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Appl. Sci. 2017, 7(5), 469; doi:10.3390/app7050469

Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

Propulsion Engineering Centre, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
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Received: 29 January 2017 / Revised: 21 April 2017 / Accepted: 26 April 2017 / Published: 30 April 2017
(This article belongs to the Special Issue Gas Turbines Propulsion and Power)

Abstract

In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft–engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude–Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft’s J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel. View Full-Text
Keywords: aircraft/engine integration; trajectory optimization; multi-objective optimization; particle swarm optimization aircraft/engine integration; trajectory optimization; multi-objective optimization; particle swarm optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Antonakis, A.; Nikolaidis, T.; Pilidis, P. Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization. Appl. Sci. 2017, 7, 469.

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