NEUP CINR-23-29490: Improving Reliability of Novel TRISO Fuel Forms for Advanced Reactors via Multiscale, High-Throughput Characterization and Modeling

We propose the use of a parallelized thermal conductivity (k) measurement device coupled with multiscale models to accurately predict the thermal conductivity of TRISO fuel composites. This proposal overcomes the issue plaguing many “localized” microscale measurements, namely the inability to scale local measurements up to engineering scale properties. We will do this by using Bayesian inference techniques and finite element models to predict effective thermal conductivity.

Additional Info

Field	Value
Awarded Institution	Brigham Young University
PI	Troy Munro