Abstract

Projectile fusion is a new approach to inertially confined fusion (ICF) that is simpler, more energy efficient, and has lower physics risk. Inertial fusion is a pulsed process, like an internal combustion engine. Each target releases a large amount of energy. A pulsed approach gives great design flexibility, trading off energy per shot and frequency. Our aim is the lowest risk plant design possible. High energy per shot reduces physics risk, and slower frequency and small overall plant size reduce the engineering risk.

The key enabler is First Light’s unique new target technology. Successful target design is facilitated by rapid iteration. Enormous parameter spaces must be explored in a tractable and accurate way to identify robust optima. This is achieved by employing hierarchies of numerical model sophistication and fidelity, which generally trade-off execution time. Modern machine-learning approaches have the potential to tip this balance. Final target assessment comes from complex codes covering a vast range of physical scales and phenomena. Comprehensive verification and validation of these codes is essential to the system design cycle. Software quality must be assured at all times; algorithms must precisely represent the mathematical model and its solution, simulations must be reproducible, and the impact of code changes on historical results must be tracked. High software quality is essential to high scientific quality.

This approach has been successfully demonstrated in our experimental campaigns, most notably with the first ever observations of nuclear fusion from projectile driven targets.