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Titel:

Methods for neural network based prediction of roughness-induced crossflow-like vorticity in re-entry scenarios

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Ulrich, Friedrich; Sedlmeyr, Thomas; Stemmer, Christian
Abstract:
Laminar-turbulent transition plays a critical role in the design of re-entry vehicles. This study investigates roughness-induced transition caused by a randomly distributed roughness patch. In the wake of the roughness patch, a cross-flow-like vortex is formed by the distributed roughness. The cross-flow-like vortex generates regions of strong wall-normal and spanwise gradients in the streamwise velocity where unstable acoustic modes are strongly amplified leading to transition. This study is us...     »
Stichworte:
Machine learning Laminar-turbulent transition High-enthalpy boundary-layer flows Saliency maps Distributed roughness
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
International Journal of Heat and Fluid Flow
Jahr:
2024
Band / Volume:
107
Seitenangaben Beitrag:
109375
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.1016/j.ijheatfluidflow.2024.109375
WWW:
https://www.sciencedirect.com/science/article/pii/S0142727X24001000
Verlag / Institution:
Elsevier BV
E-ISSN:
0142-727X
Hinweise:
Acknowledgments This research was supported by funds from the TUM International Graduate School of Science and Engineering (IGSSE) and the Cusanuswerk e.V scholarship. Further, the authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu ) for supporting this project by providing computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de).
Eingereicht (bei Zeitschrift):
15.06.2023
Angenommen (von Zeitschrift):
04.04.2024
Publikationsdatum:
01.07.2024
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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