large eddy simulation for aerodynamics: status and perspective

8/19/2019 Large Eddy Simulation for Aerodynamics: status and perspective

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pressing issue since a wide range of unsteady phenomena that have seriousimplications in terms of achievable performance, acoustic environment or safetyhas to be considered, and therefore requires to be accurately predicted as soon aspossible in the design cycle of flight vehicles or cars. In such configurations, asteady Reynolds-averaged Navier–Stokes (RANS) solution would not be what

the engineer needs.In addition to the modelling issue, the challenge of handling complex

geometries relates not only to computing cost, but also to solution quality interms of meshing and validation of the computed unsteady field as will bediscussed in §§3 and 5.

2. Large eddy simulation and sequels

One of the main properties of a turbulent flow is its multiscale aspect and the

concept of kinetic energy cascade is often cited. Direct numerical simulation(DNS) enables one to get an accurate three-dimensional and time-dependentdescription of all the active scales in the turbulent flow without resorting to anymodelling assumptions. The total number of nodes may be scaled as OðRe 3LÞ,where Re L denotes the Reynolds number based on the spatial integral scale.Furthermore, if walls are present, the near wall structures need to be resolvedleading to an even stronger dependence on the Reynolds number. Unfortunately,turbulent flows encountered in engineering applications exhibit such a wide rangeof excited length and time scales that DNS remains too costly by several orders of magnitude at relevant Reynolds numbers (z105–108). This is the reason whymodelling is necessary to study turbulent flows, prior to solving the Navier–Stokes equations. Nevertheless, DNS remains a research tool which allows one toget significant insight into turbulence physics and to explore control strategies(Moin & Mahesh 1998).

The large eddy simulation (LES) technique relies on a decomposition of theaerodynamic field between the large scales (responsible for turbulenceproduction) and the small scales of the flow, the former being directly resolvedwhile the effect of the latter is taken into account through the use of a model. Theprimary obstacle to practical use of LES on industrial flows which involveattached wall boundary layers at high Reynolds number remains computationalpower resources. Indeed, the scales of motion responsible for turbulence

production impose severe demands on the grid resolution near solid walls.As an example, the grid extension sizes Dx Cz50, Dy Cz1 and Dz Cz15 are thoseclassically retained in LES to capture the near wall structures. In Spalart (2000),it is proposed that LES of wall turbulence should be considered as a quasi DNSsince the resolution of the near wall layer is roughly only 10 times less expensivethan DNS. For instance, Spalart et al . (1998) report that full LES of the flowaround a three-dimensional wing will not be tractable until the year 2045, evenassuming that wall modelling has been achieved.

LES has been applied to many flow regimes, most of them being illustrated onacademic configurations, e.g. compressible flows with shock induced separation

(Garnier et al . 2001, 2002), compressible cavity flows with complex aeroacousticcouplings (Larcheveque et al . 2003, 2004) and heat transfer in low-speedseparated flows (Labbe et al . 2002). Most existing application-driven simulations

P. Sagaut and S. Deck 2850

Phil. Trans. R. Soc. A (2009)

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deal with simplified or reduced geometries, e.g. thin slices of wings or blades(Manoha et al . 2000; Mary & Sagaut 2002; Raverdy et al . 2003). Among keyissues in the field of LES, one can mention the definition of robust subgrid modelswhich perform equally in a wide range of flow parameters ( Meyers et al . 2006),the prescription of unsteady turbulent inflow conditions (Sagaut et al . 2004) andthe development of subgrid models for new physical mechanisms such as subgridnoise production (Seror et al . 2001).

