Designing Multimodel Calibration for System Operations

The complexity of aerospace systems often leads to the use of multiple computer simulation models. These can support decision-making but are difficult to integrate because of their heterogeneity. In addition, most modeling efforts focus on system design instead of operations. Calibrating multiple models uniformly is difficult, as uncertainties must be aggregated and quantified. We introduce a method based on Bayesian networks capturing multimodel dependencies to infer model parameters. The method automates calibration for decision-makers to conduct “what-if” simulations on unanticipated operational scenarios. There is little literature studying how Bayesian networks should be designed to address multimodel calibration as, usually, such networks are used to represent real-world entities rather than model parameters. We address this gap by introducing a design process that uses model-based systems engineering resources and comparing it with a baseline process. These processes are demonstrated in a deep space exploration habitat study. We found that network construction can benefit from 1) defining utility metrics that quantify the operational value of parameters, 2) delaying computational cost reduction efforts, and 3) combining topology-driven and causality-driven architectures when defining the graph structure.