 3). A python based control script was developed 

Computational Structure

Dynamics (CSD)

High-order 

Computational Fluid 

Dynamics (CFD)

High-fidelity Wake 

Modeling

High-order 

Computational Fluid 

Dynamics (CFD)

High-order 

Computational Fluid 

Dynamics (CFD)

Noise Propagation 

(WopWop, RNM)
3

to control the coupling processes among CFD, CSD and WOPWOP 3. For acoustic predictions, 

both permeable surface and impermeable surface methods were implemented in the coupling 

process. The acoustic prediction results were compared with the DNW high-speed test case, and 

the HART II BVI noise case. 
4

Chapter II

CFD Coupled with CSD for Accurate Source Noise Prediction

2.1 Computational Fluid Dynamics (CFD) / Computational Structural Dynamics (CSD) 

Coupling Procedure

Aerodynamic/structural analysis of helicopter rotors is a challenging problem that requires 

multidisciplinary methods to predict the rotor performance. In general, a finite element 

computational structural dynamics (CSD) analysis can predict blade motions and structural

dynamics accurately if correct surface aerodynamic loadings are provided. However, due to the 

overwhelming complexity of the comprehensive analysis, the aerodynamic components inside a 

CSD analysis are based on lower-order lifting line theory with wake/inflow models and twodimensional airfoil tables. These cannot physically model some complicated phenomena such as 

unsteady, three-dimensional viscous flow-fields, transonic flow with shocks, reverse flow and 

dynamic stall. On the other hand, computational fluid dynamics (CFD) analysis will provide high 

fidelity, nonlinear aerodynamics if the elastic rotor motions can be obtained from a CSD 

analysis. Thus, combining CFD and CSD as a systematic rotorcraft analysis tool is getting more 

and more attention. 

Coupling between CFD and CSD can be accomplished in two ways. The first method is 

loose (weak) coupling methodology, where information is transferred on a one per revolution, 

periodic basis between CFD and CSD. The second is tight (strong) coupling, where the CFD and 

CSD are coupled at every time step and marched forward in time simultaneously. Although tight 

coupling is more rigorous and can solve both steady and unsteady problems, the choice of 

algorithm is not trivial and many issues have to be carefully considered. On the other hand, the 

loose coupling approach is more mature and has been widely used in predicting blade loadings at 

different steady flight conditions. 

Currently, a Reynolds averaged Navier-Stokes CFD code (TURNS) [2.1] and a rotorcraft 

comprehensive analysis/CSD code (CAMRAD II) [2.2] have been coupled for all of the 

following simulation results. The loose coupling approach has been used for these validation 

cases since they are all under steady and periodic flight conditions. 
5

Figure 2.1 CFD/CSD Coupling Procedures between TURNS/Free-Wake and CAMRAD

(Red Box --- Main CFD, CSD and Wake Analysis Solvers)

(Blue Box --- Interface codes for CFD/CSE coupling iterations)

(Black Box --- Data/Information exchanged between these main solvers)

TURNS 

• CFD Single blade forward flight simulation with 

elastic motion

• High order STVD scheme

Wake geometric data 

(FWGEOM.dat & 

rcore)

CAMRAD II • Comprehensive rotorcraft 

structural analysis

CAMRAD II output 

(uh60.out)

CAMRADII rigid motion 

output (uh60rigid.out)

Program to extract motions from CII 

by comparing sensor position 

differences between regular output 

and rigid motion output 

Blade elastic motion 

file 

Test case input 

(uh60-c8534.scr)

Rigid motion input w/o 

aero & trim etc.

CFD blade surface loading 

at each azimuth angle 

(cfd-force-1.dat)

Program to extract blade 

loading and calculate the delta 

force between CFD and CII

Aero-loading 

Difference 

(delta force)

(delta-8534-1)

Free-Wake Module 

& PVTM

• Wake analysis
6

As shown in Figure 2.1, during the coupling procedure, a CAMRAD II blade motion file

is first generated with the position of the un-deflected blade quarter chord and the relative 

displacement of three translations and three rotations of the deflected quarter chord as a function

of the radial location and azimuth angle. TURNS reads this file as input, deforms the grid, and 

computes the results for the next revolution with these motions. To improve convergence, a 

“delta” method has been used to feed back the loading into CAMRAD II. The blade loading 

increment, ∆F, is updated from the difference between the CFD loads and the comprehensive 

analysis calculations,

( ) 1 1 1

CSD

i

CFD

i

CSD CSD Fi F F F ∆ = ∆ i− + − − − . (1)

In this manner, it is not necessary to separate out the lifting line analysis embedded inside the 

comprehensive analysis code (CAMRAD II). CAMRAD II uses this delta force file as input for 

the next iteration. The coupling iterations repeat the above procedure until the predicted loading 

between two iterations is converged. 

2.2 Rotorcraft Wake Capturing/Modeling Methods Studies

For rotorcraft aeromechanics simulation, correctly capturing the wake effects is very 

important under many flight conditions. Traditionally, there are two ways to capture/model 

rotorcraft wake effect for CFD simulations. First is the free-wake method, which is based on 

potential flow theory and is dependent on empirical inputs. The second is the CFD overset grid 

methodology, where a large background grid is used with CFD to directly capture the wake 

effect from the blade. This is a physics based, high resolution wake capturing method. However, 

the results are heavily dependent on the background grid resolution, thus are limited by the 

available computational resources. Additionally, the numerical dissipation embedded in the 

algorithm may diffuse the tip vortex too rapidly.

A new Particle Vortex Transport Method (PVTM) [2.3] has been developed under another 

NRA contract (NNL07AA32C). This is also a physics based high-fidelity wake modeling 

method, where the incompressible vortex transport equation is solved using a Lagrangian 

approach. 
7

In task 1, the first two wake capturing/modeling methods, overset grid and free-wake, were

investigated and validated with UH60a experimental data to assess the advantages and 

limitations of these approaches during different flight conditions. Then, the overset grid 

methodology was applied to the DNW high-speed case and HART II baseline BVI case, to 

obtain more accurate noise source prediction for further acoustic analysis.

In task 2, the PVTM method was utilized and validated against the same experimental data 

to assess the model limitations and to improve the accuracy and efficiency of the approach. 

2.2.1 CFD with overset grid methodology simulation results and discussions for UH60a 

high-speed level flight, low-speed level flight and dynamic stall flight conditions

Figure 2.2 Overset Grid (near body grid + background grid)

In the overset grid method, the full grid around the rotor was divided into two parts, a 

very fine near-body grid to simulate the flowfield around the blade and a large cylindrical

background grid to capture the wake effects. The RANS solver was used for both grids, thus this 

method can provide physics based high-fidelity flow simulations. A typical overset grid used by 

TURNS is shown in Figure 2.2, where the near-body grid has 3.2 million grid points and the 
8

background grid has 2.2 million grid points. This method is also used by NASA with the 

OVERFLOW CFD code. 

The first set of experimental/flight test data used in this project was for a UH-60A 

helicopter in level flight. The data were obtained during the NASA/Army UH-60A Airloads 

program. Currently, the database provides the aerodynamic pressure, rotor forces and moments 

etc. at 9 span locations r/R = 0.225, 0.40, 0.55, 0.675, 0.775, 0.865, 0.92, 0.965, and 0.99, for 

three level flight conditions, allowing for the validation of the aerodynamic models. These three 

flight conditions are: a high-speed level flight (counter no. 8534) with an advance ratio 0.368, a 

low-speed level flight (counter no. 8513) with an advance ratio 0.149, and a high-thrust dynamic 

stall flight (counter no. 9017) with an advance ratio 0.237. The blade loading predictions from 

CFD with overset grid coupled with CSD are compared with these test data and are discussed in 

the following sections for all three flight conditions.

