6 papers
Data-driven RANS closures for three-dimensional flows around bluff bodies
Jasper P. Huijing, Richard P. Dwight, Martin Schmelzer
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: na…
Data-driven RANS closures for wind turbine wakes under neutral conditions
Julia Steiner, Richard P. Dwight, Axelle Viré
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 computation…
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…
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…
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…
Data-Driven Modelling of the Reynolds Stress Tensor using Random Forests with Invariance
Mikael L. A. Kaandorp, Richard P. Dwight
A novel machine learning algorithm is presented, serving as a data-driven turbulence modeling tool for Reynolds Averaged Navier-Stokes (RANS) simulations. This machine learning alg…