Artificial intelligence optimization method for nuclear fuel triso-elements in high-temperature reactor

DOI: 10.62564/M4-АK2225

Аlexander Kul'ment'ev

Institute of Applied Physics of NAS of Ukraine


In the present era nuclear energy, has unique advantages compared to other energy sources. Now significant research and development related to TRISO-coated fuels is underway worldwide as part of the activities of the Generation IV International Forum on Very-High-Temperature Reactors. The focus is largely on extending the capabilities of the TRISO-coated fuel system for higher operating temperatures (1250°C) and higher burnups (10 – 20 %). Of greatest concern is the influence of higher fuel temperatures and burnups on fission product interactions with the SiC layer leading to the release of fission products. One of the possible solution consist in addition additional layers with special properties. For example, to prevent the corrosion of the SiC layer by fission product palladium, several types of new combinations of the coating layers have been proposed and tested. The idea is to add a layer that traps palladium by chemical reaction inside the SiC layer. Earlier several kinds of additional layers have been selected: an SiC + PyC layer and an SiC layer. For optimization of TRISO particle it is necessary to determine the number of additional layers, their thickness and composition. This is combinatorial optimization problem (continuous + discrete). Traditional methods rely on manual adjustment and human experience, which is inefficient and difficult to obtain the optimal solution. Therefore it is necessary to develop an automated design method. In the present report variant of such method is proposed based on artificial intelligence approach. There are several meta-heuristic algorithms such as genetic algorithm, neural network and particle swarm optimization algorithm (PSO) which have the ability to solve continuous, discrete and combinatorial optimization problems. Namely PSO algorithm looks especially attractive. Early this method was proven to be reliable and effective in nuclear power problems by applying it in designing a Savannah marine reactor shielding.

Keywords
high-temperature reactor, TRISO-coated fuels, artificial intelligence

Acknowledgments
Not provided

References
[1] Wrochna G, Fütterer M, Hittner D., Nuclear cogeneration with high temperature reactors, EPJ Nucl. Sci. Technol., 2020. V.6, N31. P.7. https://doi.org/10.1051/epjn/2019023. [2] R. Eberhart, J. Kennedy, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, 1995. Citeseer. [3] X. Wu, et al., Multi-objective optimization method for nuclear reactor radiation shielding design based on PSO algorithm, Ann. Nucl. Energy, 2021. V.160.