Probabilistic Optimization of a Hybrid-Electric Canard-Wing General Aviation Aircraft

This paper presents a probabilistic optimization framework for the conceptual design of a hybrid-electric, canard-wing general aviation aircraft that integrates epistemic and aleatory sources of uncertainty. Motivated by global goals to reduce greenhouse gas emissions in aviation, the study examines hybrid-electric propulsion as a near-term solution to improve redundancy while maintaining the operational capabilities of conventional fuel-powered aircraft. The Parametric Energy-Based Aircraft Configuration Evaluator was used as an aircraft sizing and optimization framework. Uncertainties in component state-of-the-art and operational variabilities are propagated using a latin hypercube sampling method for the design of experiments. Surrogate models were constructed for mission-level metrics, and probabilistic constraints were enforced using a 95% satisfaction threshold. The resulting optimization highlights the sensitivity of hybrid-electric performance to propulsion system state-of-the-art and to geometric variables such as wing and canard planform parameters and their locations. Comparative analyses quantify the increase in fuel weight associated with hybridization relative to a baseline aircraft to achieve similar performance metrics, like range. The optimal designs differ between deterministic and probabilistic optimization, which demonstrates the importance of uncertainty-aware early-stage design for emerging hybrid-electric concepts.