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A nuclear reactor based on MIT BEAVRS benchmark was used as a typical powergenerating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion coresimulator was used as a full-core reactor physics solver to emulate theoperation of a reactor and to generate training, and validation data for theANN. The ANN was implemented with dedicated Python 3.8 code with Google'sTensorFlow 2.0 library. The effort was based to a large extent on the processof appropriate automatic transformation of data generated by PARCS simulator,which was later used in the process of the ANN development. Various methodsthat allow obtaining better accuracy of the ANN predicted results were studied,such as trying different ANN architectures to find the optimal number ofneurons in the hidden layers of the network. Results were later compared withthe architectures proposed in the literature. For the selected bestarchitecture predictions were made for different core parameters and theirdependence on core loading patterns. In this study, a special focus was put onthe prediction of the fuel cycle length for a given core loading pattern, as itcan be considered one of the targets for plant economic operation. Forinstance, the length of a single fuel cycle depending on the initial coreloading pattern was predicted with very good accuracy (>99%). This workcontributes to the exploration of the usefulness of neural networks in solvingnuclear reactor design problems. Thanks to the application of ANN, designerscan avoid using an excessive amount of core simulator runs and more rapidlyexplore the space of possible solutions before performing more detailed designconsiderations.

Prediction of the evolution of the nuclear reactor core parameters using artificial neural network