2.2.1.1 UH-60A High-speed Level Flight Results and Discussions (c8534)

Figure 2.3 Convergence History (Blade Loading Prediction by TURNS at 96.5% Span)

r/R = 0.965

-300

-200

-100

0

100

200

300

400

500

0 90 180 270 360

Azimuth

Normal Force (lb/ft)

 0th iteration

 1st iteration

 2nd iteration

 3th iteration

 4th iteration

 5th iteration

 6th iteration

Flight Test Data
9

 

 Figure 2.4 Total Normal Force Comparisons with Flight Test Data

 

 Figure 2.5 Pitch Moment Comparisons with Flight Test Data

r/R = 0.40

0

50

100

150

200

250

300

350

400

450

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.775

-50

0

50

100

150

200

250

300

350

400

450

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.865

-300

-200

-100

0

100

200

300

400

500

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.965

-300

-200

-100

0

100

200

300

400

500

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.40

-20

-15

-10

-5

0

5

10

15

20

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.775

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.865

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

40

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.965

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

40

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II
10

For the UH-60A test, flight test counter number 8534, is a high-speed level flight case, 

where the advance ratio is 0.368. The rotor speed is 258 RPM, with a tip Mach number 0.6415. 

The trimmed thrust is at CT/σ = 0.08433. As shown in Figure 2.3, it takes 7 CFD/CSD coupling 

iterations to get a converged result for this case. As shown in Figures 2.4 and 2.5, the total 

normal forces and pitch moments are in good agreement with the flight test data. Especially in 

the tip region, the near-body CFD solver has accurately captured the transonic flow phenomena 

and the shock wave interaction at the advancing side. This is important for the improved 

prediction of these forces and moments in the tip region compared to the comprehensive analysis 

simulation. 

2.2.1.2 UH-60A Low-speed Level Flight Results and Discussions (c8513)

Figure 2.6 Convergence History (Blade Loading Prediction by TURNS at 96.5% Span, c8513)

r/R = 0.965

0

50

100

150

200

250

300

350

400

450

0 90 180 270 360

Azimuth

Normal Force (lb/ft)

 0th iteration

 1st iteration

 2nd iteration

 3th iteration

 4th iteration

 5th iteration

 6th iteration

Flight Test Data
11

 

 

Figure 2.7 Total Normal Force Comparison with Flight Test Data (c8513)

 

 Figure 2.8 Pitch Moment Comparison with Flight Test Data (c8513)

r/R = 0.40

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.775

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.865

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.965

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.40

-40

-30

-20

-10

0

10

20

30

40

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.775

-20

-15

-10

-5

0

5

10

15

20

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.865

-20

-15

-10

-5

0

5

10

15

20

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II

r/R = 0.965

-25

-20

-15

-10

-5

0

5

10

15

20

25

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD: TURNS_overset / CAMRAD II
12

The flight test counter number 8513 is a low-speed level flight case with an advance ratio 

of 0.149. The rotor speed is also 258 RPM, with a tip Mach number 0.6646. The trimmed thrust 

is slightly lower than c8534, at CT/σ = 0.0763. As shown in Figure 2.6, it also takes 7 CFD/CSD 

coupling iterations to get a converged result for this case. However, because of the low freestream velocity, the CFD requires two revolutions per iteration to obtain a periodic solution. In 

general, the total normal forces and pitch moments are also in good agreement with the 

experimental data as shown in Figures 2.7 and 2.8. Since this is a low-speed level flight case, the 

blade vortex interaction is not dominant. It can be seen from the test data that there are no highfrequency BVI effects on the blade loading. However, the tip vortex wake effect from the 

preceding blade is still represented by low-frequency blade loading variations, which are just 

fairly predicted by the CFD overset method. Some of the tip vortex effects from the preceding 

blade are diffused by the overset methodology, and improvement of the low-speed CFD 

predictions was investigated with the PVTM method.

2.2.1.2 UH-60A High-thrust Dynamic Stall Flight Results and Discussions (c9017)

Figure 2.9 Convergence History (Blade Loading Prediction by TURNS at 96.5% Span, c9017)

r/R = 0.965

-50

-40

-30

-20

-10

0

10

20

30

40

50

0 90 180 270 360

Azimuth

Pitch Moment

 0th iteration

 3th iteration

 5th iteration

7th iteration

8th iteration

9th iteration

Flight Test Data
13

 

Figure 2.10 Total Normal Force Comparison with Flight Test Data (c9017)

Figure 2.11 Pitch Moment Comparison with Flight Test Data (c9017)

r/R = 0.40

-200

-150

-100

-50

0

50

100

150

200

250

300

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD Prediction

r/R = 0.775

50

100

150

200

250

300

350

400

450

500

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD Prediction

r/R = 0.865

100

150

200

250

300

350

400

450

500

550

600

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD Prediction 

r/R = 0.965

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Total Normal Forces (lb/ft)

Flight Test Data

 CFD Prediction

r/R = 0.40

-20

-15

-10

-5

0

5

10

15

20

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD Prediction

r/R = 0.775

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

40

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD Prediction

r/R = 0.865

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

40

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD Prediction

r/R = 0.965

-50

-40

-30

-20

-10

0

10

20

30

40

0 90 180 270 360

Azimuth

Pitch Moment (1/rev)

Flight Test Data

 CFD Prediction 
14

The flight test c9017 is an intermediate speed case with advance ratio of 0.237, high 

thrust with CT/σ at 0.129, and level flight test point flown at 17,000 ft. This is a challenging 

rotorcraft test case due to the wide effects of unsteady flow conditions, especially the dynamic 

stall effects, with noticeable wake interactions. 

As shown in Figure 2.9, it takes 9 iterations to get a fairly converged result for this case. 

It is difficult to obtain a converged result at the tip region on the retreating side due to the strong 

unsteady effects from the dynamic stall. As for the forces and moments comparison, as shown in 

Figures 2.10 and 2.11, reasonable agreement is still reached between the CFD predictions and 

the flight test data. In Figure 2.10, the total normal forces are matched with the experiment, 

except at the root region, r/R = 0.40, where the rotor hub effects are not simulated by CFD. The 

low-frequency wake effect from the preceding blade is also captured. In Figure 2.11, it can be 

seen that the two dynamic stall peaks at the tip region in retrieving side are also fairly captured 

by the CFD. It is essential to have a physics based, high-fidelity solver around the near-blade 

region to capture such unsteady flow characteristics. 
15

2.2.2 CFD with Free-wake method simulations and validation

The following figure shows the typical approach to employing the CFD+Free-wake 

method. Here, the CFD solver is used for the near-body grid to accurately capture the physics of 

flow characteristics around the blade, and a free-wake module is used to get the wake geometry 

and the wake effect quickly. Then, the wake effect is fed back to the CFD solver using a fieldvelocity approach. Compared to the other two methods, this method can generate results much 

faster and has great potential for design purposes. 

 

2.2.2.1 UH-60A High-speed Level Flight (c8534)

The UH-60A high-speed case (c8534) has a large advance ratio of 0.368, so the wake 

effect will be expected to be small. The following figures show the comparison of blade loading 

predictions (normal forces and pitch moment) from the CFD+Free-wake method with 

CFD+Overset grid method and the flight test data.

Figure 2.12 shows the convergence history of the CFD+Free-wake method. For this highspeed case, both methods converge very quickly, requiring only 6 iterations to get a fully 

converged result. Actually, the CFD+Free-wake method converges even faster than the overset 

grid method, needing only 3-4 iterations to approach a converged result. 

The blade loading predictions are compared in Figures 2.13 and 2.14. It can be seen that 

the CFD+Free-wake method agrees well with both the overset grid predictions and flight test 

data. Particularly in the root to in-board region (Figures 2.13), the CFD+Free-wake method gives 

slightly better predictions than the overset-grid method. This is because the free-wake module 

has a root-wake component, while the overset grid cannot fully capture the root wake due to the 
16

grid resolution in that region. Around the tip region (Figure 2.14), the CFD+Overset grid 

method gives a slightly better prediction than the free-wake method. This is due to the fact that 

the full CFD method captures the physics of the transonic effect better than the free-wake 

approach.

