Treffer: Study of machine learning techniques for accelerating finite element simulations of Stokes flows
Title:
Study of machine learning techniques for accelerating finite element simulations of Stokes flows
Authors:
Contributors:
Universitat Politècnica de Catalunya. Departament de Resistència de Materials i Estructures a l'Enginyeria, Hernández Ortega, Joaquín Alberto, Drougkas, Anastasios
Publisher Information:
Universitat Politècnica de Catalunya
Publication Year:
2023
Collection:
Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
Subject Terms:
Àrees temàtiques de la UPC::Enginyeria mecànica::Mecànica de fluids, Finite element method, Navier-Stokes equations, Computational fluid dynamics, Machine learning, Reduced Order Modelling, Coding, Fluid mechanics, Partial differential equations, Elements finits, Mètode dels, Equacions de Navier-Stokes, Dinàmica de fluids computacional, Aprenentatge automàtic
Document Type:
Dissertation
bachelor thesis
File Description:
application/pdf
Language:
English
Relation:
Availability:
Rights:
Attribution 4.0 International ; http://creativecommons.org/licenses/by/4.0/ ; Open Access
Accession Number:
edsbas.3F9A368
Database:
BASE
Weitere Informationen
This project starts by studying the finite element method for the steady state Navier-Stokes equations, afterwards it is implemented in Matlab and optimized via Vectorization achieving up to 5000x speed-up in some calculations. Then a reduced order model is studied to decrease the computational time of performing different simulations with slight modifications to the input parameters. Finally, the results obtained are compared against an already tested FEM code, Kratos Multiphysics, and against literature, and the performance of the developed solver for the equations is analyzed. It has been observed that the results obtained with the present work’s solver are almost equal to those made by the reference alternatives.