Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment
The reactor core of a functioning power plant usually contains hundreds of fuel assemblies. When various fuel assembly designs coexist in the core—often due to changes in fuel suppliers, the introduction of improved designs, or other factors—it is known as a mixed core. To maintain nuclear safety, i...
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Elsevier
2025-06-01
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| author | A. Koraniany G.R. Ansarifar |
| author_facet | A. Koraniany G.R. Ansarifar |
| author_sort | A. Koraniany |
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| description | The reactor core of a functioning power plant usually contains hundreds of fuel assemblies. When various fuel assembly designs coexist in the core—often due to changes in fuel suppliers, the introduction of improved designs, or other factors—it is known as a mixed core. To maintain nuclear safety, it is essential to carefully assess the interactions between the various fuel assemblies to prevent any adverse effects within the reactor core.This article analyzes the neutronic and thermal-hydraulic modeling of a VVER-1000 reactor core, which includes TVS-2 M fuel assemblies. It also investigates a mixed core configuration created by adding a UTVS fuel assembly. The study aimed to identify the optimal fuel composition and loading location for the UTVS assembly based on various neutronic and thermal-hydraulic parameters. Finally, fuel burnup calculations were conducted on the optimized mixed core, with results compared to those of the original core. This study comprehensively ensures that the UTVS fuel assembly is placed in the reactor core with minimal stress regarding neutronic and thermal-hydraulic parameters while maintaining core performance. The VVER-1000 reactor core was initially modeled using the DRAGON and PARCS codes. Next, the new UTVS fuel assembly was placed in each fuel assembly position with varying levels of UTVS fuel enrichment. Neutronic parameters were then calculated for each configuration using computational tools. In the next stage, the hot channel equivalent cell of the reactor core and the hot channel equivalent cell of the UTVS fuel assembly were modeled using Fluent software, and the thermal-hydraulic parameters were calculated for all the created configurations. The obtained results were employed to develop a machine learning-based artificial neural network in MATLAB. By integrating this neural network with a genetic algorithm, optimization was carried out to identify the optimal fuel placement and enrichment for loading the UTVS fuel assembly into the targeted reactor core. Finally, fuel burnup calculations were performed using the PARCS code for a complete operational cycle, comparing the optimized mixed core and the original reactor core results. |
| format | Article |
| id | doaj-art-308c3de4d0ec402b916d2a4eefe296f1 |
| institution | OA Journals |
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| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-308c3de4d0ec402b916d2a4eefe296f12025-08-20T02:32:54ZengElsevierResults in Engineering2590-12302025-06-012610529710.1016/j.rineng.2025.105297Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessmentA. Koraniany0G.R. Ansarifar1Department of Nuclear Engineering, Faculty of Physics, University of Isfahan, Isfahan, 81746-73441, IranCorresponding author.; Department of Nuclear Engineering, Faculty of Physics, University of Isfahan, Isfahan, 81746-73441, IranThe reactor core of a functioning power plant usually contains hundreds of fuel assemblies. When various fuel assembly designs coexist in the core—often due to changes in fuel suppliers, the introduction of improved designs, or other factors—it is known as a mixed core. To maintain nuclear safety, it is essential to carefully assess the interactions between the various fuel assemblies to prevent any adverse effects within the reactor core.This article analyzes the neutronic and thermal-hydraulic modeling of a VVER-1000 reactor core, which includes TVS-2 M fuel assemblies. It also investigates a mixed core configuration created by adding a UTVS fuel assembly. The study aimed to identify the optimal fuel composition and loading location for the UTVS assembly based on various neutronic and thermal-hydraulic parameters. Finally, fuel burnup calculations were conducted on the optimized mixed core, with results compared to those of the original core. This study comprehensively ensures that the UTVS fuel assembly is placed in the reactor core with minimal stress regarding neutronic and thermal-hydraulic parameters while maintaining core performance. The VVER-1000 reactor core was initially modeled using the DRAGON and PARCS codes. Next, the new UTVS fuel assembly was placed in each fuel assembly position with varying levels of UTVS fuel enrichment. Neutronic parameters were then calculated for each configuration using computational tools. In the next stage, the hot channel equivalent cell of the reactor core and the hot channel equivalent cell of the UTVS fuel assembly were modeled using Fluent software, and the thermal-hydraulic parameters were calculated for all the created configurations. The obtained results were employed to develop a machine learning-based artificial neural network in MATLAB. By integrating this neural network with a genetic algorithm, optimization was carried out to identify the optimal fuel placement and enrichment for loading the UTVS fuel assembly into the targeted reactor core. Finally, fuel burnup calculations were performed using the PARCS code for a complete operational cycle, comparing the optimized mixed core and the original reactor core results.http://www.sciencedirect.com/science/article/pii/S2590123025013672Mixed coreVVER-1000Neutronic analysisThermal-hydraulic calculationGenetic algorithmFuel burnup calculation |
| spellingShingle | A. Koraniany G.R. Ansarifar Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment Results in Engineering Mixed core VVER-1000 Neutronic analysis Thermal-hydraulic calculation Genetic algorithm Fuel burnup calculation |
| title | Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment |
| title_full | Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment |
| title_fullStr | Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment |
| title_full_unstemmed | Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment |
| title_short | Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment |
| title_sort | machine learning based optimizing the neutronic and thermal hydraulic performance in a vver 1000 mixed core as well as fuel burnup assessment |
| topic | Mixed core VVER-1000 Neutronic analysis Thermal-hydraulic calculation Genetic algorithm Fuel burnup calculation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025013672 |
| work_keys_str_mv | AT akoraniany machinelearningbasedoptimizingtheneutronicandthermalhydraulicperformanceinavver1000mixedcoreaswellasfuelburnupassessment AT gransarifar machinelearningbasedoptimizingtheneutronicandthermalhydraulicperformanceinavver1000mixedcoreaswellasfuelburnupassessment |