Genetic algorithms for nuclear reactor fuel loading and reloading optimization problems
The article provides the prerequisites for the use of a genetic algorithm for optimization of loading and subsequent overloads of fuel assemblies in the nuclear reactor core. The reason why the use of classical methods based on continuous scanning of phase space or gradient approaches is unacceptable is given. The questions of choosing an optimization criterion are briefly discussed, in the quality of which the burn up depth of fuel is used. The burn up depth is estimated after the fuel assembly is unloaded from the core, i. e. after working off 3 campaigns.
An important aspect determining the efficiency of the use of the genetic algorithm in the considered task is the performance of the physical calculation of the reactor core with the detailing allowing to «feel» the change in the relative location of the fuel assemblies. The use of a coarse instrument leads to the uselessness of the proposed approach to optimizing the loading of the reactor core. On the other hand, excessive detailing entails a significant increase in the expenditure of computer time. In the presented work, the TRIGEX software package was used to analyze the neutron-physical characteristics of the reactor core, which provides an acceptable detailing and sensitivity of the results to changes in the reactor load.
The genetic algorithm implies the use of at least two basic procedures - selection and mutation. One of the most important questions for the application of the genetic algorithm is the definition of the basic concepts such as mutation, crossing, and specimen. The answers to these questions for this problem are given in the article. In addition, the main recommendations for the organization of procedures for crossing and mutation are also given.
The efficiency of using the developed model of the genetic algorithm is demonstrated in a test example of a BN type reactor. The results of the test application showed that the use of the proposed approach allows to search for optimal reactor loads, in the sense of the fuel placement chart at each reloading. The main goal of the work performed was to demonstrate the suitability and efficiency of a new, effective, modern approach to solving the problem of fuel loading into a nuclear reactor, which can give the quality of another, higher class.
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Link for citing the article: Sobolev A.V., Gazetdinov A.S., Samohin D.S. Genetic algorithms for nuclear reactor fuel loading and reloading optimization problems. Izvestiya vuzov. Yadernaya Energetika. 2017, no. 2, pp. 71-80; DOI: https://doi.org/10.26583/npe.2017.2.07 (in Russian).