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Prediction of Crack Growth in Steam Generator Tubes Using Monte Carlo Simulation

Jae Bong Lee1, Jai Hak Park1, Sung Ho Lee2, Hong-Deok Kim2, Han-Sub Chung2

Chungbuk National Univ., Korea.
Korea Electric Power Research Institute.

Computer Modeling in Engineering & Sciences 2006, 11(1), 9-16. https://doi.org/10.3970/cmes.2006.011.009

Abstract

The growth of stress corrosion cracks in steam generator tubes is predicted using the Monte Carlo simulation and statistical approaches. The statistical parameters that represent the characteristics of crack growth and crack initiation are derived from in-service inspection (ISI) non-destructive evaluation (NDE) data. Based on the statistical approaches, crack growth models are proposed and applied to predict crack distribution at the end of cycle (EOC). Because in-service inspection (ISI) crack data is different from physical crack data, a simple method for predicting the physical number of cracks from periodic in-service inspection data is proposed in this study. Actual number of cracks is easily estimated using the method, and the statistical crack growth is simulated using the Monte Carlo method. Probabilistic distributions of the number of cracks and maximum crack size at EOC are obtained from the simulation. Comparing the predicted EOC crack data with the known EOC data the usefulness of the proposed method is examined and satisfactory results are obtained.

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APA Style
Lee, J.B., Park, J.H., Lee, S.H., Kim, H., Chung, H. (2006). Prediction of crack growth in steam generator tubes using monte carlo simulation. Computer Modeling in Engineering & Sciences, 11(1), 9-16. https://doi.org/10.3970/cmes.2006.011.009
Vancouver Style
Lee JB, Park JH, Lee SH, Kim H, Chung H. Prediction of crack growth in steam generator tubes using monte carlo simulation. Comput Model Eng Sci. 2006;11(1):9-16 https://doi.org/10.3970/cmes.2006.011.009
IEEE Style
J.B. Lee, J.H. Park, S.H. Lee, H. Kim, and H. Chung "Prediction of Crack Growth in Steam Generator Tubes Using Monte Carlo Simulation," Comput. Model. Eng. Sci., vol. 11, no. 1, pp. 9-16. 2006. https://doi.org/10.3970/cmes.2006.011.009



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