Tradespace Exploration With Statistical Modeling Techniques and Immersive Visual Representation in Virtual Environments

Statistical modeling techniques combined with virtual reality (VR) visualization offer powerful new approaches to tradespace exploration in engineering design. The research presented addresses the challenge of analyzing and communicating insights from complex multidimensional datasets, particularly for autonomous ground vehicle systems. Beginning with a review of statistical methods—including Principal Component Analysis (PCA), Analysis of Variance (ANOVA), and correlation analysis—the study examines their applications in tradespace exploration. Building on this foundation, three distinct visualization pathways connecting MATLAB data to virtual environments are developed and evaluated: VRML representation, STL conversion, and Blender integration. Each approach is assessed for its ability to maintain data integrity, support interactive exploration, and effectively communicate complex relationships. Findings indicate that while VRML and STL approaches offer basic visualization capabilities, they have significant limitations in representing data relationships and supporting user interaction. The Blender implementation proves superior, providing enhanced visualization quality, richer interaction possibilities, and better preservation of both data structure and context, enabling more intuitive exploration of tradespace relationships. The research contributes a methodological framework for transforming statistical analyses into immersive VR environments, carefully addressing data fidelity concerns throughout the visualization process. These findings provide practical insights for engineering design and systems engineering professionals looking to leverage immersive technologies for more effective decision-making.