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Received: 2008-03-05

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.9 P.1264~1269

http://doi.org/10.1631/jzus.A0820217


Optimal design of pressure vessel using an improved genetic algorithm


Author(s):  Peng-fei LIU, Ping XU, Shu-xin HAN, Jin-yang ZHENG

Affiliation(s):  Institute of Chemical Machinery and Process Equipment, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   pfliu1980@yahoo.com, pingxu@zju.edu.cn

Key Words:  Pressure vessel, Optimal design, Genetic algorithm (GA), Simulated annealing (SA), Finite element analysis (FEA)


Peng-fei LIU, Ping XU, Shu-xin HAN, Jin-yang ZHENG. Optimal design of pressure vessel using an improved genetic algorithm[J]. Journal of Zhejiang University Science A, 2008, 9(9): 1264~1269.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820217"
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%A Shu-xin HAN
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%J Journal of Zhejiang University SCIENCE A
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T1 - Optimal design of pressure vessel using an improved genetic algorithm
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820217


Abstract: 
As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure constraint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Blachut, J., Eschenauer, H.A., 2001. Emerging Methods for Multidisciplinary Optimization. Springer, Wien, New York.

[2] Blachut, J., Vu, V.T., 2007. Burst pressures for torispheres and shallow spherical caps. Strain, 43(1):26-36.

[3] de Vicente, J., Lanchares, J., Hermida, R., 2003. Placement by thermodynamic simulated annealing. Physics Letters A, 317(5-6):415-423.

[4] Holland, J.H., 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, p.1-44.

[5] Hyder, J.M., Asif, M., 2008. Optimization of location and size of opening in a pressure vessel cylinder using ANSYS. Engineering Failure Analysis, 15(1-2):1-19.

[6] Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., 1983. Optimization by simulated annealing. Science, 220(4598):671-680.

[7] Liu, J.S., Parks, G.T., Clarkson, P.J., 2001. Shape optimization of axisymmetric cylindrical nozzles in spherical pressure vessels subject to stress constraints. International Journal of Pressure Vessels and Piping, 78(1):1-9.

[8] Liu, P.F., Zheng, J.Y., Ma, L., Miao, C.J., Wu, L.L., 2008. Calculations of plastic collapse load of pressure vessel using FEA. Journal of Zhejiang University SCIENCE A, 9(7):900-906.

[9] Magnucki, K., Lewiński, J., Stasiewicz, P., 2004. Optimal sizes of a ground-based horizontal cylindrical tank under strength and stability constraints. International Journal of Pressure Vessels and Piping, 81(12):913-917.

[10] Schmitt, L.M., 2001. Theory of genetic algorithms. Theoretical Computer Science, 259(1-2):1-61.

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