Aircraft Departure Research Articles

Evaluation of aircraft emissions at London Heathrow Airport

A study to monitor Ultrafine Particles (UFP) at Heathrow Airport was undertaken in the autumn of 2017. The campaign followed on from a similar study in 2016, which put UFP at the airport into context with nearby measurements. The objective of the 2017 study was to undertake UFP monitoring at higher time resolution (60 second scans) and in a narrower particle size range (6–100 nm). High resolution data from the NOx, PM and Black Carbon analysers on site was also collected during the survey. Measurements were made at the runway station, LHR2 to attempt to characterise individual aircraft using the runway. Nucleation mode particles are again seen to predominantly originate from the airport, with highest concentrations associated with departing aircraft. While there is some correlation of nucleation particles with NOx and BC, these pollutants, together with PM mass and Aitken mode particles, also show strong associations with winds from off-airport directions. There is some evidence that BC emissions from landing aircraft are enriched in UV-active BC (UVPM), most likely as a result of tyre abrasion upon landing. Comparison of UFP measurements with the 2016 survey was not possible because of the differences in configuration of the SMPS for the two surveys. This observation demonstrates the importance of documenting SMPS configuration, to determine if comparison between published data is possible. Analysis of the 1 minute measurement data with associated aircraft departure information was used to group the data by aircraft type. Larger aircraft departing from the runway recorded higher measurements of nucleation particles and NOx compared to smaller aircraft, while emissions of BC, UVPM and NO2 appear to be dependent upon the age of the engine design, rather than the size of the aircraft.

Optimal model for the aircraft arrival and departure scheduling problem with fuzzy runway incursion time.

This paper presents an optimization model for assigning a set of arrival and departure flights to multiple runways and determining their actual times with consideration of incursions. Due to the lack of data, fuzzy incursion time is used to describe the uncertainty with the help of artificial experience. Moreover, the multiple-goal priority considerations of air traffic controllers are also fully considered in this model. The two objectives are to simultaneously minimize delays in arrival and departure flights. Since this problem is NP-hard, a novel polynomial algorithm based on queuing theory is also proposed to obtain acceptable solutions efficiently. Finally, a real-world example is provided to analyze the effect of different times and places of incursion events on the scheduling scheme, which can verify the correctness of the model. Results show that higher runway incursion times lead to longer queue lengths for take-off and landing flights, resulting in more flight delays.

Predictability improvement of Scheduled Flights Departure Time Variation using Supervised Machine Learning

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air traffic flow management slot changes. This paper presents a model that predicts departure time variance based on the previous leg departure time using a combination of exponential moving average and machine learning methods. The model correctly classifies the departure time (Early, On Time, Delay) based on the previous leg departure state, allowing the ATFM system to measure the arrival time of a capacity constrained airport with greater accuracy and better assess demand requirements. The results show that the proposed model with M5P Regression tree provides the best results, with Mean Absolute Error and Root Mean Square Error (RMSE) of 3.43 and 4.83, respectively, indicating a 50% improvement over previous research findings. Whereas, with logistic regression, the classification of departure time (Early, On Time, Delay) is achieved a better accuracy of 91 %, which is higher than previous works.

Bayesian network modeling of accident investigation reports for aviation safety assessment

Safety assurance is of paramount importance in the air transportation system. In this paper, we analyze the historical passenger airline accidents that happened from 1982 to 2006 as reported in the National Transportation Safety Board (NTSB) aviation accident database. A four-step procedure is formulated to construct a Bayesian network to capture the causal relationships embedded in the sequences of these accidents. First of all, with respect to each accident, a graphical representation is developed to facilitate the visualization of the escalation of initiating events into aviation accidents in the system. Next, we develop a Bayesian network representation of all the accidents by aggregating the accident-wise graphical representations together, where the causal and dependent relationships among a wide variety of contributory factors and outcomes in terms of aircraft damage and personnel injury are captured. In the Bayesian network, the prior probabilities are estimated based on the accident occurrence times and the aircraft departure data from the Bureau of Transportation Statistics (BTS). To estimate the conditional probabilities in the Bayesian network, we develop a monotonically increasing function, whose parameters are calibrated using the probability information on single events in the available data. Finally, we develop a computer program to automate the generation of the Bayesian network in compliance with the XML format used in the commercial GeNIe modeler. The constructed Bayesian network is then fed into GeNIe modeler for accident analysis. The mapping of the NTSB data to a Bayesian network facilitates both forward propagation and backward inference in probabilistic analysis, thereby supporting accident investigations and risk analysis. Several accident cases are used to demonstrate the developed approach.

