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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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Nuclear Materials and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352179125000845 |
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| author | Seiki Saito Masato Iida Hiroaki Nakamura Keiji Sawada Kazuo Hoshino Masahiro Kobayashi Masahiro Hasuo Yuki Homma Shohei Yamoto |
| author_facet | Seiki Saito Masato Iida Hiroaki Nakamura Keiji Sawada Kazuo Hoshino Masahiro Kobayashi Masahiro Hasuo Yuki Homma Shohei Yamoto |
| author_sort | Seiki Saito |
| collection | DOAJ |
| description | 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 and molecules released from the plasma-facing materials. Neutral transport simulations, utilizing distributions obtained from the MD model as boundary conditions, are also a powerful tool for analyzing the impact of recycled hydrogens on edge plasma. However, the MD model requires significant computational resources to obtain distributions under varying material and irradiation conditions such as material temperature and incident energy. Therefore, developing effective models that seamlessly integrate neutral transport simulation with hydrogen recycling models is crucial. Machine learning techniques are employed to develop predictive models capable of forecasting distributions of energies and rovibrational states of released hydrogen atoms and molecules. Furthermore, a model considering the incident energy distribution (shifted-Maxwellian) is developed by integrating the monochromatic distribution with the shifted-Maxwellian distribution. |
| format | Article |
| id | doaj-art-bfdd07a33d1248319eaa6335ddf3f192 |
| institution | Kabale University |
| issn | 2352-1791 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Materials and Energy |
| spelling | doaj-art-bfdd07a33d1248319eaa6335ddf3f1922025-08-20T03:52:55ZengElsevierNuclear Materials and Energy2352-17912025-06-014310194210.1016/j.nme.2025.101942Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulationsSeiki Saito0Masato Iida1Hiroaki Nakamura2Keiji Sawada3Kazuo Hoshino4Masahiro Kobayashi5Masahiro Hasuo6Yuki Homma7Shohei Yamoto8Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata 992-8510, Japan; Corresponding author.Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata 992-8510, JapanDepartment Research, National Institute for Fusion Science, Toki Gifu 509-5292, Japan; Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8601, JapanGraduate School of Science and Technology, Shinshu University, Nagano 380-8553, JapanDepartment of Applied Physics and Physico-Informatics, Keio University, Yokohama, Kanagawa 223-8521, JapanDepartment Research, National Institute for Fusion Science, Toki Gifu 509-5292, JapanGraduate School of Engineering, Kyoto University, Kyoto, JapanNational Institutes for Quantum Science and Technology, Rokkasho, Aomori 039-3212, JapanNational Institutes for Quantum Science and Technology, Naka, Ibaraki 311-0193, JapanUnderstanding 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 and molecules released from the plasma-facing materials. Neutral transport simulations, utilizing distributions obtained from the MD model as boundary conditions, are also a powerful tool for analyzing the impact of recycled hydrogens on edge plasma. However, the MD model requires significant computational resources to obtain distributions under varying material and irradiation conditions such as material temperature and incident energy. Therefore, developing effective models that seamlessly integrate neutral transport simulation with hydrogen recycling models is crucial. Machine learning techniques are employed to develop predictive models capable of forecasting distributions of energies and rovibrational states of released hydrogen atoms and molecules. Furthermore, a model considering the incident energy distribution (shifted-Maxwellian) is developed by integrating the monochromatic distribution with the shifted-Maxwellian distribution.http://www.sciencedirect.com/science/article/pii/S2352179125000845Rovibrational statesMolecular hydrogenRecyclingMachine LearningMolecular Dynamics |
| spellingShingle | Seiki Saito Masato Iida Hiroaki Nakamura Keiji Sawada Kazuo Hoshino Masahiro Kobayashi Masahiro Hasuo Yuki Homma Shohei Yamoto Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations Nuclear Materials and Energy Rovibrational states Molecular hydrogen Recycling Machine Learning Molecular Dynamics |
| title | Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations |
| title_full | Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations |
| title_fullStr | Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations |
| title_full_unstemmed | Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations |
| title_short | Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations |
| title_sort | machine learning based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations |
| topic | Rovibrational states Molecular hydrogen Recycling Machine Learning Molecular Dynamics |
| url | http://www.sciencedirect.com/science/article/pii/S2352179125000845 |
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