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...

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Bibliographic Details

Published in:	Proceedings of the Combustion Institute Vol. 40; no. 1-4; p. 105382
Main Authors:	Zapata Usandivaras, José F., Bauerheim, Michael, Cuenot, Bénédicte, Urbano, Annafederica
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 2024 Elsevier
Subjects:	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 Access:	Get full text
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