Modeling control room crews for accident sequence analysis

Abstract

This report describes a systems-based operating crew model designed to simulate the behavior of an nuclear power plant control room crew during an accident scenario. This model can lead to an improved treatment of potential operator-induced multiple failures, since it deals directly with the causal factors underlying individual and group behavior. It is intended that the model, or more advanced developments of the model, will be used in the human reliability analysis portion of a probabilistic risk assessment study, where careful treatment of multiple, dependent failures is required. The model treats the members of the control room crew as separate, reasoning entities. These entities receive information from the plant and each other, process that information, perform actions that affect the plant, and provide information to the other crew members.

The information retrieval, processing, and output activities are affected by the characteristics of the individual operator (e.g., his technical ability) and his relationship (measured in terms of "confidence level") with his fellow operators. Group behavior is modeled as the implicit result of individual operator behavior and the interactions between operators. The model is applied towards the analysis of steam generator tube rupture (SGTR) accidents at a non-U.S. pressurized water reactor, using the SIMSCRIPT 11.5 programming language. Benchmark runs, comparing the model predictions with videotaped observations of the performances of three different crews during SGTR training exercises, are performed to tune a small number of model parameters. The tuned model is then applied in a blind test analysis of a fourth crew. In both the benchmarking and blind test runs, the model performs quite well in predicting the occurrence, ordering, and timing of key events.

The model is also employed in a number of sensitivity analyses that demonstrate the robustness of the model (it generates plausible results even when the model parameters are assigned values not representative of observed crews) and the model's usefulness in investigating key issues (e.g., the effect of stress buildup on crew performance). i