A Smart Data Approach to Determine an Aircraft Performance Model From an Operational Flight Data Base

Abstract

High-quality flight performance models are essential for the reliable prediction of the aircraft flight trajectory and accurate flight planning. An innovative process to determine an aircraft performance model from operational flight data with limited a priori knowledge is developed to target this goal. 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 artificial intelligence methods or other data analysis strategies to solve such big data problems. An a priori given aerodynamic model is updated to express the characteristics of an Airbus A320neo aircraft on the example of a given large database of operational flights; after the successful determination of an engine thrust model formulation based on the same flight data. The updated aerodynamic models for the different flap/slat configurations are compared to the information available from flight data and the results are discussed in terms of model quality. Finally, the model is validated with a dynamic simulation for an example flight data set.