Abstract
This study addresses the uncertainties in hybrid-electric powertrain technology for a 19-passenger commuter aircraft, focusing on two future Entry-Into-Service timeframes: 2030 and 2040. The meth-odology is split into a preliminary optimization of aircraft design based on nominal technology scenari-os followed by Monte Carlo simulations to investigate the impact of diverse technology projections and distribution types. Advanced surrogate modeling techniques, leveraging deep neural networks trained on a dataset from an aircraft design framework, are employed.
Key outcomes from this work reveal a marked increase in computational efficiency, with a speed-up factor of approximately 500 times when utilizing surrogate models. The results indicate that the 2040 EIS scenario could achieve larger reductions in fuel and total energy consumption—20.4% and 15.8% respectively—relative to the 2030 scenario, but with higher uncertainty. Across all scenarios examined, the hybrid-electric model showcased superior performance compared to its conventional counterpart. The battery specific energy density is proved to be a critical parameter of the aircraft's performance across both timeframes. The findings emphasize the importance of continuous innovation in battery and motor technologies to target towards greater system-level efficiency and reduced environmental impact.
