Deep learning-driven analysis for cellular structure characteristics of spherical premixed hydrogen-air flames

Zhang, G. orcid.org/0000-0002-0553-0778, Xu, H., Wu, D. orcid.org/0000-0003-4500-4390 et al. (5 more authors) (2024) Deep learning-driven analysis for cellular structure characteristics of spherical premixed hydrogen-air flames. International Journal of Hydrogen Energy, 68. pp. 63-73. ISSN 0360-3199

Abstract

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2024 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords: Premixed combustion; Instability; Spherical flame; Deep learning; Image processing
Dates:
  • Published: 28 May 2024
  • Published (online): 25 April 2024
  • Accepted: 20 April 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Thermofluids, Surfaces & Interfaces (iETSI) (Leeds)
Funding Information:
Funder
Grant number
EPSRC (Engineering and Physical Sciences Research Council)
EP/W002299/1
Depositing User: Symplectic Publications
Date Deposited: 11 Jun 2024 10:51
Last Modified: 11 Jun 2024 10:51
Published Version: https://www.sciencedirect.com/science/article/pii/...
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.ijhydene.2024.04.232
Open Archives Initiative ID (OAI ID):

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