Hybrid RANS–LES methods have been developed to alleviate this resolutionconstraint in the near-wall region, another strategy being the use of local increase inthe grid resolution (Quemere et al . 2001; Terracol et al . 2001). According to Sagautet al . (2006), hybrid methods can be classified into two major classes, namely theglobal and the zonal hybrid methods. Similarly, Frolich & von Terzi (2008) proposeto classify the hybrid approaches as unified models and segregated models. Anotherway may consist in distinguishing weak and strong RANS–LES coupling methods(see figure 1). The zonal hybrid methods are based on a discontinuous treatment of the RANS–LES interface. In practice, information must be exchanged at the

RANS–LES interface between two solutions with very different spectral content.The global hybrid methods are based on a continuous treatment of the flow variablesat the interface between RANS and LES. These methods, like detached eddysimulation (DES; Spalart et al . 1998) introduce a ‘grey area’ in which the solution isneither pure RANS nor pure LES since the switch from RANS to LES does not implyan instantaneous change in the resolution level. These methods can be considered asweak RANS–LES coupling methods since there is no mechanism to transfer themodelled turbulence energy into resolved turbulence energy. Despite a wide numberof approaches (and acronyms), these methods are close to each other (Sagaut et al .2006; Frolich & von Terzi 2008) and can very often be rewritten as variants of a

small group of generic approaches. In practice, they tend to decrease the level of RANS eddy viscosity, thus permiting strong instabilities to develop. Let us note thathybrid RANS–implicit LES approaches are also potentially of great interest.

modelled

resolved

unsteady statistical approaches

global hybrid methods

« weak RANS–LES coupling »

« PHYSICAL »

« NUMERICAL »

zonal methods

« strong RANS–LES coupling »

LES

C

O M P U T A T I O N A L C O S T I N C R E

A S I N G

DNS

m e t h o d s

P H Y S I C S

h y b r i d R A N S – L E S

effect of grid

refinement

what we

have

what we need

R E A D I N E S S

Figure 1. Classification of unsteady approaches according to levels of modelling and readiness.Adapted from Sagaut et al . (2006).

2851LES for aerodynamics









It is now commonly accepted that hybrid RANS–LES is the main strategy todrastically reduce computational cost (compared to LES) in a wide range of complex industrial applications if attached boundary layers have a significantimpact on the global flow dynamics. Again, this raises the challenge of validatingunsteady simulations on complex geometries.

3. Need for validation

Computational power has dramatically increased over the last few decades.A consequence of this upsurge in computational power is the rapid increase in thesize of subsequent datasets, with unsteady 50–100 million point grid simulationsbeing now conducted with increasing regularity.

As the need for higher accuracy simulations has increased, the computationalfluid dynamics (CFD) community has in turn put emphasis on assessing the

quality of the results and now focuses a great deal of its effort on validation of advanced methods. Let us remember that the validation of inviscid calculationswas primarily focused on the capability to evaluate the wall pressure distributionwhile the validation of steady viscous calculations was mainly based on thecorrect assessment of the boundary layer integral quantities. Now the flow-fieldmodel has to include a comprehensive unsteady description of turbulenceincluding fluctuations both in pressure and velocities. It is worth adding thatnumericists have at their disposal the temporal evolution of all hydrodynamicquantities in the entire volume of the flow with the best accuracy which allows adeep investigation of the flow physics.

Disappointingly, many authors present only one-point and first-order statisticsto assess their unsteady simulations. This may appear paradoxal since thedevelopment of advanced methods is precisely motivated by their potentialcapability to predict the fluctuating field in complex configurations. Therefore,we introduce in table 1 a classification of the level of validation of unsteady dataissued from CFD. Level 1 concerns the comparison of integral quantities such aslift or drag with measurements. The ability of a method to reproduce first-(e.g. mean velocity profile) and second-order statistics (such as mean squaredvalue, r.m.s. profiles) represents level 2 and 3 respectively. Single-point spectralanalysis describes how the r.m.s. levels are distributed in frequency since gettingthe correct amount of energy does not necessarily mean that the frequency

content is well reproduced. Classical spectral analysis (level 4) may be completedwith a two-point analysis (level 5) which allows us to get further insight into thespatial organization of the flow at a given frequency. When the nature of the mechanisms of interest is time-dependent, e.g. in transient processes such asflow bifurcation, the random process is said to be non-stationary and may requiretime–frequency and nonlinear coupling analysis (level 6). Levels 4, 5 and 6 areillustrated in Larcheveque et al . (2003, 2004, 2007), in which both post-processing tools and extensive comparisons with particle image velocimetry andhot wire data are displayed.

