Bayesian discovery of optimal reduced order models from mechanistic and experimental data: A case study of Pd penetration in TRISO fuels using BISON

TRistructural ISOtropic (TRISO) particle fuel relies on a silicon carbide (SiC) layer as the primary structural material and barrier to metallic fission products (FPs) release. Accurate prediction of palladium (Pd) transport and penetration into the SiC is therefore critical for qualifying TRISO fuels for advanced reactors. The empirical correlation for Pd penetration in BISON is derived from historical particle-fuel data, but it cannot explain the large scatter in the experimental data that arises from varying experimental conditions. To aid fuel qualification, we previously developed a mechanistic reduced order model (ROM) using BISON that resolves these dependencies (Bhave et al., 2025). In this work we built on that mechanistic ROM, validated it, and quantified its uncertainty using Bayesian uncertainty quantification (UQ). We calibrated against a suite of in-pile and out-of-pile experiments spanning particle compositions, geometries, and operating conditions, and benchmarked the mechanistic ROM against the empirical correlation. We used Bayesian UQ to identify influential parameters and calibrate them to data, which yielded predictive intervals. Results show that while the empirical correlation can be tuned to fit a single experiment type, it transfers poorly; the mechanistic ROM sustains accuracy with credible uncertainty across disparate conditions. This process demonstrates a practical path — via Bayesian UQ applied to mechanistic ROMs — to leverage single-effect experiments for inferring in-reactor behavior and supporting TRISO fuel qualification.