Capturing Experimental Design Insights in Support of the Model-based System Engineering Approach

Model-Based Systems Engineering (MBSE) continues to mature into a popular approach as systems become more complex. Integrating MBSE architectural diagrams with external simulations that model a variety of different domains allows designers to perform sophisticated engineering design and analysis. To better understand how these external models support design decisions, we can leverage the statistical methods of design of experiments to identify insights into a complex system design problem. These insights include identifying the most important design parameters, the nature of their behaviour, their synergies between them, their diminishing or increasing rates of change, and thresholds that achieve a desired level of effectiveness. The purpose of this paper is to propose a MBSE methodology that captures the insights identified during an experimental design study within the integrated system model while applying the MBSE approach.