Universität zu Köln

Eichinger, Matthias, Heinlein, Alexander ORCID: 0000-0003-1578-8104 and Klawonn, Axel ORCID: 0000-0003-4765-7387 (2019). Stationary flow predictions using convolutional neural networks. Technical Report.

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Abstract

Computational Fluid Dynamics (CFD) simulations are a numerical tool to model and analyze the behavior of fluid flow. However, accurate simulations are generally very costly because they require high grid resolutions. In this paper, an alternative approach for computing flow predictions using Convolutional Neural Networks (CNNs) is described; in particular, a classical CNN as well as the U-Net architecture are used. First, the networks are trained in an expensive offline phase using flow fields computed by CFD simulations. Afterwards, the evaluation of the trained neural networks is very cheap. Here, the focus is on the dependence of the stationary flow in a channel on variations of the shape and the location of an obstacle. CNNs perform very well on validation data, where the averaged error for the best networks is below 3%. In addition to that, they also generalize very well to new data, with an averaged error below 10%.

Item Type:	Monograph (Technical Report)
Creators:	Creators Email ORCID ORCID Put Code Eichinger, Matthias eichingm@smail.uni-koeln.de UNSPECIFIED UNSPECIFIED Heinlein, Alexander alexander.heinlein@uni-koeln.de https://orcid.org/0000-0003-1578-8104 UNSPECIFIED Klawonn, Axel axel.klawonn@uni-koeln.de https://orcid.org/0000-0003-4765-7387 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-104409
Series Name at the University of Cologne:	Technical report series. Center for Data and Simulation Science
Volume:	2019,20
Date:	16 December 2019
Language:	English
Faculty:	Central Institutions / Interdisciplinary Research Centers
Divisions:	Weitere Institute, Arbeits- und Forschungsgruppen > Center for Data and Simulation Science (CDS)
Subjects:	Natural sciences and mathematics Mathematics Technology (Applied sciences)
Uncontrolled Keywords:	Keywords Language computational fluid dynamics English machine learning English convolutional neural networks English
URI:	http://kups.ub.uni-koeln.de/id/eprint/10440