Solution Space Analysis for Robust Conceptual Design Solutions in Aeronautics

The use of novel technologies for low-emission and more efficient aviation requires not only the achievement of a given technology readiness level, but also their integration into aircraft concepts. Furthermore, the assessment of unconventional configurations requires robustness considerations already in the conceptual aircraft design phase. In this context, the next developmental milestone of the Advanced Morphological Approach (AMA) as a conceptual aircraft design method is presented by introducing design parameter uncertainties for disruptive technologies. The purpose of this work is the integration verification of Bayesian networks (BNs) into the AMA process for semi-quantitative system modeling and uncertainty propagation. This allowed for the visualization of uncertainties in the solution space, and therefore the depiction and initial estimation of configuration robustness. The verification is demonstrated on an existing conceptual design use case of a regional aircraft for 50 passengers, similar to the ATR 42-600. It investigated hybrid-electric and fuel-cell-based hybrid propulsion systems for 2030, 2040, and 2050 as potential years of entry into service. A BN-based system model has been developed by verifying its quality, adding parameter uncertainty and three energy price scenarios. The executed Bayesian inference propagated the uncertainties through the system and allowed for the visualization of a solution space. The presented uncertainties for the mission energy, mission energy price, and emission criteria for each design solution yield a more reliable basis for robustness analysis and decision-making.