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Doctoral thesis . 2025
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Thesis . 2024
Data sources: Datacite
ZENODO
Thesis . 2025
License: CC BY
Data sources: Datacite
ZENODO
Thesis . 2025
License: CC BY
Data sources: Datacite
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Neural Network based Shock Detection in Compressible Flow at Space-re-entry Conditions

descriptionPublicationkeyboard_double_arrow_right Thesis , Doctoral thesis 01 Jan 2024 English Publisher:Unpublished
Authors: Zhao, Jianai;

doi: 10.13140/rg.2.2.25681.70241 , 10.5281/zenodo.16894795 , 10.5281/zenodo.16894796

Neural Network based Shock Detection in Compressible Flow at Space-re-entry Conditions

Abstract

Master Thesis by Jianai Zhao at TU Darmstadt FDY institute, 05/2024 - 11/2024. Developed AI-trained learning-based artificial viscosity stabilization algorithms from the existing model-based Persson Sensor, and integrated them into the existing customized CFD (BoSSS Solver) for online CFD simulations in high-performance computing (HPC).

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Keywords

Artificial intelligence, Aerospace engineering, Fluid dynamics, Machine learning, Fluid mechanics, Computational fluid dynamics, Supercomputers, Supervised learning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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