Accurate modeling of aircraft performance is a key factor in estimating aircraft noise, emissions and fuel burn. Within the Aviation Environmental Design Tool (AEDT), many assumptions are made for aircraft performance modeling with respect to aircraft weight and departure procedure coupled with the fact that, typically the aircraft departure is modeled assuming full rated takeoff power/thrust is used. There is a need to examine those assumptions and to improve the modeling accuracy with flight data. In recent years, flight data has been used more and more in order to enhance models and bring model estimation a step closer to reality. Research is needed to build on prior work with a view to develop a robust set of recommendations for improved estimation processes for takeoff weight, reduced thrust takeoffs, and departure profiles within AEDT.
Georgia Tech will leverage domain expertise in aircraft and engine design and analysis to evaluate gaps in the current AEDT APM algorithms with respect to takeoff and climb performance. A detailed comparison will be generated identifying where the AEDT APM algorithms are lacking the capability to capture real aircraft performance and operations. The team will use publically and commercially available data sources such as DOT’s BTS data, FlightAware data, and specific airline data for OD pairs, planned distances, actual distances, average payload, airport weather, takeoff weight, and takeoff thrust data. The research team will utilize the results of ASCENT Project 35 as the basis of the investigation to conduct additional statistical analysis identifying how aircraft takeoff thrust, fuel burn, and weight vary with ambient conditions, aircraft and engine type, and flight profile. As an example, for a given origin-destination pair and aircraft type, histograms of historical takeoff weight and de-rated thrust will be obtained to provide real-world flight trajectories versus the nominal profiles within AEDT to enable a parametric study. Finally, a tuning method will be developed to match EDS or high fidelity data and AEDT performance to measured flight data. The tuning method will predict the takeoff weight and takeoff thrust. The result of this research will culminate in a methodology suitable for implementation within AEDT that improves terminal area performance modeling to more accurately predict noise, emissions, and fuel impacts.