Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics

Nissan, Ephraim; Siegelmann, Hava T.; Galperin, A.; Kimhi, Shuky

doi:10.1007/bf01201857

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…An alternative version just checks adjacent positions. It was next shown how to translate this code into a network: into either a simple feedforward network that tests the 20 positions simultaneously, or into a recurrent network that tests them serially [193,194]. (Also see [195][196][197], concerning the symbolic-to-neural project.…”

Section: Ruleset Improvement and The Involvement Of Neural Revisionmentioning

confidence: 99%

“…Algorithm 1 Code written by Hava Siegelmann for the rule elimination rule: "Do not load a fresh assembly in such a position that is adjacent to another position where there is another assembly of the same kind, except when one of those two positions is in a corner position." [193,194]. neural computation.…”

Section: Ruleset Improvement and The Involvement Of Neural Revisionmentioning

confidence: 99%

“…Algorithm 2 Code written by Hava Siegelmann for the rule preference rule: "If it is a once-burned assembly that is currently being considered, then choose for it -from amongst those positions that were not forbidden by Rules 1 to 6 (the elimination rules)-that position whose distance from the centre of the core is minimal." [193,194].…”

Section: Ruleset Improvement and The Involvement Of Neural Revisionmentioning

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: Ruleset Improvement and The Involvement Of Neural Revisionmentioning

confidence: 99%

Section: Ruleset Improvement and The Involvement Of Neural Revisionmentioning

confidence: 99%

Section: Ruleset Improvement and The Involvement Of Neural Revisionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Since the system is interactive, rule sets may be expanded or refined according to the feedback that a decision maker/analyst receives. This manual revision of rule sets may also be performed by an automated procedure that employs neural network learning algorithms [11].…”

Section: Introductionmentioning

confidence: 99%