Approximation of the criticality margin of WWR-c reactor using artificial neuron networks

I Belyavtsev, D Legchikov, S Starkov, V Kolesov, E Nikulin
2018 Journal of Physics, Conference Series  
Citation: Belyavtsev IP, Starkov SO (2018) Reactivity margin evaluation software for WWR-c reactor. Nuclear Energy and Technology 4(3): 197-201. https://doi. Abstract The WWR-c reactor reactivity margin can be calculated using a precision reactor model. The precision model based on the Monte Carlo method (Kolesov et al. 2011) is not well suited for operational calculations. The article describes the work on creating a software package for preliminary evaluations of the WWR-c reactor reactivity
more » ... reactor reactivity margin. The research has confirmed the possibility of using an artificial neural network to approximate the reactivity margin based on the reactor core condition. Computational experiments were conducted on training the artificial neural network using the precision model data and real reactor measured data. According to the results of the computational experiments, the maximum relative approximation error ∆k/k for fuel burnup was 3.13 and 3.56%, respectively. The mean computation time was 100 ms. The computational experiments showed it possible to construct the artificial neural network architecture. This architecture became the basis for building a software package for evaluating the WWR-c reactor reactivity margin -REST API based web-application -which has a convenient user interface for entering the core configuration. It is also possible to replenish the training sample with new measurements and train the artificial neuron network once again. The reactivity margin evaluation software is ready to be tested by the WWR-c reactor personnel and to be used as a component of the automated reactor refueling system. With minor modifications, the software package can be used for reactors of other types.
doi:10.1088/1742-6596/945/1/012031 fatcat:w6qyzh3z5vgvdmn3saq77zffwa