LES Lecture

8/10/2019 LES Lecture

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School of somethingFACULTY OF OTHER

CFD CentreEnergy Technology Innovation Initiative

Large Eddy Simulation:

an introduction

University of Leeds, 4-5-2012


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Turbulent Flows

Eddies (structures) with wide range of length and timescales.

3-D

Highly unsteady

A. Pranzitelli (2012). Large Eddy Simulation: an introduction


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CFD approaches

DNS

LES

RANS C o

m p u t a t i o n a l c o s t



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RANS, URANS, LES and DNS

URANS

LES

RANS

DNS

Direct calculation of the statisticalaverage of the solution

Direct calculation of only certain low-frequency modes in time and theaverage field

Direct calculation of only the

low-frequency modes in space

Simulation of all the scales



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The rationale for the LES

Momentum, mass, energy and other passive scalars aretransported mostly by large eddies.

Large eddies are anisotropic, subject to history effects andproblem-dependent (flow configuration, boundaryconditions, etc.)

Small eddies are less dependent on the geometry, tend tobe more isotropic, and are consequently more universal.

The chance of finding a universal turbulence model is much

higher for small eddies.



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Why LES?

Bluff body aerodynamics Manoeuvrability of vehicles

Safety and comfort (wake of building and ships)

Unsteady loading on structures

Aerodynamically generated noise Landing gear

Jet engine, propeller

Multi-component airfoils (flaps)

Fluid-structure interaction buffeting

Combustion instability A. Pranzitelli (2012). Large Eddy Simulation: an introduction


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Kolmogorovs theory

The energy cascade

Large eddies are unstable and break up, transferring energy to thesmaller eddies, energy continues to be passed to smaller scaled eddies

This process continues until Re( l ) u(l)l / is sufficiently small that the

eddy motion is stable, and molecular viscosity is effective in dissipating thekinetic energy

At these small scales, the kinetic energy of turbulence is converted intoheat

No dissipation in the energy cascade

determined by the first process, proportional to u03

/l 0 independent of

(at high Re)



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Kolmogorovs theory

Kolmogorovs hypotheses: At sufficiently high Reynolds numbers, the small-scale turbulent motions (l l EI) andthe small scale isotropic eddies ( l


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Kolmogorovs theory

Inertial subrangeDissipation range

Energycontaining

range

Universal equilibrium range

l DI l EI l 0 L

Kolmogorovlength scale

Integrallength scale

41

3

1Re



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Filtering

Transport equations are filtered such that only larger eddiesneed be resolved.

N-Sequation

Filtered N-S

equation

Random variable filtered usinga space-filter function G

Need to be modelledSub-grid scale models



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Filtering



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Sub-grid Scale models

Smagorinsky

In Fluent: Smagorinsky-Lilly

Dynamic models (e.g. Germano et al ., 1991): variable

In Fluent: Dynamic Smagorinsky-Lilly model, with clipped at 0 and0.23 by default

Shortcomings: constant. No value universally applicable to different types of flow Difficulty with transitional (laminar) flows Damping function needed in near-wall region



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Sub-grid Scale models

Wall-Adapting Local Eddy-Viscosity (WALE) Model

The WALE SGS model adapts to local near-wall flow structure

In Fluent



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Quality of your LES

The resolved kinetic energy depends on the filter width

Most of commercial software useimplicit filtering

The filter width isdetermined by the grid

resolution

The cell size determines the amount of energy that isbeing resolved

80% of turbulent kinetic energy should be resolved for anaccurate LES



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Quality of your LES

The grid plays a key-role in the LES

Need for knowing the turbulence scales of our own case

Need for knowing the distribution of the turbulent kineticenergy on these scales

Simulation of enough energy to catch the importantfeatures of our flow field



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What is usually done

Use of the same grid used for RANS Incorrect, grid too coarse

Not related to the vortex distribution

Grid independence study, as for the RANS simulations Expensive

No local refinement

Not possible in case of implicit filtering



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Quality of your LES

To assess the quality of your LES:

