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Aspects of Industrial Flow Prediction Using LES in STAR-CCM+

A D Gosman

CD-adapco

Japan STAR Conference 2012, Yokohama

INTRODUCTION

1. Motivation for and nature of LES

2. LES and hybrid variants (in STAR-CCM+)

3. Quality assessment criteria

4. Best practices

5. Validation

6. Industrial applications

LES AND ITS ADVANTAGES

• turbulent flows unsteady and have

wide range time & length scales

• RANS models effects of all scales, and

enables calculation of ‘mean’ motion at

low cost, but with loss of accuracy

• DNS can capture all scales, but is

very expensive

• LES models only small scales (<∆c), at

moderate cost and greater accuracy.

RANS

LES

ACTUAL

LES/DES DEVELOPMENT IN STAR-CCM+

COLLABORATIONS WITH LEADING RESEARCHERS

1. University of Manchester: Prof D Laurence

2. Penn State University: Prof D Haworth

3. Cornell University: Prof. S Pope

4. Iowa State U.: Prof P Durbin

5. TU Darmstadt: Prof. Janicka

6. University Modena: Prof. S Fontanesi

PARTICIPATION IN EU PROJECTS (ATAAC, WALLTURB, ADVANTAGE)

JOINT PROJECTS WITH INDUSTRIAL PARTNERS

BASIC EQUATIONS

Navier-Stokes Equations

Filtered Equations

Eddy viscosity modelling for subgrid stresses,

LES Equations

SUBGRID MODEL OPTIONS IN STAR-CCM+ - I

1. SMAGORINSKY

Eddy viscosity

, strain rate tensor •

• length scale

• empirical coefficient recommended values are:

0.1, for channel flows (default setting in STAR-CCM+)

0.18, for free shear flows

• model requires modification for wall-bounded flows

 

Sij =1

2

¶ui

¶x j

+¶u j

¶x i

æ

è ç ç

ö

ø ÷ ÷

 

Cs » 0.07 - 0.18;

SUBGRID MODEL OPTIONS IN STAR-CCM+: - II

2. WALE

• Sw gives correct asymptotic behaviour of eddy viscosity near wall, i.e

However modifications may still be required to predict near-wall flow.

• Length scale

SUBGRID MODEL OPTIONS IN STAR-CCM+: III

3. DYNAMIC SMAGORINSKY (coming in V8.02)

• apply second filter ∆c2 > ∆c : typically ∆c2 = 2∆c

• assume small resolved scales and subgrid scales self-similar

• assume associated stress tensors can be represented by same

Smagorinsky expression, i.e.:

• Requires evaluation on larger stencil, difficult on unstructured meshes

• Cs non-smooth, averaging and limiting necessary

• Requires no modifications for wall-bounded flows.

• Thus Cs locally evaluated from:

subgrid:

resolved:

Cs=

NEAR-WALL MODELLING: I- REQUIREMENT

• Special requirements for wall-bounded flows because:

- boundary layers contain small-scale vortex

structures

- proper resolution requires DNS-type

grids, refinement in all directions; so very

expensive.

- also requires correct near-wall behaviour of subgrid

model:

NEAR-WALL MODELLING – II NATURE

Modelling practices used for near-wall region, first node in log-law layer

• ensure subgrid viscosity model gives

- some models already have this property

• ensure length scale bounded by κy in log-law region

- a few models already have this property

• obtain wall shear stress and turbulent viscosity at first mesh point from

log-law based wall functions

• produce wall-normal mesh distribution as for RANS, ideally with aspect

ratio limits as for LES

Additional requirements for first node in buffer layer or laminar sublayer

(not advised – additional meshing and modelling requirements)

NEAR-WALL MODELLING – III IMPLEMENTATIONS

1. SMAGORINGSKY

• introduce near-wall length scale limiter and damping factor

y = wall-normal distance

• evaluate wall shear stress τw and dynamic viscosity νt from Reichart law

2. WALE

• no modifications required, provided first node in log-layer

3. DYNAMIC SMAGORINSKY

• evaluate wall shear stress τw and dynamic viscosity νt from Reichart law

• length-scale limiter

HYBRID DES MODELLING – I INTRODUCTION

• Hybrid non-zonal model:

- tends to LES in resolved flow

- tends to URANS in unresolved

• Automatic selection of length scale

according to grid:turbulence length scale

ratio

• Preferable to limit URANS to near-wall

region

• Several variants:

- DES, DDES, IDDES

• Two URANS variants:

k-ω SST

Spalart-Almaras

HYBRID DES MODELLING – II EXAMPLE

SPALART-ALMARAS DES MODEL

• one-equation model: both high-Re and low-Re versions

• tends to Smagorinsky-type LES model when CDESΔ/d < 1

d = wall normal distance, Ψ ≈ 1 at high Re

 

˜ n =n t

fn1

• dissipation rate depends on controlling turbulent length scale

 

