IAEA - International Atomic Energy Agency

11/29/2022 | Press release | Distributed by Public on 11/29/2022 10:58

How IAEA Databases Help Advance Research Towards the Commercial Use of Fusion

A lack of facilities that replicate the extreme conditions of a fusion reactor makes creating new materials for future fusion power plants complex. Using computational modelling techniques, high-performance computing platforms and analytical experimental characterization tools, experts are able to design materials that can perform well in a fusion energy environment.

Through modelling, new materials are being discovered and the reliability of existing materials can be predicted. This is especially important for the reactor's innermost wall, which is located closest to the plasma in the reactor vessel and protects the vessel components from plasma-induced damage.

"The extreme environment of a nuclear fusion reactor's first wall demands a careful choice of materials which must withstand high temperatures and particle bombardment without becoming eroded, brittle or radioactive, and without retaining the hydrogen fuel," said Christian Hill, Head of the IAEA's Atomic and Molecular Data Unit. "Only with reliable data from accurate computation and experiments can the relevant properties of candidate materials be predicted."

Researchers use IAEA databases in fusion energy research and other plasma science and technology applications. Data are collected and evaluated by the IAEA through its networks, coordinated research projects and Technical Meetings, and distributed through its free, searchable and curated online databases.

"The value in a curated, international database is in its role as a permanent, trusted and accessible repository of evaluated data that can be freely used by the fusion community. The IAEA's Atomic and Molecular Data Unit is unique in other ways, too: it has existed for more than 40 years, which is pretty old in 'fusion-data years'," said Hill.

The IAEA's databases are continuously improved and expanded based on the specific data needs of researchers - the quantification and implication of uncertainties in data, and techniques for data validation, curation and dissemination.