Multidisciplinary design optimization framework with coupled derivative computation for hybrid aircraft
A. Sgueglia, ro, P. Schmollgruber, N. Bartoli, E. Benard, J. Morlier, J. Jasa, J. R. R. A. Martins, J. T. Hwang, and J. S. Gray
Journal of Aircraft, 57(4):715–729, 2020
Hybrid-electric aircraft are a potential way to reduce the environmental footprint of aviation. Research aimed at this subject has been pursued over the last decade; nevertheless, at this stage, a full overall aircraft design procedure is still an open issue. This work proposes to enrich the procedure for the conceptual design of hybrid aircraft found in literature through the definition of a multidisciplinary design optimization (MDO) framework aimed at handling design problems for such kinds of aircraft. The MDO technique has been chosen because the hybrid aircraft design problem shows more interaction between disciplines than a conventional configuration, and the classical approach based on multidisciplinary design analysis may neglect relevant features. The procedure has been tested on the case study of a single-aisle aircraft featuring hybrid propulsion with distributed electric ducted fans. The analysis considers three configurations (with 16, 32, and 48 electric motors) compared with a conventional baseline at the same 2035 technological horizon. To demonstrate the framework’s capability, these configurations are optimized with respect to fuel and energy consumption. It is shown that the hybrid-electric concept consumes less fuel/energy when it flies on short range due to the partial mission electrification. When one increases the design range, penalties in weight introduced by hybrid propulsion overcome the advantages of electrified mission segment: the range for which hybrid aircraft have the same performance of the reference conventional aircraft is named the “breakdown range.” Starting from this range, the concept is no longer advantageous compared to conventional aircraft. Furthermore, a tradeoff between aerodynamic and propulsive efficiency is detected, and the optimal configuration is the one that balances these two effects. Finally, multiobjective optimization is performed to establish a tradeoff between airframe weight and energy consumption.