˜ d

GENERAL NUMERICAL ASPECTS

•Second order implicit time differencing

• Both CD and Bounded CD

• Non-reflecting boundary conditions

• Synthetic turbulence for inflow BC

STAR-CCM+ solver has specific features for LES/DES simulations

• second order implicit time differencing

• blended centered spatial differencing (BCD - alternative to CD for low-quality

meshes) for LES momentum

• blended second order/BCD differencing for DES

• non-reflecting boundary conditions

• synthetic turbulence inflow condition

• layered prismatic near-wall mesh

QUALITY ASSESSMENT CRITERIA FOR LES

1. A-priori ratio integral scale/mesh size = lint /Δ

 

lint = Cm

0.75k3 / 2 /e• use RANS estimate

• want ratio < 0.5

• accuracy depends on RANS solution

2. Fraction resolved kinetic energy kres/ktot

•

• want ratio > 0.8

kres 1

2u'1

2u'22u'3

2 ; ui

' ˜ u i u i; ktot kres ksgs

3. Ratio LES predicted turbulence scale/mesh size

• obtain length scale from energy spectrum

4. Ratio turbulent: laminar viscosity

• ideally close to unity, minimizes modelling error contribution

5. Other, e.g. Index of Resolution Quality

LES BEST PRACTICES

1. Generate RANS solution first and use:

- integral length scale distribution as guide to construct LES mesh.

- as initial conditions for LES

- for aeroacoustics, can also estimate frequency resolution distribution

2. Discretisation practices:

- 2nd order time,

- CD or BCD momentum

- second order scalars

3. Ensure proper boundary conditions, particularly at

- inflow: realistic turbulent simulation using SEM

- free boundaries and outflow: non-reflecting

4. Set time step to maintain Courant number Co = udt/dx ≈ 0.1- 0.5

5. Run simulation for sufficient time to:

- eliminate initial condition effects,

- get statistically representative results (e.g. true time/ensemble average)

Additional more stringent requirements for aeroacoustics

VALIDATION: I HOMOGENEOUS TURBULENCE DECAY

Comparison with DNS predictions of Wray

Wray, A. 1998 Decaying isotropic turbulence. In AGARD Advisory Rep. 345

VALIDATION: II BACKWARDS-FACING STEP

• Comparison with measurements

of Kasagi and Matsunaga

Kasagi, N., and Matsunaga, A., "Three-Dimensional Particle-Tracking Velocimetry Measurementof Turbulence

Statistics and Energy Budget in a Backward-Facing Step Flow," Int. J. Heat & Fluid Flow, Vol. 16, No. 6, (1995).

VALIDATION: III - T JUNCTION

S.T. Jayaraju, E.M.J. Komen: Nuclear Research and Consultancy Group (NRG), Petten, The Netherlands

• LES of mixing of streams of different

temperature at T junction

• Comparison with velocity and temperature

measurements

T JUNCTION (cont’d)

Mean, RMS velocities at 2.6D

Mean, RMS velocities at 1.6D

Wall temperatures

INDUSTRIAL APPLICATION: RANGE

Aerospace

• wing transition, high lift devices

• landing gear aeroacoustics

• jet noise

Automobile/truck

• full vehicle aerodynamics

• aeroacoustics mirror/window, sunroof

• HVAC fan, ducts, nozzles

• turbocharger

Combustion

• gas turbine

• reciprocating engine

• fires – building, tunnel, pool

Nuclear

• steam line/SRVs, T-junctions

• rods, spacers, turbulators, vibration

Other

• wind turbine, smoke/hazard release…………

AIRFOIL TURBULENT TRANSITION AND AEROACOUSTICS

• Wall-resolved LES of flow over

airfoil at 6o angle of attack

• Comparison with surface pressure and

noise measurements

• Relevant to wings, fans, turbines….

Surface pressure

SPL spectrum

AEROACOUSTICS: AIRCRAFT LANDING GEAR

• DES of aircraft forward landing gear

• Comparison with fluctuating surface

pressure measurements

SPL

VEHICLE EXTERNAL AERODYNAMICS: DES SIMULATIONS OF TRUCK AND SUV

Effect of yaw angle on drag coefficient of

truck

Effect of underbody modifications

on drag coefficient of SUV

VEHICLE AEROACOUSTICS – AUTOMOBILE WING MIRROR

STAR

Meas

• DES of wing mirror flow

• Comparison with fluctuating

pressure at downstream points

Deviation from measurement at estimated cut-off frequency

COMBUSTION: SANDIA FLAME D VALIDATION

• LES of Sandia D turbulent diffusion flame

• Smagorinsky, PPDF combustion model

• 4.1M cell mesh

SANDIA FLAME D (CONT’D)

Mean axial velocity RMS axial velocity

Mean mixture fraction RMS mixture fraction

SUMMARY

1. STAR-CCM+ has an extensive capability for performing LES and DES

2. The methodology has been validated for a range of industrially-relevant

cases

3. Numerous industrial applications have been made in diverse areas

including aerodynamics, thermal analysis, aeroacoustics and combustion.

4. The methodology is being improved and extended, with the help of

collaborations with leading research institutes.