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...

Full description

Bibliographic Details

Published in:	Combustion and flame Vol. 274; p. 113981
Main Authors:	Ding, Siyu, Ni, Chenxu, Chu, Xu, Lu, Qingzhou, Wang, Xingjian
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.04.2025
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!