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Title: Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors

Abstract

Moisture carryover (MCO) is modeled in the General Electric Type-4 boiling water reactor (BWR) using machine-learning methods and data from operating plants. Understanding MCO and the conditions that give rise to an elevated value is important since excessive MCO can damage critical turbine components, can result in elevated dose levels to on-site personnel, and can interfere with late-cycle power management. The analysis of MCO takes into account simplifying reactor symmetries and important geometric dependencies. The plant data are taken from several reactors and were collected over multiple years and multiple fuel cycles. A brief description of the origin of MCO in U.S. BWR plants is given. A machine-learning model is constructed from the data using applicable algorithms and data-reduction techniques. Matching model complexity with available data is one of the more challenging machine-learning tasks. Too many features and too little data will lead to overfitting. The data for each fuel cycle included over 6876 original features, 9 for each fuel bundle. Two approaches are used to reduce the data set into a manageable number of features. The first was an engineering analysis that resulted in the selection of steam quality Q and steam liquid phase velocity V L as themore » main features driving MCO. Using a Q and a V L for each fuel bundle gives 1528 Q and a V L feature describing the reactor behavior. An analysis of different functional forms of these two variables led to the actual inputs to the neural network model. The second approach involved the use of statistical techniques such as Pearson’s correlation and k-means analysis. The identified groupings of bundles behaved similarly. Treating each grouping as a single feature further reduced the input variable set to a manageable number. Here, a model selection criterion is proposed, and results are presented along with a discussion of related issues.« less

Authors:
 [1];  [2];  [2];  [3]; ORCiD logo [1]
  1. Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Purdue Univ., West Lafayette, IN (United States); Blue Wave AI Labs, Celebration, FL (United States)
  3. Exelon Generation, Kennett Square, PA (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1559008
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Nuclear Technology
Additional Journal Information:
Journal Volume: 205; Journal Issue: 8; Journal ID: ISSN 0029-5450
Publisher:
Taylor & Francis - formerly American Nuclear Society (ANS)
Country of Publication:
United States
Language:
English
Subject:
22 GENERAL STUDIES OF NUCLEAR REACTORS; Boiling Water Reactor; Data Analytics; Machine Learning; Moisture Carryover; Neural Network

Citation Formats

Wang, Haoyu, Longman, Andrew, Gruenwald, J. Thomas, Tusar, James, and Vilim, Richard. Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors. United States: N. p., 2019. Web. doi:10.1080/00295450.2019.1583957.
Wang, Haoyu, Longman, Andrew, Gruenwald, J. Thomas, Tusar, James, & Vilim, Richard. Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors. United States. doi:10.1080/00295450.2019.1583957.
Wang, Haoyu, Longman, Andrew, Gruenwald, J. Thomas, Tusar, James, and Vilim, Richard. Wed . "Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors". United States. doi:10.1080/00295450.2019.1583957.
@article{osti_1559008,
title = {Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors},
author = {Wang, Haoyu and Longman, Andrew and Gruenwald, J. Thomas and Tusar, James and Vilim, Richard},
abstractNote = {Moisture carryover (MCO) is modeled in the General Electric Type-4 boiling water reactor (BWR) using machine-learning methods and data from operating plants. Understanding MCO and the conditions that give rise to an elevated value is important since excessive MCO can damage critical turbine components, can result in elevated dose levels to on-site personnel, and can interfere with late-cycle power management. The analysis of MCO takes into account simplifying reactor symmetries and important geometric dependencies. The plant data are taken from several reactors and were collected over multiple years and multiple fuel cycles. A brief description of the origin of MCO in U.S. BWR plants is given. A machine-learning model is constructed from the data using applicable algorithms and data-reduction techniques. Matching model complexity with available data is one of the more challenging machine-learning tasks. Too many features and too little data will lead to overfitting. The data for each fuel cycle included over 6876 original features, 9 for each fuel bundle. Two approaches are used to reduce the data set into a manageable number of features. The first was an engineering analysis that resulted in the selection of steam quality Q and steam liquid phase velocity VL as the main features driving MCO. Using a Q and a VL for each fuel bundle gives 1528 Q and a VL feature describing the reactor behavior. An analysis of different functional forms of these two variables led to the actual inputs to the neural network model. The second approach involved the use of statistical techniques such as Pearson’s correlation and k-means analysis. The identified groupings of bundles behaved similarly. Treating each grouping as a single feature further reduced the input variable set to a manageable number. Here, a model selection criterion is proposed, and results are presented along with a discussion of related issues.},
doi = {10.1080/00295450.2019.1583957},
journal = {Nuclear Technology},
number = 8,
volume = 205,
place = {United States},
year = {2019},
month = {3}
}

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