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Hoefnagel, Kaj (author)
Computational fluid dynamics (CFD) is an important tool in design involving fluid flow. Scale-resolving CFD methods exist, but they are too computationally expensive for practical design. Instead, the relatively cheap Reynolds-averaged Navier-Stokes (RANS) approach is the industry standard, specifically models based on the Boussinesq hypothesis,...
master thesis 2023
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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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Miori, Nicolò (author)
Recent years have seen an increase in studies focusing on data-driven techniques to enhance modelling approaches like the two-equation turbulence models of Reynolds-averaged Navier-Stokes (RANS). Different techniques have been implemented to improve the results from these simulations. In particular, the main focus has been on overcoming the...
master thesis 2022
document
Hemmes, Jasper (author)
When simulating fluids the industry standard is Reynolds averaged Navier-Stokes (RANS). However, the results for certain flows are inaccurate. The main source of error in popular RANS turbulence models is the Boussinesq approximation, assuming a linear relationship between the Reynolds stress anisotropy and the mean rate of strain. Experiments...
master thesis 2022
Searched for: subject%3A%22Data%255C-Driven%255C%2Bturbulence%255C%2Bmodelling%22
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