Encoder-Decoder Approach to Predict Airport Operational Runway Configuration A case study for Amsterdam Schiphol airport

The runway configuration of an airport is the com- bination of runways that are active for arrivals and departures at any time. The runway configuration has a major influence on the capacity of the airport, taxiing times, the occupation of parking stands and taxiways, as well as on the management of traffic in the airspace surrounding the airport. The runway configuration of a given airport may change several times during the day, depending on the weather, air traffic demand and noise abatement rules, among other factors. This paper proposes an encoder-decoder model that is able to predict the future runway configuration sequence of an airport several hours upfront. In contrast to typical rule-based approaches, the proposed model is generic enough to be applied to any airport, since it only requires the past runway configuration history and the forecast traffic demand and weather in the prediction horizon. The performance of the model is assessed for the Amsterdam Schiphol Airport using three years of traffic, weather and runway use data.Peer ReviewedPostprint (published version

Assessment of the feasible CTA windows for efficient spacing with energy-neutral CDO

Continuous descent operations (CDO) with con- trolled times of arrival (CTA) at one or several metering fixes could enable environmentally friendly procedures at the same time that terminal airspace capacity is not compromised. This paper focuses on CTA updates once the descent has been already initiated, assessing the feasible CTA window (and associated fuel consumption) of CDO requiring neither thrust nor speed-brake usage along the whole descent (i.e. energy modulation through elevator control is used to achieve different times of arrival at the metering fixes). A multiphase optimal control problem is formulated and solved by means of numerical methods. The minimum and maximum times of arrival at the initial approach fix (IAF) and final approach point (FAP) of an hypothetical scenario are computed for an Airbus A320 descent and starting from a wide range of initial conditions. Results show CTA windows up to 4 minutes at the IAF and 70 seconds at the FAP. It has been also found that the feasible CTA window is affected by many factors, such as a previous CTA or the position of the top of descent. Moreover, minimum fuel trajectories almost correspond to those trajectories that minimise the time of arrival at the metering fix for the given initial conditionPeer ReviewedPostprint (published version

Optimal trajectory management for aircraft descent operations subject to time constraints

