Physics-based common cause failure modeling in probabilistic risk analysis: A mechanistic perspective

The modeling of dependent failures, specifically Common Cause Failures (CCFs), is one of the most important topics in Probabilistic Risk Analysis (PRA). Currently, CCFs are treated using parametric methods, which are based on historical failure events. Instead of utilizing these existing data-driven approaches, this paper proposes using physics-based CCF modeling which refers to the incorporation of underlying physical failure mechanisms into risk models so that the root causes of dependencies can be "explicitly" included. This requires building a theoretical foundation for the integration of Probabilistic Physics-Of-Failure (PPOF) models into PRA in a way that the interactions of failure mechanisms and, ultimately, the dependencies between the multiple component failures are depicted. To achieve this goal, this paper highlights the following methodological steps (1) modeling the individual failure mechanisms (e.g. fatigue and wear) of two dependent components, (2) applying a mechanistic approach to deterministically model the interactions of their failure mechanisms, (3) utilizing probabilistic sciences (e.g. uncertainty modeling, Bayesian analysis) in order to make the model of interactions probabilistic, and (4) developing appropriate modeling techniques to link the physics-based CCF models to the system-level PRA. The proposed approach is beneficial for (a) reducing CCF occurrence in currently operating plants and (b) modeling CCFs for plants in the design stage.