Using Flight Manual Data to Derive Aero-Propulsive Models for Predicting Aircraft Trajectories

Abstract

The Center/TRACON Automaton System (CTAS) is a set of air traffic management tools developed by NASA in conjunction with the FAA. As part of its functionality, CTAS predicts aircraft flight trajectories using aeropropulsive models and the kinetic equations of motion for various flight conditions including climbs. Precise aeropropulsive models for all aircraft types are not yet available to NASA researchers. In an effort to improve climb trajectory prediction of jet aircraft for which CTAS does not have a precise aero-propulsive model, a technique was developed to derive an aero-propulsive model from readily available time-to-climb data found in flight manuals. A case study was performed on a Boeing 737-400, for which time-to-climb data and aero-propulsive model data were known. A new aero-propulsive model, identified by three aerodynamic and one propulsive parameter, was derived from the time-to-climb data. The results showed it was possible to derive an aero-propulsive model for an aircraft type that will allow CTAS to compute time-to-climb for a range of climb speeds that agree closely with known data. This technique was then applied to a Learjet 60, an aircraft type for which a precise aero-propulsive model is not available. A comparison of top-of-climb predictions made with a derived aero-propulsive model and actual top-ofclimb from Learjet 60 radar track data reveal close agreement.

Top-of-Climb Matching Method for Reducing Aircraft Trajectory Prediction Errors

“…However, some flights will have altitude errors that exceed the vertical separation standard of 1000 ft even when RMSE is less than 1000 ft. As such, the percentage of climbing flights whose absolute altitude trajectory prediction error is greater than 1000 ft is a complementary metric that will also be reported. Note that all reductions in trajectory prediction error achieved by the TOC-matching method in this study are in addition to what was already attained in prior research [12][13] that improved the aircraft performance models used by the CTAS TS.…”

“…The trajectory predictor evaluated in this study is the Center/TRACON Automation System (CTAS) Trajectory Synthesizer (TS) [23] that was analyzed in prior work [6][7][8][9][11][12][13][14][15]. CTAS is a real-time research prototype system developed at NASA that includes mature capabilities for 4-D trajectory prediction, conflict detection, conflict resolution, and other functions [24].…”

“…These maneuvers increase both fuel burn and communication frequency congestion and could be avoided if the trajectory predictions for climbing flights were more accurate (especially in the vertical dimension) [10]. The TOC-matching method does not focus on improving horizontal trajectory prediction accuracy for climbing flights because only about one percent have horizontal errors that exceed the 5-nautical mile (nmi) horizontal separation standard [9,11] Researchers have improved climb trajectory prediction accuracy by adjusting aircraft performance models based on publicly available flight manuals [12] and analyzing trajectory errors [13]. The authors of the first study utilized model parameter identification to determine the combinations of thrust and drag that best modeled the time-to-TOC data specified in the Boeing 737-400 flight manual.…”

“…Compared to methods that utilize the same adjusted aircraft performance model for all flights of the same aircraft type [12][13]19], the TOC-matching method proposed in this study is more effective because it is applied on a per-flight basis like the adaptive weight algorithm [9,11]. While the adaptive weight algorithm studies demonstrated the improvement in climb trajectory prediction accuracy that can be achieved using currently available data, this paper on the TOC-matching method demonstrates what may be possible in the near future.…”

Parameter Estimation of Fundamental Technical Aircraft Information Applied to Aircraft Performance

“…Given the first three parameters mentioned, the derivation of these aero-propulsive functions is possible. 3 Gong and Chan 3 solved this inverse problem in a more simplified manner than will be attempted here, using a known engine deck to find the parameters in a simplified drag equation. They used only the time-to-climb performance metric and attempted to find the drag parameters for both a Boeing 737 and Learjet 60, and were successful in their attempts.…”