Hydrogen jet and diffusion modeling by physics-informed graph neural network-Reference-Cited by-同舟云学术

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Hydrogen jet and diffusion modeling by physics-informed graph neural network

Published:2025-01 Issue: Volume:207 Page:114898
ISSN:1364-0321
Container-title:Renewable and Sustainable Energy Reviews
language:en
Short-container-title:Renewable and Sustainable Energy Reviews

Author:

Zhang Xinqi,Shi Jihao,Li Junjie,Huang Xinyan^ORCID,Xiao Fu^ORCID,Wang Qiliang,Usmani Asif Sohail^ORCID,Chen Guoming

Funder

National Natural Science Foundation of China

Hong Kong Arts Development Council

National Key Research and Development Program of China

Research Grants Council, University Grants Committee

National Key Research and Development Program of China Stem Cell and Translational Research

Publisher

Elsevier BV

Reference68 articles.

1. A long-term capacity investment and operational energy planning model with power-to-X and flexibility technologies;Feijoo;Renew Sustain Energy Rev,2022

2. Challenges and prospects of renewable hydrogen-based strategies for full decarbonization of stationary power applications;Maestre;Renew Sustain Energy Rev,2021

3. Strategies for flexible operation of power-to-X processes coupled with renewables;Qi;Renew Sustain Energy Rev,2023

4. A Green Hydrogen Energy System: optimal control strategies for integrated hydrogen storage and power generation with wind energy;Schrotenboer;Renew Sustain Energy Rev,2022

5. Hydrogen and the decarbonization of the energy system in europe in 2050: a detailed model-based analysis;Seck;Renew Sustain Energy Rev,2022

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