Bayesian Monte Carlo Evaluation Framework for Imperfect Nuclear Data

Conference · Fri Dec 03 04:00:00 EST 2021 · Transactions of the American Nuclear Society

DOI:https://doi.org/10.13182/T125-36738· OSTI ID:1846534

Brown, Jesse M ^[1]; Arbanas, Goran ^[1]; Holcomb, Andrew M. ^[1]; Wiarda, Dorothea ^[1]

---

1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Bayesian evaluation of resolved resonance region (RRR) nuclear data has historically been carried out using the generalized least squares (GLS) formalism, as implemented in, e.g., SAMMY. We have recently developed a prototype of Bayesian Monte Carlo (BMC) evaluation framework, implemented using a Markov Chain Monte Carlo (MCMC) method with a Metropolis-Hastings (MH) acceptance criterion. This was done in order to remove the approximations underlying the conventional GLS evaluations, namely, the linear approximation, and the approximation that all probability density functions (PDFs) are of the normal kind. Recent works by others have used similar stochastic approaches to quantify cross section uncertainties from ENDF evaluated co-variances, and/or, from integral benchmark data, but those have not been conceived as an evaluation framework like the one presented here.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA), Nuclear Criticality Safety Program (NCSP)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1846534
• Conference Information: Journal Name: Transactions of the American Nuclear Society Journal Issue: 1 Journal Volume: 125
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
BMC
Markov Chain Monte Carlo (MCMC)
Nuclear Criticality Safety Program (NCSP)
Nuclear Data
Resolved Resonance Region (RRR)