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Document type:
Zeitschriftenaufsatz 
Author(s):
Rozov, Vladyslav; Breitsamter, Christian 
Title:
Data-driven prediction of unsteady pressure distributions based on deep learning 
Abstract:
In the present work, an efficient Reduced-Order Model is developed for the prediction of motion-induced unsteady pressure distributions. The model is trained on the basis of synthetic data generated by full-order Computational Fluid Dynamics (CFD) simulations. The nonlinear identification task is to predict a snapshot representing the pressure distribution for the current time step based on respective snapshots of previous time steps and applied excitation. Once a Reduced-Order Model is conditio...    »
 
Keywords:
Computational Fluid Dynamics; Deep learning; LANN model; Reduced-Order Models; Transonic flight; Unsteady aerodynamics 
Dewey Decimal Classification:
620 Ingenieurwissenschaften 
Journal title:
Journal of Fluids and Structures 
Year:
2021 
Journal volume:
104 
Pages contribution:
103316 
Covered by:
Scopus 
Language:
en 
Publisher:
Elsevier BV 
E-ISSN:
0889-9746 
Notes:
Funding text The authors gratefully acknowledge the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for funding this work in the framework of the research unit FOR 2895 (Unsteady flow and interaction phenomena at high speed stall conditions), subproject TP7, grant number BR1511/14-1 . The Gauss Centre for Supercomputing e.V. ( www.gauss-centre.eu ) is gratefully acknowledged for funding this project by providing computing time on the Linux-Cluster at Leibniz Supercomputing Cen...    »
 
Accepted:
12.05.2021 
Date of publication:
01.07.2021 
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik