https://www.mdu.se/

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 methodology is split into a preliminary optimization of aircraft design based on nominal technology scenarios 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.

Small fans which are primarily axial machines induce the air movement of a relatively large mass flow within comparatively low velocities for reasons of thermal management. In the present investigation six axial fans of the same type were assessed for their structural (inactivity), geometrical and dynamic similarity. The results indicated that the fans including the stators and the rotors were operationally and dynamically similar. A series of experiments was conducted for a single stage, 2-stage and 3-stage fan configurations. The experimental data were used for the derivation of a linear algebraic model, as well as for calibrating a stage stacking model developed by Cranfield University for predicting the overall performance of multi-stage axial flow machines. The comparison between the computed values and the experimental data indicated very good agreement in the entire range of speed lines. The algebraic model can be used with high confidence for predicting the fan performance for rotational speeds where experimental data are not available. The validated stage stacking model can be used for predicting the performance of multi-stage lowvelocity axial fans when experimental data are only available for a single stage.