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Title: Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety

Abstract

The manuscript provides a description of the application of machine-learning (ML) tools to the prediction of bias in criticality safety analysis. In particular, a set of over 1000 experiments included in the Whisper package were fed into a variety of ML algorithms (notably Random Forest and AdaBoost) implemented in SciKit-Learn using k-eigenvalue sensitivities (with and without energy dependence) for individual nuclides, and optionally, the simulated k eff as the training features. Ultimately, the ML model was used to predict the bias (k sim - k exp). The results indicate that use of energy-integrated sensitivity profiles with k sim as training features led to the best predictions as quantified by root-mean square and mean absolute errors. In particular, the best-case estimates came from AdaBoost, with a mean absolute error of 0.00174, which is less than the mean experimental uncertainty of 0.00328 for the experiments included.

Authors:
 [1]; ORCiD logo [2];  [1]
  1. Oregon State Univ., Corvallis, OR (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1544713
Report Number(s):
LA-UR-18-24800
Journal ID: ISSN 2332-4309
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Computational and Theoretical Transport
Additional Journal Information:
Journal Volume: 47; Journal Issue: 4-6; Journal ID: ISSN 2332-4309
Publisher:
Taylor and Francis
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; machine learning; Monte Carlo; criticality

Citation Formats

Grechanuk, Pavel Aleksandrovi, Rising, Michael Evan, and Palmer, Todd. Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety. United States: N. p., 2019. Web. doi:10.1080/23324309.2019.1585877.
Grechanuk, Pavel Aleksandrovi, Rising, Michael Evan, & Palmer, Todd. Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety. United States. doi:10.1080/23324309.2019.1585877.
Grechanuk, Pavel Aleksandrovi, Rising, Michael Evan, and Palmer, Todd. Thu . "Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety". United States. doi:10.1080/23324309.2019.1585877.
@article{osti_1544713,
title = {Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety},
author = {Grechanuk, Pavel Aleksandrovi and Rising, Michael Evan and Palmer, Todd},
abstractNote = {The manuscript provides a description of the application of machine-learning (ML) tools to the prediction of bias in criticality safety analysis. In particular, a set of over 1000 experiments included in the Whisper package were fed into a variety of ML algorithms (notably Random Forest and AdaBoost) implemented in SciKit-Learn using k-eigenvalue sensitivities (with and without energy dependence) for individual nuclides, and optionally, the simulated keff as the training features. Ultimately, the ML model was used to predict the bias (ksim - kexp). The results indicate that use of energy-integrated sensitivity profiles with ksim as training features led to the best predictions as quantified by root-mean square and mean absolute errors. In particular, the best-case estimates came from AdaBoost, with a mean absolute error of 0.00174, which is less than the mean experimental uncertainty of 0.00328 for the experiments included.},
doi = {10.1080/23324309.2019.1585877},
journal = {Journal of Computational and Theoretical Transport},
number = 4-6,
volume = 47,
place = {United States},
year = {2019},
month = {3}
}

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Works referenced in this record:

Random Forests
journal, January 2001