شبیه سازی دینامیکی استدلال دانش مبتنی بر اپراتور نیروگاه هسته ای در شرایط حادثه: مدل سازی و پایه های شبیه سازی

This paper describes major additions to the modeling and simulation capabilities of the Accident Dynamic Simulator paired with the Information, Decision, and Action in a Crew context (ADS-IDAC), a platform for conducting dynamic probabilistic risk assessment (DPRA) of nuclear power plants. The new advancements are mostly in modeling of operator knowledge-based behavior in accident conditions, enhancing realism of the IDAC model, and simulation approach to Human Reliability Analysis (HRA). The focus is situation assessment and diagnosis of the accident cause. Knowledge-based reasoning plays an important role in this phase. A reasoning module has been developed and implemented in ADS-IDAC to simulate operators’ knowledge-based reasoning. This paper describes the cognitive architecture of the reasoning module, including knowledge representation (model of operator’s understanding of the plant systems and functions), a memory representation, information processing flow, reasoning sequence generation, and rules for accident diagnosis. Some theoretical and empirical insights for human error prediction are embedded in this causal model as simulation rules. Human cognitive limitations and heuristics that potentially contribute to human errors are explicitly modeled. Together with the model description, several example simulations are provided to demonstrate different features of the reasoning module. Examples of the simulation show that the reasoning module in ADS-IDAC produces realistic knowledge-based responses by capturing cognitive limitations, deliberative reasoning, and dynamic of accident progression.