Advanced and flexible genetic algorithms for BWR fuel loading pattern optimization

Martín‐del‐Campo, Cecilia; Palomera-Pérez, Miguel-Ángel; François, Juan‐Luis

doi:10.1016/j.anucene.2009.07.013

Cited by 26 publications

(3 citation statements)

References 4 publications

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“…Several other teams reported about adopting genetic computation for the purposes of generating reload patterns [214][215][216][217][218][219][220][221][222][223][224][225][226][227][228][229][230], or for other applications to nuclear reactors [159,160]. As mentioned early, a combination of a fuzzy ruleset and neural computations being applied to optimal fuel loading pattern design was reported in [179][180][181].…”

Section: A Survey Of the Application Of Other Techniques: Genetic Fumentioning

confidence: 99%

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Nissan

2019

Designs

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An important aspect of managing a nuclear reactor is how to design refuellings, and from the 1980s to the present different artificial intelligence (AI) techniques have been applied to this problem. A section of the reactor core resembles a symmetrical grid; long fuel assemblies are inserted there, some of them new, some of them partly spent. Rods of “burnable poisons” dangle above, ready to be inserted into the core, in order to stop the reactor. Traditionally, manual design was made by shuffling positions in the grid heuristically, but AI enabled to automatically generate families of candidate configurations, under safety constraints, as well as in order to optimize combustion, with longer cycles of operation between shutdown periods, thus delaying the end-of-cycle point (except in France, where shutdown is on an annual basis, and Canada, where individual fuel assemblies are replaced, with no need for shutdown for rearranging the entire batch). Rule-based expert systems, the first being FUELCON,1 were succeeded by projects combining neural and rule-based processing (a symbolic-to-neural compilation of rules we did not implement), and later on, genetic algorithms in FUELGEN.2 In the literature, one also comes across the application of fuzzy techniques, tabu search, cellular automata and simulated annealing, as well as particle swarms. Safety regulations require simulating the results using a parameter prediction tool; this is done using either nodal algorithms, or neural processing.

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Section: A Survey Of the Application Of Other Techniques: Genetic Fumentioning

confidence: 99%

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Nissan

2019

Designs

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“…Additionally, they were also used in the fi eld of nuclear engineering. In the available literature, loading pattern [1], core design [5,6], BA arrangement [7,8], online refueling [9], and thorium loading [10] were considered. However, due to the required computing power, these optimizations were typically simplifi ed and focused on one parameter or used signifi cantly simplifi ed models.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the loading pattern of the PWR core using genetic algorithms and multi-purpose fitness function

2021

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The study demonstrates an application of genetic algorithms (GAs) in the optimization of the first core loading pattern. The Massachusetts Institute of Technology (MIT) BEAVRS pressurized water reactor (PWR) model was applied with PARCS nodal-diffusion core simulator coupled with GA numerical tool to perform pattern selection. In principle, GAs have been successfully used in many nuclear engineering problems such as core geometry optimization and fuel configuration. In many cases, however, these analyses focused on optimizing only a single parameter, such as the effective neutron multiplication factor (k eff), and often limited to the simplified core model. On the contrary, the GAs developed in this work are equipped with multiple-purpose fitness function (FF) and allow the optimization of more than one parameter at the same time, and these were applied to a realistic full-core problem. The main parameters of interest in this study were the total power peaking factor (PPF) and the length of the fuel cycle. The basic purpose of this study was to improve the economics by finding longer fuel cycle with more uniform power/flux distribution. Proper FFs were developed, tested, and implemented and their results were compared with the reference BEAVRS first fuel cycle. In the two analysed test scenarios, it was possible to extend the first fuel cycle while maintaining lower or similar PPF, in comparison with the BEAVRS core, but for the price of increased initial reactivity.

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“…GAs implement natural selection mechanisms and the principle of heredity to solve complex optimization problems. They are especially useful in the design process, as they are universal, simple to implement, and global, unlike other popular methods; therefore, GAs were used in several studies related to nuclear engineering [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Application of genetic algorithms in optimization of SFR nuclear reactor design

et al. 2021

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This work presents a demonstrational application of genetic algorithms (GAs) to solve sample optimization problems in the generation IV nuclear reactor core design. The new software was developed implementing novel GAs, and it was applied to show their capabilities by presenting an example solution of two selected problems to check whether GAs can be used successfully in reactor engineering as an optimization tool. The 3600 MWth oxide core, which was based on the OECD/NEA sodium-cooled fast reactor (SFR) benchmark, was used a reference design [1]. The first problem was the optimization of the fuel isotopic inventory in terms of minimizing the volume share of long-lived actinides, while maximizing the effective neutron multiplication factor. The second task was the optimization of the boron shield distribution around the reactor core to minimize the sodium void reactivity effect (SVRE). Neutron transport and fuel depletion simulations were performed using Monte Carlo neutron transport code SERPENT2. The simulation resulted in an optimized fuel mixture composition for the selected parameters, which demonstrates the functionality of the algorithm. The results show the efficiency and universality of GAs in multidimensional optimization problems in nuclear engineering.

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Advanced and flexible genetic algorithms for BWR fuel loading pattern optimization

Cited by 26 publications

References 4 publications

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Optimization of the loading pattern of the PWR core using genetic algorithms and multi-purpose fitness function

Application of genetic algorithms in optimization of SFR nuclear reactor design

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