Startseite Technik A non-dominated discrete differential evolution for fuel loading pattern optimization of a nuclear research reactor
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

A non-dominated discrete differential evolution for fuel loading pattern optimization of a nuclear research reactor

  • Quang Binh Do ORCID logo EMAIL logo
Veröffentlicht/Copyright: 9. Oktober 2023
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

This paper presents a detailed description of a new variant of differential evolution for nuclear reactor refueling optimization problem. This variant combines the elitism strategy with a discrete differential evolution. The elitism strategy allows non-dominated solutions found during the search and stored in the archive to participate in the differential evolution operation. The population size is the same as the archive size, and the number of non-dominated solutions participating in the search at a particular generation is controlled by a specific probability. The proposed method is successfully applied to a nuclear research reactor for its first refueling time to search for optimal loading patterns that both maximize the effective multiplication keff and minimize the power peaking factor PPF of the reactor. The optimal loading patterns can significantly improve the operational time and safety of the reactor compared to the loading pattern used in practice.


Corresponding author: Quang Binh Do, Sai Gon University, 273 An Duong Vuong Street, Ho Chi Minh City, 700000, Vietnam, E-mail:

  1. Research ethics: Not applicable.

  2. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The author states no conflict of interest.

  4. Research funding: This work was funded by Sai Gon University, Ho Chi Minh City, Viet Nam under grant CSA2021-17.

  5. Data availability: Not applicable.

References

AEA Technology (1997). WIMSD – a neutronics code for standard lattice physics analysis. ANSWERS Software Service, NEA 1507/02.Suche in Google Scholar

Bäck, T., Rudolph, G., and Schwefel, H.-P. (1993). In: Fogel, D.G. and Atmar, W. (Eds.), Proceedings of the second annual conference on evolutionary programming, February 1993: evolutionary programming and evolution strategies: similarities and differences. Evolutionary Programming Society, La Jolla, CA, USA.Suche in Google Scholar

Bilal, Pant, M., Zaheer, H., Garcia-Hernandez, L., and Abraham, A. (2020). Differential evolution: a review of more than two decades of research. Eng. Appl. Artif. Intell. 90: 1–24, https://doi.org/10.1016/j.engappai.2020.103479.Suche in Google Scholar

Charles, A. and Parks, G. (2019). Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design. Ann. Nucl. Energy 127: 165–177, https://doi.org/10.1016/j.anucene.2018.12.002.Suche in Google Scholar

Ding, H., Sun, G., Hao, L., Wu, B., and Wu, Y. (2020). A loading pattern optimization method based on discrete differential evolution. Ann. Nucl. Energy 137: 1–7, https://doi.org/10.1016/j.anucene.2019.107057.Suche in Google Scholar

Do, B.Q. and Nguyen, L.P. (2007). Application of a genetic algorithm to the fuel reload optimization for a research reactor. Appl. Math. Comput. 187: 977–988, https://doi.org/10.1016/j.amc.2006.09.024.Suche in Google Scholar

Do, Q.B., Ngo, Q.H., and Nguyen, H.H. (2014). A binary mixed integer coded genetic algorithm for multi-objective optimization of nuclear research reactor fuel reloading. Kerntechnik 79: 511–517, https://doi.org/10.3139/124.110447.Suche in Google Scholar

Do, Q.B., Phan, G.T.T., Nguyen, K.C., Ngo, Q.H., and Tran, H.N. (2019). Criticality and rod worth analysis of the DNRR research reactor using the SRAC and MCNP5 codes. Nucl. Eng. Des. 343: 197–209, https://doi.org/10.1016/j.nucengdes.2019.01.011.Suche in Google Scholar

Eltaeib, T. and Mahmood, A. (2018). Differential evolution: a survey and analysis. Appl. Sci. 8: 3–25, https://doi.org/10.3390/app8101945.Suche in Google Scholar

Fowler, T.B., Vondy, D.R., and Kemshell, F.B. (1971). Nuclear reactor core analysis code: CITATION, RSICC, ORNL-TM-2496.Suche in Google Scholar

Goldberg, D.F. (1989). Genetic algorithms in search optimization and machine learning. Addison Wesley, Reading, Massachusetts.Suche in Google Scholar