Figure 2.12 Convergence History of CFD+Free-wake 

(Blade Loading Prediction by TURNS at 96.5% Span, c8534)

Figure 2.13 Normal Forces and Pitch Moment Comparisons at r/R = 55% (c8534)

r/R = 0.965

-400

-300

-200

-100

0

100

200

300

400

500

0 90 180 270 360

Azimuth

Normal Force (lb/ft)

 0th iteration

 1st iteration

 2nd iteration

 3th iteration

 4th iteration

 5th iteration

 6th iteration

Flight Test Data

r/R = 0.55

0

100

200

300

400

500

0 45 90 135 180 225 270 315 360

Azimuth

Normal Forces (lb/ft)

EXP

CFD + Free-Wake

CFD + Overset Grid 

r/R = 0.55

-30

-20

-10

0

10

20

0 45 90 135 180 225 270 315 360

Azimuth

Pitch Moment (ft-lb/ft)

EXP

CFD + Free-Wake

CFD + Overset Grid 
17

Figure 2.14 Normal Forces and Pitch Moment Comparisons at r/R = 96.5% (c8534)

2.2.2.2 UH-60A Low-speed Level Flight (c8513)

The UH-60A low-speed case has an advance ratio of 0.149. Since it is a level flight

condition, the high-frequency BVI effect is not dominant for this low-speed case, and the force 

variations are mostly low-frequency.

Figure 2.15 shows the convergence history of the CFD+Free-wake method for this low 

speed case. It can be seen that for the free-wake method, the CFD/CSD coupling does not 

converge to a single trimmed value. Eventually, the predictions jump between two values. As 

shown in the figure, iteration numbers 5, 7, and 9 trend toward the same result, while iteration 

numbers 6, 8, and 10 converge to another. Thus, the computational results from iteration 9 were 

selected for the following comparisons since they were slightly closer to the experimental values

on the advancing side. However, both results are comparable to each other and the experimental 

values on the retreating side.

Figures 2.16 and 2.17 show the normal force and pitch moment comparison for iteration 

number 9 in Figure 2.15. The CFD+Overset grid method gives better predictions for this case on 

the advancing side of the tip region. However, it is seen that the CFD+Free-wake method also 

gives reasonable predictions for this low-speed case for most of the blade regions. Therefore, for 

this kind of low-speed level flight, the CFD+Free-wake is still an appropriate method for fast 

evaluations.

r/R = 0.965

-500

-400

-300

-200

-100

0

100

200

300

400

500

0 45 90 135 180 225 270 315 360

Azimuth

Normal Forces (lb/ft)

EXP

CFD + Free-Wake

CFD + Overset Grid 

r/R = 0.965

-100

-80

-60

-40

-20

0

20

40

60

0 45 90 135 180 225 270 315 360

Azimuth

Pitch Moment (ft-lb/ft)

EXP

CFD + Free-Wake

CFD + Overset Grid 
18

Figure 2.15 Convergence History of CFD+Free-wake Method

Figure 2.16 Normal Forces and Pitch Moment Comparisons at r/R = 55% (c8513)

r/R = 0.775

0

50

100

150

200

250

300

350

400

450

500

0 50 100 150 200 250 300 350 400

Azimuth

Normal Force (lb/ft)

 0th iteration

 5th iteration

 6th iteration

7th iteration

8th iteration

9th iteration

10th iteration

Flight Test Data

r/R = 0.55

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Normal Forces (lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid

r/R = 0.55

-20

-15

-10

-5

0

5

10

15

20

0 90 180 270 360

Azimuth

Pitch Moment (ft-lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid
19

Figure 2.17 Normal Forces and Pitch Moment Comparisons at r/R = 96.5% (c8513)

2.2.2.3 UH-60A Dynamic Stall Level Flight (c9017)

UH60-A case 9017 is a high-thrust, dynamic-stall case. This is a highly unsteady case, 

with two wake induced stall regions on the retrieving side. Figure 2.18 shows the convergence 

history of the CFD+Free-wake predictions. It can be seen that, after 10 iterations, the CFD / 

CSD coupling almost converges to a single value.

However, the forces predicted by the CFD+Free-wake method do not compare with the 

measured values as well as those predicted with the CFD+Overset grid method. As shown in the 

Figure 2.19 and 2.20, the CFD+Free-wake method generally did not capture the two stall regions 

on the retrieving side, while the CFD+Overset grid method captured most of the stall regions. 

Thus, for this kind of highly unsteady cases, the CFD+Free-wake method may not be a good 

choice for fast evaluations. 

r/R = 0.965

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Normal Forces (lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid

r/R = 0.965

-25

-20

-15

-10

-5

0

5

10

15

20

25

0 90 180 270 360

Azimuth

Pitch Moment (ft-lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid
20

Figure 2.18 Convergence History of CFD+Free-wake Method

Figure 2.19 Normal Forces and Pitch Moment Comparisons at r/R = 55% (c9017)

r/R = 0.965

0

50

100

150

200

250

300

350

400

450

0 90 180 270 360

Azimuth

Normal Force (lb/ft)

 6th iteration

 7th iteration

 8th iteration

9th iteration

10th iteration

Flight Test Data

r/R = 0.55

0

50

100

150

200

250

300

350

400

450

500

0 90 180 270 360

Azimuth

 Normal Forces (lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid

r/R = 0.55

-20

-15

-10

-5

0

5

10

15

20

0 90 180 270 360

Azimuth

Pitch Moment (ft-lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid
21

Figure 2.20 Normal Forces and Pitch Moment Comparisons at r/R = 96.5% (c9017)

2.3 High-order Near-Blade CFD Algorithm Studies for Accurate Source Noise Prediction

For High Speed Impulsive (HSI) noise 

prediction, the permeable surface (offsurface) Ffowcs Williams – Hawkings (FWH) equation method (see Figure 2.21), 

which is dependent on CFD to provide 

sufficiently accurate, unsteady flow-field 

data on the acoustic data surface, has 

become popular. This approach has been 

proven to give better predictions compared 

with the traditional impermeable surface 

(on-surface) method, because the noise 

generated by the shock wave interaction can 

be included in this method [2.4]. However, 

many of these first-principle CFD schemes suffer from numerical dissipation and dispersion 

errors. Numerical dissipation causes a non-physical gradual decrease in the amplitude of an 

acoustic wave during its propagation from source to acoustic data surface. Numerical dispersion 

causes the acoustic waves of different wavelengths to propagate incorrectly at different speeds. 

Because of these numerical errors, the waves may be distorted in a non-physical manner by the 

r/R = 0.965

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Normal Forces (lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid

r/R = 0.965

-50

-40

-30

-20

-10

0

10

20

30

40

0 90 180 270 360

Azimuth

Pitch Moment (ft-lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid 

Figure 2.21 High-order CFD scheme 

provides sufficient spatial and 

temporal accuracy to propagate

acoustic characteristic waves to the 

acoustic data surface.

Permeable Surface 

surrounding the blade

or

Acoustic Data Surface

Physical Blade 

Surface
22

time they arrive at the acoustic data surface, so that the accuracy of the flow-field description on 

the acoustic surface will be reduced. Hence, the far-field propagation based on the permeable 

surface FW-H equation method will provide non-realistic solutions. 

This activity entailed the implementation of the low dispersion and low dissipation 

Symmetric Total Variation Diminishing (STVD) scheme developed by Helen Yee into the 

current CFD solver. With a 6th (or even higher 8th order) spatial accuracy algorithm, and with a 

2nd order temporal accuracy scheme, the unsteady flow solutions and shock wave interactions 

can be accurately predicted. Furthermore, this high-order near-blade CFD solver was coupled 

with the proposed PVTM method, which resolves the transmission of the vortices into the rest of 

the flow field, to accurately model the physics of the source of BVI noise.

