Searched for: subject%3A%22Data%255C-driven%255C+turbulence%255C+modeling%22
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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
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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+turbulence%255C+modeling%22
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