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Development of Artificial Neural Networks with Integrated Conditional Random Fields Capable of Predicting Non-linear Dynamics of the Flow Around Cylinders

Herzog, Sebastian and Wagner, Claus (2020) Development of Artificial Neural Networks with Integrated Conditional Random Fields Capable of Predicting Non-linear Dynamics of the Flow Around Cylinders. In: 21st STAB/DGLR Symposium on New Results in Numerical and Experimental Fluid Mechanics, 142, pp. 71-79. Springer Nature. 21. STAB/DGLR Symposium 2018, 6.-7. Nov. 2018, Darmstadt, Deutschland. doi: 10.1007/978-3-030-25253-3_7. ISBN 978-3-030-25252-6. ISSN 1612-2909.

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Official URL: https://doi.org/10.1007/978-3-030-25253-3_7

Abstract

This paper presents a new approach intended to predict flow dynamics based on observed data. The approach uses artificial neural networks extended by an adapted conditional random field. This artificial neural network is trained end-to-end and the embedded conditional random field memorizes previous events and uses this memory for flow predictions. The prediction capability of the proposed method is demonstrated for flows around cylinders which are computed with a Lattice Boltzmann method in order to train the artificial neural network.

Item URL in elib:https://elib.dlr.de/135609/
Document Type:Conference or Workshop Item (Speech)
Additional Information:Print ISBN 978-3-030-25252-6 Online ISBN 978-3-030-25253-3 ISSN-electronic 1860-0824
Title:Development of Artificial Neural Networks with Integrated Conditional Random Fields Capable of Predicting Non-linear Dynamics of the Flow Around Cylinders
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Herzog, Sebastiansebastian.herzog (at) dlr.deUNSPECIFIED
Wagner, ClausClaus.Wagner (at) DLR.deUNSPECIFIED
Date:2020
Journal or Publication Title:21st STAB/DGLR Symposium on New Results in Numerical and Experimental Fluid Mechanics
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:142
DOI :10.1007/978-3-030-25253-3_7
Page Range:pp. 71-79
Editors:
EditorsEmailEditor's ORCID iD
Dillmann, AndreasAndreas.Dillmann@dlr.deUNSPECIFIED
Heller, GerdAirbusUNSPECIFIED
Krämer, EwaldInstitut für Aerodynamik und Gasdynamik Universität StuttgartUNSPECIFIED
Wagner, ClausClaus.Wagner@dlr.deUNSPECIFIED
Tropea, CameronFachgebiet Strömungslehre und Aerodynamik, TU DarmstadtUNSPECIFIED
Jarkilic, SuadTU DarmstadtUNSPECIFIED
Publisher:Springer Nature
Series Name:Notes on Numerical Fluid Mechanics and Multidisciplinary Design
ISSN:1612-2909
ISBN:978-3-030-25252-6
Status:Published
Keywords:System modeling, Machine learning, ANNs
Event Title:21. STAB/DGLR Symposium 2018
Event Location:Darmstadt, Deutschland
Event Type:international Conference
Event Dates:6.-7. Nov. 2018
Organizer:STAB/DGLR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Rail Transport
DLR - Research area:Transport
DLR - Program:V SC Schienenverkehr
DLR - Research theme (Project):V - NGT BIT
Location: Göttingen
Institutes and Institutions:Institute for Aerodynamics and Flow Technology > Ground Vehicles
Deposited By: Wagner, Prof. Dr.-Ing. Claus
Deposited On:05 Aug 2020 09:22
Last Modified:05 Aug 2020 09:22

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