Searched for: contributor%3A%22Dwight%2C+R.P.+%28mentor%29%22
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van Ede, Matthijs (author)
Classical RANS (Reynolds-Averaged Navier-Stokes) turbulence models have limited accuracy in the prediction of the flow over the wing-body geometry. Therefore, this work focuses on improving the prediction accuracy of the classical k-ω SST turbulence model for the junction flow by means of the data-driven method SpaRTA. In the SpaRTA methodology...
master thesis 2023
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Park, ChangKyu (author)
Studies revolving around data-driven methods have been on a rise in recent years to improve highly modelled methods such as the two-equation turbulence models of Reynolds-averaged Navier-Stokes (RANS). Similarly, such data-driven methods are implemented into partially-averaged Navier-Stokes (PANS). PANS is a young bridging method that fulfils...
master thesis 2022
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Khurana, Parv (author)
In recent years, many data-driven approaches which leverage high-fidelity reference data have been developed to augment the performance of Reynolds Averaged Navier–Stokes (RANS) turbulence models by providing an improved closure to the governing fluid flow equations. The goal of this M.Sc. thesis is to apply and extend one such data-driven...
master thesis 2021
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Kokee, Louis (author)
Computational Fluid Dynamics based on RANS models remain the standard but suffer from high errors in complex flows. In particular, turbulent kinetic energy is over-produced in high strain rate regions, such as the near wake of wind turbine flows. Data-driven turbulence modelling methods aim to derive novel turbulence models with lower...
master thesis 2021
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Goderie, Michiel (author)
Wind turbine wakes cause significant reductions in power production and increased fatigue damage for downwind turbines. Thus, they affect the wind levelized cost of energy. Computational Fluid Dynamics (CFD) can be used to quantify the wake characteristics, whereby Reynolds-averaged Navier-Stokes (RANS) has the most potential for industrial...
master thesis 2020
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Kaandorp, Mikael (author)
The application of machine learning algorithms as data-driven turbulence modelling tools for Reynolds Averaged Navier-Stokes (RANS) simulations is presented. A novel machine learning algorithm, called the Tensor Basis Random Forest (TBRF) is introduced, which is able to predict the Reynolds stress anisotropy tensor. The algorithm is trained on...
master thesis 2018
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