Smarter nuclear testing: when statistics guide experiments

The three-phase validation structure (in silico, minimal physical, full physical) is methodologically sound and de-risks the research programme by validating the core mechanism in simulation before committing significant resources to physical experiments. This is a textbook example of progressive scientific de-risking.

The hypothesis demonstrates a rigorous and coherent integration of the Kennedy–O’Hagan (KOH) framework with sequential Bayesian experimental design (SBED), correctly identifying the dual role of the discrepancy term in both absorbing systematic bias and quantifying irreducible model-form uncertainty. This is a theoretically sound approach that extends beyond simple surrogate-based optimisation.

The explicit use of the Kennedy–O'Hagan framework to separate model discrepancy from measurement error is methodologically sound and constitutes an improvement over naive approaches that assume a perfect simulator.

The market is identifiable and captive: civil nuclear programmes (Canada via AECL, India, China, Japan) and SMR startups (e.g. Terrestrial Energy, Kairos Power) seeking to qualify thorium fuels for 233U production or waste reduction. The typical budget for an irradiation cycle in a research reactor (e.g. NRU, Halden) is €1–5 million; an instrument that reduces the number of cycles by 30–50% therefore constitutes a direct sales argument.

Methodological originality: the application of SBED with explicit discrepancy correction to the calibration of thorium irradiation simulations represents an innovative positioning that combines Bayesian statistics and nuclear engineering, a niche seldom explored in generic calls.