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Analysis and Clustering of Flow Phenomena Using Machine Learning Methods

Gschwendtner, Philipp (2021) Analysis and Clustering of Flow Phenomena Using Machine Learning Methods. Bachelor's, DLR German Aerospace Center.

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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:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Gschwendtner, PhilippUNSPECIFIEDUNSPECIFIED
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

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