Power Reactor Embrittlement Data Base Research Articles

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

Abstract A new approach of utilizing information fusion technique is developed to predict the radiation embrittlement of reactor pressure vessel steels. The Charpy transition temperature shift data contained in the Power Reactor Embrittlement Database is used in this study. Six parameters—Cu, Ni, P, neutron fluence, irradiation time, and irradiation temperature—are used in the embrittlement prediction models. The results 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.

The development of radiation embrittlement models for US power reactor pressure vessel steels

A new approach of utilizing information fusion technique is developed to predict the radiation embrittlement of reactor pressure vessel steels. The Charpy transition temperature shift data contained in the Power Reactor Embrittlement Database is used in this study. Six parameters-Cu, Ni, P, neutron fluence, irradiation time, and irradiation temperature – are used in the embrittlement prediction models. The results 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.

New Methodologies for Developing Radiation Embrittlement Models and Trend Curves of the Charpy Impact Test Data

Abstract A new methodology is developed for the prediction of RPV embrittlement that utilizes a combination of domain models and nonlinear estimators including neural networks and nearest neighbor regressions. The Power Reactor Embrittlement Database is used in this study. The results from newly developed nearest neighbor projective fuser indicate that the combined embrittlement predictor achieved about 67.3% and 52.4% reductions in the uncertainties for General Electric Boiling Water Reactor plate and weld data compared to Regulatory Guide 1.99, Revision 2, respectively. The implications of irradiation temperature effects to the development of radiation embrittlement models are then discussed. A new methodology that incorporates the chemical compositions into the Charpy trend curve is also developed. The purpose of this new fitting procedure is to generate a new multi-space topography that can properly reflect the inhomogeneity of the surveillance materials and utilize this multi-space trend surface to link and project the surveillance test results to that of reactor pressure vessel steels.

A new technique for the prediction of non-linear material behavior

A new methodology is developed for the prediction of material behavior, such as aging processes, by utilizing a combination of domain models and non-linear estimators including neural networks and nearest neighbor regressions. This methodology is applied to the problem of predicting embrittilement levels in light-water reactors by combining the existing models with the conventional non-linear estimators. The Power Reactor Embrittlement Database is used in this study. The results indicate that the combined embrittlement predictor achieved about 56.5% and 32.8% reductions in the uncertainties for General Electric Boiling Water Reactor plate and weld data compared to Regulatory Guide 1.99, Revision 2, respectively. The implications of irradiation temperature effect to the development of radiation embrittlement model are then discussed.