Articles
Nuclear Engineering and Technology (17385733)(8)pp. 1603-1610
Due to ever-growing advancements in computers and relatively easy access to them, many efforts have been made to develop high-fidelity, high-performance, multi-physics tools, which play a crucial role in the design and operation of nuclear reactors. For this purpose in this study, the neutronic Monte Carlo and thermal-hydraulic sub-channel codes entitled MCNP and COBRA-EN, respectively, were applied for external coupling with each other. The coupled code was validated by code-to-code comparison with the internal couplings between MCNP5 and SUBCHANFLOW as well as MCNP6 and CTF. The simulation results of all code systems were in good agreement with each other. Then, as the second problem, the core of the VVER-1000 v446 reactor was simulated by the MCNP4C/COBRA-EN coupled code to measure the capability of the developed code to calculate the neutronic and thermohydraulic parameters of real and industrial cases. The simulation results of VVER-1000 core were compared with FSAR and another numerical solution of this benchmark. The obtained results showed that the ability of the MCNP4C/COBRA-EN code for estimating the neutronic and thermohydraulic parameters was very satisfactory. © 2020
Since estimating the minimum departure from nucleate boiling ratio (MDNBR) requires complex calculations, an alternative method has always been considered. One of these methods is neural network. In this study, the Back Propagation Neural network (BPN) and Radial Basis Function Neural network (RBFN) are introduced and compared in order to estimate MDNBR of the VVER-1000 light water reactor. In these networks, the MDNBR were predicted with the inputs including core mass flux, core inlet temperature, pressure, reactor power level and position of the control rods. To obtain the data required to design these neural networks, an externally coupledcode was developed and its ability to estimate the thermo-hydraulic parameters of the VVER-1000 reactor was compared with other numerical solutions of this benchmark and the Final Safety Analysis Report (FSAR). After ensuring the accuracy of this coupled-code, MDNBR was calculated for 272 different conditions of reactor operating, and it was used to design BPN and RBFN. Comparison of these two neural networks revealed that when the output SMEs of the two systems were approximately the same, the training process in RBFN was much faster than in BPN and the maximum network error in RBFN was less than in BPN. © Carl Hanser Verlag, München.
