Acoustics a and nd T Turbule lenc nce: : Aerodyna ynami mics A Appli lications ns o of S STAR- CCM+ CCM+ Milovan Peri ć
Int Introduction n � 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 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).
Us Use o of S STAR-C -CCM+ f for A Aerodyna ynami mics � 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)
Whi hich T h Turbule lenc nce M Model? l? � STAR-CCM+ offers many turbulence models (eddy-viscosity type, Reynolds-stress, transition, LES/DES…) � CD-adapco collaborates with experts in academia to further develop turbulence models… � Optimal model choice depends on flow under consideration and 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…
Whi hich S h Solv lver T Typ ype? � 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…
Whi hich S h Set-U -Up? � Steady-state computations often do not fully converge… � The reason is usually inherent local flow unsteadiness… � Fine grids resolving details of geometry and 2 nd -order discretization capture the flow instability… � Averaging intermediate “solutions” over a range of iterations is 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…
Acoustics i in S n STAR-C -CCM+, I , I � Overview of acoustics tools in STAR-CCM+ Ae Aero roaco coust stics ics Simu Simula latio ion Optio ions s St Steady y st state Tra ransie sient Synthesize Syn sized Bro Broadband ES LES Flu luct ctuatio ions s SN SNGR Corre rrela latio ions s ES DES EE LEE CURLE su ce CURLE surf rface ANS Tra ransie sient RAN illey Lille MAN vo me PR PROUDMAN volu lume Poin Po int/Su Surf rface ce FFTs s and iF iFFTs GOLDST STEI EIN 2D-a -axi xi Exp Export rt to Au Auto and Cro ross ss Sp Spect ctra ra – – co cohere rence ce and phase se Propagatio Pro ion co codes s 1D (a (and 2D) ) Wave venumb mber r analysis lysis Mesh Me sh Fre requency cy Cut-o -off FW-H -H Dire irect ct Noise ise Pro Propagatio ion Exp Export rt to pro ropagatio ion co codes s
Acoustics i in S n STAR-C -CCM+, II , II � 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
Acoustic S Sources F From DE m DES, I , I � Validation: Generic side view mirror (Daimler; Univ. of Southampton) Volume shape used to control grid refinement in the wake of mirror for a DES-study
Acoustic S Sources F From DE m DES, II , II � Validation: Generic side view mirror, grid at bottom plate
Acoustic S Sources F From DE m DES, III , III � Validation: Generic side view mirror, grid in symmetry plane (2 mm resolution in the near-mirror zone)
Acoustic S Sources F From DE m DES, IV , IV � Validation: Generic side view mirror, flow visualization
Wavenu numb mber A Ana nalys lysis 1D wavenumber-frequency diagram: - Separated wake region (upper) - Attached wake region (lower) a + a - 2D wavenumber analysis – Power Spectral Density (PSD) in wavenumber space: u - - Advection ridge (left) - Acoustic circle (right) a + a - u +
Time me S Step a and nd U Und nder-R -Rela laxation, I n, I � 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 by marching in physical time (using transient method and 1-2 iterations per time step) than in steady mode…
Time me S Step a and nd U Und nder-R -Rela laxation, II n, II � 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 to 4 (depending on time-step size and grid quality).
Numb mber o of It Iterations ns p per T Time me S Step � 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… Propagation of an acoustic wave (20 cells per wavelength, 20 time steps per period) 10 It/dt 2 It/dt
Vehi hicle le A Aerodyna ynami mics: S : Steady R y RANS, I , I � Steady-state RANS computations provide results suitable for optimization 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
Vehi hicle le A Aerodyna ynami mics: S : Steady R y RANS, II , II � 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 reasonable limit 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.
Vehi hicle le A Aerodyna ynami mics: S : Steady R y RANS, III , III � Computation on a series of grids requires substantially less computing 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.
Vehi hicle le A Aerodyna ynami mics: S : Steady R y RANS, IV , IV Example: Flow around a 3D wing attached to a wall • 4 grid levels, base size ratio 2 Wall • Finest grid 460000 polyhedral cells Section parallel to wall Section normal to wall
Vehi hicle le A Aerodyna ynami mics: S : Steady R y RANS, V , V Example: Flow around a 3D wind attached to a wall Segregated solver Coupled solver
Vehi hicle le A Aerodyna ynami mics: S : Steady R y RANS, V , VI I 0.8 Effect of yaw angle on 0.7 drag of a truck Exp 0.6 0.5 STAR- 0.4 CCM+ 0.3 -15 -10 -5 0 5 10 15 Effect of underbody geometry on drag of a car
Vehi hicle le A Aerodyna ynami mics: DE : DES, I , I � 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…
Vehi hicle le A Aerodyna ynami mics: DE : DES, II , II DES of flow around a truck: details of flow structure in one vertical and one horizontal section (vorticity)
Vehi hicle le A Aerodyna ynami mics: DE : DES, III , III � Comparison with experiment is often difficult… � Boundary conditions need to be matched for a fair comparison… Wind tunnel effects
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