Turbulence model conditioning for vortex dominated flows based on experimental results

Matteo Moioli, Christian Breitsamter, Kaare A. Sørensen

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)

Abstract

Focusing on vortex dominated flows, related to delta wings, Reynolds Averaged Navier-Stokes numerical simulation with eddy viscosity models is an appropriate tool with respect to the computational effort. Nevertheless, higher accuracy is required to improve the prediction of the vortex flow development and characteristics. More complex turbulence models like Reynolds Stress Models introduce stability issues and increase the computational costs, not always improving the flow solution; Detached Eddy Simulation and Large Eddy Simulation models are not always a viable option due to computational resources. The idea introduced is to enhance the Spalart-Allmaras one-equation eddy viscosity model with additional destruction terms, exclusively active in the vortex region, optimized with experimental data as reference. The vortex topology is adjusted by correcting the distribution of eddy viscosity. The level of improvement in data accuracy, the range of improvements for the considered test cases and the flexibility of the model variation are investigated to understand the potential of the approach.

OriginalspracheEnglisch
Titel31st Congress of the International Council of the Aeronautical Sciences, ICAS 2018
Herausgeber (Verlag)International Council of the Aeronautical Sciences
ISBN (elektronisch)9783932182884
PublikationsstatusVeröffentlicht - 2018
Veranstaltung31st Congress of the International Council of the Aeronautical Sciences, ICAS 2018 - Belo Horizonte, Brasilien
Dauer: 9 Sept. 201814 Sept. 2018

Publikationsreihe

Name31st Congress of the International Council of the Aeronautical Sciences, ICAS 2018

Konferenz

Konferenz31st Congress of the International Council of the Aeronautical Sciences, ICAS 2018
Land/GebietBrasilien
OrtBelo Horizonte
Zeitraum9/09/1814/09/18

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