The growth in traffic increased the pressure on the environmental sustainability of air transport. In this context, many research effort has been devoted to minimise the environmental impact in the different phases of flight. Continuous descent operations, ideally performed with the engines at idle from the cruise altitude to right before landing, have shown to reduce fuel, noise nuisance and gaseous emissions if compared to conventional descents. However, this type of operations suffer from a well known drawback: the loss of predictability from the air traffic control point of view in terms of overfly times at the different waypoints of the route. Due to this loss of predictability, air traffic controllers require large separation buffers, thus reducing the capacity of the airport. Previous works investigating this issue showed that the ability to meet a controlled time of arrival at a metering fix could enable continuous descent operations while simultaneously maintaining airport throughput. In this context, the planning and guidance functions of state-of-the-art flight management systems need to be modernised. On-board trajectory planners capable to generate an optimal trajectory plan satisfying time constraints introduced during the flight are seldom, mainly because the real-time optimisation of aircraft trajectories is still elusive. Furthermore, the time scale and spatial resolution of the wind forecasts used by these trajectory planners are far from being adequate to generate accurate flight time predictions. Finally, there exist guidance strategies capable to accurately comply with time constraints enforced at a certain fix in the trajectory plan, yet they are not specifically designed to minimise the environmental impact. This PhD thesis aims at investigating fast optimisation techniques to enable real-time updates of the optimal trajectory plan subject to time constraints during the descent; wind networking concepts to generate more accurate and up-to-date wind forecasts and, consequently, time predictions; and more robust an efficient guidance strategies to reduce the environmental impact at the maximum extent while complying with the time constraints of the trajectory plan. First, the feasible time window at a metering fix that could be achieved during a descent requiring neither thrust nor speed brakes usage is quantified as a function of the aircraft states (altitude, distance to the metering fix and airspeed), aiming to assess the feasibility of guidance strategies that take advantage of time and energy management concepts. Then, the performance of four of these guidance strategies is compared in terms of environmental impact mitigation and ability to satisfy operational constraints. Results from the comparison reveal that model predictive control, a strategy based on a frequent re-calculation of the optimal trajectory plan during the execution of the descent, is the most robust in terms of energy and time deviation at the metering fix, providing at the same time excellent environmental impact mitigation figures. However, the execution time required to solve a rigorous trajectory optimisation problem at each re-calculation instant remains a critical limitation for practical applications. In order to address this issue, a variant of the model predictive control strategy that allows for fast updates of the optimal trajectory plan based on parametric sensitivities is proposed, which shows analogous results yet halving the time needed to update the optimal trajectory plan. Finally, the potential benefits of using wind observations broadcast by nearby aircraft to reconstruct the wind profile downstream right before updating the optimal trajectory plan when using model predictive control is also investigated. Promising results show that the combination of model predictive control with wind networking concepts could enable optimal descents without degrading the capacity of the airport.El creixement del trànsit ha augmentat la pressió sobre la sostenibilitat ambiental del transport aeri. En aquest àmbit s'han dedicat molts esforços en recerca per reduir l'impacte ambiental en les diferents fases del vol. Les operacions de descens continu, en les quals l'aeronau descendeix amb els motors a ralentí des de l'altitud de creuer fins just abans d'aterrar, han demostrat ser una solució atractiva per reduir el combustible, el soroll i les emissions en la fase de descens. Desafortunadament, aquest tipus d'operacions tenen un inconvenient molt important: la pèrdua de predictibilitat des del punt de vista dels controladors de trànsit aeri, en termes de temps de sobrevol als diferents punts de pas de la ruta. Per aquesta raó, els controladors necessiten aplicar més separació entre aeronaus, reduint així la capacitat de l'aeroport. Treballs anteriors han demostrat que si les aeronaus fossin capaces de satisfer restriccions de temps de sobrevol a un o més punts de pas, seria possible implementar operacions de descens continu sense degradar la capacitat de l'aeroport. Malauradament, avui en dia existeixen pocs sistemes de gestió de vol capaços de generar trajectòries òptimes que satisfacin restriccions de temps, principalment perquè l’optimització de trajectòries en temps real continua sent una tasca difícil. A més, la resolució espacial i temporal dels models de vent utilitzades per els planificadors de trajectòria no son suficients per generar prediccions de temps de sobrevol prou fiables. Finalment, les estratègies de guiatge que fins i tot avui en dia permetrien satisfer amb exactitud restriccions de temps de sobrevol, no estan dissenyades específicament per minimitzar l’impacte ambiental. Aquesta tesi té com a objectiu explorar algoritmes de d'optimització ràpids i robustos que permetin actualitzar la trajectòria òptima en temps real durant l'execució del descens, satisfent al mateix temps restriccions de temps de sobrevol; també s'investigaran nous conceptes de que permetin generar models de vent molt exactes a partir d'observacions emeses per aeronaus veïnes; i estratègies de guiatge més intel·ligents que minimitzin l'impacte ambiental de les operacions de descens continu subjectes a restriccions de temps de sobrevol. En primer lloc, es quantifica la finestra de temps disponible al punt on s'aplica la restricció de temps de sobrevol, en funció dels estats de l'aeronau (altitud, velocitat i distància al punt) i assumint que els motors es mantenen ralentí i que no s'utilitzen aerofrens durant tot el descens. Els resultats de l'experiment indiquen que es podrien utilitzar estratègies de guiatge que gestionessin l'energia cinètica i potencial de l'aeronau per satisfer restriccions de temps sense necessitat de gastar més combustible. A continuació, es compararen quatre d'aquestes estratègies. Els resultats d'aquests segon experiment indiquen que el control predictiu, una estratègia que contínuament actualitza la trajectòria òptima durant el descens, es molt robusta en termes d'errors de temps i energia, i que també redueix l'impacte ambiental. Malauradament, es tarda massa a actualitzar la trajectòria òptima cada cop que s’actualitza, fet que limita la implementació d'aquesta estratègia. Per tal d'afrontar aquesta limitació, es proposa una variant que utilitza sensitivitats paramètriques per reduir el temps d'execució a l'hora d'actualitzar la trajectòria òptima, sense degradar significativament la seva exactitud. Finalment, s'investiguen els possibles beneficis d'aprofitar observacions de vent enviades per les aeronaus del volant per millorar el model de vent i, conseqüentment, l'exactitud de la trajectòria calculada. Resultats prometedors demostren que si s’implementés model predictiu com a estratègia de guiatge i les aeronaus cooperessin per compartir observacions de vent, es reduiria l'impacte ambiental sense degradar la capacitat de l'aeroport.Postprint (published version

Effects of speed reduction in climb, cruise and descent phases to generate linear holding at no extra fuel cost

