Print Email Facebook Twitter Machine Learning for Data-Driven RANS Turbulence Modelling Title Machine Learning for Data-Driven RANS Turbulence Modelling Author Kaandorp, Mikael (TU Delft Aerospace Engineering; TU Delft Aerodynamics, Wind Energy & Propulsion) Contributor Dwight, R.P. (mentor) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2018-03-01 Abstract 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 several flow cases using DNS/LES data, and used to predict the Reynolds stress anisotropy tensor on multiple test flow cases. Predictions are then propagated with a custom OpenFOAM solver to yield an improved flow field. Results show that the TBRF algorithm is able to accurately predict the anisotropy tensor for various flow cases, with most of the predictions being realizable and close to the DNS/LES reference data. Resulting mean flows for a square duct and a backward facing step show great resemblance to corresponding DNS and experimental data-sets. Subject machine learningRANSRandom ForestTurbulence To reference this document use: http://resolver.tudelft.nl/uuid:f833e151-7c0f-414c-8217-5af783c88474 Part of collection Student theses Document type master thesis Rights © 2018 Mikael Kaandorp Files PDF Thesis_MikaelKaandorp.pdf 40.13 MB Close viewer /islandora/object/uuid:f833e151-7c0f-414c-8217-5af783c88474/datastream/OBJ/view