Read More The Accident Dynamics Simulator (ADS) paired with the Information, Decision, and Action in a Crew context (IDAC) cognitive model and coupled with a thermal-hydraulic model (e.g., RELAP5) dynamically simulates the crew performance during nuclear power plant events and generates a discrete dynamic event tree.


Read More IRIS software is a platform to perform traditional probabilistic risk analysis (PRA) based on the Hybrid Causal Logic (HCL) methodology. The HCL methodology employs a model-based approach to system analysis. The framework contains a multi-layer structure that integrates event sequence diagrams (ESDs), fault trees (FTs), and bayesian belief networks (BBNs) without converting the entire system into a large BBN.


Read More HCLA software is a cross-platform command-line tool to perform traditional probabilistic risk analysis (PRA) based on the Hybrid Causal Logic (HCL) methodology with advanced time-to-failure models, importance measures, and uncertainty quantification.


Developed for NASA for Space Shuttle mission risk management, 1997, 2001, 2002 developed for NASA (currently a commercial software used by several government agencies and industries worldwide).


Developed for risk-based design of complex hybrid systems under a grant from NASA, 2005, SimPRA is an adaptive-scheduling simulation-based DPRA environment developed at the University of Maryland under NASA funding. SimPRA provides an extensive and multi-layered risk model building capability to capture engineering knowledge, design information, and any available information from operating experience, simplifying (and in part automating) the tasks typically undertaken by the risk analysts. In the SimPRA framework, the estimation of end state probabilities is based on the simulation of system behavior under stochastic and epistemic uncertainties. A new scenario exploration strategy is employed to guide the simulation in an efficient and targeted way. The SimPRA environment provides the analysts with a user-friendly interface and a rich DPRA library for the construction of the system simulation model. In SimPRA, a high-level simulation scheduler is constructed to control the simulation process, generally by controlling the occurrence of the random events inside the system model. To stimulate the desired types of scenarios, the input to the simulation model is also controlled, using scheduling algorithms. Rather than using a generic wide-scale exploration, the scheduler is able to pick up the important scenarios, which are essential to the final system risk, thus increasing the simulation efficiency. To do that, a high-level simulation planner is constructed to guide the scheduler to simulate the scenarios of interest. Therefore the SimPRA environment has three key elements: planner, scheduler, and simulator. The planner serves as a map for exploration of risk scenario space. The scenarios of interest are highlighted in the planner. The scheduler manages the simulation process, including saving system states, deciding the scenario branch selection, and restarting the simulation. The scheduler guides the simulation toward the plan generated by the planner. The scenarios with high importance would be explored with higher priority, while all other scenarios also have a chance to be simulated. Scheduler would favor the events with higher information and importance values. This is done with an entropy-based algorithm.