Pengaruh Pengoperasian Wide-Body Aircraft Terhadap Kelancaran Arus Lalu Lintas Penerbangan di Makassar Air Traffic Service Center

This study aims to obtain a description of whether there is an influence between the movement of wide-body aircraft both departure and arrival on flight delays as a measure of the smooth flow of flight traffic at the Makassar Air Traffic Service Center. The research concludes that there is a positive relationship between the operation of wide-body aircraft and flight delays of 0.870. The positive correlation indicates that the more wide-body aircraft operations, the flight delay will increase, which if there is an increase in flight delays, the smooth flow of flight traffic will decrease. The coefficient of determination is 75%, which means that the effect of the operation of wide-body aircraft on flight delays is 75% and the rest is determined by other factors not included in the research section. The equation obtained from the regression test is Y = -5,679 + 1,872 X. So, if the operation of wide-body aircraft increases by 1, the flight delay will increase by 1,872 at a constant of -5,679.

A decision support framework and prototype for aircraft dispatch assessment

When an aircraft experiences an unexpected issue during flight operations, a technician determines whether the aircraft can safely perform the next flight. This operational decision process - known as dispatch assessment - has to happen within limited available time between aircraft arrival and departure. Currently, technicians face two main problems during the assessment: lack of access to decision support information and a time-consuming process for finding relevant information in extensive maintenance manuals. These issues often lead to delays and additional costs and are indicative of three larger challenges in the decision support domain: 1) a paucity of decision support models and applications for operational processes in maintenance; 2) relatively few efforts in applying and evaluating artifacts in experimental and real-life operational settings; and 3) a lack of systematic development, application and evaluation of digitization and automation efforts of complex decision processes in maintenance. This paper applies a design science research approach to address these challenges and introduces two novel artifacts: a decision support framework for real-time decision making in aircraft dispatch, and a web-based prototype tool accessible through mobile solutions. The practical relevance of the framework and prototype is validated through two representative application and evaluation studies, one in an experimental setting and one in an operational environment. Results show significant time savings and strong qualitative indications towards a higher incentive to use documentation and reducing human risk factors that lead to maintenance error.

A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runways.

This study proposes a multi-objective mixed integer linear programming (MOMILP) model for assigning a set of flights to different runways and determining their actual arrival and departure times. The proposed model envisages unique operation model of each runway (i.e., takeoff, landing, or mixed takeoff and landing). Further, interference in two flights between adjacent runways are also fully considered in this model. The work aims at reveal the optimal relationship between traffic stream characteristics, operation mode of each runway and flight scheduling to simultaneously minimizing flight delays and maximizing runway utilization. Since the problem of interest has a non-deterministic polynomial (NP-hard) complexity, a heuristic-based non-dominated sorting genetic algorithm (NSGA-II) is also presented to find Pareto-optimal solutions in a reasonable amount of time, where coding structure and heuristic algorithm for producing initial population are defined. Finally, a real-world example is provided to compare the difference in quality between the proposed and traditional models, and reveal changes in trends between delay time of flights and idle time of the runways, which can verify the correctness of the model.

Aircraft noise level calculation during take-off

A mathematical model with 4 degree of freedom created in Matlab for aircraft departure trajectory is described in this article. As a reference aircraft a midsize commercial passenger aircraft similar to an Airbus A320 has been chosen. The aircraft is represented by the rigid body and the parameters of model are collected from Airbus and the simulated departure trajectory at the Munich airport is based on a Standard Instrumental Departure. A semi-empirical model of Stone for predicting the jet noise has been used. The proposed model is validated against 10 real flights obtained from aircraft noise and flight track monitoring system at Munich airport. The computed error between the real data and modelling is reported on. Obtained results are presented numerical and graphically. The observed effects of flight operational parameters affecting the aircraft noise emission level during take-off represent subjects of discussions in the paper.

Aircraft Departure Fuel Consumption Improvement Model Based On Flight Data

Accurate assessment of aircraft fuel consumption is an important means for airlines to reduce flight costs and control fuel emissions. By analyzing the actual flight data of QAR, the functional relationship between the aerodynamic parameters in the fuel consumption model of the departure climb phase is fitted, and the crosswind is added to the fuel consumption assessment model. Based on the analysis of the departure training of Qingdao Airport, the fuel consumption model of the exit climbing stage was calculated using the improved fuel consumption model. The results showed that the calculation accuracy of the wind tunnel experiment was improved by 3.6%, and the theory of the fuel evaluation experiment considering the crosswind was considered. The accuracy of the model calculation and QAR data is 93.3%.