In the baseline TURNS, Roe’s approximate Riemann solver [2.5] has been used for the 

calculation of inviscid fluxes. Considering a cell face at i+1/2, in the Roe scheme, the flux Fi+1/ 2

is computed as:

[ ] [ ( , ( )] 2

1 ( ) ( ) 2

1

1/ 2 i L i R L R R L Fi+ = F q + F q − A q q q −q

where the first part [ ( ) ( ) ] 2

1

i L i R F q + F q is the Physical Flux and the second part 

[ ( , ( )] 2

1

L R R L A q q q −q is the Numerical Viscosity filter. In the baseline TURNS, the physical 

flux is solved with the Roe solver, which is second-order. The numerical viscosity filters are 3rd

order MUSCL [2.6] (Monotone Upstream-Centered Scheme for the Conservation Laws), and 5th

order WENO [2.7] (Weighted Essentially Non-Oscillatory) schemes.

In the low dissipation shock-capturing schemes of Yee et al. [2.8], there are two steps. 

The first step is the high-order spatial base scheme. The second step is the appropriate 

characteristic-based filter to improve stability, shock and fine flow structure capturing. Thus, the 

flux at i+1/2, Fi+1/ 2 can be computed as:

Fi+1/ 2 = Base Scheme + Numerical Filter Term

For the base scheme, a sixth-order central difference scheme (STVD 6) is currently used:

[ ] [ ( , ( )] 2

1 8 37 37 8

30

1

3 2 1 1 2 1/ 2 i i i i i i L R R L Fi+ = F+ − F+ + F+ + F − F− + F− − A q q q −q
23

For the numerical filter term, the MUSCL and WENO schemes included in the baseline 

TURNS to dissipate the numerical viscosity are already good characteristic-based high-order 

filters. Therefore, in the second step of STVD scheme, these numerical filter terms are retained 

to simplify the implementation.

The results shown below compare combinations of these four different schemes, which 

are MUSCL+ROE, WENO+ROE, MUSCL+STVD6 and WENO+STVD6.

The test case for the high-order scheme is again the UH60-A high speed level flight. The 

structural analysis using CAMRAD II was included in the computation and coupling iterations 

between CSD and CFD were conducted until the blade loading predicted by CFD converged. 

The CFD code used here was the single blade TURNS code with a free-wake module as 

described in the previous section. In the future, the free-wake module will be replaced by the 

PVTM module and the code will be used for both high-speed and low-speed BVI simulations. 

Figures 2.22 and 2.23 show the normal force and pitch moment predictions of the four

different schemes (MUSCL-ROE, MUSCL-STVD6, WENO-ROE, and WENO-STVD6)

compared with experimental data. The results for the WENO and MUSCL schemes are similar.

However, the STVD6 scheme gives slightly better predictions than the ROE scheme, particularly

when there is a large gradient like that at the tip region where transonic flow effects are present. 

From the computational time perspective, for this high speed case, the STVD6 scheme 

generally takes approximately 3% more time than ROE scheme. However, the WENO scheme 

takes 24% more time than MUSCL scheme. So the MUSCL-STVD6 scheme was selected for the

current simulations.
24

Figure 2.22 Normal Forces and Pitch Moment Comparisons at r/R = 55% (c8534)

r/R = 0.55

0

100

200

300

400

500

0 50 100 150 200 250 300 350 400

Azimuth

Normal Forces (lb/ft)

EXP MUSCL + ROE

MUSCL + STVD 6 WENO + ROE 

WENO + STVD 6

r/R = 0.55

-30

-20

-10

0

10

20

0 50 100 150 200 250 300 350 400

Azimuth

Pitch Moment (ft-lb/ft)

EXP

MUSCL + ROE

MUSCL + STVD 6

WENO + ROE 

WENO + STVD 6
25

Figure 2.23 Normal Forces and Pitch Moment Comparisons at r/R = 96.5% (c8534)

r/R = 0.965

-500

-400

-300

-200

-100

0

100

200

300

400

500

0 50 100 150 200 250 300 350 400

Azimuth

Normal Forces (lb/ft)

EXP MUSCL + ROE

MUSCL + STVD 6 WENO + ROE 

WENO + STVD 6

r/R = 0.965

-100

-80

-60

-40

-20

0

20

40

60

0 50 100 150 200 250 300 350 400

Azimuth

Pitch Moment (ft-lb/ft)

EXP MUSCL + ROE

MUSCL + STVD 6 WENO + ROE 

WENO + STVD 6
26

2.4 CFD+Overset grid Coupled with CSD for DNW High-speed Source Noise Prediction

For the whole CFD/CSD/Acoustic frame work testing, initial validations were performed

using the DNW wind-tunnel test data [2.9, 2.10]. Here, the CFD+Overset grid method was used, 

and the overset grid is shown in Figure 2.24. Compared to the UH60-A cases, a refined grid was

used with 4.82 million near-body grid points (4*155*139*56) and 5.69 million back-ground grid 

points (4*69 *129*160).

The test case is a high-speed case 

with advance ratio of 0.310 and advance 

tip Mach number of 0.873. Figure 2.25

shows the convergence history, where 6 

iterations are needed to obtain a 

converged result. 

Comparison of the predicted 

force and moment coefficients with the 

experimental data are shown Figures

2.26 and 2.27. It can be seen that for this 

DNW high-speed case, both the lift and 

pitch moment coefficient predictions 

follow trends similar to the measurements. 

However, the measured data shows some 

weak BVI effect on the advancing side

which is not captured by the CFD predictions with this grid resolution. Conversely, the shock 

effect on the advancing side for this high-speed case has been captured.

Figure 2.24 The Overset Grid for DNW HighSpeed Simulations
27

Figure 2.25 The Convergence History for DNW High-Speed Case

Figure 2.26 Normal Forces and Pitch Moment Comparisons at r/R = 55% (DNW)

r/R = 0.965

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 90 180 270 360

Azimuth

M^2 * Cn

 0th iteration

 1st iteration

 2nd iteration

 3th iteration

 4th iteration

 5th iteration

 6th iteration

Flight Test Data

r/R = 0.550

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 45 90 135 180 225 270 315 360

Azimuth

Lift Coefficient

DNW Test Data

CFD/CSD Coupling

Predictions

r/R = 0.550

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0 45 90 135 180 225 270 315 360

Azimuth

Pitch Moment Coefficient

DNW Test Data

CFD/CSD Coupling

Predictions
28

Figure 2.27 Normal Forces and Pitch Moment Comparisons at r/R = 96.5% (DNW)

2.5 Simulations for HART Baseline Low-speed BVI Case with CFD+Overset and 

CFD+Free-wake Methods

Simulations have also been performed for the HART low-speed BVI case using the 

CFD+Overset Grid method. The refined grid was also used with 4.82 million near-body grid 

points (4*155*139*56) and 5.69 million back-ground grid points (4*69 *129*160). 

Figure 2.28 shows the convergence history with the refined grid, illustrating that the code 

predicts a converged blade loading after 7 iterations with 2 revolutions per iteration. Blade 

loading comparisons at the 87% span location are shown in the Figure 2.29. Here, the CFD/CSD 

prediction does not capture the high-frequency BVI variations seen in the experimental data, 

although the low frequency variations follow similar trends. Thus, it is expected that the BVI 

noise will not be well predicted by WOPWOP3. For this case, the PVTM method is needed to 

better capture the wake, or a very find grid is needed for the overset grid method to capture the 

far-wake affecting the next blade. 

r/R = 0.965

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 45 90 135 180 225 270 315 360

Azimuth

Lift Coefficient

DNW Test Data

CFD/CSD Coupling

Predictions

r/R = 0.965

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0 45 90 135 180 225 270 315 360

Azimuth

Pitch Moment Coefficient

DNW Test Data

CFD/CSD Coupling

Predictions
29

Figure 2.28 Convergence History for the HART low-speed BVI case 

Figure 2.29 The Blade Loading Comparison at 87% Span Location

r/R = 0.8700

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 90 180 270 360

Azimuth

M2CN

Exp

0 Iteration

1st Iteration

2nd Iteration

3rd Iteration

4th Iteration

5th Iteration

6th Iteration

r/R = 0.8700

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 90 180 270 360

Azimuth

M2CN

Exp

CFD Overset Grid

CAMRAD II
30

For this HART case, the CFD+Free-wake method was also used to predict the BVI effects. 

After a few tests, a refined grid (3.2 million grid points) was used, as shown in Figure 2.30.

