Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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FUZZY SUPPORT VECTOR REGRESSION MODEL FOR THE CALCULATION OF THE COLLAPSE MOMENT FOR WALL-THINNED PIPES

  • Published : 2008.12.31
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Abstract

Since pipes with wall-thinning defects can collapse at fluid pressure that are lower than expected, the collapse moment of wall-thinned pipes should be determined accurately for the safety of nuclear power plants. Wall-thinning defects, which are mostly found in pipe bends and elbows, are mainly caused by flow-accelerated corrosion. This lowers the failure pressure, load-carrying capacity, deformation ability, and fatigue resistance of pipe bends and elbows. This paper offers a support vector regression (SVR) model further enhanced with a fuzzy algorithm for calculation of the collapse moment and for evaluating the integrity of wall-thinned piping systems. The fuzzy support vector regression (FSVR) model is applied to numerical data obtained from finite element analyses of piping systems with wall-thinning defects. In this paper, three FSVR models are developed, respectively, for three data sets divided into extrados, intrados, and crown defects corresponding to three different defect locations. It is known that FSVR models are sufficiently accurate for an integrity evaluation of piping systems from laser or ultrasonic measurements of wall-thinning defects.

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References

  1. B. Chexal, J. Horowitz, B. Dooley, P. Millett, C. Wood, and R. Jones, 'Flow-Accelerated Corrosion in Power Plant,' EPRI/TR-106611-R2 (1998)
  2. V. C. Martzen and L. Yu, 'Elbow Stress Indices using Finite Element Analysis,' Nucl. Eng. & Des., 181, 257 (1998) https://doi.org/10.1016/S0029-5493(97)00352-X
  3. M. A. Shalaby and M. Y. A. Younan, 'Limit Loads for Pipe Elbows Subjected to In-Plane Opening Moments and Internal Pressure,' J. Press. Ves. Tech., 121, 17 (1999) https://doi.org/10.1115/1.2883661
  4. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York (1995)
  5. A. Kulkarni, V. K. Jayaraman, and B. D. Kulkarni, 'Control of Chaotic Dynamical Systems Using Support Vector Machines,' Physics Letters A, 317, 429 (2003) https://doi.org/10.1016/j.physleta.2003.09.004
  6. P. -F. Pai and W. -C. Hong, 'Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting,' Energy Conversion and Management, 46, 2669 (2005) https://doi.org/10.1016/j.enconman.2005.02.004
  7. W. Yan, H. Shao, and X. Wang, 'Soft Sensing Modeling Based on Support Vector Machine and Bayesian Model Selection,' Computers and Chemical Engineering, 28, 1489 (2004) https://doi.org/10.1016/j.compchemeng.2003.11.004
  8. M. G. Na, J. W. Kim, and I. J. Hwang, 'Collapse Moment Estimation by Support Vector Machines for Wall-Thinned Pipe Bends and Elbows,' Nucl. Eng. Des., 237, 451 (2007) https://doi.org/10.1016/j.nucengdes.2006.07.004
  9. M. G. Na, H. Y. Yang, and D. H. Lim, 'A Soft-Sensing Model for Feedwater Flow Rate Using Fuzzy Support Vector Regression,' Nucl. Eng. Tech. 40, 69 (2008) https://doi.org/10.5516/NET.2008.40.1.069
  10. K. Yahiaoui, D. G. Moffat, and D. N. Moreton, 'Piping Elbows with Cracks, Part 2: A Parametric Study of the Influence of Crack Size on Limit Loads due to Pressure and Opening Bending,' J. Strain Anal., 35, 35 (2000) https://doi.org/10.1243/0309324001513991
  11. K. Yahiaoui, D. G. Moffat, and D. N. Moreton, 'Piping Elbows with Cracks, Part 2: Global Finite Element and Experimental Plastic Loads under Opening Bending,' J. Strain Anal., 35, 47 (2000) https://doi.org/10.1243/0309324001514008
  12. J. Chattopadhyay, 'The Effect of Internal Pressure on In-Plane Collapse Moment of Elbows,' Nucl. Eng. Des., 202, 133 (2002)
  13. A. Robertson, H. Li, and D. Mackenzie, 'Plastic Collapse of Pipe Bends under Combined Internal Pressure and In-Plane Bending,' Int. J. Pres. Ves. & Piping, 82, 407 (2005) https://doi.org/10.1016/j.ijpvp.2004.09.005
  14. H. D. Hibbitt, B. I. Karlsson, and E. P. Sorensen, 'ABAQUS/Standard User's Manual,' Hibbitt, Karlsson & Sorensen, Inc., Pawtucket, RI (2001)
  15. K. Vojislav, Learning and Soft Computing -Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA (2001)
  16. S. L. Chiu, 'Fuzzy Model Identification Based on Cluster Estimation,' J. Intell. Fuzzy Systems, 2, 267 (1994) https://doi.org/10.1109/91.324806
  17. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA (1989)
  18. M. Mitchell, An Introduction to Genetic Algorithms, The MIT Press, Cambridge, MA (1996)