Deep convolutional autoencoders for the time–space reconstruction of liquid rocket engine flames

The integration of high-fidelity numerical simulations into the rocket engine design-cycle promises to cut costs in an ever more competitive launch market. Although still being intractable in the optimization of most of combustion devices, their potential can be harnessed via appropriate reduced ord...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Proceedings of the Combustion Institute Jg. 40; H. 1-4; S. 105382
Hauptverfasser:	Zapata Usandivaras, José F., Bauerheim, Michael, Cuenot, Bénédicte, Urbano, Annafederica
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 2024 Elsevier
Schlagworte:	Autoencoders Deep learning Fluid Dynamics Physics Reduced order modeling Rocket engine Deep learning Autoencoders Reduced order modeling Rocket engine
ISSN:	1540-7489, 1540-7489
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!