In addition, it is worthwhile to stress that comparing numerical data

with experiment is by far not trivial, mainly because the datasets are of verydifferent duration. Indeed, experimental time-series often exceed 10 s while 1 s isconsidered a ‘small eternity’ in CFD. Note the antinomic aspect between the










needs for statistics and the constraints imposed by CFD. Indeed, to performa statistical analysis in good conditions, the signal has to be well sampled on asufficient duration because the spectral information needs to be averaged onmany blocks to be statistically converged. In practice, unsteady signals issuedfrom CFD are most often oversampled on a short duration (due to high CPUcost). From a statistical point of view, it is strictly speaking not relevant tocompare two sequences of data whose duration differs significantly. In otherwords, a comparison between experiment and numerical data has to beconducted cautiously with full knowledge of the facts.

4. Illustrative examples

We now address the issue of the state-of-the-art knowledge about the use of LESand hybrid RANS–LES techniques to predict the flow in complex three-dimensional systems. We deliberately choose here to include only works publishedin international peer-reviewed journals, since they have been validated by acommonly agreed selection process and are available to the whole CFD practionercommunity. Some exceptions are made for some RANS–LES applications, sinceonly a very few of them have been published in international journals.

LES is now a mature technique (let us recall that the seminal paper bySmagorinsky was published 45 years ago), but most published works in peer-reviewed international journals are restricted to fundamental studies of academicflows and developments related to LES theory. Nevertheless, LES is now used to

compute full-scale complex three-dimensional systems. Some significant land-mark achievements are reported in the top half of table 2. These applications arerecent. A detailed analysis of the related papers reveals that they rely on ‘old’methods, i.e. on numerical algorithms and subgrid modelling approachespublished 10–15 years ago. The delay between the original publication of themethods and their practical use is certainly due to the exponential increase of computing power and the time needed to develop general purpose Navier–Stokessolvers with LES capabilities. As a matter of fact, it is observed that the amountof memory needed for these recent LESs is much larger than those of thelandmark DNSs recorded in the early 1990s. But an interesting fact is that LES is

successfully used to predict the global behaviour of complex systems in whichattached boundary layers are of minor importance, such as a full-scale five-stagecentrifugal pump or engine combustor chamber.

Table 1. Levels of validation of simulation techniques: nomenclature.

grade level of validation

1 integral forces (lift, drag and pitch)

2 mean aerodynamic field (velocity or pressure profiles)3 second-order statistics (r.m.s. quantities)4 one-point spectral analysis (power spectral densities)5 two-point spectral analysis (correlation, coherence and phase spectra)6 high-order and time–frequency analysis (time–frequency, bicoherence spectra)










T a b l e 2

. T y p i c a l L E S - l i k e s i m u l a t i o n s

f o r c o m p l e x a p p l i c a t i o n s . ( V a l i d a t i o n l e v e l n o m e n c l a t u r e : ( n ) m

e a n s t h a t i n f o r m a t i o n o f l e v e l

n ( a s d e fi n e d i n

t a b l e 1 ) i s p r o v i d e d i n t h e p a p e r b u t n o t c o m p a r e d w i t h r e f e r e n c e d a t a

( i . e . e x p e r i m e n t a l / D N S d a t a ) .

F i r s t a n d s e c o n d b l o c k s a r e r e l a t e d t o c l a s s i c a l

L E S a n

d R A N S – L E S , r e s p e c t i v e l y . a

a n d L d e n o t e t h e a n g l e o f a t t a c k a n d t h e l e n g t h s c a l e , r e s p e c t i v e l y . )

c o n fi g u r a t i o n

r e f e r e n c e

M N

a

( 8 )

L ( m )

R e L ! 1 0 6

N x y z ! 1 0 6

v a l i d a t i o n

fi v e - s t a g e c e n t r i f u g a l p u m p

K a t o e t a l . ( 2 0 0 3 , 2 0 0 7 )

0

—

1

1 0

3 7

4

u r b a n fl o w

N o z u e t a l . ( 2 0 0 8 )

