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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
Fulltext / DOI:
doi:10.1016/j.jfluidstructs.2021.103316
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...     »
Submitted:
19.09.2020
Accepted:
12.05.2021
Date of publication:
01.07.2021
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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