Application of an Evolutionary Algorithm to LES Modelling of Turbulent Premixed Flames

M. Schöpplein, J. Weatheritt, M. Talei, M. Klein () and R. D. Sandberg
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M. Schöpplein: Bundeswehr University Munich, Department of Aerospace Engineering
J. Weatheritt: University of Melbourne, Department of Mechanical Engineering
M. Talei: University of Melbourne, Department of Mechanical Engineering
M. Klein: Bundeswehr University Munich, Department of Aerospace Engineering
R. D. Sandberg: University of Melbourne, Department of Mechanical Engineering

Chapter Chapter 13 in Data Analysis for Direct Numerical Simulations of Turbulent Combustion, 2020, pp 253-271 from Springer

Abstract: Abstract Gene Expression Programming (GEP) has been used successfully for modelling the unclosed terms in the context of Reynolds Averaged Navier–Stokes (RANS) and Large Eddy Simulation (LES)-based turbulence modelling. In contrast to deep-learning-based methodologies, this approach has the advantage that the model can be documented in the form of a mathematical expression; it can be interpreted and easily implemented in existing solvers. Recently, application of GEP to a priori LES modelling has demonstrated the efficiency of the approach to find high fidelity LES closures. The present contribution explains the methodology, reviews recent work in the field and focuses on the robustness of the method and the scope for future efficiency improvements, by applying it to the modelling of the unclosed stress tensor in turbulent premixed statistically planar flames.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-44718-2_13

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DOI: 10.1007/978-3-030-44718-2_13

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