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The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) is a dynamic probabilistic risk assessment (PRA) simulation method used for nuclear power plant PRAs. It provides rich contextual information and explicit consideration of feedback from complex equipment dependencies and operator actions. Compared to traditional risk assessment methods, ADS-IDAC offers a more realistic representation of plant accident response, directly simulates plant procedures and actions, and captures complex interdependencies. It also represents the timing and sequencing of events, simulating the impact of variations in hardware and operator performance on the plant model.
ADS-IDAC is a mature dynamic PRA platform with a development history spanning over 30 years. Its architecture includes a scheduler module, a control panel module, a system reliability model, a hybrid causal logic module, a crew module, and a system module. It generates Discrete Dynamic Event Trees (DDET) using simplified branching rules to model variations in system and crew responses. The IDAC model contains a cognitive engine and a reasoning module to simulate operator decision-making, capturing cognitive resource limitations and top-down attention control. The experience and training of each crew operator are reflected in the ADS-IDAC knowledge base, which captures the information needed to assess the plant state, execute procedural actions, and match memorized response actions to perceived plant needs.
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The OpenPRA Initiative is aimed at advancing the next generation of probabilistic risk assessment (PRA) methods and software. It seeks to provide a transparent public forum for the dissemination of information, independent review of new ideas, and promotion of open co-operation among researchers, practitioners, corporations, and regulators.
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Traditional and dynamic PRA methods are often insufficient for analyzing complex systems or systems that incorporate deeply coupled error propagation failure modes found in control systems. Tracking the propagation of errors in such systems from discrete sub-components to the system or functional level presents a unique challenge in failure analysis. While traditional error propagation methods have been around for some time, recent methods introduce the ability to separately model control and data flows within a system. This approach is based on the Dual Error Propagation Method (DEPM), where failures can be defined descriptively or systematically extracted from SysML/UML graphs or by modeling the flow of control and associated data in a logical software block.
OpenEPL is a C++ open source tool that implements the DEPM methodology and quantifies PRISM models using Storm, a modern model checker for probabilistic systems.
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SUPRA is a command-line based SUpply-chain Probabilistic Risk Assessment, quantification and post-processing tool, written in Python. It is being developed to quantify pharmaceutical drug and nuclear supply chain shortage risk.
SUPRA is capable of generating fault trees and reports from supply-chain data based on the OpenPSA Model Exchange Format, using the underlying SCRAM engine, including probability calculations with importance analysis, and uncertainty analysis with Monte Carlo simulations.