A dynamic probabilistic risk assessment platform for nuclear power plants under single and concurrent transients

Dynamic probabilistic risk assessment (DPRA) of nuclear power plants (NPPs) has become one of the most critical research areas, especially in the aftermath of the 2011 Fukushima Daiichi nuclear accident. Uncertainty in NPP behavior is key when considering its safety under different operating conditions. Such uncertainty typically results from operation parameters, system conditions, and modeling assumptions. This study integrates the system dynamics (SD) modelling approach with an uncertainty analysis method to quantify the dynamic probabilistic risk in NPPs. To demonstrate the approach's applicability, the average fuel temperature is used to estimate the probability of reactor core damage under different transients, representing perturbations in reactivity and steam valve coefficient. A Monte Carlo simulation is employed to investigate the effect of uncertainties associated with the different model parameters. A global sensitivity analysis demonstrates that the total delayed neutron fraction, the heat transfer coefficient from fuel to coolant, the coolant temperature coefficient of reactivity, and the fuel temperature coefficient of reactivity are the primary controllers of the plant response variability under the transients considered. In summary, the integration of SD modelling and uncertainty analysis presents an effective DPRA approach that overcomes the limitations of static counterparts while minimizing the computational resources required.