Best paper Award in Trajectory Optimisation Track - ICRAT 2016Speed reduction strategies have proved to be useful to recover delay if air traffic flow management regulations are cancelled before initially planned. Considering that for short- haul flights the climb and descent phases usually account for a considerable percentage of the total trip distance, this paper extends previous works on speed reduction in cruise to the whole flight. A trajectory optimization software is used to compute the maximum airborne delay (or linear holding) that can be performed without extra fuel consumption if compared with the nominal flight. Three cases are studied: speed reduction only in cruise; speed reduction in the whole flight, but keeping the nominal cruise altitude; and speed reduction for the whole flight while also optimizing the cruise altitude to maximize delay. Three representative flights have been simulated, showing that the airborne delay increases significantly in the two last cases with nearly 3-fold time for short-haul flights and 2-fold for mid- hauls with the first case. Results also show that fuel and time are traded along different phases of flight in such a way the airborne delay is maximized while the total fuel burn is kept constant.Peer ReviewedAward-winningPostprint (published version

Assessing the impact of relaxing cruise operations with a reduction of the minimum rate of climb and/or step climb heights

A compromise solution to increase flight efficiency in cruise, but without penalising capacity (or even safety), would be perhaps to remove (or relax) the minimum rate of climb (ROC) constraint and/or to reduce the height of the step climbs in cruise. In this paper, the benefits (in terms of total operating costs) and the associated impact on the air traffic management (ATM) of such “relaxed cruise” operations are quantified for a representative medium-haul aircraft under different scenarios, by means of an in-house trajectory optimisation software. Results show that by reducing the minimum ROC from 500 to 300 ftmin-1, whilst keeping the step climb height according to current reduced vertical separation minima (RVSM) standard would give a good compromise between cost savings and impact on the ATM.Peer ReviewedPostprint (published version

Optimal trajectory management for aircraft descent operations subject to time constraints

