Genetic algorithms for nuclear reactor fuel load and reload optimization problems

Sobolev, Artem Vladimirovich; Gazetdinov, Azamat; Samokhin, D. S.

doi:10.1016/j.nucet.2017.07.002

Cited by 24 publications

(3 citation statements)

References 1 publication

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“…The basic idea of the GA starts with the population for the potential solution of a complex problem that evolves iteratively over generations. The GA is applied for the optimization of load and reloading of fuel assemblies in the nuclear reactor core (Sobolev et al, 2017). It is also employed for designing and simulating safe and effective fuel-loading patterns in nuclear reactors (Zhao et al, 1998).…”

Section: Genetic Algorithmmentioning

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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Section: Genetic Algorithmmentioning

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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“…Genetic algorithm is a stochastic method with the ability to find true or approximate solutions to the global optimization. In the recent years, genetic algorithm is applied to nuclear-related applications, such as the nuclear fuel loading and reloading optimization (Sobolev et al, 2017) and shielding design of the PWRs (pressurized water reactors) (Chen et al, 2019;Chen et al, 2020). In this paper, the typical multi-objective optimization problem of the shadow shield design of the space nuclear reactor is converted into a single-objective optimization problem by setting the rest of the sub-objectives as constraints.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Design of Radiation Shadow Shield for Space Nuclear Power With Genetic Algorithm

Liu¹,

Lv²,

Lan³

et al. 2022

Front. Energy Res.

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The radiation shadow shield of the space nuclear power is an important component to protect the electronic device at the dose plane. In this paper, design of the radiation shadow shield is first modeled as a multi-objective optimization problem, and then converted into a single objective optimization problem by setting the other sub-objectives as constraints. An optimization method combining a single objective genetic algorithm with Monte Carlo simulation is developed to solve the optimization problem of shadow shield design. Through optimization, weight of the radiation shadow shield can be greatly reduced (14.7% of the weight of the initial design and 16.4% of the weight of the best individual of the first generation), while radiation dose at the dose plane is lower than the acceptable tolerance. The optimization method developed in this work is an automated optimization strategy by searching for the parameter space, which will not be subject to human preferences. The global optimal solutions can be obtained without the tedium of human manual work.

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“…The finite element method is an efficient numerical method and has been widely used in the electrochemical field (Sun and Sun, 2009; Lucas, 2012; Palani et al , 2014; Montoya et al , 2011; Onishi et al , 2012; Vankeerberghen et al , 2001; Kenny and Ramón, 2009; Martín et al , 2010; (Verónica and Carlos, 2007; Rolich et al , 2010; Shi et al , 2015; Shi et al , 2014; Cai et al , 1999; (Alcantar et al , 2017; Mohammadi et al , 2018; Matic et al , 2017; Zamzamian et al , 2017; Choi et al , 2017; Sobolev et al , 2017; Kim et al , 2008; Paul, 2013), especially for objects with complex geometric boundaries. In this method, the integral formula of an energy functional can be generated based on the boundary value problem of the partial differential equation used for solving the electrolyte electric field.…”

Section: Introductionmentioning

confidence: 99%

Determination of the auxiliary anode position via finite element method in impressed current cathodic protection

Liu

Zhang

et al. 2019

ACMM

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Purpose It is a challenge in the design to determine the feasible anode position and the supply current when the hull is protected by the impressed current cathodic protection method. It is difficult to obtain these parameters through traditional experimental methods due to the huge hull surface area and geometric complexity. This study aims to solve the problem by finite element method. Design/methodology/approach First, a great number of experiments need to be conducted; second, experiments are empirical; finally, there exist measurement errors, etc. All these factors make the experimental results less reliable. The application of the finite element method, combined with other technologies, is expected to overcome these deficiencies. In this paper, the combined Matlab and Comsol method was used to calculate various anode positions and corresponding protection areas with a series of input current conditions. The calculation is implemented via the script in Matlab. Findings As a result, the best design can be obtained. The results show that the method provided in this paper can replace the experiment to a certain extent, save human and material resources and reduce the design time. The method also can be applied to other similar fields, having a good universality. Originality/value This optimization method can be extended to other areas of relevant production and research, having a good universality.

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Genetic algorithms for nuclear reactor fuel load and reload optimization problems

Cited by 24 publications

References 1 publication

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

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

Multi-Objective Optimization Design of Radiation Shadow Shield for Space Nuclear Power With Genetic Algorithm

Determination of the auxiliary anode position via finite element method in impressed current cathodic protection

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