A priori study

A posteriori study

Determination of the mesh resolution

Accuracy is difficult to measure in DNS and LES because of the nature itself of the turbulent flows



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Cell-size estimation

A first approach, from the integral length scale estimate from correlation

from a RANS simulation (e.g. with the standard k- ) on a coarse gridand l 0 =(k 1.5 )/

cell size l 0 /6, 80% turb. kinetic energy resolved (l 0 /6= l EI demarcation line between Energy and Inertial ranges)

Issues: k is grid dependent / solution dependent (?)

rough estimation of the turbulent kinetic energy to be resolved



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Example:E.ON Combustion Test Facility

Located in Ratcliffe-on-Soar, UK

1 MW th

Single, wall-fired, low NO xburner

Variety of fuels (coal, gas,biomass, )

Time-temperature scaledto 500 MW facility



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CFD simulations: approach

commercial code: ANSYS FLUENT V12.1

Preparation of thegeometry

Mesh generation

Steady RANS simulation

LES

Hardware:

RANS: 1 quad-core intel Q9550, 2.83GHzprocessor, 8GB RAM LES: Leeds ARC1 HPC cluster, 50 intel NehalemX5560, 2.8GHz CPU cores, 2GB RAM/core

CPU time:

RANS: 25 days LES: 15 days/second of simulation



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Example:E.ON Combustion Test Facility (CTF)

Mesh:

3M cells Unstructured, but regular cell

distribution Mostly hexahedral cells,small number of polyhedralcells

High quality, low cell aspectratio and squish, notetrahedral cells

5.1

0k

L 12

0 Ll cell Cell sizing: A. Pranzitelli (2012). Large Eddy Simulation: an introduction


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E.ON CTF:results

RANS LES (instantaneous values)

Predicted Temperature distribution (K)

Air-coal combustionRANS vs LES



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E.ON CTF:results

vertical mid plane,distance from the burner centre line

Gas temperature



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Oxy-coal combustionRANS vs LES

RANS LES (instantaneousvalues)

Temperature distribution (K)



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Oxy-coal combustionflame

RANS LES (inst.) LES (mean) A. Pranzitelli (2012). Large Eddy Simulation: an introduction


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Oxy-coal combustionLES



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References

Sagaut, P. Large Eddy Simulation for Incompressible Flows. Springer, 2006.

Ferziger J.H., Peri, M. Computational methods for Fluid Dynamics. Springer, 2002

ANSYS Fluent Users Manual

Pope, Stephen B. Turbulent Flows. Cambridge University Press, 2000

Andr Bakker, lecture on Applied Computational Fluid Dynamics, Fluent Inc. 2002

Best practice advice for Large Eddy Simulation, ANSYS Celik, I., Klein, M., Janica, J. Assessment Measures for Engineering LES Applications.

Journal of Fluids Engineering, Vol. 131 (2009)

M. Gharebaghi, R.M.A. Irons, L. Ma, M. Pourkashanian, A. Pranzitelli. Large eddysimulation of oxy-coal combustion in an industrial combustion test facility. Int. J.Greenhouse Gas Control (2011), doi:10.1016/j.ijggc.2011.05.030

D. Caridi, D. Couling, M. Gharebaghi, S.R. Gubba, R.M.A. Irons, L. Ma, M. Pourkashanian, A. Pranzitelli and A. Williams. Comparison of RANS and LES Turbulence Models for Predicting Air-coal and Oxy-coal Combustion Behaviours. 2nd Oxyfuel CombustionConference, 12th 16th September 2011, Yeppoon ,QLD, Australia.http://www.ieaghg.org/index.php?/20110526255/occ2-programme-ts-5.html

http://www.ieaghg.org/index.php?/20110526255/occ2-programme-ts-5.htmlhttp://www.ieaghg.org/index.php?/20110526255/occ2-programme-ts-5.html


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Any questions?

Contact details: [email protected] rd floor, Energy Building

mailto:[email protected]:[email protected]

LES Lecture

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