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Titel:

Turbulence Model Extension for Vortex Dominated Flows and Optimization with Experimental Data

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Moioli, M.; Breitsamter, C.; Sørensen, K.
Abstract:
This document provides information and instructions for preparing a Full Paper to be included in the Proceedings of 14th WCCM – ECCOMAS CONGRESS 2020. © 2021, Univelt Inc., All rights reserved.
Stichworte:
Artificial Neural Network; Data-Driven Models; Delta Wing; Modeling; Optimization; RANS; Turbulence; Vortex Dominated Flows
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
14th WCCM-ECCOMAS Congress
Verlag / Institution:
CIMNE
Publikationsdatum:
01.01.2021
Jahr:
2021
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.23967/wccm-eccomas.2020.349
WWW:
https://www.scipedia.com/public/Moioli_et_al_2021a
Hinweise:
Funding text 1 The funding of parts of these investigations within the LUFO VI-1 project DIGIfly-I (Digital Flight of Air Vehicles - Adaptive turbulence model with neural network conditioning applied to wing flows featuring leading edge vortex systems, FKZ: 20X1909I) by the German Federal Ministry for Economic Affairs and Energy (BMWi) is gratefully acknowledged. Furthermore, the authors thank Airbus Defence and Space for the fruitful cooperation and the German Aerospace Center (DLR) for provid...     »
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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