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Title: Using Machine Learning to Predict MCNP Bias

Abstract

For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental k eff) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles, and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.

Authors:
 [1]
  1. 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
OSTI Identifier:
1416276
Report Number(s):
LA-UR-18-20175
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; Machine learning; Monte Carlo; nuclear criticality

Citation Formats

Grechanuk, Pavel Aleksandrovi. Using Machine Learning to Predict MCNP Bias. United States: N. p., 2018. Web. doi:10.2172/1416276.
Grechanuk, Pavel Aleksandrovi. Using Machine Learning to Predict MCNP Bias. United States. doi:10.2172/1416276.
Grechanuk, Pavel Aleksandrovi. Tue . "Using Machine Learning to Predict MCNP Bias". United States. doi:10.2172/1416276. https://www.osti.gov/servlets/purl/1416276.
@article{osti_1416276,
title = {Using Machine Learning to Predict MCNP Bias},
author = {Grechanuk, Pavel Aleksandrovi},
abstractNote = {For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental keff) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles, and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.},
doi = {10.2172/1416276},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 09 00:00:00 EST 2018},
month = {Tue Jan 09 00:00:00 EST 2018}
}

Technical Report:

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