Session: 07-04: Experiments and Analyses - III

Paper Number: 135192

135192 - Inverse Uncertainty Quantification for Subchannel Code With Psbt Experimental Benchmark 

Abstract: 

Inverse uncertainty quantification analysis for subchannel programs has been conducted to decide uncertainty distribution of important model parameters based on the modified Markov Chain Monte Carlo (MCMC) algorithm using the PSBT void fraction experiment. The posterior probability distribution expression for input parameter uncertainty can be derived using Bayesian principles, typically representing a proportional relationship. However, obtaining the direct normalization coefficient proves challenging, prompting the use of the MCMC algorithm to address this issue. Given the substantial external forward program calculations associated with stochastic methods, the Back-propagation Neural Network (BPNN) is employed to construct a surrogate model, serving as a substitute for intricate forward calculations. To significantly enhance the precision of the surrogate model, an adaptive approach based on relative entropy minimization is implemented to densify training sample points. The uncertainty of key input model parameters (slip ratio and turbulent mixing coefficient) was quantified, revealing that the uncertainty of the slip ratio is the primary source of void fraction uncertainty. After obtaining information on input parameter uncertainty, forward uncertainty analysis based on input uncertainty was conducted. The resulting uncertainty band for void fraction was obtained under a tolerance limit of 95%-95%. The results indicate that the uncertainty band effectively envelops the experimental data for void fraction. Using the statistical mean of parameter uncertainty obtained, the baseline values were calibrated. The predicted results of the model correction values were found to be more accurate than those of the baseline predictions.

In this paper, Bayesian framework-based MCMC method proposed by Li et al. will be used to analyze model parameter uncertainties of the subchannel program COBRA-IV. As an update, the surrogate model has been replace by BPNN to increase the accuracy.

In the present work, steady state void fraction experiments with 5×5 rod bundles are selected from the PSBT (NUPEC PWR Sub-channel and Bundle Tests) benchmark. the subchannel code COBRA-IV was utilized to simulate the experimental data of void fraction in PSBT benchmark steady-state fuel rods. The unknown uncertainty of the physical model parameters in the thermal-hydraulic code COBRA-IV was addressed using the Bayesian framework-based MCMC method described. The use of surrogate models established based on BPNN significantly alleviated the computational burden associated with sampling from the posterior probability distribution using the MCMC method. After obtaining the uncertainty distribution of the model parameters, considering the existing uncertainty in the boundary condition parameters, further uncertainty quantification and sensitivity analysis were conducted for the forward propagation, quantifying the impact of these parameters on void fraction.

The subchannel program COBRA-IV was evaluated using steady-state void fraction experimental data from the PSBT benchmark with the modified MCMC algorithm. Uncertainties distribution of non-observed key input model parameters (slip ratio and turbulent mixing coefficient) were quantified. Following the inverse uncertainty quantification, a forward uncertainty analysis based on input model uncertainty was conducted. It can be seen that an uncertainty band for void fraction under a 95%-95% tolerance limit effectively envelope the experimental void fraction data. Besides, compared to the original code, COBRA-IV code calibrated by the mean value of input parameter uncertainty achieves better prediction accuracy.

Presenting Author: Xiaojing Liu Shanghai Jiao Tong University

Presenting Author Biography: Xiaojing Liu, working in Shanghai Jiao Tong University, with research interests in digital nuclear energy and advanced nuclear energy.

Authors:

Hanyu Luo Shanghai Jiao Tong University
Xiaojing Liu Shanghai Jiao Tong University