Estimation of collapse moment for the wall-thinned pipe bends using fuzzy model identification

Nuclear Engineering and Design

Volumn 236, Issue 13, 2006, Pages 1335-1343

  Na, Man Gyun ^a   Kim, Jin Weon ^a   Hwang, In Joon ^a  

^a Chosun University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COLLAPSIBLE TUBES; ERROR ANALYSIS; FUZZY SETS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS;

COLLAPSE MOMENT; FUZZY MODELS; ROBUST ALGORITHMS; SUBTRACTIVE CLUSTERING;

PIPELINE BENDS;

EID: 33646528468     PISSN: 00295493     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.nucengdes.2005.12.003     Document Type: Article

References (27)

1. Bartal Y., Lin J., and Uhrig R.E. Nuclear power plant transient diagnostics using artificial neural networks that allow "don't-know" classifications. Nucl. Technol. 110 3 (1995) 436-449
2. Bartlett E.B., and Uhrig R.E. Nuclear power plant diagnostics using an artificial neural network. Nucl. Technol. 97 (1992) 272-281
3. Chattopadhyay J. The effect of internal pressure on in-plane collapse moment of elbows. Nucl. Eng. Des. 202 (2002) 133-144
4. Chen J., and Liu J. Mixture principal component analysis models for process monitoring. Ind. Eng. Chem. Res. 38 4 (1999) 1478-1488
5. Chexal, B., Horowitz, J., Dooley, B., Millett, P., Wood, C., Jones, R., 1998. Flow-accelerated corrosion in power plant, EPRI/TR-106611-R2.
6. Chiu S.L. Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2 (1994) 267-278
7. Fantoni, P., Figedy, S., Racz, A., 1998. A neuro-fuzzy model applied full range signal validation of PWR nuclear power plant data. FLINS-98, Antwerpen, Belgium.
8. Goldberg D.E. Genetic algorithms in search. Optimization and Machine Learning (1989), Addison-Wesley, Reading, MA
9. Hines J.W., Wrest D.J., and Uhrig R.E. Signal validation using an adaptive neural fuzzy inference system. Nucl. Technol. 119 2 (1997) 181-193
10. Kim H.G., Chang S.H., and Lee B.H. Optimal fuel loading pattern design using an artificial neural network and a fuzzy rule-based system. Nucl. Sci. Eng. 115 2 (1993) 152-163
11. Kumar P., Merchant S.N., and Desai U.B. Improving performance in pulse radar detection using Bayesian regularization for neural network training. Digital Signal Process. 14 (2004) 438-448
12. Lee J.H., Sim H.J., Jang C.S., and Kim C.H. Incorporation of neural networks into simulated annealing algorithm for fuel assembly loading pattern optimization in a PWR. Proceedings of the International Conference on Physics of Nuclear Science and Technology. October 5-8 (1998), American Nuclear Society, Long Island, New York 75
13. Mamdani E.H., and Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7 (1975) 1-13
14. Marseguerra M., and Zio E. Fault diagnosis via neural networks: the Boltzmann machine. Nucl. Sci. Eng. 117 3 (1994) 194-200
15. Martzen V.C., and Yu L. Elbow stress indices using finite element analysis. Nucl. Eng. Des. 181 (1998) 257-265
16. Mitchell M. An Introduction to Genetic Algorithms (1996), MIT Press, Cambridge, MA
17. Na M.G. Design of a genetic fuzzy controller for the nuclear steam generator water level control. IEEE Trans. Nucl. Sci. 45 4 (1998) 2261-2271
18. Na M.G., Sim Y.R., Park K.H., Lee S.M., Jung D.W., Shin S.H., Upadhyaya B.R., Zhao K., and Lu B. Sensor monitoring using a fuzzy neural network with an automatic structure constructor. IEEE Trans. Nucl. Sci. 50 2 (2003) 241-250
19. Na M.G., and Sim Y.R. An input feature selection method applied to fuzzy neural networks for signal estimation. J. Kor. Nucl. Soc. 33 5 (2001) 457-467
20. Robertson A., Li H., and Mackenzie D. Plastic collapse of pipe bends under combined internal pressure and in-plane bending. Int. J. Press. Vess. Piping 82 (2005) 407-416
21. Roh C.H., Chang H.S., Kim H.G., and Chang S.H. Identification of reactor vessel failures using spatiotemporal neural networks. IEEE Trans. Nucl. Sci. 43 6 (1996) 3223-3229
22. Shalaby M.A., and Younan M.Y.A. Limit loads for pipe elbows subjected to in-plane opening moments and internal pressure. J. Press. Vess. Technol. 121 (1999) 17-23
23. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. MC-1 1 (1985) 116-132
24. Tian L., and Noore A. Evolutionary neural network modeling for software cumulative failure time prediction. Reliab. Eng. Syst. Safety 87 (2005) 45-51
25. Wang X.Z., and Li R.F. Combining conceptual clustering and principal component analysis for state space based process monitoring. Ind. Eng. Chem. Res. 38 11 (1999) 4345-4358
26. Yahiaoui K., Moffat D.G., and Moreton D.N. 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 (2000) 35-46
27. Yahiaoui K., Moffat D.G., and Moreton D.N. Piping elbows with cracks. Part 2. Global finite element and experimental plastic loads under opening bending. J. Strain Anal. 35 (2000) 47-57