Aerodynamic model adjustment for an accurate flight performance representation using a large operational flight data base

Abstract

A novel process to determine an aircraft performance model from operational flight data with limited a priori knowledge is developed. The given big data problem is solved by application of fundamental engineering knowledge and a specific data evaluation strategy. The resulting smart data approach is fundamentally different from existing deep learning methods to solve such big data problems. A given aerodynamic model is updated to represent the characteristics of an Airbus A320neo aircraft based on a given large database of operational flights. The updated aerodynamic model implementation for one specific flap/slat configuration is exemplarily compared to the information available from flight data and the results are discussed in terms of model quality.