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 learning
RANS
Random Forest
Turbulence

 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
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