A description of the results for a Station Black-Out analysis for Atucha 2 Nuclear Power Plant is presented here. Calculations were performed with MELCOR 1.8.6 YV3165 Code. Atucha 2 is a pressurized heavy water reactor, cooled and moderated with heavy water, by two separate systems, presently under final construction in Argentina. The initiating event is loss of power, accompanied by the failure of four out of four diesel generators. All remaining plant safety systems are supposed to be available. It is assumed that during the Station Black-Out sequence the first pressurizer safety valve fails stuck open after 3 cycles of water release, respectively, 17 cycles in total. During the transient, the water in the fuel channels evaporates first while the moderator tank is still partially full. The moderator tank inventory acts as a temporary heat sink for the decay heat, which is evacuated through conduction and radiation heat transfer, delaying core degradation. This feature, together with the large volume of the steel filler pieces in the lower plenum and a high primary system volume to thermal power ratio, derives in a very slow transient in which RPV failure time is four to five times larger than that of other German PWRs.

RMPS methodology is used for estimating the uncertainties in the fulfillment of a target related with the design of the isolation condenser of a “CAREM-like” integral reactor. The passive-system assessment is made on a basis of a loss of heat sink transient. Given this scenario, the safety function is to remove the core decay heat after the actuation of the shutdown system, thus reducing the primary system pressure and leading the plant to a safe condition. The design target to evaluate is the avoidance of the RPV safety valves opening. In order to accomplish the evaluation, the following RMPS steps were followed:system identification, system modeling, characterization of TH phenomena, direct Monte Carlo simulation, sensitivity analysis, and quantitative reliability estimation.As main outcomes, a ranking of parameters' importance and an estimate of the failure probability, from a design target point of view, were achieved by sensitivity analysis and Monte Carlo simulations based on a response surface model.

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