A new hybrid turbulence modelling strategy for industrial CFD

Basara, Branislav; Jakirlić, Suad

doi:10.1002/fld.492

Cited by 40 publications

(31 citation statements)

References 37 publications

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“…However, so far Reynolds stress models have not been widely used in the engineering applications despite their theoretical superiority. A key shortcoming of the Reynolds stress models is their lack of stability and robustness, i.e., the difficulty in achieving convergence (Basara & Jakirlic 2003;Maduta & Jakirlic 2017) and the high sensitivity to the modeling of unclosed terms (particularly the pressure-rate-of-strain tensor) in the Reynolds stress transport equations.…”

Section: Introductionmentioning

confidence: 99%

Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned

et al. 2019

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Reynolds-averaged Navier-Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy viscosity models are intrinsically unable to handle flows with nonequilibrium turbulence (e.g., flows with massive separation). Reynolds stress models, on the other hand, are plagued by their lack of robustness. Recent studies in plane channel flows found that even substituting Reynolds stresses with errors below 0.5% from direct numerical simulation (DNS) databases into RANS equations leads to velocities with large errors (up to 35%). While such an observation may have only marginal relevance to traditional Reynolds stress models, it is disturbing for the recently emerging data-driven models that treat the Reynolds stress as an explicit source term in the RANS equations, as it suggests that the RANS equations with such models can be ill-conditioned. So far, a rigorous analysis of the condition of such models is still lacking. As such, in this work we propose a metric based on local condition number function for a priori evaluation of the conditioning of the RANS equations. We further show that the ill-conditioning cannot be explained by the global matrix condition number of the discretized RANS equations. Comprehensive numerical tests are performed on turbulent channel flows at various Reynolds numbers and additionally on two complex flows, i.e., flow over periodic hills and flow in a square duct. Results suggest that the proposed metric can adequately explain observations in previous studies, i.e., deteriorated model conditioning with increasing Reynolds number and better conditioning of the implicit treatment of Reynolds stress compared to the explicit treatment. This metric can play critical roles in the future development of data-driven turbulence models by enforcing the conditioning as a requirement on these models.

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Section: Introductionmentioning

confidence: 99%

Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned

et al. 2019

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“…Therefore, after calculating SAE notchback reference body (Basara et al 2002), Ahmed Body (Basara and Jakirlic 2003) or Morel Body (Basara and Alajbegovic 1998) etc., the same procedure was applied for more complex models. Therefore, after calculating SAE notchback reference body (Basara et al 2002), Ahmed Body (Basara and Jakirlic 2003) or Morel Body (Basara and Alajbegovic 1998) etc., the same procedure was applied for more complex models.…”

Section: Resultsmentioning

confidence: 99%

“…4. The HTM approach was previously tested on many simple examples including all cases shown in the introduction (Basara and Jakirlic 2003). However, it was possible to perform such numerical study as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

The Aerodynamics of Heavy Vehicles: Trucks, Buses, and Trains

2004

Lecture Notes in Applied and Computational Mechanics

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“…While the propagation of random eddy viscosity is straightforward and doesn't require modification of the solver in general, the propagation of random Reynolds stresses is numerically more challenging. To achieve numerically stable performance of the solver, we adopt a blending of the random Reynolds stress, which we wish to propagate, and a contribution based on the Boussinesq assumption [42]. While the latter alters the propagated effective Reynolds stress, it promotes numerical convergence of the solver.…”

Section: Mlmc-rans Implementationmentioning

confidence: 99%

Stochastic turbulence modeling in RANS simulations via multilevel Monte Carlo

2020

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A multilevel Monte Carlo (MLMC) method for quantifying model-form uncertainties associated with the Reynolds-Averaged Navier-Stokes (RANS) simulations is presented. Two, high-dimensional, stochastic extensions of the RANS equations are considered to demonstrate the applicability of the MLMC method.The first approach is based on global perturbation of the baseline eddy viscosity field using a lognormal random field. A more general second extension is considered based on the work of [Xiao et al.(2017)], where the entire Reynolds Stress Tensor (RST) is perturbed while maintaining realizability. For two fundamental flows, we show that the MLMC method based on a hierarchy of meshes is asymptotically faster than plain Monte Carlo. Additionally, we demonstrate that for some flows an optimal multilevel estimator can be obtained for which the cost scales with the same order as a single CFD solve on the finest grid level.The Reynolds-Averaged Navier-Stokes (RANS) equations combined with turbulence closure models are widely utilized in engineering to predict flows with high Reynolds number. These turbulence closure models are used to obtain an approximate Reynolds stress tensor that is responsible for coupling the mean flow with turbulence. Although many turbulence models exist in the literature, there is no single model that generalizes well to all classes of turbulent flows [1,2]. Specifically, the performance depends on the modeling assumptions and the type of flow used to calibrate the so-called closure coefficients that are needed as inputs to a turbulence model.Since the dominant source of error in the flow prediction comes from the turbulence modeling, a number of approaches have already been developed for the model-form uncertainty quantification (UQ) of RANS simulations, see e.g. [3,4] for recent reviews. The majority of these approaches are based on the perturbation of baseline RANS models. One way to achieve this is by injecting uncertainties in the closure coefficients [5,6,7,8] of turbulence models. Another more general physics-based approaches exists, which typically introduces randomness directly into the modeled Reynolds Stress Tensor (RST), either by perturbing its eigenvalues [9,10,11], tensor invariants [12,13] or the entire RST field [14]. One can also classify these stochastic models in terms of global and local perturbation (in space). For global approaches, such as in [5,6,7,10], the magnitude of perturbations in closure coefficients, eigenvalues of RST, etc. is the same throughout the flow domain. This translates to a low-dimensional UQ problem which can be efficiently handled by deterministic sampling techniques like stochastic collocation or just by simulating flows for Email addresses: pkumar@cwi.nl (Prashant Kumar), m.schmelzer@tudelft.nl (Martin Schmelzer), r.p.dwight@tudelft.nl (Richard P. Dwight) arXiv:1811.00872v1 [physics.comp-ph] 1 Nov 2018

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A new hybrid turbulence modelling strategy for industrial CFD

Cited by 40 publications

References 37 publications

Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned

Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned

The Aerodynamics of Heavy Vehicles: Trucks, Buses, and Trains

Stochastic turbulence modeling in RANS simulations via multilevel Monte Carlo

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