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Inverse Uncertainty Quantification (IUQ) method has been widely used toquantify the uncertainty of Physical Model Parameters (PMPs) in nuclear ThermalHydraulics (TH) systems. This paper introduces a novel hierarchical Bayesianmodel which aims to mitigate two existing challenges in IUQ: the highvariability of PMPs under varying experimental conditions, and unknown modeldiscrepancies or outliers causing over-fitting issues.  The proposed hierarchical model is compared with the conventionalsingle-level Bayesian model using TRACE code and the measured void fractiondata in the BFBT benchmark. A Hamiltonian Monte Carlo Method - No U-TurnSampler (NUTS) is used for posterior sampling. The results demonstrate theeffectiveness of the proposed hierarchical model in providing better estimatesof the posterior distributions of PMPs and being less prone to over-fitting.The proposed method also demonstrates a promising approach for generalizing IUQto larger databases with broad ranges of experimental conditions.

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