Nuclear Fuel Lattice Optimization Using Neural Networks and a Fuzzy Logic System

Ortiz, Juan José; Castillo, Alejandro; Montes, José Francisco Aldana; Perusquía, Raúl; Hernández, José L.

doi:10.13182/nse162-148

Cited by 21 publications

(3 citation statements)

References 7 publications

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“…Once a design for how to load the fuel into the reactor core has been produced and selected by engineers, it is a statutory obligation that simulations of the reactor physics parameter prediction -under the assumption that that particular design is adopted -be carried out by special software. Nodal algorithms have traditionally been used, but neural computation has also been applied [177,178] (by the latter two teams, see [179][180][181] on neural processing being applied to optimal fuel loading pattern design, which is [179,181] in combination with a fuzzy ruleset).…”

Section: Validation By Simulations Of the Reactor Physicsmentioning

confidence: 99%

“…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]. Also see [231][232][233] on fuzzy representations in the nuclear sector.…”

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

confidence: 99%

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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: Validation By Simulations Of the Reactor Physicsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…In a similar application, a genetic algorithm technique was used by Martin-delCampo et al (2007) to find a radial distribution of enrichment and gadolinia for BWR fuel lattices. Ortiz et al (2009) used a multi state recurrent neural network to minimize the local power peaking factor and to keep the k-infinity neutron factor inside a proposed interval. Guzman et al (2010) proposed a method focused on finding the radial distribution of fuel rods having different transuranic composition to obtain a neutron infinite multiplication factor (k ∞ ) with a prescribed value and to minimize the rod power peaking factor (PPF).…”

Section: Introductionmentioning

confidence: 99%