StarCCM - AeroAcoustics

7/27/2019 StarCCM - AeroAcoustics

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Acoustics and Turbulence:

Aerodynamics Applications of STAR-

CCM

Milovan Peri!


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Use of STAR-CCM+ for aerodynamics applications

Which turbulence model for which application?

Simulation of acoustics phenomena with STAR-CCM+

Best-practice guidelines

Examples of application

Future developments

Introduction

This presentation is based on reports prepared by CD-adapco experts

for Vehicle Aerodynamics(Fred Ross), Defence and Aerospace

(Deryl Snyder) and Acoustics(Fred Mendonca).


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Vehicle aerodynamics (cars, trucks, sport vehicles)

Train aerodynamics

Aerodynamics of aircraft and rotorcraft

Military applications (airplanes, missiles)

Flow around buildings etc.

Main aims of simulation:

Predict mean forces and moments (optimize geometry)

Predict unsteady loads (reduce vibrations)

Predict turbulence structure (minimize noise)

Use of STAR-CCM for Aerodynamics


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STAR-CCM+ offers many turbulence models (eddy-viscositytype, Reynolds-stress, transition, LES/DES)

CD-adapco collaborates with experts in academia to further

develop turbulence models

Optimal model choice depends on flow under considerationand the aim of simulation

Eddy-viscosity type models are usually suitable to predict

mean forces and moments

Reynolds-stress model predicts better flows with swirling

and turbulence-driven secondary flows

LES/DES type models are capable of predicting all flow

details (including acoustics), but are more costly

Which Turbulence Model?


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Coupled and segregated solver in STAR-CCM+ differ in

discretization (results not the same)

Coupled solver is recommended for steady-state flows

exhibiting strong coupling between variables (compressi-

bility, buoyancy).For transient flows, segregated solver is usually more

efficient

It is also more accurate when computing propagation of

acoustic waves

Double precision is sometimes important for acoustics

computations

Which Solver Type?


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Steady-state computations often do not fully converge

The reason is usually inherent local flow unsteadiness

Fine grids resolving details of geometry and 2nd-orderdiscretization capture the flow instability

Averaging intermediate solutions over a range of iterationsis unreliable (especially if residuals are high).

Recommended approach:

Switch to transient segregated solver;

Select time step to resolve the fluctuations of interest;

Average the result over few periods of oscillation

Which Set-Up?


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Overview of acoustics tools in STAR-CCM+

Acoustics in STAR-CCM , I

Aeroacoustics Simulation Options

Steady state Transient

Broadband

Correlations

Synthesized

Fluctuations SNGR

CURLE surface

PROUDMAN volume

GOLDSTEIN 2D-axi

LEE

Lilley

Mesh Frequency Cut-off

LES

DES

Transient RANS

Point/Surface FFTs and iFFTs

Auto and Cross Spectra coherence and phase

FW-H

Export to propagation codes

Export to

Propagation codes

Direct Noise Propagation

1D and 2D) Wavenumber analysis


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Essential features for transient analysis in STAR-CCM+:

Suitable turbulence models (LES, DES)

Non-reflecting boundary conditions (inlet, outlet, far field)

Accurate computation of compressible flow at low Mach no.

Reliable estimate of cut-off frequency on given mesh (a guide

for mesh resolution)

Spectral analysis:

FFT at points and surfaces

Auto- and cross-spectra

Frequency and wavenumber Fourier analysis

Acoustics in STAR-CCM , II


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Validation: Generic side view mirror (Daimler; Univ. of Southampton)

Acoustic Sources From DES, I

Volume shape used to controlgrid refinement in the wake of

mirror for a DES-study


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Validation: Generic side view mirror, grid at bottom plate

Acoustic Sources From DES, II


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Validation: Generic side view mirror, grid in symmetry plane (2 mmresolution in the near-mirror zone)

Acoustic Sources From DES, III


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Validation: Generic side view mirror, flow visualization

Acoustic Sources From DES, IV


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Wavenumber Analysis

a+ a-

u-

a+ a-

u+

1D wavenumber-frequency diagram:- Separated wake region (upper)

- Attached wake region (lower)

2D wavenumber analysis Power SpectralDensity (PSD) in wavenumber space:

- Advection ridge (left)- Acoustic circle (right)


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Under-relaxation in segregated solver can be interpreted as

marching in a pseudo-time (one iteration per step)

For Implicit Euler time integration, the relation is:

A constant under-relaxation factor corresponds to a variable

time step and vice versa

Sometimes one can obtain steady-state solution easier bymarching in physical time (using transient method and 1-2

iterations per time step) than in steady mode

Time Step and Under-Relaxation, I


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When solving transient problems with sufficiently small time

steps, under-relaxation is not needed

For typical aero-acoustic studies using segregated solver,

the recommended under-relaxation settings are:

For all transport equations (velocities, temperature and other

scalar equations): 1.0

For the pressure-correction equation: 0.5 to 1.0 (smaller

values for highly non-orthogonal grids).

The recommended number of iterations per time step is 2 to4 (depending on time-step size and grid quality).

