Tradespace analysis for multiple performance measures

To meet the changing demands of operational environments, future Department of Defense solutions require the engineering of resilient systems. Scientists, engineers and analysts rely on modeling, simulation, and tradespace analysis to design future resilient systems. During conceptual system design, high performance computing clusters and models from multiple domains are leveraged to conduct large-scale simulation experiments that generate multi-dimensional data for tradespace exploration. Despite recent breakthroughs in computation capabilities, the world's most powerful computers cannot effectively explore a high-dimensional tradespace using a brute-force approach. This paper outlines a viable methodology and process to generate large numbers of variant solutions for tradeoff analysis. Design of experiments is used to efficiently explore a high-dimensional tradespace and identify system design drivers. These drivers are used to identify model inputs that help focus tradespace generation in areas that promise viable solutions. A dashboard illustrates how viable variant exploration can be conducted to illuminate trade decisions.