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Title: The Information Fusion Embrittlement Models for U.S. Power Reactor Pressure Vessel Steels

Abstract

The complex nonlinear dependencies observed in typical reactor pressure vessel (RPV) material embrittlement data, as well as the inherent large uncertainties and scatter in the radiation embrittlement data, make prediction of radiation embrittlement a difficult task. Conventional statistical and deterministic approaches have only resulted in rather large uncertainties, in part because they do not fully exploit domain-specific mechanisms. The domain models built by researchers in the field, on the other hand, do not fully exploit the statistical and information content of the data. As evidenced in previous studies, it is unlikely that a single method, whether statistical, nonlinear, or domain model, will outperform all others. More generally, considering the complexity of the embrittlement prediction problem, it is highly unlikely that a single best method exists and is tractable, even in theory. In this paper, we propose to combine a number of complementary methods including domain models, neural networks, and nearest neighbor regressions (NNRs). Such a combination of methods has become possible because of recent developments in measurement-based optimal fusers in the area of information fusion. The information fusion technique is used to develop radiation embrittlement prediction models for reactor RPV steels from U.S. power reactors, including boiling water reactors andmore » pressurized water reactors. The Charpy transition temperature-shift data is used as the primary index of RPV radiation embrittlement in this study. Six Cu, Ni, P, neutron fluence, irradiation time, and irradiation-parameters are used in the embrittlement prediction models. The results-temperature indicate that this new embrittlement predictor achieved reductions of about 49.5% and 52% in the uncertainties for plate and weld data, respectively, for pressurized water reactor and boiling water reactor data, compared with the Nuclear Regulatory Commission Regulatory Guide 1.99, Rev. 2. The implications of dose-rate effect and irradiation temperature effects for the development of radiation embrittlement models are also discussed.« less

Authors:
 [1];  [1];  [2]
  1. ORNL
  2. AOL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Center for Computational Sciences
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
931640
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: ASTM 23rd Symposium on Effects of Radiation on Materials, San Jose, CA, USA, 20060612, 20060615
Country of Publication:
United States
Language:
English
Subject:
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; 36 MATERIALS SCIENCE; EMBRITTLEMENT; IRRADIATION; NEURAL NETWORKS; NEUTRON FLUENCE; POWER REACTORS; PRESSURE VESSELS; PWR TYPE REACTORS; RADIATIONS; REGULATORY GUIDES; STEELS; TEMPERATURE DEPENDENCE; WATER

Citation Formats

Wang, Jy-An John, Rao, Nageswara S, and Konduri, Savanthi. The Information Fusion Embrittlement Models for U.S. Power Reactor Pressure Vessel Steels. United States: N. p., 2007. Web.
Wang, Jy-An John, Rao, Nageswara S, & Konduri, Savanthi. The Information Fusion Embrittlement Models for U.S. Power Reactor Pressure Vessel Steels. United States.
Wang, Jy-An John, Rao, Nageswara S, and Konduri, Savanthi. Mon . "The Information Fusion Embrittlement Models for U.S. Power Reactor Pressure Vessel Steels". United States. doi:.
@article{osti_931640,
title = {The Information Fusion Embrittlement Models for U.S. Power Reactor Pressure Vessel Steels},
author = {Wang, Jy-An John and Rao, Nageswara S and Konduri, Savanthi},
abstractNote = {The complex nonlinear dependencies observed in typical reactor pressure vessel (RPV) material embrittlement data, as well as the inherent large uncertainties and scatter in the radiation embrittlement data, make prediction of radiation embrittlement a difficult task. Conventional statistical and deterministic approaches have only resulted in rather large uncertainties, in part because they do not fully exploit domain-specific mechanisms. The domain models built by researchers in the field, on the other hand, do not fully exploit the statistical and information content of the data. As evidenced in previous studies, it is unlikely that a single method, whether statistical, nonlinear, or domain model, will outperform all others. More generally, considering the complexity of the embrittlement prediction problem, it is highly unlikely that a single best method exists and is tractable, even in theory. In this paper, we propose to combine a number of complementary methods including domain models, neural networks, and nearest neighbor regressions (NNRs). Such a combination of methods has become possible because of recent developments in measurement-based optimal fusers in the area of information fusion. The information fusion technique is used to develop radiation embrittlement prediction models for reactor RPV steels from U.S. power reactors, including boiling water reactors and pressurized water reactors. The Charpy transition temperature-shift data is used as the primary index of RPV radiation embrittlement in this study. Six Cu, Ni, P, neutron fluence, irradiation time, and irradiation-parameters are used in the embrittlement prediction models. The results-temperature indicate that this new embrittlement predictor achieved reductions of about 49.5% and 52% in the uncertainties for plate and weld data, respectively, for pressurized water reactor and boiling water reactor data, compared with the Nuclear Regulatory Commission Regulatory Guide 1.99, Rev. 2. The implications of dose-rate effect and irradiation temperature effects for the development of radiation embrittlement models are also discussed.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}

Conference:
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