Expanded analysis of machine learning models for nuclear transient identification using TPOT

Journal Article · 27 February 2022 · Nuclear Engineering and Design

DOI: https://doi.org/10.1016/j.nucengdes.2022.111694 · OSTI ID:1981726

Mena, Pedro ^[1]; Borrelli, R. A. ^[2]; Kerby, Leslie ^[3]

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1. Idaho State Univ., Pocatello, ID (United States)
2. Univ. of Idaho, Idaho Falls, ID (United States); Idaho National Laboratory (INL), Idaho Falls, ID (United States). Center for Advanced Energy Studies (CAES)
3. Idaho State Univ., Pocatello, ID (United States); Idaho National Laboratory (INL), Idaho Falls, ID (United States). Center for Advanced Energy Studies (CAES)

Industries around the world are becoming more and more data driven. The nuclear field is no exception with several different applications being proposed. One popular area of research is the use of machine learning in transient detection. This paper seeks to build upon a previous study which made use of the AutoML package TPOT to train traditional machine learning models to classify transient events occurring with a reactor. Synthetic data was once again collected using a GPWR reactor simulator. Data on 12 different events was collected using 15 different initial conditions. Here, a dataset consisting of over 100,000 data points was compiled and used to train 7 different machine learning models using a pre-defined TPOT dictionary with 12 different preprocessing techniques. Three of the trained models were able to produce validation results in the 90s with the expanded dataset. Once the models were trained, it was possible to look into where during the simulation, misclassifications occurred. Using these three models, analysis was done to determine if TPOT could be used to train models that were effective if important features were missing. The results from this were positive with the newly trained models scoring close to the original models. Finally, to conclude this study, the three high performing models were retrained using different random states to see if there was any major variation when different states were used.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• Grant/Contract Number: AC07-05ID14517
• OSTI ID: 1981726
• Journal Information: Nuclear Engineering and Design, Journal Name: Nuclear Engineering and Design Vol. 390; ISSN 0029-5493
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English