The coupling iterations with CSD to get trim solutions were converged after 10 revolutions. 

However, the predicted blade loadings show little of the BVI effects as seen in Figure 2.31.

Figure 2.30 The CFD-Freewake Grid

Figure 2.31 The CFD-Freewake Blade Loading Predictions for HART II Baseline Case

r/R = 0.8700

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 90 180 270 360

Azimuth

M2CN

Exp

CFD FreeWake Prediction
31

Chapter III 

Particle Vortex Transport Method (PVTM) for Accurate Wake Modeling

Modeling the highly nonlinear convective 

wake flow of rotorcraft is challenging. Two

major approaches, full CFD overset grid method 

and free-wake method, were investigated in Task 

1 against a variety set of experimental data with 

different configuration and flight conditions. It 

has been found that, although the overall blade 

loading predictions by CFD are in good 

agreement with the flight test data for a wide range of flight conditions (high-speed, low-speed, 

and dynamic stall), there are limitations in preserving the wake effects away from the blade for 

these two methods, such as numerical dissipation and grid dependency. 

There is another relatively new method to model the vortex, the Vortex Transport Method 

(VTM) [3.1], which solves vortex transport equations to determine the evolution of the vorticity 

field on a uniform Cartesian grid. The approach satisfactorily models the evolution of the 

vorticity field, but the vortex source used is derived from a simplified 2D aerodynamics model.

The Co-I, Dr. Phuriwat Anusonti-Inthra, developed a combined approach under another 

NRA contract (NNL07AA32C). This approach uses CFD in the vicinity of the rotor blades to 

capture the generation of the vortices, and PVTM to resolve the evolution of the vortices in the 

rest of the flow field (see Figure 3.1). The fully-coupled CFD-PVTM is implemented by 

appropriate information exchange between CFD and PVTM at every time step. This approach 

for wake capturing is relatively new and requires further investigation and validation.

In this task work, this fully coupled CFD-PVTM approach was used to model the wake 

flow from the rotor systems. A series of systematic and comprehensive validations was

conducted to assess modeling limitations and to improve the accuracy and efficiency of the 

approach.

Figure 3.1 Gridless PVTM prescribes 

and preserves evolution of wake from 

rotor systems.
32

3.1 UH60 simulations using coupled CII/TURNS/PVTM method

The coupled CII/TURNS/PVTM methodology was applied to simulate two UH60 cases 

(c8534 and c8513). The near body grid for UH60 blade used by TURNS is presented in Fig. 3.2. 

The grid has about 1.6 million points and extends about 1 chord length in all directions, except 

behind the blade tip where the grid only extends about 0.5 chord. The variations in blade 

profiles and twist distribution of the UH60 blade grid are shown in Fig. 3.3. The PVTM cell 

resolution is 0.5 chord. A loose coupling methodology is used to obtain coupled trim solution.

More detailed explanations of the PVTM method and the validation results with UH60 forward 

flight data can be found at Ref. 3.2.

UH60 Case c8534

Simulations for case c8534, μ = 0.34, are obtained using a coarse PVTM grid. The PVTM grid 

for this case is shown in Fig. 3.4. With the PVTM grid, the finest resolution zone covers about 2 

full revolutions of the rotor wake. Nine coupled trim iterations are simulated, and the 

convergence history of the trim parameters and vehicle trust are presented in Figs. 3.5 and 3.6, 

respectively. The results are shown using 5 and 20 TURNS sub-iterations. It is observed that the 

20 sub-iteration run converge to different trim values. The comparison of the resulting rotor 

blade normal force and the experimental data at nine radial stations is presented in Fig. 3.7. 

Overall the correlations of the blade normal force are good for all radial stations. The 20 subiteration results provide slightly better correlation than the 5 sub-iteration results. The vorticity 

field results are shown in Fig. 3.8. Overall, the vorticity fields with 5 or 20 sub-iterations are 

similar, with only slight differences in the vorticity with the wake age of more than 1 revolution.

UH60 Case c8513

For case c8513, μ = 0.1489, the coupled simulations are obtained using 20 sub-iterations for 

TURNS and the same PVTM grid system (Fig. 3.4). The convergence history of the thrust and 

trim parameters are given in Fig. 3.9. The blade normal force correlation between the simulation 

and experimental data as seen in Fig. 3.10 is generally good, except at a few radial stations. 

Another comparison of the normal force predicted from CFD/PVTM simulations with full CFD 

and CFD/free wake results are shown in Fig. 3.11 for completeness. The converged vorticity 

field results from PVTM in Fig. 3.12 show the presence of super-vortices behind the rotor. 
33

Figure 3.2 UH60 blade grid for near body CFD calculation (Dimension: 219×131×55)

Figure 3.3 (a) UH60 blade profiles and (b) blade twist distribution

0.5c

0.957c Blade Pitch (Deg.)

x/c Radial station

z/c 

(a) (b) 
34

Resolution : 2c

Resolution : 1c

Resolution: 0.5c

5R

10R

15R

1.25R

3.75

R

6.25

R

Figure 3.4 Schematic of PVTM grid with multi-resolution zones for c8534

 (a) with 5 sub-iterations (b) with 20 sub-iterations

Figure 3.5 Convergence history for rotor trim parameters (UH-60A c8534)

 

 (a) with 5 sub-iterations (b) with 20 sub-iterations

Figure 3.6 Convergence history for rotor thrust (UH-60A c8534)

-4

-2

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10

COLL

LAT_CYC

LONG_CYC

-4

-2

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10

COLL

LAT_CYC

LONG_CYC

0

5000

10000

15000

20000

25000

0 2 4 6 8 10

CT-CII

CT-CFD

0

5000

10000

15000

20000

25000

0 2 4 6 8 10

CT-CII

CT-CFD

Trim parameters (Deg.)

Iterations

Trim parameters (Deg.)

Iterations

Iterations Iterations

Thrust (lbs)

Thrust (lbs)
35

(a) with 5 sub-iterations for TURNS

(b) with 20 sub-iterations for TURNS

Figure 3.7 Normal force comparison (UH-60A c8534)

(o: flight test data, --- intermediate iterations, --- converged CFD/CSD/PVTM solution)
36

 (a) with 5 sub-iterations (b) with 20 sub-iterations

Figure 3.8 Converged vorticity field (UH-60A c8534)

Figure 3.9 Convergence history of thrust and trim parameters (UH-60A c8513)

0

5000

10000

15000

20000

25000

30000

1 3 5 7 9 11

CII_T

CFD_T

-6

-4

-2

0

2

4

6

8

10

12

1 3 5 7 9 11

COLL

LATCYC

LNGCYC

Iterations

Thrust (lbs)

Trim parameters (Deg.)

Iterations
37

(a) Intermediate CSD/CFD/PVTM results

(b) Converged CSD/CFD/PVTM results

Figure 3.10 Comparison of normal force (UH-60A c8513)

(o: flight test data, ---: intermediate results, ---: converged CFD/CSD/PVTM result)
38

Figure 3.11 Comparisons of normal force (UH-60A c8513)

 (a) Isotropic view (b) Back view

(c) Top view

Figure 3.12 Converged vorticity field (UH-60A c8513)

r/R = 0.55

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Normal Forces (lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid

CFD + PVTM

r/R = 0.775

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Normal Forces (lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid

CFD + PVTM

r/R = 0.865

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Normal Forces (lb/ft)

Flight Test Data

CFD + Free-wake

CFD + Overset Grid

CFD + PVTM

r/R = 0.965

-50

0

50

100

150

200

250

300

350

400

0 90 180 270 360

Azimuth

Normal Forces (lb/ft)

EXP

CFD + Free-wake

CFD + Overset Grid

CFD + Overset Grid (U. of Maryland)

CFD + PVTM
39

3.2 HART II simulations using coupled CII/TURNS/PVTM method

The coupled CII/TURNS/PVTM method was also used to simulate the HART II baseline 

case [3.3], descent condition at μ = 0.15. The near body grid for TURNS and the blade twist 

profile are shown in Figs. 3.13 and 3.14, respectively. A wind tunnel trim is used to simulate the 

wind tunnel test condition. The TURNS grid has about 1.5 million cells. The case is simulated 

using low and high PVTM resolutions.