The growth in traffic increased the pressure on the environmental sustainability of air transport. In this context, many research effort has been devoted to minimise the environmental impact in the different phases of flight. Continuous descent operations, ideally performed with the engines at idle from the cruise altitude to right before landing, have shown to reduce fuel, noise nuisance and gaseous emissions if compared to conventional descents. However, this type of operations suffer from a well known drawback: the loss of predictability from the air traffic control point of view in terms of overfly times at the different waypoints of the route. Due to this loss of predictability, air traffic controllers require large separation buffers, thus reducing the capacity of the airport. Previous works investigating this issue showed that the ability to meet a controlled time of arrival at a metering fix could enable continuous descent operations while simultaneously maintaining airport throughput. In this context, the planning and guidance functions of state-of-the-art flight management systems need to be modernised. On-board trajectory planners capable to generate an optimal trajectory plan satisfying time constraints introduced during the flight are seldom, mainly because the real-time optimisation of aircraft trajectories is still elusive. Furthermore, the time scale and spatial resolution of the wind forecasts used by these trajectory planners are far from being adequate to generate accurate flight time predictions. Finally, there exist guidance strategies capable to accurately comply with time constraints enforced at a certain fix in the trajectory plan, yet they are not specifically designed to minimise the environmental impact. This PhD thesis aims at investigating fast optimisation techniques to enable real-time updates of the optimal trajectory plan subject to time constraints during the descent; wind networking concepts to generate more accurate and up-to-date wind forecasts and, consequently, time predictions; and more robust an efficient guidance strategies to reduce the environmental impact at the maximum extent while complying with the time constraints of the trajectory plan. First, the feasible time window at a metering fix that could be achieved during a descent requiring neither thrust nor speed brakes usage is quantified as a function of the aircraft states (altitude, distance to the metering fix and airspeed), aiming to assess the feasibility of guidance strategies that take advantage of time and energy management concepts. Then, the performance of four of these guidance strategies is compared in terms of environmental impact mitigation and ability to satisfy operational constraints. Results from the comparison reveal that model predictive control, a strategy based on a frequent re-calculation of the optimal trajectory plan during the execution of the descent, is the most robust in terms of energy and time deviation at the metering fix, providing at the same time excellent environmental impact mitigation figures. However, the execution time required to solve a rigorous trajectory optimisation problem at each re-calculation instant remains a critical limitation for practical applications. In order to address this issue, a variant of the model predictive control strategy that allows for fast updates of the optimal trajectory plan based on parametric sensitivities is proposed, which shows analogous results yet halving the time needed to update the optimal trajectory plan. Finally, the potential benefits of using wind observations broadcast by nearby aircraft to reconstruct the wind profile downstream right before updating the optimal trajectory plan when using model predictive control is also investigated. Promising results show that the combination of model predictive control with wind networking concepts could enable optimal descents without degrading the capacity of the airport.El creixement del trànsit ha augmentat la pressió sobre la sostenibilitat ambiental del transport aeri. En aquest àmbit s'han dedicat molts esforços en recerca per reduir l'impacte ambiental en les diferents fases del vol. Les operacions de descens continu, en les quals l'aeronau descendeix amb els motors a ralentí des de l'altitud de creuer fins just abans d'aterrar, han demostrat ser una solució atractiva per reduir el combustible, el soroll i les emissions en la fase de descens. Desafortunadament, aquest tipus d'operacions tenen un inconvenient molt important: la pèrdua de predictibilitat des del punt de vista dels controladors de trànsit aeri, en termes de temps de sobrevol als diferents punts de pas de la ruta. Per aquesta raó, els controladors necessiten aplicar més separació entre aeronaus, reduint així la capacitat de l'aeroport. Treballs anteriors han demostrat que si les aeronaus fossin capaces de satisfer restriccions de temps de sobrevol a un o més punts de pas, seria possible implementar operacions de descens continu sense degradar la capacitat de l'aeroport. Malauradament, avui en dia existeixen pocs sistemes de gestió de vol capaços de generar trajectòries òptimes que satisfacin restriccions de temps, principalment perquè l’optimització de trajectòries en temps real continua sent una tasca difícil. A més, la resolució espacial i temporal dels models de vent utilitzades per els planificadors de trajectòria no son suficients per generar prediccions de temps de sobrevol prou fiables. Finalment, les estratègies de guiatge que fins i tot avui en dia permetrien satisfer amb exactitud restriccions de temps de sobrevol, no estan dissenyades específicament per minimitzar l’impacte ambiental. Aquesta tesi té com a objectiu explorar algoritmes de d'optimització ràpids i robustos que permetin actualitzar la trajectòria òptima en temps real durant l'execució del descens, satisfent al mateix temps restriccions de temps de sobrevol; també s'investigaran nous conceptes de que permetin generar models de vent molt exactes a partir d'observacions emeses per aeronaus veïnes; i estratègies de guiatge més intel·ligents que minimitzin l'impacte ambiental de les operacions de descens continu subjectes a restriccions de temps de sobrevol. En primer lloc, es quantifica la finestra de temps disponible al punt on s'aplica la restricció de temps de sobrevol, en funció dels estats de l'aeronau (altitud, velocitat i distància al punt) i assumint que els motors es mantenen ralentí i que no s'utilitzen aerofrens durant tot el descens. Els resultats de l'experiment indiquen que es podrien utilitzar estratègies de guiatge que gestionessin l'energia cinètica i potencial de l'aeronau per satisfer restriccions de temps sense necessitat de gastar més combustible. A continuació, es compararen quatre d'aquestes estratègies. Els resultats d'aquests segon experiment indiquen que el control predictiu, una estratègia que contínuament actualitza la trajectòria òptima durant el descens, es molt robusta en termes d'errors de temps i energia, i que també redueix l'impacte ambiental. Malauradament, es tarda massa a actualitzar la trajectòria òptima cada cop que s’actualitza, fet que limita la implementació d'aquesta estratègia. Per tal d'afrontar aquesta limitació, es proposa una variant que utilitza sensitivitats paramètriques per reduir el temps d'execució a l'hora d'actualitzar la trajectòria òptima, sense degradar significativament la seva exactitud. Finalment, s'investiguen els possibles beneficis d'aprofitar observacions de vent enviades per les aeronaus del volant per millorar el model de vent i, conseqüentment, l'exactitud de la trajectòria calculada. Resultats prometedors demostren que si s’implementés model predictiu com a estratègia de guiatge i les aeronaus cooperessin per compartir observacions de vent, es reduiria l'impacte ambiental sense degradar la capacitat de l'aeroport

How much fuel and time can be saved in a perfect flight trajectory? Continuous cruise climbs vs. conventional operations

Continuous climb, cruise and decent operations (referred as continuous operations) may contribute to significantly reduce fuel and emissions. Nevertheless, it is obvious that the introduction of such procedures at large scale is not possible with the current air traffic management concept of operations, since flying at constant altitudes is one of the key aspects to strategically separate flows of aircraft. This paper tries to quantify what would be the potential savings of flying such optimised vertical profiles. A multiphase optimal control problem is formulated and solved by means of numerical optimisation. Optimal conventional trajectories (subject to realistic air traffic management practices and constraints) are compared with optimal continuous (and ideal) operations, only subject to aircraft performance constraints. Results show that the continuous cruise phase can lead to fuel savings between 1% and 2% of the total trip fuel for an Airbus A320. Interestingly, continuous operations show also a reduction of trip times between 1% and 5% of the total trip time, depending on the trip distance between origin and destination airports.Peer ReviewedAward-winningPostprint (published version

