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Grabe, Cornelia (author), Jäckel, Florian (author), Khurana, Parv (author), Dwight, R.P. (author)Purpose: This paper aims to improve Reynolds-averaged Navier Stokes (RANS) turbulence models using a data-driven approach based on machine learning (ML). A special focus is put on determining the optimal input features used for the ML model. Design/methodology/approach: The field inversion and machine learning (FIML) approach is applied to...journal article 2023
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Xu, R. (author), Zhou, Xu Hui (author), Han, Jiequn (author), Dwight, R.P. (author), Xiao, Heng (author)In fluid dynamics, constitutive models are often used to describe the unresolved turbulence and to close the Reynolds averaged Navier–Stokes (RANS) equations. Traditional PDE-based constitutive models are usually too rigid to calibrate with a large set of high-fidelity data. Moreover, commonly used turbulence models are based on the weak...journal article 2022
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Steiner, J. (author), Dwight, R.P. (author), Viré, A.C. (author)The state-of-the-art in wind-farm flow-physics modeling is Large Eddy Simulation (LES) which makes accurate predictions of most relevant physics, but requires extensive computational resources. The next-fidelity model types are Reynolds-Averaged Navier–Stokes (RANS) which are two orders of magnitude cheaper, but resolve only mean quantities...journal article 2022
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Huijing, Jasper P. (author), Dwight, R.P. (author), Schmelzer, M. (author)In this short note we apply the recently proposed data-driven RANS closure modelling framework of Schmelzer et al.(2020) to fully three-dimensional, high Reynolds number flows: namely wall-mounted cubes and cuboids at Re=40,000, and a cylinder at Re=140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based...journal article 2021
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Schmelzer, M. (author), Dwight, R.P. (author), Cinnella, Paola (author)A novel deterministic symbolic regression method SpaRTA (Sparse Regression of Turbulent Stress Anisotropy) is introduced to infer algebraic stress models for the closure of RANS equations directly from high-fidelity LES or DNS data. The models are written as tensor polynomials and are built from a library of candidate functions. The machine...journal article 2019