0

—

1 5 0

5 0 – 1 0 0

1 0

1 – 2

t o r p e d o

F u r e b y ( 2 0 0 8 )

0

—

—

1 2

1 . 5 – 6

1 – 2 – 3

m a n o e u

v r i n g s u b m a r i n e

F u r e b y ( 2 0 0 8 )

0

0 – 2 5 . 5

—

5

4 . 4 – 8 . 6

1 – 2

c o m b u s

t o r c h a m b e r

B o i l e a u e t a l . ( 2 0 0 8 )

0 . 1

—

0 . 5

0 . 3

2 0 – 4 0

1 – ( 2 ) – ( 3 )

f o u r - v a l v e c o m b u s t i o n e n g i n e

R i c

h a r d e t a l . ( 2 0 0 6 )

0 . 2

—

0 . 1

—

0 . 6

1 – ( 3 )

l a n d i n g

g e a r

H e d g e s e t a l . ( 2 0 0 2 )

0 . 1

—

—

0 . 6

2 . 5

2 – ( 3 ) – ( 4 )

l a n d i n g

g e a r

L o c k a r d e t a l . ( 2 0 0 4 )

0 . 2

—

0 . 0 9 4

—

1 3 . 3

( 2 ) – ( 3 ) – ( 4 )

m i s s i l e

f o r e b o d y

V i s

w a n a t h a n & S q u i r e s ( 2 0 0 8 )

0 . 2 1

6 0 a n d 9 0

—

2 . 1

2 . 1 – 8 . 7 5

( 1 ) – 2

m i l i t a r y fi g h t e r

M o

r t o n e t a l . ( 2 0 0 4 )

0 . 2 7

3 0

—

1 3

3 . 9

1 – ( 3 )

m i l i t a r y fi g h t e r

F o r s y t h e e t a l . ( 2 0 0 4 )

0 . 3

6 5

—

2 . 8 5

2 . 8 5 – 1 0

1

g a s t u r b i n e

M e

d i c e t a l . ( 2 0 0 8 )

—

—

—

—

8 – 5 7

2

a f t e r b o d y

D e c k & T h o r i g n y ( 2 0 0 7 )

0 . 7

—

0 . 1

1 . 1

8 . 3

( 1 ) – 2 – 3 – 4 – 5

c i v i l a i r c r a f t

B r u n e t & D e c k ( 2 0 0 8 )

0 . 8 2

4 . 2

0 . 3 4

2 . 8

1 0

2 – 3 – 4

fi g h t e r

j e t

C h a u v e t e t a l . ( 2 0 0 7 )

1

—

0 . 0 4 4

0 . 2

1 1

2 – ( 3 )

s u p e r s o

n i c i n l e t

T r a p i e r e t a l . ( 2 0 0 8 )

1 . 8

—

0 . 1

2 . 9

1 0 – 2 0

2 – 3 – 4 – 6

l a u n c h e

r n o z z l e

D e c k ( i n p r e s s )

6

—

0 . 0 1 3

2 1

1 1

1 – 2 – 3 – 4










Hybrid RANS–LES approaches have received increasing attention amongturbulence modelling specialists, code developers and industrial CFD engineers.So far, the validation effort has been mainly focused on two-dimensionalgeometries, the span being treated as a homogeneous direction. These aremandatory problems in relatively simple geometries but are now becomingsupported by a wider set of applications on more complicated geometries. For thesake of conciseness, only examples of three-dimensional applications are gatheredin table 2.

Several off-design unsteady applications (such as high angle of attack wing

aerodynamics or afterbody flows) can now be simulated accurately. Conversely,thin layer separations as well as shock/boundary layer interactions remainchallenging configurations. Nevertheless, figure 2 shows that the simulation of the unsteady flow around a civil aircraft configuration is feasible without waitingfor 2045. Such a simulation has been performed thanks to the use of a zonal DESapproach (Deck 2005) allowing for the reduction of the cost of the simulationcompared to a LES by limiting explicitly the extent of the DES zones. Note thatfrom an engineering standpoint, full LES of a complete wing would give toomuch information in some ‘useless’ regions such as the leading edge orpressure side where traditional RANS modelling is often sufficient. Therefore,

the authors promote the use of a zonal treatment of turbulence to treatcomplex configurations while other authors (Spalart 2009) may differ, but debateis useful.

