Covariance and sensitivity data generation at ORNL
Covariance data are required to assess uncertainties in design parameters in several nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the US Evaluated Nuclear Data Library, EN...
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Veröffentlicht in: | Radiation protection dosimetry 2005-01, Vol.115 (1-4), p.133-135 |
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creator | Leal, L. C. Derrien, H. Larson, N. M. Alpan, A. |
description | Covariance data are required to assess uncertainties in design parameters in several nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the US Evaluated Nuclear Data Library, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. In this paper we address the generation of covariance data in the resonance region done with the computer code SAMMY. SAMMY is used in the evaluation of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on the generalised least-squares formalism (Bayesian theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, it provides the resonance parameter covariances. For resonance parameter evaluations where there are no resonance parameter covariance data available, the alternative is to use an approach called the ‘retroactive’ resonance parameter covariance generation. In this paper, we describe the application of the retroactive covariance generation approach for the gadolinium isotopes. |
doi_str_mv | 10.1093/rpd/nci126 |
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The data fitting of cross sections is based on the generalised least-squares formalism (Bayesian theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, it provides the resonance parameter covariances. For resonance parameter evaluations where there are no resonance parameter covariance data available, the alternative is to use an approach called the ‘retroactive’ resonance parameter covariance generation. In this paper, we describe the application of the retroactive covariance generation approach for the gadolinium isotopes.</description><identifier>ISSN: 0144-8420</identifier><identifier>EISSN: 1742-3406</identifier><identifier>DOI: 10.1093/rpd/nci126</identifier><identifier>PMID: 16381699</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Computer-Aided Design ; Data Interpretation, Statistical ; Databases, Factual ; Equipment Design - methods ; Equipment Failure Analysis - methods ; Nuclear Reactors ; Radiation Protection - instrumentation ; Radiation Protection - methods ; Radioisotopes - analysis ; Radiometry - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Software ; Statistics as Topic ; Tennessee</subject><ispartof>Radiation protection dosimetry, 2005-01, Vol.115 (1-4), p.133-135</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c323t-d338b216cbd6fe30a8ddcca267f77db8280c44befabd5c252b7a479a55f7f9f03</citedby><cites>FETCH-LOGICAL-c323t-d338b216cbd6fe30a8ddcca267f77db8280c44befabd5c252b7a479a55f7f9f03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16381699$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Leal, L. C.</creatorcontrib><creatorcontrib>Derrien, H.</creatorcontrib><creatorcontrib>Larson, N. M.</creatorcontrib><creatorcontrib>Alpan, A.</creatorcontrib><title>Covariance and sensitivity data generation at ORNL</title><title>Radiation protection dosimetry</title><addtitle>Radiat Prot Dosimetry</addtitle><description>Covariance data are required to assess uncertainties in design parameters in several nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the US Evaluated Nuclear Data Library, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. In this paper we address the generation of covariance data in the resonance region done with the computer code SAMMY. SAMMY is used in the evaluation of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on the generalised least-squares formalism (Bayesian theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, it provides the resonance parameter covariances. For resonance parameter evaluations where there are no resonance parameter covariance data available, the alternative is to use an approach called the ‘retroactive’ resonance parameter covariance generation. In this paper, we describe the application of the retroactive covariance generation approach for the gadolinium isotopes.</description><subject>Computer-Aided Design</subject><subject>Data Interpretation, Statistical</subject><subject>Databases, Factual</subject><subject>Equipment Design - methods</subject><subject>Equipment Failure Analysis - methods</subject><subject>Nuclear Reactors</subject><subject>Radiation Protection - instrumentation</subject><subject>Radiation Protection - methods</subject><subject>Radioisotopes - analysis</subject><subject>Radiometry - methods</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Software</subject><subject>Statistics as Topic</subject><subject>Tennessee</subject><issn>0144-8420</issn><issn>1742-3406</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpFz0tLAzEUhuEgiq3VjT9AZi2MzW2SzFKKtUJpoVYQN-HkJlE7Lcm02H9vZYquzuI8fPAidE3wHcE1G6aNGzY2EipOUJ9ITkvGsThFfUw4LxWnuIcucv7AmMq64ueoRwRTRNR1H9HRegcpQmN9AY0rsm9ybOMutvvCQQvFu298gjaumwLaYr6YTS_RWYCv7K-Od4Bexg_L0aSczh-fRvfT0jLK2tIxpgwlwhongmcYlHPWAhUySOmMogpbzo0PYFxlaUWNBC5rqKogQx0wG6Dbbtemdc7JB71JcQVprwnWv-H6EK678AO-6fBma1be_dNj6QGUHYi59d9_f0ifWkgmKz15fdNLMlazyfhZL9gP1LNkSA</recordid><startdate>20050101</startdate><enddate>20050101</enddate><creator>Leal, L. 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M. ; Alpan, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c323t-d338b216cbd6fe30a8ddcca267f77db8280c44befabd5c252b7a479a55f7f9f03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Computer-Aided Design</topic><topic>Data Interpretation, Statistical</topic><topic>Databases, Factual</topic><topic>Equipment Design - methods</topic><topic>Equipment Failure Analysis - methods</topic><topic>Nuclear Reactors</topic><topic>Radiation Protection - instrumentation</topic><topic>Radiation Protection - methods</topic><topic>Radioisotopes - analysis</topic><topic>Radiometry - methods</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Software</topic><topic>Statistics as Topic</topic><topic>Tennessee</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leal, L. C.</creatorcontrib><creatorcontrib>Derrien, H.</creatorcontrib><creatorcontrib>Larson, N. M.</creatorcontrib><creatorcontrib>Alpan, A.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><jtitle>Radiation protection dosimetry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leal, L. C.</au><au>Derrien, H.</au><au>Larson, N. M.</au><au>Alpan, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Covariance and sensitivity data generation at ORNL</atitle><jtitle>Radiation protection dosimetry</jtitle><addtitle>Radiat Prot Dosimetry</addtitle><date>2005-01-01</date><risdate>2005</risdate><volume>115</volume><issue>1-4</issue><spage>133</spage><epage>135</epage><pages>133-135</pages><issn>0144-8420</issn><eissn>1742-3406</eissn><abstract>Covariance data are required to assess uncertainties in design parameters in several nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the US Evaluated Nuclear Data Library, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. In this paper we address the generation of covariance data in the resonance region done with the computer code SAMMY. SAMMY is used in the evaluation of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on the generalised least-squares formalism (Bayesian theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, it provides the resonance parameter covariances. For resonance parameter evaluations where there are no resonance parameter covariance data available, the alternative is to use an approach called the ‘retroactive’ resonance parameter covariance generation. In this paper, we describe the application of the retroactive covariance generation approach for the gadolinium isotopes.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>16381699</pmid><doi>10.1093/rpd/nci126</doi><tpages>3</tpages></addata></record> |
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subjects | Computer-Aided Design Data Interpretation, Statistical Databases, Factual Equipment Design - methods Equipment Failure Analysis - methods Nuclear Reactors Radiation Protection - instrumentation Radiation Protection - methods Radioisotopes - analysis Radiometry - methods Reproducibility of Results Sensitivity and Specificity Software Statistics as Topic Tennessee |
title | Covariance and sensitivity data generation at ORNL |
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