Machine learning of fire hazard model simulations for use in probabilistic safety assessments at nuclear power plants

Clarence Worrell, Louis Luangkesorn, Joel Haight and Thomas Congedo

Reliability Engineering and System Safety, 2019, vol. 183, issue C, 128-142

Abstract: This study explored the application of machine learning to generate metamodel approximations of a physics-based fire hazard model. The motivation to generate accurate and efficient metamodels is to improve modeling realism in probabilistic safety assessments where computational burden has prevented broader application of high fidelity models. The process involved scenario definition, generating training data by iteratively running the fire hazard model called CFAST over a range of input space using the RAVEN software, exploratory data analysis and feature selection, an initial testing of a broad set of metamodel methods, and finally metamodel selection and tuning using the R software.

Keywords: Machine learning; Metamodeling; Probabilistic safety assessment; Fire; Nuclear (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:183:y:2019:i:c:p:128-142

DOI: 10.1016/j.ress.2018.11.014

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