QAR Data-Driven Calibration of Physics-based Aircraft Performance Models using a Machine-Learning Approach

Abstract

Aircraft performance has always been a focus of attention in aviation. The work of aircraft designers, certifying agencies, aircraft operators, and air traffic controllers relies on aircraft performance models. Current aircraft performance models are based on performance data of brand-new aircraft, independent of airline configuration and customizations. Nonetheless, over time aircraft suffer structure, engine and aerodynamic deterioration, as well as maintenance actions. These factors, which vary with tail number, make aircraft performance deviate from the theoretical and create the need for aircraft performance monitoring, and ultimately for aircraft performance tailoring. This research work proposes a novel approach to develop up-to-date, tail-specific performance models based on the use of Quick Access Recorder (QAR) data and machine-learning techniques. In particular, a methodology was designed to calibrate Base of Aircraft DAta (BADA), a widely consolidated physics-based performance model. As a result, more accurate performance models are generated, maintaining the same applicability over the entire flight envelope and during all phases of flight as BADA nominal models.