Increases in computational power and pressure for
more accurate simulations and estimations of accident scenario consequences are driving the need for Dynamic
Probabilistic Risk Assessment (PRA) [1] of very complex models. While more sophisticated algorithms and
computational power address the back end of this challenge, the front end is still handled by engineers that
need to extract meaningful information from the large amount of data and build these complex models.
Compounding this problem is the difficulty in knowledge transfer and retention, and the increasing speed of
software development. The above-described issues would have negatively
impacted deployment of the new high fidelity plant simulator RELAP-7 (Reactor Excursion and Leak
Analysis Program) at Idaho National Laboratory. Therefore, RAVEN that was initially focused to be the
plant controller for RELAP-7 will help mitigate future
RELAP-7 software engineering risks. In order to accomplish such a task Reactor Analysis
and V