Low Resolution PVTM

The low resolution PVTM cells are similar to the one described in Fig. 3.4. The convergence 

history of the thrust and trim parameters are shown in Fig. 3.15. The comparisons the normal 

blade force and the experimental data are given in Figs. 3.16-17. The only experimental data 

available is for one radial station (r/R = 0.87). The correlation of the converged low resolution 

result is fair. Some BVI events are observed in the first and fourth quadrants for the blade, but 

the magnitude and duration of the events are very different from the experimental data. Figure 

3.18 shows the converged vorticity field for this case. The super-vortices behind the rotor are 

apparent. 

High Resolution PVTM

The high resolution PVTM grid structure is shown in Fig. 3.19. Two sets of high resolution 

results are presented with different initial conditions: (A) using the converged low resolution 

wake results as the initial condition, (B) using no initial wake. Figures 3.20-22 show the results 

from the Case (A). It is seen that the trim variables obtained from the converged low resolution 

PVTM wake is very different from the required trim for high resolution PVTM wake. However, 

the high resolution results are still not converged. The normal force comparison is presented in 

Figs. 3.21-23. Slightly better correlation is seen for the high resolution results than the low 

resolution results. Case (B) results are presented in Figs. 3.23-24. The converged high resolution 

results and the acoustic predictions were not available for this final report, but they will be sent 

for a paper publication in the future. 
40

Figure 3.13 HART II blade grid

(a) Grid

(b) Blade Profiles (r/R = 0.25, 0.78, 0.99)

Figure 3.14 HART II blade twist profile

r/R

Blade Twist (deg.)

-

Figure 3.15 Trim convergence history (HART II: BL, low resolution)

0

500

1000

1500

2000

2500

3000

3500

4000

1 3 5 7 9 11

CII_T

CFD_T

-4

-3

-2

-1

0

1

2

3

4

5

6

1 3 5 7 9 11

COLL

LATCYC

LNGCYC

Iterations

Iterations

Trim variables (Deg.)

Rotor Thrust (N)
41

Figure 3.16 Comparison of blade normal force (HART II: BL, low res.)

(oo: exp. data, --: intermediate results, --: converged CFD/CSD/PVTM result)

Figure 3.17 Comparison of blade normal force at r/R = 0.87 (HART II: BL, low res.) 

(oo: exp. data, --: intermediate results, --: converged CFD/CSD/PVTM result)
42

(a) View from retreating side (b) view from back

Figure 3.18: Converged vorticity field (HART II: BL, low resolution)

Resolution : 2c

Resolution : 1c

Res: 0.5c

5R

10R

15R

1.25R

3.75

R

6.25

R

Res: 0.25c

2.5R

Figure 3.19 Schematic of PVTM grid with multi-resolution zones for HART high resolution 

results

Figure 3.20 Trim convergence history (HART II: BL, high resolution: A)

(Iteration 1-10: low resolution PVTM, Iterations 11-12: high resolution PVTM)

0

500

1000

1500

2000

2500

3000

3500

4000

1 3 5 7 9 11

CII_T

CFD_T

-4

-3

-2

-1

0

1

2

3

4

5

6

1 3 5 7 9 11

COLL

LATCYC

LNGCYC

Iterations

Iterations

Trim variables (Deg.)

Rotor Thrust (N)
43

Figure 3.21Comparison of blade normal force (HART II: BL, high res. A)

(oo: exp. data, --: intermediate results, --: last CFD/CSD/PVTM result)

Figure 3.22 Comparison of blade normal force at r/R = 0.87 (HART II: BL, high res. A) 

(oo: exp. data, --: intermediate results, --: last CFD/CSD/PVTM result)
44

Figure 3.23 Comparison of blade normal force (HART II: BL, high res. B)

(oo: exp. data, --: intermediate results, --: last CFD/CSD/PVTM result)

Figure 3.24Comparison of blade normal force (HART II: BL, high res. B)

(oo: exp. data, --: intermediate results, --: last CFD/CSD/PVTM result)
45

Chapter IV

Improved and Efficient Interfaces with Acoustic Propagation Codes

Another essential element in the MUTE 

tool is the noise prediction code. Currently, 

WOPWOP3 (also known as PSU-WOPWOP) 

[4.1] provided by LaRC has been used as the main 

acoustic propagation code. WOPWOP3 solves 

Farassat’s retarded-time formulation 1A [4.2] of the 

Ffowcs Williams-Hawkings (FW-H) equation [4.3]

and computes the rotorcraft tone noises at given 

observer locations. These predictions are based on 

the blade loading on an impermeable surface or the unsteady flow-field description provided by 

CFD simulations on a permeable surface. 

The traditional method of rotor noise propagation uses the rotor blade surface as the 

acoustic data surface, which is an impermeable surface. The blade loadings provided either by 

the comprehensive analysis (CAMRAD II) or the CFD (TURNS) are integrated into the FW-H 

equation as the source term. This method can accurately propagate and calculate the thickness 

noise and loading noise. However, for high-speed cases when the high-speed impulsive (HSI) 

noise is dominant, errors are incurred with the impermeable surface method because the 

quadrupole term from the shock wave interaction is not included in the FW-H equation. Thus, 

recently, a new method of using the FW-H equation on a permeable surface was developed 

within WOPWOP3. With the availability of an accurate flow-field description on this permeable 

acoustic data surface from the CFD, it is expected that this approach would improve the accuracy 

of the HSI noise prediction. 

Without Control With High Harmonic Control

MAX

MIN

Figure 4.1 Noise propagation from the 

rotorcraft to observer locations 

in the far-field.
46

4.1 DNW High-Speed Noise Prediction with Impermeable Surface Method and Permeable 

Surface Method

Figure 4.2 The Acoustic Experiments Set-up (Microphone 1 & 7)

In the DNW High-speed data set [4.4], some acoustic measurements are also available for 

comparison. As seen in Figure 4.2, the acoustic sound pressure from two microphones 

(Microphone 1 and 7) are recorded. These two microphones are located ahead of the rotor blade

and in the same plane as the rotor surface. 

Figures 4.3 and 4.4 show the time averaged total sound pressure read by microphones 1 

and 7 in the experiments. In these two figures, the high-negative peak is due to compressible

flow effects which include the shock wave interactions. The noise due to vortex interactions can 

also be seen clearly in the experiments

Mic. 1

3R

3R

30o

Mic. 1

Mic. 7

Wind

Top View

Mic. 7

3R

Side View R

Mic. 1

3R

3R

30o

Mic. 1

Mic. 7

Wind

Top View

Mic. 7

3R

Side View R
47

Figure 4.3 The Sound Pressure of Microphone 1

Figure 4.4 The Sound Pressure of Microphone 7

-100

-50

0

50

100

0 0.25 0.5 0.75 1

Normalized Time

Sound Pressure (Pa)

EXP-Microphone 1

-100

-50

0

50

100

0 0.25 0.5 0.75 1

Normalized Time

Sound Pressure (Pa)

EXP-Microphone 7

HSI Noise

Noise due to vortex

HSI Noise

Noise due to vortex
48

Two acoustic prediction methods were used for the acoustic analysis with WOPWOP3, 

the impermeable surface method and the permeable surface method. At first, the impermeable 

surface method with compact loading patches is used, where the blade surface loadings (Cn, Cd 

and Cm) at each span location are used to obtain the acoustic signature at the observer points. 

As shown in Figure 4.5 and 4.6 for both Microphone 1 and 7, the impermeable surface method 

with loading patches did not fully capture the negative peak of the HSI noise, nor did it capture

any of the vortex effects. Subsequently, the impermeable surface method with surface pressure 

as input was used, as shown in Figures 4.7 and 4.8. When using the surface pressure for the 

impermeable surface method, some improvement of the negative noise peak predictions was

obtained. However, it is still below the measured levels and the noise due to the vortex effects 

was not captured. 

