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Title: Error Analysis of CM Data Products Sources of Uncertainty

Abstract

This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program. This is a widely recognized shortfall, the resolution of which would provide a great deal of value and defensibility to the analysis results, data products, and the decision making process that follows this work. A global approach to this problem is necessary because multiple sources of error and uncertainty contribute to the ultimate production of CM data products. Therefore, this project will require collaboration with subject matter experts across a wide range of FRMAC skill sets in order to quantify the types of uncertainty that each area of the CM process might contain and to understand how variations in these uncertainty sources contribute to the aggregated uncertainty present in CM data products. The ultimate goal of this project is to quantify the confidence level of CM products to ensure that appropriate public and worker protections decisions are supported by defensible analysis.

Authors:
 [1];  [1];  [1];  [1];  [1];  [2];  [3];  [4]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. National Security Technologies, Joint Base Andrews, MD (United States)
  3. National Security Technologies, LLC. (NSTec), Las Vegas, NV (United States)
  4. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1343383
Report Number(s):
SAND2017-1289R
651031
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Hunt, Brian D., Eckert-Gallup, Aubrey Celia, Cochran, Lainy Dromgoole, Kraus, Terrence D., Allen, Mark B., Beal, Bill, Okada, Colin, and Simpson, Mathew. Error Analysis of CM Data Products Sources of Uncertainty. United States: N. p., 2017. Web. doi:10.2172/1343383.
Hunt, Brian D., Eckert-Gallup, Aubrey Celia, Cochran, Lainy Dromgoole, Kraus, Terrence D., Allen, Mark B., Beal, Bill, Okada, Colin, & Simpson, Mathew. Error Analysis of CM Data Products Sources of Uncertainty. United States. doi:10.2172/1343383.
Hunt, Brian D., Eckert-Gallup, Aubrey Celia, Cochran, Lainy Dromgoole, Kraus, Terrence D., Allen, Mark B., Beal, Bill, Okada, Colin, and Simpson, Mathew. Wed . "Error Analysis of CM Data Products Sources of Uncertainty". United States. doi:10.2172/1343383. https://www.osti.gov/servlets/purl/1343383.
@article{osti_1343383,
title = {Error Analysis of CM Data Products Sources of Uncertainty},
author = {Hunt, Brian D. and Eckert-Gallup, Aubrey Celia and Cochran, Lainy Dromgoole and Kraus, Terrence D. and Allen, Mark B. and Beal, Bill and Okada, Colin and Simpson, Mathew},
abstractNote = {This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program. This is a widely recognized shortfall, the resolution of which would provide a great deal of value and defensibility to the analysis results, data products, and the decision making process that follows this work. A global approach to this problem is necessary because multiple sources of error and uncertainty contribute to the ultimate production of CM data products. Therefore, this project will require collaboration with subject matter experts across a wide range of FRMAC skill sets in order to quantify the types of uncertainty that each area of the CM process might contain and to understand how variations in these uncertainty sources contribute to the aggregated uncertainty present in CM data products. The ultimate goal of this project is to quantify the confidence level of CM products to ensure that appropriate public and worker protections decisions are supported by defensible analysis.},
doi = {10.2172/1343383},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Feb 01 00:00:00 EST 2017},
month = {Wed Feb 01 00:00:00 EST 2017}
}

Technical Report:

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