Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment
Abstract
In general, the fatigue life of a safety critical pressure component is estimated using best-fit fatigue life curves (S-N curves). These curves are estimated based on underlying in-air condition fatigue test data. The best-fitting approach requires a large safety factor to accommodate the uncertainty associated with large scatter in fatigue test data. In addition to this safety factor, reactor component fatigue life prognostics requires an additional correction factor that in general is also estimated deterministically. This additional factor known as the environmental correction factor Fen is to cater the effect of the harsh coolant environment that severely reduces the life of these components. The deterministic Fen factor may also lead to further conservative estimation of fatigue life leading to unnecessary early retirement of costly reactor components. To address the above-mentioned issues, we propose a data-analytics framework which uses Weibull and Bootstrap probabilistic modeling techniques for explicitly quantifying the uncertainty/scatter associated with fatigue life rather than estimating the lives based on a best-fit based deterministic approach. We assume the proposed probabilistic approach would provide the first hand information for assessing the maximum and minimum effects of pressurized water reactor water on the reactor component. In the discussed approach, in addition tomore »
- Authors:
-
- Pusan National Univ., Busan (South Korea)
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- National Research Foundation of Korea (NRF); Korea Institute of Energy Technology Evaluation and Planning (KETEP); USDOE Office of Nuclear Energy (NE), Nuclear Reactor Technologies (NE-7). Light Water Reactor Sustainability Program
- OSTI Identifier:
- 1579939
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
- Additional Journal Information:
- Journal Volume: 3; Journal Issue: 1; Journal ID: ISSN 2572-3901
- Publisher:
- ASME
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 22 GENERAL STUDIES OF NUCLEAR REACTORS; pressurized water reactor; environmental fatigue; probabilistic life estimation; Weibull distribution; maximum likelihood estimation; Bootstrap method; data analytics; predictive modeling; probabilistic prognostics; reliability modeling; failure analysis; harsh or extreme environments; materials testing; mechanical engineering; service life prediction; structural engineering
Citation Formats
Park, Jae Phil, Mohanty, Subhasish, Bahn, Chi Bum, Majumdar, Saurin, and Natesan, Krishnamurti. Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment. United States: N. p., 2019.
Web. doi:10.1115/1.4045162.
Park, Jae Phil, Mohanty, Subhasish, Bahn, Chi Bum, Majumdar, Saurin, & Natesan, Krishnamurti. Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment. United States. https://doi.org/10.1115/1.4045162
Park, Jae Phil, Mohanty, Subhasish, Bahn, Chi Bum, Majumdar, Saurin, and Natesan, Krishnamurti. Tue .
"Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment". United States. https://doi.org/10.1115/1.4045162. https://www.osti.gov/servlets/purl/1579939.
@article{osti_1579939,
title = {Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment},
author = {Park, Jae Phil and Mohanty, Subhasish and Bahn, Chi Bum and Majumdar, Saurin and Natesan, Krishnamurti},
abstractNote = {In general, the fatigue life of a safety critical pressure component is estimated using best-fit fatigue life curves (S-N curves). These curves are estimated based on underlying in-air condition fatigue test data. The best-fitting approach requires a large safety factor to accommodate the uncertainty associated with large scatter in fatigue test data. In addition to this safety factor, reactor component fatigue life prognostics requires an additional correction factor that in general is also estimated deterministically. This additional factor known as the environmental correction factor Fen is to cater the effect of the harsh coolant environment that severely reduces the life of these components. The deterministic Fen factor may also lead to further conservative estimation of fatigue life leading to unnecessary early retirement of costly reactor components. To address the above-mentioned issues, we propose a data-analytics framework which uses Weibull and Bootstrap probabilistic modeling techniques for explicitly quantifying the uncertainty/scatter associated with fatigue life rather than estimating the lives based on a best-fit based deterministic approach. We assume the proposed probabilistic approach would provide the first hand information for assessing the maximum and minimum effects of pressurized water reactor water on the reactor component. In the discussed approach, in addition to the probabilistic fatigue curves, we suggest using a probabilistic environment correction factor Fen. Here, we assume the probabilistic fatigue curve and Fen would capture the S-N data scatter associated with the bulk effect of material grades, surface finish, strain rate, etc. on the material/component fatigue life.},
doi = {10.1115/1.4045162},
journal = {Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems},
number = 1,
volume = 3,
place = {United States},
year = {2019},
month = {11}
}
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