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A comparative study has been conducted to find out how design approximations and simulation methods of a prismatic-type fuel block of a high-temperature gas-cooled reactor (HTGR) may affect the calculation accuracy of neutronic characteristics of fuel assemblies. To study the impact, a detailed three-dimensional computational model of a typical fuel block including fuel compacts, burnable absorber compacts, and coolant passages was developed. Changes in neutronic characteristics in the process of fuel assembly irradiation were calculated. The burnup was analyzed based on the SCALE 6.2.4 software package using a calculation module implementing the Monte Carlo method with a multigroup library of cross-sections on the basis of ENDF/B-VII.1 files of assessed nuclear data and the ORIGEN burnup analysis module included in this package. Different ways of modeling fuel compacts and burnable absorber compacts have been considered: using a built-in tool (DOUBLEHET cell type), by specifying fuel particles in the graphite matrix, and their combination. The calculations were made using the 252-group library of constants except for the option in which fuel compacts and burnable absorber compacts were simulated explicitly by particles in the graphite matrix. In the latter case, a library with a pointwise (CE) representation of cross-sections was used. A series of calculations were also made to assess the way computational statistic parameters affect the results. The results confirm correct operation of the SCALE complex built-in tool, i.e. cells with the DOUBLEHET-type double heterogeneity, and its prospective use to calculate neutronic characteristics of HTGR fuel. The calculations have also shown that it is acceptable to model burnable absorber compacts both by setting a DOUBLEHET-type cell and explicitly by particles in the graphite matrix. In general, the calculation results for these options agree quite well, within 1-2%, with the direct calculation using the pointwise library of cross-sections. Based on the computational statistic parameters, it may be recommended to set at least 200,000 histories and the number of particles in a generation or the number of generations should be at least 250.
Salyaev et al. (Fri,) studied this question.