Navigating the Tradespace: Empowering Decision-Making With Data Manipulation

Abstract In engineering design and decision-making, the exploration of tradespace is crucial for arriving at optimal solutions. Traditional methods, such as morphological charts, have been instrumental in organizing design possibilities. Our approach integrates functional objectives, enhancing focus on performance and efficiency. While morphological charts have been fundamental, our work introduces advanced techniques like ANOVA and PCA, expanding tradespace exploration. This empowers researchers to uncover insights and correlations and make informed decisions, driving innovation. We illustrate how methodologies like ANOVA, PCA and correlation open new horizons in tradespace exploration, capturing the richness of design alternatives and understanding relationships between variables. Embracing functional objectives aligns engineering design with evolving technological demands. As graduate students and researchers tackle complex challenges, our work bridges the past and future of design methodologies. The US Army DEVCOM GVSC invested years to develop a comprehensive tradespace encompassing vast vehicle configurations. A ‘Testbed’ tradespace with about 13 variables has been formulated to facilitate smaller-scale exploration. It serves as a resource for future studies, providing a standardized framework for advancing engineering design optimization. This collaborative effort highlights the need for effective tradespace exploration approaches to inform decision-making.