For the permeable surface method, four different sized acoustic data surfaces (ADS) were

chosen to investigate the effects of the permeable surface size on the acoustic predictions. The 

first one, referred to as the original surface, was used to verify the accuracy of the interface codes 

and WOPWOP3 input. The grid index range of the original surface, relative to the near-body 

CFD grid, is: I from 11 to 123, J from 5 to 125, K from 1 to 43, where I is the chord-wise 

direction, J span-wise and K normal directions. Three additional ADS grids were extracted from 

the CFD solutions for comparisons, where for Grid 1, I ranged from 9 to 125, J from 5 to 125 

and K from 1 to 44; for Grid 2 I from 8 to 126, J from 5 to 125 and K from 1 to 43; and for Grid 

3 I from 10 to 124, J from 5 to 125 and K from 1 to 43. Figures 4.9--4.11 show the comparison 

of the original grid with the additional grids. The acoustic predictions at Microphone 1 for these 

four acoustic data surfaces are compared at Figure 4.12. It is seen that the predictions using the 

original surface and Grids 2 and 3 are quite similar. However, a larger negative peak is predicted 

with Grid 2 because the aft surface of Grid 2 surface is farther away from the blade trailing edge

and can capture more of the shock wave effects. The Grid 1 predictions are the least accurate, 

owing to the larger distance from the blade surface and therefore greater numerical dissipation of 

the aerodynamic input. 

For the permeable surface method, Figure 4.13 shows the prediction comparisons at

Microphone 1. It is clearly seen that the permeable surface method can not only capture the high 

negative peak due to the shock wave interaction, but can also capture a good deal of the noise 

due to the vortex interaction. Figure 4.14 shows the comparisons for Microphone 7 predictions. 
49

Here, the predictions for the permeable surface method are much better than those obtained with 

the impermeable surface method, as the noise due to the vortex interactions is also captured.

Figure 4.5 The Sound Pressure Comparison for Microphone 1 (Compact Patch Method)

Figure 4.6 The Sound Pressure Comparison for Microphone 7 (Compact Patch Method)

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

No-dimension Time

Sound Pressure-Total

Impermeable Surface Method -

Loading

EXP - Microphone 1

-100

-80

-60

-40

-20

0

20

40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

No-dimension Time

Sound Pressure-Total

Impermeable Surface Method -

Loading

EXP - Microphone 7
50

Figure 4.7 The Sound Pressure Comparison for Microphone 1 

(Impermeable Surface method with Surface Pressure as Input)

Figure 4.8 The Sound Pressure Comparison for Microphone 7 

(Impermeable Surface method with Surface Pressure as Input)

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

No-dimension Time

Sound Pressure-Total

EXP - Microphone 1

Impermeable Pressure

-100

-80

-60

-40

-20

0

20

40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

No-dimension Time

Sound Pressure-Total

EXP - Microphone 7

Impermeable Pressure
51

Figure 4.9 Acoustic Data Surface (Baseline-Grid vs. Grid 1)

(Red – Blade; Blue – Baseline Grid; Yellow – Grid 1)

Figure 4.10 Acoustic Data Surface (Baseline-Grid vs. Grid 2)

(Red – Blade; Blue – Baseline Grid; Yellow – Grid 2)
52

Figure 4.11 Acoustic Data Surface (Baseline-Grid vs. Grid 3)

(Red – Blade; Blue – Baseline Grid; Yellow – Grid 3)

Figure 4.12 WOPWOP3 Predictions for Different Acoustic Surface Grids

-100

-80

-60

-40

-20

0

20

40

0 0.01 0.02 0.03 0.04

Time

Sound Pressure-Total

Baseline Grid

Grid No. 1

Grid No. 2

Grid No. 3

EXP
53

Figure 4.13 The Sound Pressure Comparison for Microphone 1 

(Permeable Surface Method)

Figure 4.14 The Sound Pressure Comparison for Microphone 7 

(Permeable Surface Method)

-100

-80

-60

-40

-20

0

20

40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

No-dimension Time

Sound Pressure-Total

Permeable Surface Method

EXP - Microphone 1

-100

-80

-60

-40

-20

0

20

40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

No-dimension Time

Sound Pressure-Total

Permeable Surface Method

EXP - Microphone 7
54

4.2 HART II BVI Noise Prediction with Impermeable Surface Method 

The aerodynamic parameters on the blade surface predicted with the above methods 

(CFD+Overset grid, CFD+Free-wake method and CFD+PVTM) were used to obtain the acoustic 

signature on a microphone plane below the rotor disk. Because the BVI noise is mostly 

generated by the blade surface pressure variation, the impermeable surface method was used to 

predict the acoustic signature on the microphone plane. As mentioned above, there are two 

different approaches available in WOPWOP3 for specifying the blade loading for the 

impermeable surface method. One uses the surface loading compact patch, while the other uses

the surface pressure directly. Both of these methods were investigated to check the accuracy of 

the acoustic predictions. 

Figure 4.15 shows the BVI SPL contour from the experiment [4.5], where the two BVI noise 

peaks on the advancing side and retrieving side are clearly observed. The microphone plane in 

the experiment, located 2.215 meter below the rotor disk with an area of 8 meter * 5.4 meter, is 

also presented here.

Figure 4.16 shows the BVI SPL contours from the CFD+Overset Grid method, where 4.16(a) 

is the acoustic signature obtained using compact patches and (b) is from the pressure. As shown 

in these figures, the BVI noise predicted with the overset grid method does not generally agree 

well with the measured data, due at least in part to insufficient grid resolution. Also, the 

maximum noise predicted with the compact patches is well below the levels obtained using the 

surface pressure. This indicates that the compact patch method may not be optimal in this case.

Figure 4.17 shows the contours from the CFD+Free-wake method, where the noise peak 

location also differs from that seen in the experiment. As shown in Figure 4.17(b), two 

maximum BVI noise peaks are predicted by the CFD+Free-wake method. However, the 

directivity (i.e., locations of the maximum noise levels) differs from the experiment

measurements. Additionally, the predicted maximum BVI noise levels are also lower than the 

experimental values.

Figure 4.18 shows the contours from the CFD+PVTM method with a low resolution wake. 

Again, the acoustic prediction using the compact patch approach does not compare well with 

measured levels as seen in Figure 3.18(a). However, using the surface pressure produces 

predicted BVI SPL contours that compare much more favorably with measurement for both the 
55

maximum noise level and location on the advancing and retrieving side. Direct comparison with 

the measured contours is shown in Figure 3.19 for further clarification. 

Figure 4.15 Experiment BVI SPL Contour

(a) Loading (b) Pressure

Figure 4.16 BVI SPL Contour with CFD+Overset Grid Method 

Tunnel Wind

4 m

4 m

2.7 m 2.7 m

Rotor Disk Plane

 (Radius = 2m)

Microphone Plane

2.215 m below the 

rotor disk 
56

 (a) Loading (b) Pressure 

Figure 4.17 BVI SPL Contour with CFD+Free-wake Method

 (a) Loading (b) Pressure

Figure 4.18 BVI SPL Contour with CFD+PVTM Low Resolution Wake
57

 (a) Experiments (b) PVTM_Low_Resolution_Wake

Figure 4.19 BVI SPL Contours Comparison with PVTM Method
58

Chapter V 

Concluding Remarks

This report summarizes the development and validation of a physics-based, high-fidelity, 

multi-disciplinary tool (MUTE) for accurate and efficient rotorcraft source noise predictions. The 

MUTE tool uses a systematic coupling of approaches from multiples disciplines. Namely,

Computational Fluid Dynamics with high-order near body algorithm(STVD 6), a high-fidelity 

wake modeling method (PVTM), Computational Structural Dynamics with feedbacks of the

rotor blade deformation information and rotorcraft trim solutions, and high-fidelity acoustic 

propagation analysis are incorporated. This coupled CFD/CSD/Acoustic procedure is very 

flexible and controlled by a Python script, so that these CFD, CSD or acoustic modules can be 

easily replaced with other similar codes. The blade loading and acoustic signature predictions 

from the MUTE tool are compared with several experimental data sets with a wide range of 

flight conditions.

