# Prediction of temperature field of moderator of heavy- water reactor based on cellular neural network

3/22/2017 2017 - #01 Modelling processes at nuclear facilities

https://doi.org/10.26583/npe.2017.1.09

### UDC: 621.039.517.3

Calculation of 2D temperature fields in structural elements of installations with internal sources of heat is the necessary condition required for maintaining their safe operation. Methodology is suggested for forecasting moderator temperature in heavy-water reactors with application of cellular neural network. Application of intellectual computational structure allows simplifying the procedure of calculation of moderator temperature in different cross-sections of calandria heating of which is achieved both directly from the surfaces of fuel channels and, as well, as the result of volume heating. The difficulty to apply numerical methods is explained by the computational difficulty of determination of temperature gradients and heat fluxes along the fuel channel, as well as by the complex geometry of fuel assemblies. Neural network modeling facilitates removing potential critical situations by implementing the control of variation of temperature fields during transients, emergency situations and in the estimation of thermal stresses. Application of the technique of cellular neural networks is justified by the specific features of the architecture allowing targeted limitation of communications between neurons. Specific features of cellular networks correspond to the principles of quick tunable transformations which can be efficiently realized on hardware level. Reduction of the number of synoptical communications increases computational efficiency and makes it possible to use this class of networks for processing the high dimensionality data. Structural synthesis of cellular network with optical communications between neurons possessing high efficiency of data processing is examined. The suggested type of communications constitutes the basis for realization of modular intellectual structures consisting of homogeneous fragments. Training the neural network is accomplished using methods of local training with elements of co-evolutional interaction. Presence of powerful 32-bit microcontroller in each neural core justifies the above strategy despite associated high memory requirements. The suggested modular organization efficiently combines local training of neural cores with global training of the whole network. Comparison of results of forecasting with behavior of the reactor model demonstrated the efficiency of reconstructive analysis of complex systems with application of reconfigurable cellular neural networks, structure of which can be optimized for the specific calculational task. Modularity of the structure allows simultaneously constructing large number of networks on the same electronic chip which makes it possible to forecast moderator temperature fields in different sections of calandria and constitutes the basis for constructing 3D models.

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cellular neural networks heavy water reactor oscillating fractal network optical neurons forecasting temperature fields the heating medium neutron moderator stochastic algorithms for neural networks training