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<p>Inertial Confinement Fusion energy is one of the most promising technologies for the future energy transition, yet several challenges must be addressed before a fully operational reactor can be realized. Among the most relevant open issues is the design of the reaction vessel, including material selection, geometric configuration and functional characteristics. Computational frameworks offer a powerful means to explore the design space, and several examples already exist in the context of magnetic confinement fusion. This work reviews the main physics-based computational tools used to investigate Inertial Confinement Fusion systems, including dedicated codes such as BUCKY-1 and CHIC, as well as more general-purpose solvers for thermal and mechanical stress analysis commonly adopted to study fusion-related problems arising from plasma generation and containment. In addition, the paper introduces recent developments in machine learning for design optimization, with particular attention to Multi-Fidelity Bayesian Optimization Methods, which have been successfully applied to inertial fusion targets and represent a promising approach for guiding future vessel design.</p>

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Computational framework for direct drive Inertial Confinement Fusion ICF chamber design

  • 期刊论文

  • 发表日期：

    2026年8月2日

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  • Piergiovanni Domenighini,
  • Gianluca Vinti,
  • Franco Cotana

摘要

Inertial Confinement Fusion energy is one of the most promising technologies for the future energy transition, yet several challenges must be addressed before a fully operational reactor can be realized. Among the most relevant open issues is the design of the reaction vessel, including material selection, geometric configuration and functional characteristics. Computational frameworks offer a powerful means to explore the design space, and several examples already exist in the context of magnetic confinement fusion. This work reviews the main physics-based computational tools used to investigate Inertial Confinement Fusion systems, including dedicated codes such as BUCKY-1 and CHIC, as well as more general-purpose solvers for thermal and mechanical stress analysis commonly adopted to study fusion-related problems arising from plasma generation and containment. In addition, the paper introduces recent developments in machine learning for design optimization, with particular attention to Multi-Fidelity Bayesian Optimization Methods, which have been successfully applied to inertial fusion targets and represent a promising approach for guiding future vessel design.