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Title: New probabilistic fracture mechanics approach with neural network-based crack modeling: Its application to multiple cracks problem

Abstract

Studies on efficient utilization and life extension of operating nuclear power plants (NPPs) have become increasingly important since ages of the first-generation NPPs are approaching their design lives. In order to predict a remaining life of each plant, it is necessary to select those critical components that strongly influence the plant life, and to evaluate their remaining lives by considering aging effects of materials and other factors. This paper proposes a new method to incorporate sophisticated crack models, such as interaction and coalescence of multiple surface cracks, into probabilistic fracture mechanism (PFM) computer programs using neural networks. First, hundreds of finite element (FE) calculations of a plate containing multiple surface cracks are performed by parametrically changing crack parameters such as sizes and locations. A fully automated 3D FE analysis system is effectively utilized here. Second, the back-propagation neural network is trained using the FE solutions, i.e. crack parameters vs. their corresponding stress intensity factors (SIFs). After a sufficient number of training iterations, the network attains an ability to promptly output SIFs for arbitrary combinations of crack parameters. The well trained network is then incorporated into the parallel PFM program which runs on one of massively parallel computers composed of 512more » processing units. To demonstrate its fundamental performances, the present computer program is applied to evaluate failure probabilities of aged reactor pressure vessels considering interaction and coalescence of two dissimilar semi-elliptical surface cracks.« less

Authors:
; ;  [1]; ;  [2]
  1. Univ. of Tokyo (Japan)
  2. Science Univ. of Tokyo (Japan)
Publication Date:
OSTI Identifier:
122630
Report Number(s):
CONF-950740-
ISBN 0-7918-1335-5; TRN: 95:024247
Resource Type:
Conference
Resource Relation:
Conference: Joint American Society of Mechanical Engineers (ASME)/Japan Society of Mechanical Engineers (JSME) pressure vessels and piping conference, Honolulu, HI (United States), 23-27 Jul 1995; Other Information: PBD: 1995; Related Information: Is Part Of Fatigue and fracture mechanics in pressure vessels and piping. PVP-Volume 304; Mehta, H.S.; Wilkowski, G.; Takezono, S.; Bloom, J.; Yoon, K.; Aoki, S.; Rahman, S.; Nakamura, T.; Brust, F.; Yoshimura, S. [eds.]; PB: 594 p.
Country of Publication:
United States
Language:
English
Subject:
22 NUCLEAR REACTOR TECHNOLOGY; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; FRACTURE MECHANICS; NEURAL NETWORKS; NUCLEAR POWER PLANTS; PRESSURE VESSELS; SYSTEMS ANALYSIS; PARAMETRIC ANALYSIS; THREE-DIMENSIONAL CALCULATIONS; CRACK PROPAGATION; PARALLEL PROCESSING; SERVICE LIFE; COMPUTER CODES

Citation Formats

Yoshimura, Shinobu, Lee, J S, Yagawa, Genki, Sugioka, Kiyoshi, and Kawai, Tadahiko. New probabilistic fracture mechanics approach with neural network-based crack modeling: Its application to multiple cracks problem. United States: N. p., 1995. Web.
Yoshimura, Shinobu, Lee, J S, Yagawa, Genki, Sugioka, Kiyoshi, & Kawai, Tadahiko. New probabilistic fracture mechanics approach with neural network-based crack modeling: Its application to multiple cracks problem. United States.
Yoshimura, Shinobu, Lee, J S, Yagawa, Genki, Sugioka, Kiyoshi, and Kawai, Tadahiko. Wed . "New probabilistic fracture mechanics approach with neural network-based crack modeling: Its application to multiple cracks problem". United States.
@article{osti_122630,
title = {New probabilistic fracture mechanics approach with neural network-based crack modeling: Its application to multiple cracks problem},
author = {Yoshimura, Shinobu and Lee, J S and Yagawa, Genki and Sugioka, Kiyoshi and Kawai, Tadahiko},
abstractNote = {Studies on efficient utilization and life extension of operating nuclear power plants (NPPs) have become increasingly important since ages of the first-generation NPPs are approaching their design lives. In order to predict a remaining life of each plant, it is necessary to select those critical components that strongly influence the plant life, and to evaluate their remaining lives by considering aging effects of materials and other factors. This paper proposes a new method to incorporate sophisticated crack models, such as interaction and coalescence of multiple surface cracks, into probabilistic fracture mechanism (PFM) computer programs using neural networks. First, hundreds of finite element (FE) calculations of a plate containing multiple surface cracks are performed by parametrically changing crack parameters such as sizes and locations. A fully automated 3D FE analysis system is effectively utilized here. Second, the back-propagation neural network is trained using the FE solutions, i.e. crack parameters vs. their corresponding stress intensity factors (SIFs). After a sufficient number of training iterations, the network attains an ability to promptly output SIFs for arbitrary combinations of crack parameters. The well trained network is then incorporated into the parallel PFM program which runs on one of massively parallel computers composed of 512 processing units. To demonstrate its fundamental performances, the present computer program is applied to evaluate failure probabilities of aged reactor pressure vessels considering interaction and coalescence of two dissimilar semi-elliptical surface cracks.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1995},
month = {11}
}

Conference:
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