Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Application in Nuclear System Thermal-Hydraulics Codes

TL;DR

This paper presents a hierarchical Bayesian approach for inverse uncertainty quantification in nuclear thermal-hydraulics, effectively handling variability and outliers, and demonstrating improved parameter estimation over traditional methods.

Contribution

Introduces a hierarchical Bayesian model for IUQ that reduces over-fitting and manages variability in PMPs across different experimental conditions.

Findings

Hierarchical model outperforms single-level Bayesian in accuracy.

Model reduces over-fitting and handles outliers effectively.

Demonstrates applicability to large, diverse datasets.

Abstract

Inverse Uncertainty Quantification (IUQ) method has been widely used to quantify the uncertainty of Physical Model Parameters (PMPs) in nuclear Thermal Hydraulics (TH) systems. This paper introduces a novel hierarchical Bayesian model which aims to mitigate two existing challenges in IUQ: the high variability of PMPs under varying experimental conditions, and unknown model discrepancies or outliers causing over-fitting issues. The proposed hierarchical model is compared with the conventional single-level Bayesian model using TRACE code and the measured void fraction data in the BFBT benchmark. A Hamiltonian Monte Carlo Method - No U-Turn Sampler (NUTS) is used for posterior sampling. The results demonstrate the effectiveness of the proposed hierarchical model in providing better estimates of the posterior distributions of PMPs and being less prone to over-fitting. The proposed method also demonstrates a promising approach for generalizing IUQ to larger databases with broad ranges of experimental conditions.