Figure 2. Zonal detached eddy simulation of the flow over a subscale civil aircraft at 4.2 degreeangle of attack (courtesy of V. Brunet, ONERA; from Brunet & Deck (2008)).










Besides, table 2 brings out the fact that the validation (i.e. thoroughcomparison with measurements) of the unsteady solution is often insufficient(level of validation!3). This is both surprising and disappointing since the mainpurpose of hybrid methods is precisely to provide an unsteady description of theflow. On top of this observation, this promotes the acquisition of unsteady

databases to validate LES and RANS–LES methods.An interesting point is that, as far as massively separated flows are addressed

and that a detailed description of the boundary-layer dynamics (e.g. prediction of the turbulent heat transfer) is not considered, classical LES seems to be as usefulas hybrid RANS–LES methods. Another observation is that the capability of hybrid methods to provide a more accurate description of heat transfer and skinfriction in fully three-dimensional complex systems than usual LES (eventuallysupplemented with a crude algebraic wall model) remains to be assessed.Nevertheless, a recent simulation seems to indicate that some DES variants areable to yield an accurate description of shock/boundary layer interactions at a

lower cost than classical LES.

5. Modern validation procedures: uncertainty and error quantification

The above discussion dealt with ‘classical’ validation, i.e. with a procedure inwhich results coming from a deterministic simulation of an exactly definedsystem are compared with reference data that are assumed to be perfect andassociated with an identical system. In practice, the validation process is muchmore complex and less accurate. The main reasons are the following. First, reallycomplex systems (such as a five-stage pump) are never perfectly known, in thesense that very fine details of both the geometry and boundary conditions are notavailable. Therefore, the computational model can never exactly reproduce thephysical system. The second problem, which has already been mentioned, is thatthe CFD and measurements exhibit very different sampling time and samplingfrequency, leading to sometimes very different statistical convergence rate andaccuracy of quantities selected for comparison. A last uncertainty sourceoriginates in measurement errors.

Therefore, uncertainties and errors should be taken into account to performfully relevant comparisons. A key point is the capability to evaluate thesensitivity of the CFD solution with respect to computational parameters, a

reliable solution being a solution with low sensitivity. The computation of thesensitivity amounts to evaluating the gradient of the solution with respect toparameters of interest. Many mathematical methods can be used for thatpurpose, ranging from local linearization (e.g. adjoint problem-based approaches)to fully nonlinear response surface reconstruction. The latter has been veryrecently applied to LES. The generalized polynomial chaos approach, whichallows for a Galerkin-type reconstruction of the response surface with pseudo-spectral accuracy, was used by Lucor et al . (2007) to compute the sensitivity of the kinetic energy spectrum in decaying isotropic turbulence with respect to thearbitrary constant which appears in the Smagorinsky subgrid model. A typical

output is the probability density function of the energy spectrum at all resolvedscales. Another method, namely the Kriging method, which is an optimalstatistical linear interpolation technique, was used by Jouhaud et al . (2008) to










investigate the sensitivity of the LES of the flow in the cooling system of anaeronautical engine. Both the numerical stabilization tuning parameter and theSmagoginsky model constant were considered as uncertain parameters, sincetheir optimal values are known to be case-dependent. A typical output here was

the prediction of the range of variation of the computed mean flow thanks to theresponse surface (see figure 3). It is observed that the computed mean velocityfield sensitivity varies very significantly, depending on both the location and the

00

0.5

1.0

1.5

2.0

2.5

0.5

1.0

1.5

2.0

2.5

3.0

0

0.5

1.0

1.5

2.0

2.5

3.0

0

0.5

1.0

1.5

2.0

2.5

3.0

0

0.5

1.01.5

2.0

2.5

3.0

0

0.5

1.01.5

2.0

2.5

3.0(a) (i) (i)

(ii) (ii)

(iii) (iii)

(b)

