Multidisciplinary design optimization of distributed electric propulsion aircraft with trim and flying qualities constraints using surrogate models

Hybrid-electric regional aircraft characterized by distributed electric propulsion offer promising benefits but introduce strong couplings problems between aerodynamics, structures, propulsion, and power systems, requiring a multidisciplinary design and optimization (MDO) approach to capture relevant trade-offs. This work presents an extended MDO framework applied to the Clean Aviation HERA configuration, developed within a collaboration between Politecnico di Milano and Airbus Defence and Space. The framework enhances traditional aero-structural optimization considering the use of the aerodynamic solver DUST, based on Vortex Particle Method (VPM) and by explicitly incorporating stability, control, and automatic trim requirements as design constraints. To limit the computational cost of aerodynamic simulations, surrogate models based on supervised machine learning regression and design of experiments (DOE) are employed within a multi-fidelity strategy. Results show consistent performance improvements in trimmed flight conditions, with successful enforcement of stability and flying qualities constraints. Furthermore, comparison between surrogate-based and high-fidelity optimizations highlights both the accuracy of the reduced-order models and their effectiveness in lowering computational costs.