Time and Energy Managed Operations (TEMO): Cessna Citation II Flight Trials

From 9-26 October 2015 the Netherlands Aerospace Centre (NLR) in cooperation with Delft University of Technology (DUT) has executed Clean Sky flight trials with the Cessna Citation II research aircraft. The trials consisted of several descents and approaches at the Eelde airport near Groningen, demonstrating the TEMO (Time and Energy Managed Operations) concept developed in the Clean Sky Joint Technology Initiative research programme as part of the Systems for Green Operations (SGO) Integrated Technology Demonstrator. A TEMO descent aims to achieve an energy-managed idle-thrust continuous descent operation (CDO) while satisfying ATC time constraints, to maintain runway throughput. An optimal descent plan is calculated with an advanced on-board real-time aircraft trajectory optimisation algorithm considering forecasted weather and aircraft performance. The optimised descent plan was executed using the speed-on-elevator mode of an experimental Fly-By-Wire (FBW) system connected to the pitch servo motor of the Cessna Citation II aircraft. Several TEMO conceptual variants have been flown. It has been demonstrated that the TEMO concept enables arrival with timing errors below 10 seconds. The project was realised with the support of CONCORDE partners Universitat Politècnica de Catalunya (UPC) and PildoLabs from Barcelona, and the Royal Netherlands Meteorological Institute (KNMI).Peer ReviewedPostprint (published version

Flight testing Time and Energy Managed Operations (TEMO)

The expected growth in air traffic combined with an increased public concern for the environment, have forced legislators to rethink the current air traffic system design. The current air traffic system operates at its capacity limits and is expected to lead to increased delays if traffic levels grow even further. Both in the United States and Europe, research projects have been initiated to develop the future Air Transportation System (ATS) to address capacity, and environmental, safety and economic issues. To address the environmental issues during descent and approach, a novel Continuous Descent Operations (CDO) concept, named Time and Energy Managed Operations (TEMO), has been developed co-sponsored by the Clean Sky Joint Undertaking. It uses energy principles to reduce fuel burn, gaseous emissions and noise nuisance whilst maintaining runway capacity. Different from other CDO concepts, TEMO optimizes the descent by using energy management to achieve a continuous engine-idle descent, while satisfying time constraints on both the Initial Approach Fix (IAF) and the runway threshold. As such, TEMO uses timemetering at two control points to facilitate flow management and arrival spacing. TEMO is in line with SESAR step 2 capabilities, since it proposes 4D trajectory management and is aimed at providing significant environmental benefits in the arrival phase without negatively affecting throughput, even in high density and peak-hour operations. In particular, TEMO addresses SESAR operational improvement (OI) TS-103: Controlled Time of Arrival (CTA) through use of datalink [1]. TEMO has been validated starting from initial performance batch studies at Technology Readiness Level (TRL) 3, up to Human-in-the-Loop studies in realistic environments using a moving base flight simulator at TRL 5 ([2]-[6]). In this paper the definition, preparation, performance and analysis of a flight test experiment is described with the objective to demonstrate the ability of the TEMO algorithm to provide accurate and safe aircraft guidance toward the Initial Approach Fix (IAF), and further down to the Stabilization Point (1000 ft AGL), to demonstrate the ability of the TEMO algorithm to meet absolute time requirements at IAF and/or runway threshold and to evaluate the performance of the system under test (e.g. fuel usage).Peer ReviewedPostprint (published version

Improving the predictability of take-off times with Machine Learning : a case study for the Maastricht upper area control centre area of responsibility

The uncertainty of the take-off time is a major contribution to the loss of trajectory predictability. At present, the Estimated Take-Off Time (ETOT) for each individual flight is extracted from the Enhanced Traffic Flow Management System (ETFMS) messages, which are sent each time there is an event triggering a recalculation of the flight data by the Network Man- ager Operations Centre. However, aircraft do not always take- off at the ETOTs reported by the ETFMS due to several factors, including congestion and bad weather conditions at the departure airport, reactionary delays and air traffic flow management slot improvements. This paper presents two machine learning models that take into account several of these factors to improve the take- off time prediction of individual flights one hour before their estimated off-block time. Predictions performed by the model trained on three years of historical flight and weather data show a reduction on the take-off time prediction error of about 30% as compared to the ETOTs reported by the ETFMS.Peer ReviewedPostprint (published version