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physics.comp-ph2020
Customized data-driven RANS closures for bi-fidelity LES-RANS optimization
Yu Zhang, Richard P. Dwight, Martin Schmelzer +3
Multi-fidelity optimization methods promise a high-fidelity optimum at a cost only slightly greater than a low-fidelity optimization. This promise is seldom achieved in practice, d…
physics.comp-ph2019
Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
Martin Schmelzer, Richard P. Dwight, Paola Cinnella
** This article is published (open-access). ** A novel deterministic symbolic regression method SpaRTA (Sparse Regression of Turbulent Stress Anisotropy) is introduced to infer alg…
physics.comp-ph2018
Stochastic turbulence modeling in RANS simulations via Multilevel Monte Carlo
Prashant Kumar, Martin Schmelzer, Richard P. Dwight
A multilevel Monte Carlo (MLMC) method for quantifying model-form uncertainties associated with the Reynolds-Averaged Navier-Stokes (RANS) simulations is presented. Two, high-dimen…