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Title: Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment

Journal Article · · Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
DOI: https://doi.org/10.1115/1.4045162 · OSTI ID:1579939
 [1];  [2];  [1];  [2];  [2]
  1. Pusan National Univ., Busan (South Korea)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)

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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
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
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1579939
Journal Information:
Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, Vol. 3, Issue 1; ISSN 2572-3901
Publisher:
ASMECopyright Statement
Country of Publication:
United States
Language:
English

References (9)

Fatigue and Reliability Analysis of Unidirectional GFRP Composites under Rotating Bending Loads journal February 2003
Effects of Cracking Test Conditions on Estimation Uncertainty for Weibull Parameters Considering Time-Dependent Censoring Interval journal December 2016
The genetical theory of natural selection. book January 1930
Statistical Investigation of the Behaviour of Small Cracks and Fatigue life in Carbon Steels with Different Ferrite Grain Sizes journal June 1994
Reliability analysis of GFRP pultruded composite rods journal March 1996
Design of Pressure Vessels for Low-Cycle Fatigue journal September 1962
Statistical analysis of bending fatigue life data using Weibull distribution in glass-fiber reinforced polyester composites journal January 2008
Bootstrap Methods: Another Look at the Jackknife journal January 1979
Statistical analysis of parameter estimation of a probabilistic crack initiation model for Alloy 182 weld considering right-censored data and the covariate effect journal February 2018