Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Loading pattern optimization of VVER-1000 reactor core based on the discrete golden eagle optimization algorithm

  • Received : 2023.11.20
  • Accepted : 2024.03.28
  • Published : 2024.08.25
Download PDF
⟨ Previous Next ⟩

Abstract

The main features of the loading pattern optimization (LPO) problem, such as high-dimensionality, multi-modality, and non-linearity, make it difficult to achieve a truly optimal configuration. In recent years, metaheuristic methods have been successfully used to solve this problem. In this research, a discrete golden eagle optimization (DGEO) algorithm has been developed to solve the LPO problem in the first cycle of VVER-1000 reactor core. To evaluate the proposed algorithm, a linear multi-purpose fitness function has been used to improve the safety parameters of the reactor core by obtaining a flatter power distribution during the first cycle, and also to enhance the economic parameters by increasing the cycle length and reducing the cost of fuel recycling. For this purpose, a FORTRAN program has been written to map the DGEO algorithm for the LPO problem using the PMAX and PARCS core calculation code to compute the fitness function in each iteration. To speed up the calculations, parallel computing has been applied in the written program. The results demonstrated that the loading pattern, which is suggested by the DGEO algorithm, enhances all the safety and economic parameters in the fitness function. Thus, the DGEO algorithm is highly reliable for the LPO problems in the VVER 1000 reactor core.

