System engineers rely on a variety of models and simulations to help understand multiple perspectives in several domains throughout a system’s life-cycle. These domain models include operational simulations, life-cycle cost models, physics-based computational models, and many more. Currently, there is a technical gap with regard to our ability to untangle system design drivers within system life-cycle domains. This article provides a procedural workflow that addresses this technical gap by leveraging the methods of experimental design in order to clearly identify tradable variables and narrow the search for viable system variants. Our purpose is to illuminate trade decisions across several different viewpoints by integrating metamodels that approximate the behavior of multiple domain models; a metamodel is a statistical function that acts as a surrogate to a model. Model inputs often represent value properties that define a system alternative configuration or environmental conditions that represent uncertain factors within the system boundary. Model outputs represent measures of performance or effectiveness that allow us to compare alternatives and understand the tradeoffs among several objectives. In order to illuminate the tradeoffs that exist in a complex system design problem we propose an approach that approximates model input and output behavior using the functional form of statistical metamodels. After performing an experimental design, we can fit a metamodel with a functional form known as a response surface. We utilize contour profilers that show horizontal cross sections of multiple response surfaces to visualize where key trade decisions exist. Our research supports the tradespace analytics pillar for the development of the engineered resilient system (ERS) architecture. The article concludes with instructions on how to perform simulation experiments to construct a dynamic dashboard that illuminates system tradeoffs.
Dimensioning reserve bus fleet using life cycle cost models and condition based/predictive maintenance: a case study
