Gschwendtner, Philipp (2021) Analysis and Clustering of Flow Phenomena Using Machine Learning Methods. Bachelor's, DLR German Aerospace Center.
![]() |
PDF
- Only accessible within DLR
7MB |
Abstract
The present work investigates how unsupervised machine learning methods can be applied to detect complex phenomena in flow simulation data. For this purpose, a variational autoencoder (VAE) is implemented. It allows for non-linear dimensionality reduction of a data set, resulting in the so-called feature space. Suitable parameters for a VAE are determined, and a number of algorithms for finding clusters in the feature space are introduced and tested. The resulting clusters are investigated in a 3D plotting software, showing that the model can detect complex phenomena, e.g., a shock or a shear layer.
Item URL in elib: | https://elib.dlr.de/140062/ | ||||||
---|---|---|---|---|---|---|---|
Document Type: | Thesis (Bachelor's) | ||||||
Title: | Analysis and Clustering of Flow Phenomena Using Machine Learning Methods | ||||||
Authors: |
| ||||||
Date: | June 2021 | ||||||
Refereed publication: | No | ||||||
Open Access: | No | ||||||
Gold Open Access: | No | ||||||
In SCOPUS: | No | ||||||
In ISI Web of Science: | No | ||||||
Number of Pages: | 68 | ||||||
Status: | Published | ||||||
Keywords: | machine learning, flow simulation, CFD, dimensionality reduction, data, VAE, variational autoencoder, tensorflow | ||||||
Institution: | DLR German Aerospace Center | ||||||
Department: | Institute of Propulsion Technology | ||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||
HGF - Program: | Aeronautics | ||||||
HGF - Program Themes: | Clean Propulsion | ||||||
DLR - Research area: | Aeronautics | ||||||
DLR - Program: | L CP - Clean Propulsion | ||||||
DLR - Research theme (Project): | L - Virtual Engine | ||||||
Location: | Köln-Porz | ||||||
Institutes and Institutions: | Institute of Propulsion Technology > Numerical Methodes | ||||||
Deposited By: | Bleh, Alexander | ||||||
Deposited On: | 11 Feb 2021 13:53 | ||||||
Last Modified: | 11 Feb 2021 13:53 |
Repository Staff Only: item control page