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.

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Artificial neural networks are an established technique for constructing non-linear models of multi-input-multi-output systems based on sets of observations. In terms of aerospace vehicle modelling, however, these are currently restricted to either unmanned applications or simulations, despite the fact that large amounts of flight data are typically recorded and kept for reasons of safety and maintenance. In this paper, a methodology for constructing practical models of aerospace vehicles based on available flight data recordings from the vehicles' operational use is proposed and applied on the Jetstream G-NFLA aircraft. This includes a data analysis procedure to assess the suitability of the available flight databases and a neural network based approach for modelling. In this context, a database of recorded landings of the Jetstream G-NFLA, normally kept as part of a routine maintenance procedure, is used to form training datasets for two separate applications. A neural network based longitudinal dynamic model and gust identification system are constructed and tested against real flight data. Results indicate that in both cases, the resulting models' predictions achieve a level of accuracy that allows them to be used as a basis for practical real-world applications.

The recent advances in Infra-Red (IR) weapon technology have dramatically altered the rules of air combat leading to a consistent departure from 'traditional' Energy-Maneuverability philosophy in aircraft design, prioritizing stealth and sophisticated armament instead. In this modern aerial warfare environment, it is obvious that new techniques need to be applied to properly assess aircraft survivability and produce successful designs for aircraft propulsion systems. The present study focuses on the development of such a methodology, which contrary to related work in the field includes considerations for both aircraft IR signature and missile/aircraft kinematic performance. An aircraft IR signature model is constructed using a collection of methods for area & temperature estimation and exhaust plume modelling; the latter is combined with missile-vs-aircraft and aircraft-vsaircraft simulations to quantify aircraft survivability in the form of missile & aircraft lethal zones. The proposed methodology is applied to a study on propulsion system effects on aircraft survivability, in which a comparison between different engine configurations is performed: In the scenarios examined, IR signature at cruise conditions and maximum-power thrust performance are identified as key parameters for aircraft combat performance. Nomenclature (Nomenclature entries should have the units identified) A = Area AB = Afterburner

Artificial neural networks are an established technique for constructing non-linear models of multi-input–multi-output systems based on sets of observations. In terms of aerospace vehicle modeling, however, these are currently restricted to either unmanned applications or simulations, despite the fact that large amounts of flight data are typically recorded and kept for reasons of safety and maintenance. In this paper, a methodology for constructing practical models of aerospace vehicles based on available flight data recordings from the vehicles’ operational use is proposed and applied on the Jetstream G-NFLA aircraft. This includes a data analysis procedure to assess the suitability of the available flight databases and a neural network-based approach for modeling. In this context, a database of recorded landings of the Jetstream G-NFLA, normally kept as part of a routine maintenance procedure, is used to form training datasets for two separate applications. A neural network-based longitudinal dynamic model and gust identification system are constructed and tested against real flight data. Results indicate that in both cases, the resulting models’ predictions achieve a level of accuracy that allows them to be used as a basis for practical real-world applications.