A Predictive Dynamic Approach to Evaluating the Reliability of Passive Systems

Abstract

Passive systems play a vital role in ensuring the safety and advancement of Nuclear Power Plant (NPP) technology. The precise evaluation of their reliability is of paramount importance for their successful implementation in the nuclear industry. A thorough understanding of the reliability of these passive systems is essential to ensure the safety and efficiency of nuclear power plants, making them a cornerstone of nuclear energy generation. A new methodology has been developed to assess the reliability of passive systems, which are distinguished by three main components: systematic functional analysis, dynamic component analysis, and phenomenological factors. Each step of the methodology is described and commented, and a diagram of the methodology is presented. The paper presents a novel approach for analysing dynamic systems by incorporating dependencies among events and component states, as well as accounting for the impact of phenomenological factors. A state space solution to generate all possible system states and stochastic transitions is proposed, resulting in a Continuous Time Markov Chain (CTMC) representation of the system’s behaviour. To support this analysis, an algorithm that integrates multiple phenomenological factors by sampling their values from respective probability distributions is also developed. Through Monte Carlo simulation, the approach provides a comprehensive and realistic assessment of the system’s performance, enabling accurate reliability analysis and decision-making.

Keywords

• Passive system reliability. probabilistic safety assessment
• model based safety assessment
• generalized stochastic Petri net