Groşan, C., Oltean, M., and Oltean, M. (2003). The role of elitism in multiobjective optimization with evolutionary algorithms. Acta Univ. Apulensis, Acta 5: 83–90.Suche in Google Scholar

Holland, J.H. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, Michigan.Suche in Google Scholar

Jaszkiewicz, A. (2002). Genetic local search for multi-objective combinatorial optimization. Eur. J. Oper. Res. 137: 50–71, https://doi.org/10.1016/S0377-2217(01)00104-7.Suche in Google Scholar

Jayalal, M.L., Satya Murty, S.A.V., and Sai Baba, M. (2014). A survey of genetic algorithm applications in nuclear fuel management. J. Nucl. Sci. Technol. 4: 45–62.Suche in Google Scholar

Knowles, J. and Corne, D. (1999). Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), Washington D.C., July, 1999: the Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimization. IEEE.Suche in Google Scholar

Koza, J. (1992). Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA.Suche in Google Scholar

Kropaczek, D.J. and Turinsky, P.J. (1991). In-core nuclear fuel management for pressurized water reactors utilizing simulated annealing. Nucl. Technol. 95: 9–32, https://doi.org/10.13182/NT95-1-9.Suche in Google Scholar

Naft, B.N. and Sesonske, A. (1972). Pressurized water reactor optimal fuel management. Nucl. Technol. 14: 123–132, https://doi.org/10.13182/NT72-A31127.Suche in Google Scholar

Parks, G.T. (1996). Multi-objective pressurized water reactor reload core design by non-dominated genetic algorithm search. Nucl. Sci. Eng. 124: 178–187, https://doi.org/10.13182/NSE96-A24233.Suche in Google Scholar

Parks, G.T. and Miller, I. (1998). Proceedings of the 5th international conference on parallel problem solving from nature – PPSN V, Amsterdam, The Netherlands, September 27–30, 1998: selective breeding in a multiobjective genetic algorithm. Springer, Berlin, Heidelberg.Suche in Google Scholar

Phan, G.T.T., Do, Q.B., Ngo, Q.H., Tran, T.A., and Tran, H.N. (2020). Application of differential evolution algorithm for fuel loading optimization of the DNRR research reactor. Nucl. Eng. Des. 362: 1–9, https://doi.org/10.1016/j.nucengdes.2020.110582.Suche in Google Scholar

Price, K.V., Storn, R.M., and Lampinen, J.A. (2005). Differential evolution – a practical approach to global optimization. Springer-Verlag Berlin Heidelberg, Gernamy.Suche in Google Scholar

Rechenberg, I. (1973). Evolutions strategie. Frommann-Holzboog, Stuttgart.Suche in Google Scholar

Sacco, W.F., Henderson, N., Rios-Coelho, A.C., Ali, M.M., and Pereira, C.M.N.A. (2009). Differential evolution algorithms applied to nuclear reactor core design. Ann. Nucl. Energy 36: 1093–1099, https://doi.org/10.1016/j.anucene.2009.05.007.Suche in Google Scholar

Schwefel, H.P. (1995). Evolution and optimum seeking. Wiley, New York.Suche in Google Scholar

Stevens, J.G., Smith, K.S., Rempe, K.R., and Downar, T.J. (1995). Optimization of pressurized water reactor shuffling by simulated annealing with heuristics. Nucl. Sci. Eng. 121: 67–80, https://doi.org/10.13182/NSE121-67.Suche in Google Scholar

Storn, R.M. and Price, K.V. (1997). Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11: 341–359, https://doi.org/10.1023/A:1008202821328.10.1023/A:1008202821328Suche in Google Scholar

Suzuki, A. and Kiyose, R. (1971). Application of linear programming to refueling optimization for light water moderated power reactors. Nucl. Sci. Eng. 46: 112–130, https://doi.org/10.13182/NSE71-A22339.Suche in Google Scholar

Received: 2023-06-11
Published Online: 2023-10-09
Published in Print: 2023-12-15

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 13.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/kern-2023-0043/pdf?lang=de
Button zum nach oben scrollen