Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations

Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations

Understanding the hydrogen recycling process is crucial for comprehending the behavior of detached plasma in nuclear fusion devices. To achieve this, a molecular dynamics (MD) model is being developed to predict the distribution of translational energies and rovibrational states of hydrogen atoms an...

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Bibliographic Details
Main Authors: Seiki Saito, Masato Iida, Hiroaki Nakamura, Keiji Sawada, Kazuo Hoshino, Masahiro Kobayashi, Masahiro Hasuo, Yuki Homma, Shohei Yamoto
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Nuclear Materials and Energy
Subjects:
Rovibrational states
Molecular hydrogen
Recycling
Machine Learning
Molecular Dynamics
Online Access:http://www.sciencedirect.com/science/article/pii/S2352179125000845
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