SG Turbulence models for CFD

Transcription

1 SG Turbulence models for CFD Stefan Wallin Linné FLOW Centre Dept of Mechanics, KTH Dept. of Aeronautics and Systems Integration, FOI

2 There are no simple turbulent flows Turbulent boundary layer: Instantaneous velocity field (snapshot) Ref: Prof. M. Gad-el-Hak, University of Notre Dame

3 Prediction of turbulent flows Approximation level standard advanced hybrid -LES LES DNS days weeks months years Log(CPU)

4 Standard models Approximation level standard advanced Available in commercial codes k- k-, e.g. Menter SST Spalart-Allmaras hybrid -LES Good for: Attached boundary layers Thin shear flows LES DNS days weeks months years Log(CPU)

5 Advanced models Approximation level standard advanced Differential Reynolds stress models, DRSM, RST Algebraic Reynolds stress models, EARSM Structure-based modelling (Kassinos & Reynolds) Two-point correlations hybrid -LES LES Needed for: Rotation and swirl Strong 3D effects Body forces DNS days weeks months years Log(CPU)

6 Direct Numerical Simulation DNS Approximation level standard advanced Valid (!) no approximation Universal Full information But: Extremely expensive Only low Re Full aircraft ~2060!? Research tool only hybrid -LES LES DNS days weeks months years Log(CPU)

7 Large Eddy Simulation LES Valid Approximation level standard advanced little approximation Universal (if correctly done) Large scale dynamics: Also very expensive Acoustics Full aircraft ~2040!? Dynamic loads Affordable in internal flow (industrial use) hybrid -LES LES DNS days weeks months years Log(CPU)

8 Hybrid LES methods in attached BLs Approximation level standard advanced LES in free turbulence, separation Affordable, but still expensive hybrid -LES Many different methods: DES, DDES, IDDES, VLES, XLES, PANS, PITM LES DNS days weeks months years Log(CPU)

9 Prediction of turbulent flows Approximation level standard advanced hybrid -LES LES DNS days weeks months years Log(CPU)

10 DNS DNS

11 DNS DNS DNS (and also LES): 3D Time dependent Full information of turbulence scales Acoustics and dynamic loads Huge Reynolds number dependency (al least Re 3 ) Expensive!

12 DNS : Reduction of dimensions > cheap Here: 2D and steady Only statistical information of turbulence scales: Time and length scales rms values Smooth fields = easy to resolve > cheap

13 How expensive is DNS? DNS of flat plate turbulent boundary layer Schlatter, et al., KTH, Dept. of Mechanics APS meeting 2010: Re = 4300, Re = = spectral modes ( nodes) x + = 9, z + = 4 > box: L + = , H + = W + = BL relations: Re x = CPU time: CPU cores = 1 unit DNS of Airplane, same Reynolds number (Re x = ; c=0.5m, U=40m/s) Only a narrow stripe wing requires about stripes N nodes = CPU = 10 3 units

14 Empirical turbulent BL relations Skin friction coefficient: Boundary layer thickness: Boundary layer momentum thickness: Reynolds numbers:

15 Empirical relations plotted Laminar Turbulent Laminar Turbulent

16 DNS full scale airplane Re scaling wall bounded flow Nodes: Time steps: CPU time: DNS of Airplane (Re x = ) (factor of 50) N nodes = CPU = 10 9 units

17 Supercomputer development 10 9 units DNS of full airplane year 2055? present 45 year? doubling of CPU every 18 months wikipedia

18 Computational effort different approaches From Spalart, Int. J. Heat and Fluid Flow, 2000 : Reynolds Averaged Navier-Stokes U: Unsteady slowly in time DES: Detached Eddy Simulation LES: Large eddy simulation QDNS: Quasi DNS, or wall resolved LES DNS: Direct Numerical Simulation (of the Navier-Stokes eq s) Ready : When first results can be expected

19 Reynolds stresses Appears because of the non-linear term Not small Significant effects on the flow modelling Model Reynolds stresses in terms of known quantities Reduces the problem to steady (or slowly varying) 2D assumptions possible Closure problem Equation for can be derived from the N-S equation Contains higher order correlations, e.g. All equations of the statistical properties contain additional correlations.

20 Reynolds stress anisotropy map

21 anisotropy map in channel flow 2-component limit 1-component limit Axisymmetric turbulence Log layer