260 citations · 443 across the 9 of their papers we have counts for
10 papers
Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes
Deepali Singh, Richard P. Dwight, Kasper Laugesen +2
Heteroscedastic Gaussian process regression, based on the concept of chained Gaussian processes, is used to build surrogates to predict site-specific loads on an offshore wind turb…
A PDE-free, neural network-based eddy viscosity model coupled with RANS equations
Ruiying Xu, Xu-Hui Zhou, Jiequn Han +2
Most turbulence models used in Reynolds-averaged Navier-Stokes (RANS) simulations are partial differential equations (PDE) that describe the transport of turbulent quantities. Such…
Classifying regions of high model error within a data-driven RANS closure: Application to wind turbine wakes
J. Steiner, R. Dwight, A. Viré
Data-driven Reynolds-Averaged Navier-Stokes (RANS) turbulence closures are increasing seen as a viable alternative to general-purpose RANS closures, when LES reference data is avai…
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…