Three wake modeling/capturing methods were implemented into the MUTE tool for 

different applications: the free-wake method, the overset grid CFD method, and the PVTM 

method. The rotor blade aerodynamic loading predictions from these wake modeling methods 

were validated against the UH60A, DNW and HART II experimental data under high-speed, 

low-speed, and BVI flight conditions. The results show that, in the absence of strong blade 

vortex interaction, all wake modeling methods produced reasonable blade loading predictions 

when the CFD was coupled with CSD to include the blade deformation information (e.g., such as 

the UH60A level flight test cases). 

Compared with the other wake modeling methods investigated, the free-wake method 

was the fastest for most of the high-speed and low-speed level flight simulations when the blade 

wake interaction was minimal. The CFD with overset-grid method would generally be the most 

robust method over the flight conditions considered. This would also be the case for the UH60a 

dynamic stall condition, provided the CFD background grid resolution was high enough to 

preserve the far wake traveling inside the computational zone. However, this would require

considerably more computational resources. With the limited computational resources employed 

in this study, the PVTM method was the most efficient way to preserve the wake in the far-field 
59

and accurately predict the rotor wake blade interactions. For the HART II BVI test case, only the 

PVTM method gave reasonable predictions at coarse resolution. 

The acoustic predictions of the MUTE tool were also validated with the DNW high-speed 

noise experimental data and the HART II BVI noise data. In the acoustic analysis using 

WOPWOP 3, both the impermeable surface and permeable surface methods were implemented 

into the MUTE tool. The results show that, to capture the high-speed impulsive noised produced 

by the shock wave interaction, the permeable surface method gave much better predictions than 

the impermeable surface method. For the BVI noise prediction, the wake modeling method was

very important for the accuracy of the noise predictions, and the PVTM method gave the best 

results for this case.

The accuracy of the noise prediction was greatly dependent on the accuracy of the blade 

loading and rotor wake simulation results. Compared to the very fine grid resolution

requirements of the CFD overset grid method, the PVTM method has more potential in 

accurately modeling the rotor blade and wake interactions with reasonable computational 

resource requirements. Improvements of the PVTM computational efficiency may significantly 

improve the correlations and further reduce the computational costs in the future. 
60

References

Chapter II

[2.1] Srinivasan, G. R., and Baeder, J. D., “TURNS: A Free-wake Euler/Navier-Stokes 

Numerical Method for Helicopter Rotors,” AIAA Journal Vol. 31, No. 5, 1993, pp. 959-962.

[2.2] Johnson, W., “Rotorcraft Aerodynamic Models for a Comprehensive Analysis,” 

Proceedings of the American Helicopter Society Annual Forum, Washington DC, 1998.

[2.3] Anusonti-Inthra, P., “Developments and Validations of Fully Coupled CFD and Particle 

Vortex Transport Method for High-Fidelity Wake Modeling in Fixed and Rotary Wing 

Applications,” NASA CR-2010-21669.

[2.4] Lee, S., Brentner, K. S., Hennes, C. C., Flynt, B. T., Theron, J. N., and Duque, E. P. N., 

“Investigation of the Accuracy Requirements for Permeable Surfaces Used in Rotor Noise 

Prediction,” Proceeding of American Helicopter Society 62nd Forum, Phoenix, AZ, May, 2006.

[2.5] Roe, P. L. “Approximate Riemann Solvers, Parameter Vectors and Difference Schemes,” J. 

of Computational Physics, vol. 42, pp. 357-372, 1981

[2.6] Van Leer, B. “Towards the Ultimate Conservation Difference Scheme, V: A Second-Order 

Sequel to Godunov’s Method,” J. of Computational Physics, vol. 32, pp. 101-136, 1979.

[2.7] Jiang, G., and Shu, C.-W., “Efficient Implementation of Weighted ENO Schemes,” J. of 

Computational Physics, vol.126, pp202-228, 1096

[2.8] Yee, H. C., Sandham, S. D., and Djomehri, M. J., “Low Dissipative High-order Shockcapturing Method Using Characteristic Based Filters,” J. of Computational Physics, vol. 150, 

pp.199-238, 1999.

[2.9] Lorber, P. F., “Aerodynamic Results of a Pressure-Instrumented Model Rotor Test at the 

DNW,” Journal of the American Helicopter Society, Vol. 36, No. 4, 1991, pp. 66-76.

[2.10] Lorber, P. F., Stauter, R. C., and Landgrebe, A. J., “A Comprehensive Hover test of the 

Airloads and Airflow of an Extensively Instrumented Model Helicopter Rotor,” American 

Helicopter Society 45th Annual Forum, Boston, MA, May, 1989.
61

Chapter III

[3.1] Brown, R. E., “Rotor Wake Modeling for Flight Dynamic Simulation of Helicopters,” 

AIAA Journal, Vol. 38, No. 1, pp.57-63, 2000.

[3.2] Anusonti-Inthra, P. “Validations of Coupled CSD/CFD and Particle Vortex Transport 

Method for Rotorcraft Applications: Hover, Transition, and High Speed Flights” Proc. 66th AHS 

Forum, Phoenix, AZ, 2010.

[3.3] van der Wall, B. G., Junker, B., Burley, C. L., Brooks, T. F., Yu, Y., Tung, C., Raffel, M, 

Richard, H., Wagner, W., Mercker, E., Pengel, K., Holthusem, H., Beaumier, P., and Delrieux, 

Y., “The HART II Test in the LLF of DNW – a Major Step towards Rotor Wake 

Understandings,” 28th European Rotorcraft Forum, Bristol, England, 2002.

Chapter IV

[4.1] Brentner, K. S., Perez, G., Bres, G. A., and Jones, H. E., “Maneuvering Rotorcraft Noise 

Predictions,” Journal of Sound and Vibration, Vol. 39, No. 3-5, August, 2003, pp. 719-738.

[4.2] Farassat, F. and Succi, G. P., “The Prediction of Helicopter Discrete Frequency Noise,” 

Vertica, Vol. 7, No. 4, 1983, pp. 309-320.

[4.3] Ffowcs Williams, J. E., and Hawkings, D. L., “Sound Generated by Turbulence and 

Surfaces in Arbitrary Motion,” Philosophical Transactions of the Royal Society, Vol. A264, No. 

1151, 1969, pp. 321-342.

[4.4] Visintainer, J. A., Marcolini, M. A., Burley, C. L., and Liu, S. R., “Acoustic Predictions 

Using Measured Pressure from a Model Rotor in the DNW,” Journal of American Helicopter 

Society, Vol. 38, No. 3, 1993, pp.35-44.

[4.5] Boyd, D. D., Burley, C. L., and Conner, D. A., “Full Scale Rotor Acoustic Predictions 

and the Link to Model Scale Rotor Data,” AHS 4th Decennial Specialist’s conference on 

Aeromechanics, San Francisco, CA, Jan. 2004.
REPORT DOCUMENTATION PAGE Form Approved

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Development and Validation of a Multidisciplinary Tool for Accurate and 

Efficient Rotorcraft Noise Prediction (MUTE) 

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NNL07AA31C

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Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

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NASA Langley Research Center National Institute of Aerospace

Hampton, VA 23681-2199 100 Exploration Way

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14. ABSTRACT

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise is developed and validated with a 

wide range of flight configurations and conditions. MUTE is an aggregation of Computational Fluid Dynamics (CFD), Computational

Structural Dynamics (CSD), and high-fidelity acoustics tools. Predictions of the blade-vortex interaction noise in low speed flight are also 

improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and 

fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD 

and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the 

analysis. The blade loading information and/or flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach 

are used to obtain the acoustic signatures at far-field observer locations. Blade aerodynamic loadings and far-field acoustic predictions are 

compared and validated with a variation of experimental data sets. 

15. SUBJECT TERMS

Computational fluid dynamics; Computational structural dynamics; Particle Vortex Transport Method; Rotary wing aircraft; 

Acoustics; Noise prediction; Blade-vortex interaction

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