0.1 0.2 0.3 0–0.1–0.2 0.1

0–0.1 0.1 0.2 0–0.05 0.100.05

0–0.5 1.00.5 1.5 0.6 0.7 0.8 0.9 1.0 1.1

Figure 3. Kriging-based prediction of the range of variation of the LES mean flow in a complexconfiguration. (a ) X /D Z1; (b) X /D Z8. Symbols, measurements; solid lines, LES solutioncomputed with standard values of numerical stabilization parameter and Smagorinsky constant;dashed lines, envelope of the LES solution predicted thanks to the response surface. Adapted fromJouhaud et al . (2008).










selected velocity component. The new information retrieved from the responsesurface is related to the possibility to perfectly match measurements at somepoint on the computational grid or to globally minimize the discrepancies withexperimental values by adjusting computational parameters. In such a case, avalidated method is a method whose response surface encompasses reference

data. This allows for the definition of optimal LES parameters on a givencomputational grid.

6. Concluding remarks

As a conclusion, let us emphasize that even if universal methods for industrialunsteady flows will not be available in the near future, useful results for designand understanding of flow physics can be obtained by adapting the levelof modelling to the considered problem without waiting for the next generation of

supercomputers. The next foreseen challenges in applied numerical aerodynamicswill be firstly the capture of the boundary-layer dynamics including transitionand pressure-gradient-driven separation issues and secondly the capability tohandle accurately geometrically complex configurations with validated unsteadytools. In the near future, if (really) complex configurations are considered, thevalidation procedure should account for uncertainties and errors which arise fromthe very definition of the computational model and the postprocessing of data of different nature. Accurate validations of system simulations will also requireadequate experimental databases, which are still missing.

References

Boileau, M., Staffelbach, G., Cuenot, B., Poinsot, T. & Berat, C. 2008 LES of an ignition sequencein a full helicopter combustor. Combust. Flame 154, 2–22. (doi:10.1016/j.combustflame.2008.02.006)

Brunet, V. & Deck, S. 2008 Zonal detached eddy simulation of transonic buffet on a civil aircrafttype configuration. In Fluid Dynamics Conference . AIAA paper 2008-4152.

Chauvet, N., Deck, S. & Jacquin, L. 2007 Zonal-detached-eddy simulation of a controlledpropulsive jet. AIAA J. 45, 2458–2473. (doi:10.2514/1.28562)

Deck, S. 2005 Zonal-detached eddy simulation of the flow around a high-lift configuration. AIAA J.43, 2372–2384. (doi:10.2514/1.16810)

Deck, S. In press. Delayed detached eddy simulation of self-sustained unsteadiness and side-loadsin an overexpanded nozzle flow. Shock Waves . (doi:10.1007/S00193-009-0199-5)

Deck, S. & Thorigny, P. 2007 Unsteadiness of an axisymmetric separating flow: numericalinvestigation. Phys. Fluids 19, 065 103. (doi:10.1063/1.2734996)

Forsythe, J. R., Squires, K. D., Wurtzler, K. E. & Spalart, P. R. 2004 Detached-eddy simulation of the F-15E at high alpha. J. Aircraft 41, 193–200. (doi:10.2514/1.2111)

Frolich, J. & von Terzi, D. 2008 Hybrid RANS/LES methods for the simulation of turbulent flows.Prog. Aerosp. Sci. 44, 349–377. (doi:10.1016/j.paerosci.2008.05.001)

Fureby, C. 2008 Towards the use of large eddy simulation in engineering. Prog. Aerosp. Sci. 44,381–396. (doi:10.1016/j.paerosci.2008.07.003)

Garnier, E., Sagaut, P. & Deville, M. 2001 A class of explicit ENO filters with application to

unsteady flows. J. Comput. Phys. 170, 184–204. (doi:10.1006/jcph.2001.6732)Garnier, E., Sagaut, P. & Deville, M. 2002 Large-eddy simulation of the shock/boundary layer

interaction. AIAA J. 40, 1935–1944. (doi:10.2514/2.1552)




http://dx.doi.org/doi:10.1016/j.combustflame.2008.02.006


http://dx.doi.org/doi:10.2514/1.28562


http://dx.doi.org/doi:10.1007/S00193-009-0199-5



http://dx.doi.org/doi:10.1016/j.paerosci.2008.05.001


http://dx.doi.org/doi:10.1006/jcph.2001.6732












http://dx.doi.org/doi:10.1007/S00193-009-0199-5







Hedges, L. H., Travin, A. K. & Spalart, P. R. 2002 Detached eddy simulations over a simplified

landing gear. J. Fluids Eng. 124, 413–423. (doi:10.1115/1.1471532)

