Continuous Descent Operations using Energy Principles

During today’s aircraft descents, Air Traf?c Control (ATC) commands aircraft to descend to specific altitudes and directions to maintain separation and spacing from other aircraft. When the aircraft is instructed to maintain an intermediate descent altitude, it requires engine thrust to maintain speed, leading to increased fuel burn and noise being produced. By eliminating these level ?ight segments, fuel consumption, noise and gaseous emissions can be reduced as aircraft can perform the descent at an engine-idle thrust setting. The aircraft will then ?y a continuous descent, or Continuous Descent Operations (CDO), which at the same time raises the altitude pro?le, reducing the experienced noise levels at ground level. Today, CDO’s are operationally in use at various major airports, such as Amsterdam Airport Schiphol and London Heathrow. Due to dif?culties in predicting aircraft trajectories and time of arrival when performing CDOs, ATC needs to add additional spacing buffers to assure proper spacing between aircraft. As a result, airport capacity is reduced, limiting the use of CDOs to hours of low capacity demand. Researchers investigated various concepts in an aim to improve the predictability of CDOs to maintain airport capacity during CDOs. However, many of these concepts require additional thrust to correct for deviations. Therefore, this research developed a new CDO concept, named Time and Energy Managed Operations (TEMO), that allows an aircraft to perform accurate 4D engine-idle descents using energy principles. TEMO uses the principles of energy to correct deviations (replanning) without the need for additional thrust and simultaneously adhering to time constraints for spacing and sequencing. The concept uses an optimization algorithm to minimize thrust and speedbrake use and to calculate accurate trajectories. The algorithm uses energy management by exchanging kinetic and potential energy by controlling the elevator to correct deviations. Sustained deviations are corrected for through either strategic replanning, when deviations exceed a prede?ned boundary, or using tactical replanning, which instantaneously corrects deviations. To improve ?ight accuracy and maintain acceptable workload levels, a TEMO descent is ?own using the autopilot and auto-thrust systems. However, selection of ?aps and gear, and commanding the autopilot are examples of actions that are still performed by the pilot. The TEMO concept should be validated for different conditions to verify whether CDOs can be ?own using energy management and whether the concept can cope with various disturbances. A study should verify whether environmental impact is reduced while the various replanning methods should be compared. Various errors could be arti?cially introduced to evaluate to what extent energy management alone can correct errors and in what scenarios thrust or speedbrakes are required. Moreover, the role of the human pilot in the TEMO concept should be evaluated. The human pilot introduces additional uncertainties that affect the ?own descent. Another uncertainty during descent is wind and affects the trajectory accuracy greatly. Hence, can we improve wind estimation to enhance trajectory prediction? This thesis addresses these topics and questions. A ?rst experiment involved a fast-time batch simulation performed in MATLAB and aimed at identifying TEMO’s environmental bene?ts and ability to correct deviations and errors using strategic replanning. Deviations result from modeling errors in the Trajectory Predictor (TP) and algorithm to simplify trajectory prediction. A comparison of baseline scenarios between TEMO descents and current step-down descents showed that TEMO reduces the 65 dB and 75 dB Sound Exposure Level (SEL) contour areas by 20% and 13%, respectively. Moreover, a reduction in fuel used was achieved between 11% and 20% for the descent. When considering fuel use per ?ight time, the reduction is slightly reduced to values between 9% and 16%. Gaseous emissions were effectively reduced by approximately 33–47%. The comparison also showed that without additional errors, no replanning was required to correct deviations that result from modeling errors. Next, descents were simulated with introduced time, energy and wind estimation errors to evaluate how strategic replanning corrects such errors during descent. Without using additional thrust, a time error window of 8–16 seconds was achieved using energy management only. The actual dimensions of this time window depends on the wind estimation error. By allowing TEMO to command minimized amounts of thrust and speedbrakes, the algorithm was able to calculate a new trajectory that allowed the aircraft to arrive 30 seconds earlier and later than originally planned. In some extreme scenarios, the time deviation at the Initial Approach Fix (IAF) exceeded the 5 seconds required accuracy prescribed by the Required Time Performance (RTP). These larger time deviations primarily result from wind estimation errors that negatively affect time and energy performance. This continuous wind error resulted in multiple trajectory recalculations to correct for time and energy deviations. This experiment also compared results of descents ?own using strategic replanning with descents ?own using hybrid replanning under wind conditions. This hybrid replanning method used a 4D-speed controller to continuously (tactically) correct for time deviations and used a strategic replan before Terminal Maneuvering Area (TMA) entry to correct for energy deviations. The results showed that the 4D-controller effectively minimizes time deviations at the IAF with minimum cost to fuel use and noise contours, even when a wind estimation error is present. Hence, the tactical controller is ef?cient at correcting deviations resulting from a continuous disturbance. However, hybrid replanning showed larger energy deviations at localizer intercept which were not corrected using a replan but corrected upon glideslope intercept by the autopilot. Therefore, hybrid