Katkovsky Е.А.1, Katkovsky S.E.1, S. Nikonov2, I. Pasichnyk3, K. Velkov3, 1“Energoautomatika” Ltd, Moscow, Russia, 2NRC „Kurchatov Institute“, Russia, 3Gesellschaft für Anlagen- und Reaktorsicherheit (GRS) mbH, Garching, Germany

23rd Symposium of AER on VVER Reactor Physics and Reactor Safety (2013, Štrbské Pleso, Slovakia)
Reactor dynamics and safety analysis


The paper presents a methodology which will enable in the future performing of fast transient
analysis of NPP with VVER-1000 reactors. It is based on artificial neural networks (ANN)
method. For the training of the network the best-estimate system code ATHLET (GRS) is
used with detailed nodalization (multi-channel model) of the Reactor Pressure Vessel (RPV).
The present work is dedicated to the training procedure which is connected at this stage of
development only with the setting in the system the RPV hydraulic resistances. Training
samples are created by random variation from a double sided 95% confidence interval of 50
different hydraulic resistance coefficients in the RPV. For each set of hydraulic resistance
coefficients a test calculation is performed with ATHLET to obtain the corresponding thermohydraulic distributions (mass flow, pressure, temperature) within the RPV. For this training
procedure the total and the assembly power distributions used in all calculations remain
unvaried and equal the nominal values, e.g. at this stage a dynamic 3D neutron-physics model
is not being taken into account. As a result of the neural network training an application
program is created. This program is capable to give almost simultaneous answer about all
thermo-hydraulic fields at each point of the RPV. A comparison of the system results with
results obtained from the AHTLET simulation gives a good correspondence for such nodes
(fluid objects) of the active core, which have not been taken into account in the training
sequence of the artificial neural network. The paper contains also some ideas how to apply the
artificial network modeling of the RPV in the current practice.
This work is a step forward to create an artificial neural network system for performing of fast
NPP analysis. Further training of the system will be done with the help of coupled neutronphysics/thermal-hydraulics codes based on ATHLET.

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