Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods
In order to maximize both the life cycle and efficiency of a reactor core, it is essential to find the optimum loading pattern. In the case of research reactors, a loading pattern can also be optimized for flux at an irradiation site. Therefore, the development of a general-use methodology for core...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2021/1802492 |
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| author | Nadeem Shaukat Ammar Ahmad Bukhtiar Mohsin Rustam Khan Salah Ud-Din Khan Shahab Ud-Din Khan |
| author_facet | Nadeem Shaukat Ammar Ahmad Bukhtiar Mohsin Rustam Khan Salah Ud-Din Khan Shahab Ud-Din Khan |
| author_sort | Nadeem Shaukat |
| collection | DOAJ |
| description | In order to maximize both the life cycle and efficiency of a reactor core, it is essential to find the optimum loading pattern. In the case of research reactors, a loading pattern can also be optimized for flux at an irradiation site. Therefore, the development of a general-use methodology for core loading optimization would be very valuable. In this paper, general-use multiobjective core reloading pattern optimization is performed using modified genetic algorithms (MGA). The developed strategy can be applied for the constrained optimization of research and power reactor cores. For an optimal reactor core reloading design strategy, an intelligent technique GA is coupled with the Monte Carlo (MC) code SuperMC developed by the FDS team in China for nuclear reactor physics calculations. An optimal loading pattern can be depicted as a configuration that has the maximum keff and maximum thermal fluxes in the core of the given fuel inventory keeping in view the safety constraints such as limitation on power peaking factor. The optimized loading patterns for Pakistan Research Reactor-1 (PARR-1) have been recommended using the implemented strategy by considering the constraint optimization, i.e., to maximize the keff or maximum thermal neutron flux while maintaining low power peaking factor. It has been observed that the developed intelligent strategy performs these tasks with a reasonable computational cost. |
| format | Article |
| id | doaj-art-9ccd14ab482b4ff88fc8816f9def4c95 |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-9ccd14ab482b4ff88fc8816f9def4c952025-02-03T01:29:20ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832021-01-01202110.1155/2021/18024921802492Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo MethodsNadeem Shaukat0Ammar Ahmad1Bukhtiar Mohsin2Rustam Khan3Salah Ud-Din Khan4Shahab Ud-Din Khan5Center for Mathematical Sciences (CMS), Pakistan Institute of Engineering & Applied Sciences, Nilore 45650, Islamabad, PakistanDepartment of Nuclear Engineering, Pakistan Institute of Engineering & Applied Sciences, Nilore 45650, Islamabad, PakistanDepartment of Nuclear Engineering, Pakistan Institute of Engineering & Applied Sciences, Nilore 45650, Islamabad, PakistanDepartment of Nuclear Engineering, Pakistan Institute of Engineering & Applied Sciences, Nilore 45650, Islamabad, PakistanCollege of Engineering, King Saud University, PO-Box 800, Riyadh 11421, Saudi ArabiaNational Tokamak Fusion Program, Nilore 45650, Islamabad, PakistanIn order to maximize both the life cycle and efficiency of a reactor core, it is essential to find the optimum loading pattern. In the case of research reactors, a loading pattern can also be optimized for flux at an irradiation site. Therefore, the development of a general-use methodology for core loading optimization would be very valuable. In this paper, general-use multiobjective core reloading pattern optimization is performed using modified genetic algorithms (MGA). The developed strategy can be applied for the constrained optimization of research and power reactor cores. For an optimal reactor core reloading design strategy, an intelligent technique GA is coupled with the Monte Carlo (MC) code SuperMC developed by the FDS team in China for nuclear reactor physics calculations. An optimal loading pattern can be depicted as a configuration that has the maximum keff and maximum thermal fluxes in the core of the given fuel inventory keeping in view the safety constraints such as limitation on power peaking factor. The optimized loading patterns for Pakistan Research Reactor-1 (PARR-1) have been recommended using the implemented strategy by considering the constraint optimization, i.e., to maximize the keff or maximum thermal neutron flux while maintaining low power peaking factor. It has been observed that the developed intelligent strategy performs these tasks with a reasonable computational cost.http://dx.doi.org/10.1155/2021/1802492 |
| spellingShingle | Nadeem Shaukat Ammar Ahmad Bukhtiar Mohsin Rustam Khan Salah Ud-Din Khan Shahab Ud-Din Khan Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods Science and Technology of Nuclear Installations |
| title | Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods |
| title_full | Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods |
| title_fullStr | Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods |
| title_full_unstemmed | Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods |
| title_short | Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods |
| title_sort | multiobjective core reloading pattern optimization of parr 1 using modified genetic algorithm coupled with monte carlo methods |
| url | http://dx.doi.org/10.1155/2021/1802492 |
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