Utveckling av turbulensmodeller med hjälp av maskininlärning i Python
Typ
Examensarbete på kandidatnivå
Bachelor Thesis
Bachelor Thesis
Program
Publicerad
2023
Författare
Batlouni, Fadi
Elm Jonsson, Benjamin
Fjeldså, Ole
Persson, Niclas
Ånestrand, Leo
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Turbulence modelling is a central component in the field of computational fluid
dynamics (CFD), which aims to accurately simulate turbulent flows. To do this,
solving the Navier-Stokes equations numerically is necessary. However, due to the
fact that turbulence is rather chaotic, direct numerical simulation (DNS) is com putationally expensive. The idea of using Reynold-Averaged Navier Stokes (RANS)
and turbulence models such as the k −ω model is to reduce the computational costs
by simplifying the equations, at the cost of losing accuracy.
The main aim of the project is to explore if some specific parameters used in these
simplified equations, usually assumed to be constant, can be optimized with the use
of machine learning (ML), and how the improved models fare against previous mo dels. The ML methods used are support vector regression (SVR), k nearest neighbors
regression (kNN), and neural networks. The project found that optimizing Cµ with
the k − ω model is inessential, since the model badly predicts k even though the
fraction of k and ω can be correct making the optimization difficult. Nonetheless,
optimizing other parameters still proves to be rewarding. The use of machine lear ning to improve turbulence models is considered promising and should be explored
further.
Beskrivning
Ämne/nyckelord
Maskininlärning , turbulensmodellering , CDF , DNS , SVR , RANS , Python , strömningsmekanik