Optimization of the Realizable k - ε Turbulence Model Especially for the Simulation of Road Vehicle

Gu, Zhengqi; Xiao, Song; Jiang, Yejie; Gong, Xu

doi:10.4271/2012-01-0778

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The Latin Hypercube algorithm allows a regular sampling throughout the design space to attain the most of information out of the points analysed with the minimum number of design evaluations. LHC sampling schemes have been extensively used in DoE based metamodel development initiatives as evident from the studies (Awad et al, 2005;Boussouf, 2011;do Rego et al, 2006;Gao et al, 2012;Scarlat et al, 2002;Sun et al, 2011;Wang et al, 2004;Gu et al, 2012) to name a few.…”

Section: The Design Of Experimentsmentioning

confidence: 99%

ANN metamodel assisted Particle Swarm Optimization of the performance-emission trade-off characteristics of a single cylinder CRDI engine under CNG dual-fuel operation

Roy

Bose

Banerjee

2014

Journal of Natural Gas Science and Engineering

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Section: The Design Of Experimentsmentioning

confidence: 99%

ANN metamodel assisted Particle Swarm Optimization of the performance-emission trade-off characteristics of a single cylinder CRDI engine under CNG dual-fuel operation

Roy

Bose

Banerjee

2014

Journal of Natural Gas Science and Engineering

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The role of opened fire doors in enhanced heat exchange of long-distance utility tunnels

Wang,

Yin,

Kang

et al. 2024

Tunnelling and Underground Space Technology

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Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients

McConkey,

Kalia,

Yee

et al. 2024

HFF

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Purpose Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization. Design/methodology/approach The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure. Findings The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel. Originality/value To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

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Optimization of the Realizable k - ε Turbulence Model Especially for the Simulation of Road Vehicle

Cited by 3 publications

References 5 publications

ANN metamodel assisted Particle Swarm Optimization of the performance-emission trade-off characteristics of a single cylinder CRDI engine under CNG dual-fuel operation

ANN metamodel assisted Particle Swarm Optimization of the performance-emission trade-off characteristics of a single cylinder CRDI engine under CNG dual-fuel operation

The role of opened fire doors in enhanced heat exchange of long-distance utility tunnels

Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients

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