Mechanistic Multiscale Uncertainty Propagation in Support of Accelerated Fuel Qualification
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Univ. of Tennessee, Knoxville, TN (United States)
Taking a nuclear fuel concept through the research, development, and qualification stages has historically taken on the order of 20 to 25 years because of extensive irradiation tests required for a variety of conditions. The concept of accelerated fuel qualification (AFQ) has been proposed to increase the innovation pace for nuclear fuels. The goal of AFQ is not to replace the traditional qualification approach but rather to reduce the total number of experiments required to ensure approval from the regulatory authority. Of the many AFQ approaches being explored, advanced modeling—and, in particular, mechanistic modeling—is in a uniquely cross-cutting position to reduce the number of required integral tests through the inclusion of separate-effects testing, while helping to extrapolate reactor performance during rare events. We make the case that propagation of uncertainty through various computational length scales helps contextualize mechanistic modeling. We will utilize UO2 fission gas diffusion predictions from the atomistically informed cluster dynamics code Centipede to inform fuel performance rodlet simulations using the BISON finite element code as the metric for showing how multiscale mechanistic uncertainty quantification can help reduce uncertainty in fuel performance. In conclusion, by quantifying uncertainty and its reduction through multiscale modeling, the qualification process may be accelerated through the reduction of costly irradiation experiments.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
- Grant/Contract Number:
- 89233218CNA000001; NA0003525
- OSTI ID:
- 2575610
- Report Number(s):
- LA-UR--24-31789; 10.1080/00295450.2025.2505811; 1943-7471
- Journal Information:
- Nuclear Technology, Journal Name: Nuclear Technology; ISSN 0029-5450; ISSN 1943-7471
- Publisher:
- Taylor & FrancisCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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