Development of MELCOR Input Techniques for High Temperature Gas-Cooled Reactor Analysis

High Temperature Gas-cooled Reactors (HTGRs) can provide clean electricity,as well as process heat that can be used to produce hydrogen for transportation and other sectors. A prototypic HTGR, the Next Generation Nuclear Plant (NGNP),will be built at Idaho National Laboratory.The need for HTGR analysis tools and methods has led to the addition of gas-cooled reactor (GCR) capabilities to the light water reactor code MELCOR. MELCOR will be used by the Nuclear Regulatory Commission licensing of the NGNP and other HTGRs. In the present study, new input techniques have been developed for MELCOR HTGR analysis. These new techniques include methods for modeling radiation heat transfer between solid surfaces in an HTGR, calculating fuel and cladding geometric parameters for pebble bed and prismatic block-type HTGRs, and selecting appropriate input parameters for the reflector component in MELCOR.

The above methods have been applied to input decks for a water-cooled reactor cavity cooling system (RCCS); the 400 MW Pebble Bed Modular Reactor (PBMR), the input for which is based on a code-to-code benchmark activity; and the High Temperature Test Facility (HTTF), which is currently in the design phase at Oregon State University. RCCS results show that MELCOR accurately predicts radiation heat transfer rates from the vessel but may overpredict convective heat transfer rates and RCCS coolant flow rates. PBMR results show that thermal striping from hot jets in the lower plenum during steady-state operations, and in the upper plenum during a pressurized loss of forced cooling accident, may be a major design concern. Hot jets could potentially melt control rod drive mechanisms or cause thermal stresses in plenum structures.

For the HTTF, results will provide data to validate MELCOR for HTGR analyses. Validation will be accomplished by comparing results from the MELCOR representation of the HTTF to experimental results from the facility. The validation process can be automated using a modular code written in Python, which is described here.