Data-enhanced dynamic flight simulations for flight performance analysis

More often than not, airlines use aircraft in operating scenarios beyond their optimal design conditions, which negatively affects their performance characteristics. These effects are statistically reflected in flight operational data, which are indirectly constrained by air transportation management as well as aircraft design. In this research, we develop a data-enhanced methodology for modeling dynamic flight simulations of aircraft to enable accurate estimation of performance parameters. The relevant flight phases and constraints of these simulations are determined by employing supervised machine learning on the flight data. The methodology is demonstrated by simulating flights across representative short-, medium-, and long-haul sectors using data shared by our airline partner. We compare the fuel burn and flight time calculation results with those from the Breguet range and endurance equations, from an available open source flight performance model, and from the reference data for validation. The developed dynamic flight simulation model is designed in such a way to enable accurate flight performance analysis even when high-fidelity force analyses and control models are absent, which is common in preliminary design frameworks. This will further enable incorporating flight data into aircraft design processes. (c) 2022 Elsevier Masson SAS. All rights reserved.