replanning should use stricter energy boundaries to reduce energy (and altitude) deviations when the aircraft approaches the localizer. The fast-time simulations on TEMO performance included a zero-delay pilot response model that executed pilot tasks, such as con?guration changes, perfectly. Hence, the question remained how variations in pilot response to manual actions affect TEMO performance. This question was addressed in a real-time experiment with pilots in the loop. This experiment also evaluated what information support pilots best to perform accurate TEMO descents and minimize variations in pilot response. Three Human-Machine Interface (HMI)’s were developed that provide support information during TEMO descents and differed in level of information displayed. Pilots preferred the HMI variant that included a timer to support accurate selection of ?aps and gear, and responded that workload was acceptable. This con?guration timer, however, did not signi?cantly reduce time deviations at the runway threshold but reduced the variance in delay of setting con?gurations. For comparison the pilot ?own scenarios were also ?own using a zero-delay pilot response model to investigate investigation of the effects of variations in pilot response on environmental impact and TEMO performance. A comparison of these simulations showed that human response had little effect on noise contour levels and Nitrogen Oxide emissions of a TEMO descent, while the difference in time deviation with respect to the automated runs was small. Consequently, pilots were suf?ciently informed to perform their actions. The comparison also indicated that without delays in performing pilot actions, the aircraft did not arrive exactly on time either. This resulted from simpli?cations in modeling of aircraft dynamics in the TEMO algorithm and TP and guidance errors while following the prescribed speed-pro?le. In general, the aircraft arrived early and close to the early boundary of the RTP at the runway threshold for pilot ?own scenarios. This raises the question whether an RTP of 2 seconds is achievable in real life. The guidance and planning functions should be improved to reduce this offset to be able to obtain similar time accuracies in less favorable wind conditions. The analysis of all results showed that the energy deviation at the moment of intercepting the glideslope signi?cantly in?uences the time of arrival for the automated runs, while for the human runs this effect was slightly smaller. This implies that to arrive exactly on time at the runway threshold, the energy deviation at glideslope intercept should be reduced and corrections during glideslope descent should be made possible. The results from both experiments showed that TEMO is sensitive to disturbances and errors. The batch study showed that wind estimation errors contribute greatly to time and energy deviations. For this reason, it is expected that using accurate wind estimation data in the TEMO algorithm will reduce trajectory deviations. Today, aircraft primarily rely on coarse and slowly updated wind estimates resulting in gross estimates of the prevailing wind when predicting the own trajectory. Therefore, a novel method for real-time estimation of a wind pro?le was developed, named Airborne Wind Estimation Algorithm (AWEA) that increases the temporal and spatial resolution of wind estimates. AWEA uses data transmitted by nearby aircraft to construct high resolution real-time wind pro?le estimates. The AWEA algorithm uses a Kalman ?lter to relate all received measurements to the own trajectory and reduce measurement noise. The wind estimation algorithm performance was evaluated using Mode-S derived meteorological data from Amsterdam Airport Schiphol. Using these wind observations, the AWEA algorithm showed an Root Mean Square (RMS) in the wind estimation error of 1.35 KTS along the own trajectory, which is lower than the observed RMS measurement error of 1.94 KTS. Relating the measurements to the own trajectory also proved bene?cial in reducing wind estimation errors. In another experiment, estimated wind pro?les along the own trajectory constructed by AWEA showed to improve spacing performance during approach. The TEMO experiments showed promising results as clear bene?ts to the environment have been identi?ed whilst the aircraft adheres to time constraints accurately. However, some issues require further investigation before TEMO could be used in real-life. TEMO was designed for the Airbus A320 ?ying straight-in descents and evaluated in a single aircraft environment. Future work should investigate TEMO’s use in other aircraft types, include turn dynamics, and realistic wind and turbulence conditions. AWEA should be integrated with TEMO to reduce deviations resulting from wind. Next, an experiment should investigate capacity, and spacing and separation between multiple aircraft performing TEMO descents. To improve TEMO time performance at the runway, TEMO should be able to perform replans while on the glideslope. Since energy management cannot be performed while the aircraft descents down the glideslope, deviations could be corrected using ?ap-scheduling such that engine-thrust remains idle, or a tactical component could use thrust and speedbrakes to simultaneously control time and energy. Trajectory prediction will always include modeling errors as we cannot model the world explicitly, hence, effort should be put into reducing these errors to a minimum. Since strategic replanning can be considered as an open-loop (or slow, intermittent) control system, modeling errors will always result in deviations from the planned trajectory. To improve time performance by minimizing time deviations due to modeling errors and unknown disturbances, a closed-loop system should be used. Hybrid replanning augments strategic replanning with a fast closed-loop speed controller. Hence, research should investigate how hybrid replanning can be further improved and evaluate the human factors aspects of hybrid replanning in a real-time experiment with pilots in control