Space nuclear reactor fuel design based on dynamic analysis and GA &amp; PSO optimization

Rafiei, M.; Ansarifar, G.R.

doi:10.1016/j.pnucene.2021.104043

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Ansarifar. 17 Considering the result, this optimal design had a more suitable performance in comparison with the present scheme.…”

mentioning

confidence: 79%

“…The same procedures as in Reference 17 are implemented for coupled ANFIS‐GA and coupled ANFIS‐PSO optimizations. To enhance the functionality and efficiency of the optimization process, combined GA‐PSO can be implemented which is known as Hybridized GA‐PSO (HGAPSO) algorithm.…”

Section: Adaptive Neuro‐fuzzy Inference System (Anfis) Designmentioning

confidence: 99%

“…An optimal fuel design for a 100 kWe integral space nuclear reactor was introduced by M. Rafiei and G.R.Ansarifar 17 . Considering the result, this optimal design had a more suitable performance in comparison with the present scheme.…”

Section: Introductionmentioning

confidence: 95%

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Conceptual design of a new space nuclear reactor with low‐enriched uranium fuel based on the optimization of fuel composition and reflector geometry via optimized adaptive neuro‐fuzzy inference system as well as investigation of fresh core cycle length

Rafiei

Ansarifar

2022

Intl J of Energy Research

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A nuclear reactor could be a suitable option in space needs such as planetary and space exploration. A low-enriched uranium fuel (LEU) could be utilized by member states of the Non-Proliferation Treaty (NPT) instead of Highly Enriched Uranium (HEU) fuel for security reasons. This paper deals with a conceptual design and optimization of low-enriched uranium (20 wt% enriched uranium in UO 2 fuel and 100 kWe) integral space nuclear reactor core according to the neutronic and dynamic analysis. The existing HEUfueled reactor is considered for comparison. For neutronic evaluation, different fuel mixtures and reflector thicknesses are considered to derive reactivity of excess, power peaking coefficient in radial direction, reactivity factor of fuel temperature, and reactivity factor of coolant temperature. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented for data estimation. This system is a machine learning method that combines the features of neural networks and fuzzy systems. The initial ANFIS parameters are optimized using optimization algorithms. The optimized ANFIS is coupled with the HGAPSO algorithm to derive an optimally designed fuel mixture and reflector thickness considering a cost function. The simulation results show that the coupled ANFIS-HGAPSO method has better performance and superiority (less error) compared with the ANFIS-GA and ANFIS-PSO methods. In addition, the optimized fuel mixture and core geometry have better performance compared to the present design. In each optimization process (fuel mixture and reflector thickness) the optimally designed model is validated utilizing neutronic computations. The overall optimal design of this reactor can provide 7000 days of full-power operation which is significantly more than the core cycle length of the existing HEU-fueled space nuclear reactor. In addition, this scheme has more reactor safety and stability because of a more negative reactivity factor of fuel temperature.

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“…Ansarifar. 17 Considering the result, this optimal design had a more suitable performance in comparison with the present scheme.…”

mentioning

confidence: 79%

Section: Adaptive Neuro‐fuzzy Inference System (Anfis) Designmentioning

confidence: 99%

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Conceptual design of a new space nuclear reactor with low‐enriched uranium fuel based on the optimization of fuel composition and reflector geometry via optimized adaptive neuro‐fuzzy inference system as well as investigation of fresh core cycle length

Rafiei

Ansarifar

2022

Intl J of Energy Research

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“…The concept of swarm intelligence is based on the social collaboration of individuals to learn with their own experience in a group. It is applied to optimize fuel reloading problems in PWR (Meneses et al, 2009), optimization of fuel core loading pattern in a VVER nuclear reactor (Babazadeh et al, 2009), optimization of secondary circuit system in marine NPP (Zhao et al, 2023), optimize the design parameters of radiation shielding system material (Lei et al, 2023), optimization of control drum operation for a microreactor under normal and transient conditions (Price et al, 2022), and designing of space nuclear reactor fuel (Rafiei and Ansarifar, 2022). Besides, the PSO mechanism is employed in NPPs for fault diagnosis (Wang H. et al, 2021), designing maintenance and safety systems (Wang J. et al, 2021), and control system development (Coban, 2011;Safarzadeh and Noori-kalkhoran, 2021;Ejigu and Liu, 2022a;Ayele Ejigu and Liu, 2022;Muthuraj et al, 2023).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Ejigu,

Tuo,

Liu

2024

Front. Nucl. Eng.

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Nuclear power plants produce a massive amount of clean energy and necessitate safe operation through intelligence technologies. Recently, the rapid advancements in communication infrastructures including artificial intelligence, big data computing, and Internet of Things devices moving the nuclear industries towards digitalization and intelligence to improve safety. The integration of these technologies into the nuclear sector offers effective tactics in addressing several challenges in the control and safe operation of nuclear power plants. This can be achieved through the insights generated from massive amounts of data. This paper comprehensively reviews the literature on artificial intelligence technologies and big data, seeking to provide a holistic perspective on their relations and how they can be integrated with nuclear power plants. The utilization of computing platforms boosts the deployment of artificial intelligence and big data analytics effectively in nuclear power plants. Further, this review also points out the future opportunities as well as challenges for applying artificial intelligence and big data computing in the nuclear industry.

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Core design optimization of small modular dual fluid reactor based on NSGA-III in the aspect of reactor physics

Chen

Seidl²,

Wang

2022

Annals of Nuclear Energy

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Space nuclear reactor fuel design based on dynamic analysis and GA & PSO optimization

Cited by 5 publications

References 14 publications

Conceptual design of a new space nuclear reactor with low‐enriched uranium fuel based on the optimization of fuel composition and reflector geometry via optimized adaptive neuro‐fuzzy inference system as well as investigation of fresh core cycle length

Conceptual design of a new space nuclear reactor with low‐enriched uranium fuel based on the optimization of fuel composition and reflector geometry via optimized adaptive neuro‐fuzzy inference system as well as investigation of fresh core cycle length

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Core design optimization of small modular dual fluid reactor based on NSGA-III in the aspect of reactor physics

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