Time Step and Under-Relaxation, II


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The reduction of residuals is not a suitable measure for

convergence of iterations within time step

For small enough time steps, iterations are not necessary (explicit

methods)

One can verify by numerical experiments how many iterations are

needed

Number of Iterations per Time Step

10 It/dt

2 It/dt

Propagation of an acoustic wave (20 cells per wavelength,20 time steps per period)


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Steady-state RANS computations provide results suitable foroptimization studies:

Mean forces and moments

Effects of shape change

Parametric studies (speed, angle etc.)

Best practice developed for different vehicle types (F1,

commercial cars, trucks, motocycles):

Grid design (refinement zones, cell size distribution, prism

layer parameters)

Turbulence model

Solver setup

Vehicle Aerodynamics: Steady RANS, I


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Personal recommendation for fine grids: Design the finest grid according to requirements and available

resources, using Base Size as the parameter.

Increase the base size by a factor of 8 and generate the coarse

grid first; start computation on this grid using default set-up

parameters (under-relaxation, CFL-number) and a reasonablelimit on the number of iterations.

Then reduce the base size by a factor of 2, generate finer grid

and continue computation (the solution will be automatically

mapped to the new grid), but increase under-relaxation or CFL-

number. Repeat until the base size of the original fine grid is reached.

Vehicle Aerodynamics: Steady RANS, II


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Computation on a series of grids requires substantially lesscomputing time (2-4 times less) and provides a set of

solutions on different grids, allowing error estimate

Instead of a factor of 2, one can use any fixed number

between 1.5 and 2.

For a second-order method, the error on the finest grid can

be estimated as

If the base size ratio between coarser and finer grid is not 2,

the actual ratio should be used instead of 2.

Vehicle Aerodynamics: Steady RANS, III


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Vehicle Aerodynamics: Steady RANS, IV

Example: Flow around a 3D wing attached to a wall

4 grid levels, base size ratio 2

Finest grid 460000 polyhedral cells

Section parallel to wall

Section normal

to wall

Wall


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Vehicle Aerodynamics: Steady RANS, V

Example: Flow around a 3D wind attached to a wall

Segregated solver Coupled solver


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Vehicle Aerodynamics: Steady RANS, VI

0.3

0.4

0.5

0.6

0.7

0.8

-15 -10 -5 0 5 10 15

Exp

STAR-CCM+

Effect of yaw angle ondrag of a truck

Effect of underbody

geometry on drag of

a car


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DES-analysis provides:

Insight into flow features and unsteady phenomena (separation,

vortex shedding, pulsation)

Noise sources

DES is the most accurate approach, but too costly for parametric

studies

Vehicle Aerodynamics: DES, I


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Vehicle Aerodynamics: DES, II

DES of flow around a truck: details of flow structure in one vertical

and one horizontal section (vorticity)


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Comparison with experiment is often difficult

Boundary conditions need to be matched for a fair comparison

Vehicle Aerodynamics: DES, III

Wind tunnel

effects


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University of Washington wind tunneltest configuration

Excellent agreement between

simulation and experiment for all flap

configurations

F16 Validation Study


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Mach 0.2, transition model, 34 million poly-cells, 25 prism layers

AIAA HiLiftWS1-Configuration, I


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Comparison of measured and predicted lift

AIAA HiLiftWS1-Configuration, II

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Workshop conclusions:

Modeling laminar-turbulent transition is important - simple RANS

models do not produce good enough results

Local grid refinement at wing tip is important - otherwise tip vortex is

not well captured

AIAA HiLiftWS1-Configuration, II

TransitionAoA=13

AoA=21


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Hub drag is 30% of the total

Need good resolution of geometry details CAD to mesh in

a day for each of two geometries

Need transient simulation to account for rotation

Rotorcraft Hub Drag, I

Sikorsky UH-60A HubSikorsky S-92A Hub


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Surface-wrapper provides high geometric fidelity

Rotorcraft Hub Drag, II


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Trimmed grid with prism layers and a sliding interface, ca.15 million cells

Rotorcraft Hub Drag, III


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DES, time step 5 (too large for acoustics, but enough for

forces).

Rotorcraft Hub Drag, IV

PressureVelocity Magnitude

UH-60AS-92A

UH-60AS-92A


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Studied were variations in drag

with adding complexity

Results good for optimization

purposes

Rotorcraft Hub Drag, V

S-92A

UH-60A

From:M.Dombroski&T.A.Egolf,68thAnnualForum,AmericanHelicopter,FortWorth,TX

May1-3,2012.


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Simulation of store separation using overset grids a validation

study

Store Separation, I


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Good agreement between simulation and experiment

Store Separation, II

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Real application

Store Separation, III


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Acoustics Application, Vehicles

Surface FFT (dB) at 500Hz(top) and 1000Hz (bottom)


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Acoustics Application, Airplanes

Noise generation during landing by:

-

Wings-

Landing gear

Pressure fluctuation around airfoil Velocity variation around landing gear


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Numerics:

Higher-order discretization

Automatic adaptive mesh refinement

Turbulence:

Improvements to RANS-models (curvature correction, law of

the wall) Improvements to DES-model (transition from RANS to LES)

Vibro-acoustics:

Wavenumber analysis

Coupling of flow and structure

Possibly solving special set of equations for noise propagation

Future Developments

Documents

StarCCM - AeroAcoustics