Machine-learn-driven prediction of streamwise vorticity induced by a random distributed roughness path in hypersonic flow
| Autor: | Friedrich Ulrich, Christian Stemmer |
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| Přispěvatelé: | Lehrstuhl für Aerodynamik und Strömungsmechanik |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Popis: | Boundary-layer transition on the surface of a space transportation vehicle highly influences the heat-flux the thermal protection system has to withstand in a re-entry scenario. Distributed surface roughness can cause cross-flow like vortices in the wake of the roughness patch that highly destabilize the flow regime. The variety of roughness parameters which influence the generation of a cross-flow vortex is addressed with the training of a Deep Neural Network. This paper presents a database of Direct Numerical Simulations (DNS) of a restricted domain of an Apollo-like space capsule with different distributed roughness patches. This study is using machine learning to predict the streamwise vorticity of a cross-flow-like vortex generated by a distributed random roughness patch. A sensitivity analysis identifies the importance of surface derivatives and the location of the maximum and minimum peak in the roughness patch. |
| Databáze: | OpenAIRE |
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