Jouhaud, J. C., Sagaut, P., Enaux, B. & Laurenceau, J. 2008 Sensitivity analysis and

multiobjective optimization for LES numerical parameters. J. Fluids Eng. 130, 021 401.

(doi:10.1115/1.2829602)

Kato, C., Kaiho, M. & Manabe, A. 2003 An overset finite element large-eddy simulation methodwith application to turbomachinery and aeroacoustics. J. Appl. Mech. 70, 49–56. (doi:10.1115/

1.1530637)

Kato, C., Yamade, Y., Wang, H., Guo, Y., Miyazawa, M., Takaishi, T., Yoshimura, S. & Takano,

Y. 2007 Numerical prediction of sound generated from flows with a low Mach number. Comput.

Fluids 36, 53–68. (doi:10.1016/j.compfluid.2005.07.006)Labbe, O., Sagaut, P. & Montreuil, E. 2002 Large-eddy simulation of heat transfer over a

backward-facing step. Int. J. Numer. Heat Transfer A 42, 73–90. (doi:10.1080/104077802

90059431)

Larcheveque, L., Sagaut, P., Mary, I., Labbe, O. & Comte, P. 2003 Large-eddy simulation of a

compressible flow past a deep cavity. Phys. Fluids 15, 193–210. (doi:10.1063/1.1522379)

Larcheveque, L., Sagaut, P., Le, T. H. & Comte, P. 2004 Large-eddy simulation of a compressibleflow in a three-dimensional open cavity at high Reynolds number. J. Fluid Mech. 516, 265–301.

(doi:10.1017/S0022112004000709)

Larcheveque, L., Sagaut, P. & Labbe, O. 2007 Large-eddy simulation of a subsonic cavity flow

including asymmetric effects. J. Fluid Mech. 577, 105–126. (doi:10.1017/S0022112006004502)

Lockard, D. P., Khorrami, M. R. & Li, F. 2004 High resolution calculation of a simplified landing

gear. In 10th AIAA/CEAS Aeroacoustics Conference . AIAA paper 04-2887.

Lucor, D., Meyers, J. & Sagaut, P. 2007 Sensitivity analysis of LES using polynomial chaos.

J. Fluid Mech. 585, 255–279. (doi:10.1017/S0022112007006751)Manoha, E., Troff, B. & Sagaut, P. 2000 Trailing-edge noise prediction using large-eddy simulation

and acoustic analogy. AIAA J. 38, 575–583. (doi:10.2514/2.1015)

Mary, I. & Sagaut, P. 2002 Large-eddy simulation of flow around an airfoil near stall. AIAA J. 40,1139–1145. (doi:10.2514/2.1763)

Medic, G., Kalitzin, G., You, D., Weide, V. D., Alonso, J. J. & Pitsch, H. 2008 Integrated RANS-

LES computation of an entire gas turbine jet engine. In 45th AIAA Aerospace Sciences Meeting

and Exhibit . AIAA paper 2007-1117.Meyers, J., Sagaut, P. & Geurts, B. J. 2006 Optimal model parameters for multi-objective large-

eddy simulations. Phys. Fluids 18, 095 103. (doi:10.1063/1.2353402)

Moin, P. & Mahesh, K. 1998 Direct numerical simulation: a tool in turbulence research. Annu.

