starccm - aeroacoustics
TRANSCRIPT
-
7/27/2019 StarCCM - AeroAcoustics
1/40
Acoustics and Turbulence:
Aerodynamics Applications of STAR-
CCM
Milovan Peri!
-
7/27/2019 StarCCM - AeroAcoustics
2/40
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).
-
7/27/2019 StarCCM - AeroAcoustics
3/40
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
-
7/27/2019 StarCCM - AeroAcoustics
4/40
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?
-
7/27/2019 StarCCM - AeroAcoustics
5/40
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?
-
7/27/2019 StarCCM - AeroAcoustics
6/40
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?
-
7/27/2019 StarCCM - AeroAcoustics
7/40
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
-
7/27/2019 StarCCM - AeroAcoustics
8/40
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
-
7/27/2019 StarCCM - AeroAcoustics
9/40
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
-
7/27/2019 StarCCM - AeroAcoustics
10/40
Validation: Generic side view mirror, grid at bottom plate
Acoustic Sources From DES, II
-
7/27/2019 StarCCM - AeroAcoustics
11/40
Validation: Generic side view mirror, grid in symmetry plane (2 mmresolution in the near-mirror zone)
Acoustic Sources From DES, III
-
7/27/2019 StarCCM - AeroAcoustics
12/40
Validation: Generic side view mirror, flow visualization
Acoustic Sources From DES, IV
-
7/27/2019 StarCCM - AeroAcoustics
13/40
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)
-
7/27/2019 StarCCM - AeroAcoustics
14/40
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
-
7/27/2019 StarCCM - AeroAcoustics
15/40
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
-
7/27/2019 StarCCM - AeroAcoustics
16/40
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)
-
7/27/2019 StarCCM - AeroAcoustics
17/40
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
-
7/27/2019 StarCCM - AeroAcoustics
18/40
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
-
7/27/2019 StarCCM - AeroAcoustics
19/40
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
-
7/27/2019 StarCCM - AeroAcoustics
20/40
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
-
7/27/2019 StarCCM - AeroAcoustics
21/40
Vehicle Aerodynamics: Steady RANS, V
Example: Flow around a 3D wind attached to a wall
Segregated solver Coupled solver
-
7/27/2019 StarCCM - AeroAcoustics
22/40
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
-
7/27/2019 StarCCM - AeroAcoustics
23/40
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
-
7/27/2019 StarCCM - AeroAcoustics
24/40
Vehicle Aerodynamics: DES, II
DES of flow around a truck: details of flow structure in one vertical
and one horizontal section (vorticity)
-
7/27/2019 StarCCM - AeroAcoustics
25/40
Comparison with experiment is often difficult
Boundary conditions need to be matched for a fair comparison
Vehicle Aerodynamics: DES, III
Wind tunnel
effects
-
7/27/2019 StarCCM - AeroAcoustics
26/40
University of Washington wind tunneltest configuration
Excellent agreement between
simulation and experiment for all flap
configurations
F16 Validation Study
-
7/27/2019 StarCCM - AeroAcoustics
27/40
Mach 0.2, transition model, 34 million poly-cells, 25 prism layers
AIAA HiLiftWS1-Configuration, I
-
7/27/2019 StarCCM - AeroAcoustics
28/40
Comparison of measured and predicted lift
AIAA HiLiftWS1-Configuration, II
!
!#$
%
%#$
&
$
'
'#$
($ ! $ %! %$ &! &$ '! '$ )!
*+
,-./0 12 ,33456 780.900:;
09?@0-3
AB,C(**DEF D0G?H@
-
7/27/2019 StarCCM - AeroAcoustics
29/40
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
-
7/27/2019 StarCCM - AeroAcoustics
30/40
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
-
7/27/2019 StarCCM - AeroAcoustics
31/40
Surface-wrapper provides high geometric fidelity
Rotorcraft Hub Drag, II
-
7/27/2019 StarCCM - AeroAcoustics
32/40
Trimmed grid with prism layers and a sliding interface, ca.15 million cells
Rotorcraft Hub Drag, III
-
7/27/2019 StarCCM - AeroAcoustics
33/40
DES, time step 5 (too large for acoustics, but enough for
forces).
Rotorcraft Hub Drag, IV
PressureVelocity Magnitude
UH-60AS-92A
UH-60AS-92A
-
7/27/2019 StarCCM - AeroAcoustics
34/40
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.
-
7/27/2019 StarCCM - AeroAcoustics
35/40
Simulation of store separation using overset grids a validation
study
Store Separation, I
-
7/27/2019 StarCCM - AeroAcoustics
36/40
Good agreement between simulation and experiment
Store Separation, II
! # $%$$
! # $%&'
! # $%()
-
7/27/2019 StarCCM - AeroAcoustics
37/40
Real application
Store Separation, III
-
7/27/2019 StarCCM - AeroAcoustics
38/40
Acoustics Application, Vehicles
Surface FFT (dB) at 500Hz(top) and 1000Hz (bottom)
-
7/27/2019 StarCCM - AeroAcoustics
39/40
Acoustics Application, Airplanes
Noise generation during landing by:
-
Wings-
Landing gear
Pressure fluctuation around airfoil Velocity variation around landing gear
-
7/27/2019 StarCCM - AeroAcoustics
40/40
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