Hydrogen jet and diffusion modeling by physics-informed graph neural network
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DOI: 10.1016/j.rser.2024.114898
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- Yin, Xiong & Wen, Kai & Huang, Weihe & Luo, Yinwei & Ding, Yi & Gong, Jing & Gao, Jianfeng & Hong, Bingyuan, 2023. "A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods," Applied Energy, Elsevier, vol. 333(C).
- Maestre, V.M. & Ortiz, A. & Ortiz, I., 2021. "Challenges and prospects of renewable hydrogen-based strategies for full decarbonization of stationary power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
- Azadnia, Amir Hossein & McDaid, Conor & Andwari, Amin Mahmoudzadeh & Hosseini, Seyed Ehsan, 2023. "Green hydrogen supply chain risk analysis: A european hard-to-abate sectors perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
- Shi, Jihao & Li, Junjie & Usmani, Asif Sohail & Zhu, Yuan & Chen, Guoming & Yang, Dongdong, 2021. "Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach," Energy, Elsevier, vol. 219(C).
- Gordon, Joel A. & Balta-Ozkan, Nazmiye & Nabavi, Seyed Ali, 2023. "Socio-technical barriers to domestic hydrogen futures: Repurposing pipelines, policies, and public perceptions," Applied Energy, Elsevier, vol. 336(C).
- Qi, Meng & Vo, Dat Nguyen & Yu, Haoshui & Shu, Chi-Min & Cui, Chengtian & Liu, Yi & Park, Jinwoo & Moon, Il, 2023. "Strategies for flexible operation of power-to-X processes coupled with renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
- Tu, Hao & Moura, Scott & Wang, Yebin & Fang, Huazhen, 2023. "Integrating physics-based modeling with machine learning for lithium-ion batteries," Applied Energy, Elsevier, vol. 329(C).
- Schrotenboer, Albert H. & Veenstra, Arjen A.T. & uit het Broek, Michiel A.J. & Ursavas, Evrim, 2022. "A Green Hydrogen Energy System: Optimal control strategies for integrated hydrogen storage and power generation with wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
- Tsiklios, C. & Hermesmann, M. & Müller, T.E., 2022. "Hydrogen transport in large-scale transmission pipeline networks: Thermodynamic and environmental assessment of repurposed and new pipeline configurations," Applied Energy, Elsevier, vol. 327(C).
- Seck, Gondia S. & Hache, Emmanuel & Sabathier, Jerome & Guedes, Fernanda & Reigstad, Gunhild A. & Straus, Julian & Wolfgang, Ove & Ouassou, Jabir A. & Askeland, Magnus & Hjorth, Ida & Skjelbred, Hans , 2022. "Hydrogen and the decarbonization of the energy system in europe in 2050: A detailed model-based analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
- Shu, Zhiyong & Liang, Wenqing & Zheng, Xiaohong & Lei, Gang & Cao, Peng & Dai, Wenxiao & Qian, Hua, 2021. "Dispersion characteristics of hydrogen leakage: Comparing the prediction model with the experiment," Energy, Elsevier, vol. 236(C).
- Bünning, Felix & Huber, Benjamin & Schalbetter, Adrian & Aboudonia, Ahmed & Hudoba de Badyn, Mathias & Heer, Philipp & Smith, Roy S. & Lygeros, John, 2022. "Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC," Applied Energy, Elsevier, vol. 310(C).
- Ishitsuka, Kazuya & Lin, Weiren, 2023. "Physics-informed neural network for inverse modeling of natural-state geothermal systems," Applied Energy, Elsevier, vol. 337(C).
- Zhang, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
- Zhang, Jincheng & Zhao, Xiaowei, 2021. "Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements," Applied Energy, Elsevier, vol. 288(C).
- Shen, Yahao & Lv, Hong & Zheng, Tao & Liu, Yi & Zhou, Wei & Zhang, Cunman, 2023. "Temporal and spatial evolution of hydrogen leakage and diffusion from tube fittings on fuel cell vehicles under the effect of ambient wind," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
- Lv, Hong & Shen, Yahao & Zheng, Tao & Zhou, Wei & Ming, Pingwen & Zhang, Cunman, 2023. "Numerical study of hydrogen leakage, diffusion, and combustion in an outdoor parking space under different parking configurations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
- Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
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Keywords
Green hydrogen production; Power-to-Hydrogen; Hydrogen diffusion; Graph deep learning; Physics-informed neural network;All these keywords.
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