Rev. Fluid Mech. 30, 539–578. (doi:10.1146/annurev.fluid.30.1.539)

Morton, S. A., Cummings, R. M. & Kholodar, D. B. 2004 High resolution turbulence treatment of

F/A-18 tail buffet. In 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics

and Material Conference . AIAA paper 04-1676.Nozu, T., Tamura, T., Okuda, Y. & Sanada, S. 2008 LES of the flow and building wall pressures in

the center of Tokyo. J. Wind Eng. Indust. Aerodyn. 96, 1762–1773. (doi:10.1016/j.jweia.2008.

02.028)Quemere, P., Sagaut, P. & Couaillier, V. 2001 A new multidomain/multiresolution method for

large-eddy simulation. Int. J. Numer. Methods Fluids 36, 391–416. (doi:10.1002/fld.134)

Raverdy, B., Mary, I., Sagaut, P. & Liamis, N. 2003 High resolution large-eddy simulation of the

flow around a low-pressure turbine blade. AIAA J. 41, 390–397. (doi:10.2514/2.1989)

Richard, S., Colin, O., Vermorel, O., Benkenida, A., Angelberger, C. & Veynante, D. 2006

Towards large eddy simulation of combustion in spark ignition engines. Proc. Combust. Inst. 31,

3059–3066. (doi:10.1016/j.proci.2006.07.086)

Sagaut, P., Garnier, E., Tromeur, E., Larcheveque, L. & Labourasse, E. 2004 Turbulent inflowconditions for LES of subsonic and supersonic wall-bounded flows. AIAA J. 42, 469–478.

(doi:10.2514/1.3461)








http://dx.doi.org/doi:10.1016/j.compfluid.2005.07.006

http://dx.doi.org/doi:10.1080/10407780290059431



http://dx.doi.org/doi:10.1017/S0022112004000709






http://dx.doi.org/doi:10.1146/annurev.fluid.30.1.539

http://dx.doi.org/doi:10.1016/j.jweia.2008.02.028


http://dx.doi.org/doi:10.1002/fld.134


http://dx.doi.org/doi:10.1016/j.proci.2006.07.086







http://dx.doi.org/doi:10.1016/j.proci.2006.07.086


http://dx.doi.org/doi:10.1002/fld.134



http://dx.doi.org/doi:10.1146/annurev.fluid.30.1.539










http://dx.doi.org/doi:10.1016/j.compfluid.2005.07.006







Sagaut, P., Deck, S. & Terracol, M. 2006 Multiscale and multiresolution approaches in turbulence .London, UK: Imperial College Press.

Seror, C., Sagaut, P., Bailly, C. & Juve, D. 2001 On the radiated noise computed by large-eddysimulation. Phys. Fluids 13, 476–487. (doi:10.1063/1.1336150)

Spalart, P. R. 2000 Strategies for turbulence modelling and simulations. Int. J. Heat Fluid Flows 21, 252–263. (doi:10.1016/S0142-727X(00)00007-2)

Spalart, P. R. 2009 Detached-eddy simulation. Annu. Rev. Fluid Mech. 41, 181–202. (doi:10.1146/annurev.fluid.010908.165130)

Spalart, P. R., Jou, W. H., Strelets, M. & Allmaras, S. R. 1998 Comments on the feasibility of LESfor wings and on a hybrid RANS/LES approach. In Proc. 1st AFSOR Int. Conf. on DNS/LES,Ruston, LA, pp. 137–147.

Terracol, M., Sagaut, P. & Basdevant, C. 2001 A multilevel algorithm for large-eddy simulation of turbulent compressible flows. J. Comput. Phys. 167, 439–474. (doi:10.1006/jcph.2000.6687)

Trapier, S., Deck, S. & Duveau, P. 2008 Delayed detached eddy simulation and analysis of supersonic inlet buzz. AIAA J. 46, 118–131. (doi:10.2514/1.32187)

Viswanathan, A. K. & Squires, K. D. 2008 Detached eddy simulation around a forebody withrotary motion. AIAA J. 26, 2191–2201. (doi:10.2514/1.4756)





http://dx.doi.org/doi:10.1016/S0142-727X(00)00007-2

http://dx.doi.org/doi:10.1146/annurev.fluid.010908.165130














http://dx.doi.org/doi:10.1016/S0142-727X(00)00007-2


large eddy simulation for aerodynamics: status and perspective

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