Finding an optimization of the plate element of Egyptian research reactor using genetic algorithm

MISCELLANEOUS

Finding an optimization of the plate element of Egyptian research reactor using genetic algorithm

WAHED Mohamed，

IBRAHIM Wesam，

EFFAT Ahmed

Nuclear Science and Techniques

Vol.19, No.5

pp.314-320

Published in print 20 Oct 2008

DOI：10.1016/S1001-8042(09)60010-5

CLC：TL411,TL361

32100

The second Egyptian research reactor ET-RR-2 went critical on the 27th of November 1997. The National Center of Nuclear Safety and Radiation Control (NCNSRC) has the responsibility of the evaluation and assessment of the safety of this reactor. The purpose of this paper is to present an approach to optimization of the fuel element plate. For an efficient search through the solution space we use a multi objective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets. The aim of this paper is to propose a new approach for optimizing the fuel element plate in the reactor. The fuel element plate is designed with a view to improve reliability and lifetime and it is one of the most important elements during the shut down. In this present paper, we present a conceptual design approach for fuel element plate, in conjunction with a genetic algorithm to obtain a fuel plate that maximizes a fitness value to optimize the safety design of the fuel plate.

Genetic algorithmNon-dominated sortingFuel element plateEgypt nuclear reactor

References

[1]

Roughgarden J. The ory of population genetics and evolutionary ecology. Prentice-Hall, 1979.

[2]

Goldberg D E. Genetic algorithms in search, optimization and machine learning. Michigan: Addison-Wesley Publishing Company, Inc., 1989.

[3]

Coello C.

An empirical study of evolutionary techniques for multi objective optimization in engineering design. Dissertation

of Department of Computer Science, Tulane University, 1996.

Baidu Scholar

Google Scholar

[4]

Deb K. Evaluation Computing, 1999, 7: 205-230.

[5]

Zitzler E, Thiele L. IEEE Trans Evaluation computing, 1999, 3: 257-271.

[6]

Fonesca C, Fleming P.

Genetic algorithms for multiobjective optimization: formulation, discussion and generalization

. Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, CA: Morgan Kaufmann Publisher, 1993, 416-423.

Baidu Scholar

Google Scholar

[7]

Gruneninger T, Wallace D.

Multi-modal optimization using genetic algorithms. MIT CADlab - Technical Report 96.02

, 1996.

Baidu Scholar

Google Scholar

[8]

Holland J H, Adaptation in natural and artificial systems. Cambridge, Massachusetts: MIT Press, 1975.

[9]

Murato T. Genetic algorithms for multi-objective optimization, OSKA Prefecture University, 1997.

[10]

Fonseca C M, Fleming P J. IEEE Trans Systems, Man, & Cybernetics Part A: Systems & Humans, 1998, 28: 26-37.

[11]

Back T. Evolutionary computation, 1993, 1: 1-12.

[12]

Srinivas N, Deb K. J Evol Comput, 2004, 2: 221-248.

[13]

Wahed M. J Concert Application of Math, 2007, 5: 219-229.

[14]

Horn J. Multi criterion decision making. Handbook of evolutionary computation, IOP Publishing Ltd, 1997.

[15]

Ball M, Fleischer M, Church D.

A product design system employing optimization-based tradeoff analysis

. Proceedings of ASME DETC Design Theory and Methodology Conference, Baltimore, USA, September 10-13, 2000.

Baidu Scholar

Google Scholar

[16]

Shaaban N, Takahashi H. J Nuclear Science and Technology, 2006, 43: 816-818.

[17]

Invap A.

Final safety analysis report of ETRR-2. Document #0767-5325-3IBLI-001-1A

, 1999.

Baidu Scholar

Google Scholar