Keywords

References

  1. M. Jamalipour, R. Sayareh, M. Gharib, F. Khoshahval, M.R. Karimi, Quantum behaved Particle Swarm Optimization with Differential Mutation operator applied to WWER-1000 in-core fuel management optimization, Ann. Nucl. Energy 54 (2013) 134-140, https://doi.org/10.1016/j.anucene.2012.11.008.
  2. J. Stevens, A Hybrid Method for In-Core Optimization of Pressurized Water Reactor Reload Design, Purdue University, 1995.
  3. G. Beni, J. Wang, Swarm intelligence in cellular robotic systems, NATO Adv. Workshop Robots Biol. Syst. (1989) 703-712, https://doi.org/10.1007/978-3-642-58069-7_38. Tuscany, Italy.
  4. Z.M. Zahedi, R. Akbari, M. ShokouhifarF, Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks, Expert Syst. Appl. 55 (2016) 313-338, https://doi.org/10.1016/j.eswa.2016.02.016.
  5. J. Kennedy, R. Eberhart, Particle swarm optimization, in: IEEE International Conference on Neural Networks. IEEE International Conference on Neural Networks, 1995, pp. 1942-1948, https://doi.org/10.1109/ICNN.1995.488968.
  6. A. Colorni, M. Dorigo, V. Maniezzo, Distributed optimization by ant colonies, in: La Premiere Conference Europeenne Sur La Vie Artificielle, 1991, pp. 134-142.
  7. Z.W. Geem, J.H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation 76 (2001) 60-68.
  8. D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, 2005. Kayseri/Turkiye.
  9. F. Hoareau, Loading pattern optimization using ant colony algorithm, in: International Conference on the Physics of Reactors "Nuclear Power: A Sustainable Resource.", 2008.
  10. F. Khoshahval, A. Zolfaghari, H. Minuchehr, M. Sadighi, A. Norouzi, PWR fuel management optimization using continuous particle swarm intelligence, Ann. Nucl. Energy 37 (2010) 1263-1271, https://doi.org/10.1016/j.anucene.2010.05.023.
  11. D. Babazadeh, M. Boroushaki, C. Lucas, Annals of Nuclear Energy Optimization of fuel core loading pattern design in a VVER nuclear power reactors using Particle Swarm Optimization (PSO), Ann. Nucl. Energy 36 (2009) 923-930, https://doi.org/10.1016/j.anucene.2009.03.007.
  12. N. Poursalehi, A. Zolfaghari, A. Minuchehr, Multi-objective loading pattern enhancement of PWR based on the Discrete Firefly Algorithm, Ann. Nucl. Energy 57 (2013) 151-163, https://doi.org/10.1016/j.anucene.2013.01.043.
  13. S. Ishiguro, T. Endo, A. Yamamoto, Loading pattern optimization for a PWR using multi-swarm Moth Flame optimization method with predator, J. Nucl. Sci. Technol. 57 (2020) 523-536, https://doi.org/10.1080/00223131.2019.1700844.
  14. M.A. Nasr, A. Zolfaghari, M. Abbasi, Development of Loading Pattern Optimization Software of Square Fuel Assemblies by Simulated Annealing Method Using the Calculation Modules of Nodal Expansion Neutronic and Two-phase Thermal-Hydraulic, Shahid Beheshti University, 2019.
  15. A. Zameer, M. Muneeb, S.M. Mirza, M.A.Z. Raja, Fractional-order particle swarm based multi-objective PWR core loading pattern optimization, Ann. Nucl. Energy (2020), https://doi.org/10.1016/j.anucene.2019.106982.
  16. A. Naserbegi, M. Aghaie, A. Zolfaghari, Implementation of Grey Wolf Optimization (GWO) algorithm to multi-objective loading pattern optimization of a PWR reactor, Ann. Nucl. Energy 148 (2020) 107703, https://doi.org/10.1016/j.anucene.2020.107703.
  17. A. Mohammadi-Balani, M. Dehghan Nayeri, A. Azar, M. Taghizadeh-Yazdi, Golden eagle optimizer: a nature-inspired metaheuristic algorithm, Comput. Ind. Eng. 152 (2021) 107050, https://doi.org/10.1016/j.cie.2020.107050.
  18. Guo Jun-Feng, Guo Jun-Feng, Qiang Yong, Guo Zhang Jun-Feng, Bin Sheng, Zhang Xu Yong-Qiang, Trong-The Nguyen Jin-Yang Lin, A Power System Profitable Load Dispatch Based on Golden Eagle Optimizer, 2022, https://doi.org/10.53106/199115992022083304012.
  19. Dwivedi Avinash, Vipin Rai, S. Joshi, Peripheral blood cell classification using modified local-information weighted fuzzy C-means clustering-based golden eagle optimization model, Springer, Soft Comput. (2022), https://doi.org/10.1007/s00500-022-07392-2.
  20. K. Durkota, Implementation of a discrete firefly algorithm for the QAP problem within the SEAGE framework, Czech Technic. Univ. Prague (2011), https://doi.org/10.13140/RG.2.1.4131.7281.
  21. R.W. Hamming, Error detecting and error correcting codes, Bell Syst. Tech. J. 29 (1950) 147-160, https://doi.org/10.1002/j.1538-7305.1950.tb00463.
  22. F. Khoshahval, H. Minuchehr, A. Zolfaghari, Performance evaluation of PSO and GA in PWR core loading pattern optimization, Nucl. Eng. Des. 241 (2011) 799-808, https://doi.org/10.1016/j.nucengdes.2010.12.023.
  23. G. Dantzig, R. Fulkerson, S. Johnson, Solution of a large-scale traveling-salesman problem, J. Oper. Res. Soc. Am. (1954) 393-410, https://doi.org/10.1287/opre.2.4.393.
  24. F. Faghihi, S.M. Mirvakili, S. Safaei, S. Bagheri, Neutronics and sub-channel thermal-hydraulics analysis of the Iranian VVER-1000 fuel bundle, Prog. Nucl. Energy 87 (2016) 39-46.
  25. Russia Federal Agency on Nuclear Energy (RFANE), Final Safety Assessment Report (FSAR) for BNPP, 2007.
  26. M. Jamalipour, R. Sayareh, M. Gharib, F. Khoshahval, M.R. Karimi, Quantum behaved Particle Swarm Optimization with Differential Mutation operator applied to WWER-1000 in-core fuel management optimization, Ann. Nucl. Energy 54 (2013) 134-140, https://doi.org/10.1016/j.anucene.2012.11.008.
  27. Specific-Safety-Requirements, Safety of Nuclear Power Plants: Design, INTERNATIONAL ATOMIC ENERGY AGENCY, 2016, https://doi.org/10.1515/kern-2002-0007.
  28. M. Benedict, T.H. Pigford, H.W. Levi, Nuclear Chemical Engineering, McGraw-Hill Education, New York, Chicago, San Francisco, Athens, London, Madrid, Mexico City, Milan, New Delhi, Singapore, Sydney, Toronto, 1998.