  In nuclear engineering, modeling and simulations (M&Ss) are widely applied to
support risk-informed safety analysis. Since nuclear safety analysis has
important implications, a convincing validation process is needed to assess
simulation adequacy, i.e., the degree to which M&S tools can adequately
represent the system quantities of interest. However, due to data gaps,
validation becomes a decision-making process under uncertainties. Expert
knowledge and judgments are required to collect, choose, characterize, and
integrate evidence toward the final adequacy decision. However, in validation
frameworks CSAU: Code Scaling, Applicability, and Uncertainty (NUREG/CR-5249)
and EMDAP: Evaluation Model Development and Assessment Process (RG 1.203), such
a decision-making process is largely implicit and obscure. When scenarios are
complex, knowledge biases and unreliable judgments can be overlooked, which
could increase uncertainty in the simulation adequacy result and the
corresponding risks. Therefore, a framework is required to formalize the
decision-making process for simulation adequacy in a practical, transparent,
and consistent manner. This paper suggests a framework "Predictive Capability
Maturity Quantification using Bayesian network (PCMQBN)" as a quantified
framework for assessing simulation adequacy based on information collected from
validation activities. A case study is prepared for evaluating the adequacy of
a Smoothed Particle Hydrodynamic simulation in predicting the hydrodynamic
forces onto static structures during an external flooding scenario. Comparing
to the qualitative and implicit adequacy assessment, PCMQBN is able to improve
confidence in the simulation adequacy result and to reduce expected loss in the
risk-informed safety analysis.
