Improvement of two-phase closure models in CTF using Bayesian inference

Under the Consortium for Advanced Simulation of Light Water Reactors (CASL) program, extensive capabilities have been developed in CTF to analyze light-water reactors (LWRs) for normal operating conditions, departure from nucleate boiling (DNB), and system transients. However, further improvements a...

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Bibliographic Details
Published in:Nuclear engineering and design 2022-11, Vol.398 (1), p.111968, Article 111968
Main Authors: Kumar, Vineet, Gurecky, William, Salko, Robert, Hizoum, Belgacem
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian inference
Boiling
Boiling water reactors
Calibration
Computer applications
CTF
GENERAL STUDIES OF NUCLEAR REACTORS
Light water reactors
Markov chains
Mathematical models
Modelling
Nuclear energy
Nuclear reactor components
Nuclear reactors
Nucleate boiling
Parameters
Predictions
Pressure
Pressure drop
Reactors
Simulation
Statistical inference
Surrogate models
Two-phase closure models
Void fraction
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Summary:Under the Consortium for Advanced Simulation of Light Water Reactors (CASL) program, extensive capabilities have been developed in CTF to analyze light-water reactors (LWRs) for normal operating conditions, departure from nucleate boiling (DNB), and system transients. However, further improvements are required in the modeling and simulation of boiling water reactors (BWRs), which is a focus of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. In this work, CTF validation results were used to optimize selected modeling coefficients by calibrating to experimental data using a Bayesian inference approach. Calibration studies were conducted to improve (vapor) void fraction prediction without worsening the two-phase pressure drop prediction, as well as to improve the two-phase pressure drop prediction. Calibration was performed for interfacial drag and wall shear models. Surrogates were developed to alleviate the computational expense required for sampling the parameter space using Markov chain Monte Carlo (MCMC). An assessment performed with calibrated models demonstrated an improvement of CTF in its prediction of key parameters such as void fraction and two-phase pressure drop. •CTF Two-phase closure models calibrated to experimental data using Bayesian inference.•Surrogate models generated to mimic CTF code responses using Gaussian process regression.•Calibrated models show improved predictions against experimental datasets not used in the calibration stage.•Identified challenges to generating surrogates and optimizing for operating condition-agnostic coefficients.
ISSN:0029-5493
1872-759X
DOI:10.1016/j.nucengdes.2022.111968