Reduced-order modeling via convolutional autoencoder for emulating combustion of hydrogen/methane fuel blends

Numerical simulations are essential in comprehending the processes of flow and combustion in aerospace and power-generation systems. Yet, substantial computational demands render these simulations prohibitively expensive for design and optimization, owing to the large amount of design parameters to...

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Vydané v:	Combustion and flame Ročník 274; s. 113981
Hlavní autori:	Ding, Siyu, Ni, Chenxu, Chu, Xu, Lu, Qingzhou, Wang, Xingjian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.04.2025
Predmet:	Convolutional autoencoder Kriging Nonlinear feature Proper orthogonal decomposition Reduced-order model (ROM) Proper orthogonal decomposition Nonlinear feature Convolutional autoencoder Reduced-order model (ROM) Kriging
ISSN:	0010-2180
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