Lu, K.; Katsuyama, Jinya; Li, Y.; Miyamoto, Yuhei*; Hirota, Takatoshi*; Itabashi, Yu*; Nagai, Masaki*; Suzuki, Masahide*; Kanto, Yasuhiro*
Proceedings of 27th International Conference on Nuclear Engineering (ICONE-27) (Internet), 9 Pages, 2019/05
Katsuyama, Jinya; Masaki, Koichi; Miyamoto, Yuhei*; Li, Y.
JAEA-Data/Code 2017-015, 229 Pages, 2018/03
As a part of the structural integrity research for aging light water reactor components, a probabilistic fracture mechanics (PFM) analysis code PASCAL has been developed in JAEA. The PASCAL code can evaluate the conditional failure probabilities and failure frequencies for core region in reactor pressure vessels under the pressurized thermal shock events. In this study, we improved many functions such as the stress intensity factor solutions, the fracture toughness models, or confidence level evaluation function by considering epistemic and aleatory uncertainties related to influence parameters in the structural integrity assessment. We also developed the analysis module PASCAL-Manager which calculates the failure frequency for the entire core region taking into consideration the failure probabilities obtained from PACAL-RV. Based on these improvements, the new analysis code is upgraded to PASCAL Ver.4. This report provides the user's manual and theoretical background of PASCAL Ver.4.
Lu, K.; Miyamoto, Yuhei*; Mano, Akihiro; Katsuyama, Jinya; Li, Y.
Proceedings of Asian Symposium on Risk Assessment and Management 2017 (ASRAM 2017) (USB Flash Drive), 9 Pages, 2017/11
Nowadays, probabilistic fracture mechanics (PFM) has been utilized in several countries as a rational method for structural integrity assessment of important structural components such as reactor pressure vessels (RPVs). In PFM analyses, potential flaws in target components are used to evaluate the failure probability or frequency. Therefore, flaw distributions (i.e., flaw depth and density distributions) in an RPV shall be rationally set as one of the most important influential factors, which are developed during the manufacturing process such as welding. Recently, a Bayesian updating methodology was applied to reflect the inspection results into flaw distributions, and the likelihood functions applicable to the case when flaws are detected in inspections were proposed. However, there may be no flaw indication as the inspection results of some RPVs. The flaw distributions in this situation are important while the corresponding likelihood functions have not been proposed. Therefore, this study proposed likelihood functions to be applicable for both case when flaws are detected and when there is no flaw indication as the inspection results. Based on the proposed likelihood functions, several application examples were given in which flaw distributions were estimated by reflecting the inspection results through Bayesian update. The results indicate that the proposed likelihood functions are useful for estimating the flaw distribution for the case when there is no flaw indication as the inspection results.
Masaki, Koichi; Miyamoto, Yuhei*; Osakabe, Kazuya*; Uno, Shumpei*; Katsuyama, Jinya; Li, Y.
Proceedings of 2017 ASME Pressure Vessels and Piping Conference (PVP 2017) (CD-ROM), 7 Pages, 2017/07
A probabilistic fracture mechanics (PFM) analysis code PASCAL has been developed by Japan Atomic Energy Agency (JAEA). PASCAL can evaluate failure frequencies of Japanese reactor pressure vessels (RPVs) during pressurized thermal shock (PTS) events based on domestic structural integrity assessment models and data of influence factors. In order to improve the engineering applicability of PFM to Japanese RPVs, we have performed verification of the PASCAL. In general, PFM code consists of many functions such as fracture mechanics evaluation functions, probabilistic evaluation functions including random variables sampling modules and probabilistic evaluation models, and so on. The verification of PFM code is basically difficult because it is impossible to confirm such functions through the comparison with experiments. When a PFM code is applied for evaluating failure frequencies of RPVs, verification methodology of the code should be clarified and it is important that verification results including the region and process of the verification of the code are indicated. In this paper, our activities of verification for PASCAL are presented. We firstly represent the overview and methodology of verification of PFM code, and then, some verification examples are provided. Through the verification activities, the applicability of PASCAL in structural integrity assessments for Japanese RPVs was confirmed with great confidence.
Miyamoto, Nobuyoshi*; Shimasaki, Kotaro*; Yamamoto, Kosuke*; Shintate, Morio*; Kamachi, Yuichiro*; Bastakoti, B. P.*; Suzuki, Norihiro*; Motokawa, Ryuhei; Yamauchi, Yusuke*
Chemistry; A European Journal, 20(46), p.14955 - 14958, 2014/11
Osakabe, Kazuya*; Masaki, Koichi; Miyamoto, Yuhei*; Katsumata, Genshichiro*; Katsuyama, Jinya
no journal, ,
Probabilistic fracture mechanics (PFM) analysis is a useful methodology for quantitative evaluation because failure probabilities can be calculated considering uncertainties of material properties. The recent studies about the classification of uncertainties of inputs for PFM analyses are introduced. In this paper, we describe overseas study on the handling of epistemic uncertainty and aleatory uncertainty in PFM analysis considering reliability.
Katsuyama, Jinya; Miyamoto, Yuhei*; Yamaguchi, Yoshihito; Mano, Akihiro; Li, Y.
no journal, ,
Weld residual stress (WRS) is one of the most important factors with a great deal of uncertainty, which is considered as a driving force for crack growth in the structural integrity assessment of piping welds. For more rational assessments, it is important to consider the uncertainty of WRS in probabilistic fracture mechanics (PFM) analysis. In the existing PFM analysis codes, the uncertainty of WRS is set through statistical process of multiple finite element analysis (FEA) results. This process depends on persons who perform PFM analysis, and it may give different uncertainties. In this study, we developed a new WRS evaluation model based on the Fourier transformation, and the model was introduced into PASCAL-SP which has been developed by Japan Atomic Energy Agency. Through these improvements of the code, the uncertainty of WRS can be taken into account automatically and appropriately by inputting multiple WRS analysis results directly as input data of PFM analysis.