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A multidisciplinary design optimization for conceptual design of hybrid-electric aircraft

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Aircraft design has become increasingly complex since it depends on technological advances and integration between modern engineering systems. These systems are multidisciplinary, i.e., any process or division of any aircraft… Click to show full abstract

Aircraft design has become increasingly complex since it depends on technological advances and integration between modern engineering systems. These systems are multidisciplinary, i.e., any process or division of any aircraft design produces effects in all others, making the definition of each parameter a significant challenge. In this context, this work presents a general multidisciplinary design optimization method for the conceptual design of general aviation and hybrid-electric aircraft. The framework uses efficient computational methods comprising modules of engineering that include aerodynamics, flight mechanics, structures, and performance, and the integration of all of them. The aerodynamic package relies on a Nonlinear Vortex Lattice Method solver, while the flight mechanics package is based on an analytical procedure with minimal dependence on historical data. Moreover, the structural module adopts an analytical sizing approach using boom idealization, and the performance of the aircraft is computed based on energy and power required to accomplish a specific mission. The objective functions are to minimize the fuel consumption and to minimize the takeoff weight. The Pareto-optimal front encompasses aircraft with different propulsive architectures: turboelectric, hybrid electric, and fully electric. The degrees of hybridization defined by the optimization and the mission requirements chosen in this study directly affect the final weight breakdown of the aircraft, which is related to the sizing of the wings, propulsive system, and horizontal and vertical tails.

Keywords: aircraft; design optimization; multidisciplinary design; hybrid electric; design

Journal Title: Structural and Multidisciplinary Optimization
Year Published: 2021

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