Nodalization of NEK in APROS and Steady State Simulation

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A model of the primary circuit and part of the secondary circuit of the Slovenian Krško NPP – NEK was built using APROS - Advanced PROcess Simulation environment. The data used to describe the properties of the system modelled in APROS, were the data describing Krško NPP and its operational properties after the uprating and the introduction of the 18-month cycle. Basis for data collections, nodalization, structure and simplifications was NEK RELAP5\MOD3.3 Engineering Handbook and the 23rd cycle. In order to build a model describing all the important parameters, the available elements in APROS environment were used as building blocks for each system. The goal was to create a detailed model nodalization, which would give accurate results and would run on reasonable processing power. Each submodel was checked to verify that the partial results are within the allowable limits and that the description of the physical parameters is consistent with the real components. The model includes reactor pressure vessel, reactor coolant pumps and primary piping, steam generator, part of main steam, part of feedwater, pressurizer and reactor core kinetics. The regulation of pressurizer level and pressure, steam generator level and control rod is also modelled. The model consists of more than 400 thermal hydraulic volumes. The aim of building this model was a through thermal hydraulic analysis of the PWR systems present in the NPP Krško. Several simulations of the steady states at different power levels were performed. The resulting data describing the flow rates in steam generator feedwater, reactor pressure vessel, including bypass flows, heat transfer in reactor core and steam generator, thermal losses to containment, liquid level in pressurizer and steam generator, pressure drops in primary circuit and other parameters were then compared to the results of different types of calculation and to the testing data obtained from Krško NPP. The next step was to identify variations in results and determine whether they are consequence of wrong parameters, measurement deviation or numerical error. In that manner the model was verified and validated (in the sense of comparison with available system surveillance plant test results) to ensure the correct setup, initial and boundary conditions were applied in order to get reliable steady state results.