Uncertainty Propagation and Visualization of Aircraft Design, Economic, and Industrial Systems Using Design Space Exploration Methodology

The ever-increasing complexity of aerospace systems demands methodologies that can efficiently navigate high-dimensional design spaces while ensuring traceability across multiple analysis tools of varying fidelity levels. The use of Design Space Exploration (DSE) enables the systematic evaluation of multidisciplinary trade-offs by integrating aircraft performance, economic viability, and industrial system considerations within a cohesive framework. However, a key limitation of the existing DSE workflow is its assumption that all inputs are deterministic. In practice, uncertainties stemming from modeling approximations, market variations, or logistics delays can substantially alter system-level outcomes and feasibility. Neglecting these uncertainties may lead to overconfident designs that require costly redesigns when confronted with real-world variability. To bridge this gap, this paper focuses on extending the DSE framework presented in previous research by incorporating uncertainty propagation through Monte Carlo simulations and enhancing visualizations to convey probabilistic information. This research utilizes a notional derivative single-aisle aircraft to implement and validate the enhanced DSE methodology with uncertainty quantification. Additionally, an interactive dashboard is developed to visualize the results. These visualizations enable stakeholders to make informed decisions under uncertainty, leading to a robust overall design.