New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing
The construction and operation of the first generation of magnetically controlled nuclear fusion power plants require the development of proper physics and the engineering bases. The analysis of data, recently collected by the actual largest and most important tokamak in the world JET, that has succ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/10/6240 |
| _version_ | 1797601214957879296 |
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| author | Emmanuele Peluso Ekaterina Pakhomova Michela Gelfusa |
| author_facet | Emmanuele Peluso Ekaterina Pakhomova Michela Gelfusa |
| author_sort | Emmanuele Peluso |
| collection | DOAJ |
| description | The construction and operation of the first generation of magnetically controlled nuclear fusion power plants require the development of proper physics and the engineering bases. The analysis of data, recently collected by the actual largest and most important tokamak in the world JET, that has successfully completed his second deuterium and tritium campaign in 2021 (DTE2) with a full ITER like wall main chamber, has provided an important consolidation of the ITER physics basis. Thermonuclear plasmas are highly nonlinear systems characterized by the need of numerous diagnostics to measure physical quantities to guide, through proper control schemes, external actuators. Both modelling and machine learning approaches are required to maximize the physical understanding of plasma dynamics and at the same time, engineering challenges have to be faced. Fusion experiments are indeed extremely hostile environments for plasma facing materials (PFM) and plasma-facing components (PFC), both in terms of neutron, thermal loads and mechanical stresses that the components have to face during either steady operation or off-normal events. Efforts are therefore spent by the community to reach the ultimate goal ahead: turning on the first nuclear fusion power plant, DEMO, by 2050. This editorial is dedicated at reviewing some aspects touched in recent studies developed in this dynamic, challenging project, collected by the special issue titled “New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing”. |
| first_indexed | 2024-03-11T03:58:02Z |
| format | Article |
| id | doaj.art-ca830d51791b40c890444d6460d5e38c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:58:02Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ca830d51791b40c890444d6460d5e38c2023-11-18T00:22:36ZengMDPI AGApplied Sciences2076-34172023-05-011310624010.3390/app13106240New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and ManufacturingEmmanuele Peluso0Ekaterina Pakhomova1Michela Gelfusa2Department of Industrial Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo 2, 09123 Cagliari, ItalyDepartment of Industrial Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, ItalyThe construction and operation of the first generation of magnetically controlled nuclear fusion power plants require the development of proper physics and the engineering bases. The analysis of data, recently collected by the actual largest and most important tokamak in the world JET, that has successfully completed his second deuterium and tritium campaign in 2021 (DTE2) with a full ITER like wall main chamber, has provided an important consolidation of the ITER physics basis. Thermonuclear plasmas are highly nonlinear systems characterized by the need of numerous diagnostics to measure physical quantities to guide, through proper control schemes, external actuators. Both modelling and machine learning approaches are required to maximize the physical understanding of plasma dynamics and at the same time, engineering challenges have to be faced. Fusion experiments are indeed extremely hostile environments for plasma facing materials (PFM) and plasma-facing components (PFC), both in terms of neutron, thermal loads and mechanical stresses that the components have to face during either steady operation or off-normal events. Efforts are therefore spent by the community to reach the ultimate goal ahead: turning on the first nuclear fusion power plant, DEMO, by 2050. This editorial is dedicated at reviewing some aspects touched in recent studies developed in this dynamic, challenging project, collected by the special issue titled “New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing”.https://www.mdpi.com/2076-3417/13/10/6240nuclear fusionplasma physicsplasma diagnosticplasma facing componentsmachine learningneutronics |
| spellingShingle | Emmanuele Peluso Ekaterina Pakhomova Michela Gelfusa New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing Applied Sciences nuclear fusion plasma physics plasma diagnostic plasma facing components machine learning neutronics |
| title | New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing |
| title_full | New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing |
| title_fullStr | New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing |
| title_full_unstemmed | New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing |
| title_short | New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing |
| title_sort | new challenges in nuclear fusion reactors from data analysis to materials and manufacturing |
| topic | nuclear fusion plasma physics plasma diagnostic plasma facing components machine learning neutronics |
| url | https://www.mdpi.com/2076-3417/13/10/6240 |
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