Aircraft timely departure probability technique calculation

The article proposes the probability determination technique of timely aircraft departure, which is based on mathematical modeling method. It is proposed to use the indicator of timely departure probability, a calculation method of which is different from the known methods by taking into account the aircraft maintenance and repair system. This technique applies modern information technologies and the adopted approach is also supplemented with the mathematical model software implementation. The method of aircraft timely departure probability calculation is based on the model of the aircraft maintenance and repair system, utilizing the theory of mass service mathematical apparatus. The technique allows us to achieve the practical objectives of flight tests implementing aircraft maintainability evaluation and also complements the existing methodological support of the tests. The developed computer program provides the calculation of the required indicators for various values and combinations of factors which influence the result. Modeling was performed, and the study results of the main factors of probability effect, influencing timely departure are presented. Eventually the basic laws in solving the problem of aircraft timely departure, including interception of flying object are established.

An optimization framework for route design and allocation of aircraft to multiple departure routes

In this article, we present the development of a two-step optimization framework to deal with the design and selection of aircraft departure routes and the allocation of flights among these routes. The aim of the framework is to minimize cumulative noise annoyance and fuel burn. In the first step of the framework, multi-objective trajectory optimization is used to compute and store a set of routes that will serve as inputs in the second step. In the second step, the selection of routes from the sets of pre-computed optimal routes and the optimal allocation of flights to these routes are conducted simultaneously. To validate the proposed framework, we also conduct an analysis involving an integrated (one-step) approach, in which both trajectory optimization and route allocation are formulated as a single optimization problem. A comparison of both approaches is then performed, and their advantages and disadvantages are identified. The performance and capabilities of the present framework are demonstrated using a case study at Amsterdam Airport Schiphol in The Netherlands. The numerical results show that the proposed framework can generate solutions which can achieve a reduction in the number of people annoyed of up to 31% and a reduction in fuel consumption of 7.3% relative to the reference case solution.

Fuel Consumption Model of the Climbing Phase of Departure Aircraft Based on Flight Data Analysis

Accurate estimation of the fuel consumed during aircraft operation is key for determining the fuel load, reducing the airline operating cost, and mitigating environmental impacts. Aerodynamic parameters in current fuel consumption models are obtained from a static diagram extracted from the outcomes of wind tunnel experiments. Given that these experiments are performed in a lab setting, the parameters cannot be used to estimate additional fuel consumption caused by aircraft performance degradation. In addition, wind tunnel experiment results rarely involve the influence of crosswind on fuel consumption; thus, the results could be inaccurate when compared with field data. This study focuses on the departure climbing phase of aircraft operation and proposes a new fuel consumption model. In this model, the relationships between aerodynamic parameters are extracted by fitting quick access recorder (QAR) actual flight data, and the crosswind effect is also considered. Taking QAR data from two airports in China, the accuracy of the proposed model and its transferability are demonstrated. Applying the proposed model, the fuel saving of a continuous climb operation (CCO) compared with the traditional climb operation is further quantified. Finally, how aircraft mass, climbing angle, and different aircraft models could affect the fuel consumption of the climbing phase of aircraft operation is investigated. The proposed fuel consumption model fills gaps in the existing literature, and the method can be used for developing specific fuel consumption models for more aircraft types at other airports.

Integrated Surface–Airspace Model of Airport Departures

The significant growth in air traffic over the past few decades has led to increased congestion at major airports. Departure metering strategies to mitigate congestion require good models of operations at the airport, as well as in the surrounding terminal airspace. Although runways have traditionally been considered the sole capacity bottleneck in the air transportation system, large airports can exhibit multiple points of congestion, both on the surface and in the terminal area. This paper presents an integrated surface–airspace model of aircraft departure operations from pushback to the final departure fix, when the flight leaves the terminal area. The airport surface is represented as a queuing network to capture congestion at multiple locations, such as ramp, taxiways, and runways. The terminal departure airspace is modeled considering various factors, such as runway configurations, standard departure procedures, weather, and en route traffic. The proposed modeling approach is illustrated for Charlotte Douglas International Airport, the test bed for NASA’s Airspace Technology Demonstration 2 program. The model is evaluated on its ability to accurately predict various transit times, both on the surface and in the terminal area.

A Study on the Strategy for Departure Aircraft Pushback Control from the Perspective of Reducing Carbon Emissions

In order to reduce the taxiing time of departing aircraft and reduce the fuel consumption and exhaust emissions of the aircraft, Shanghai Hongqiao Airport was taken as an example to study the control strategy for aircraft departure. In this paper, the influence of the number of departure aircraft on the runway utilization rate, the takeoff rate, and the departure rate of flight departures under the conditions of airport runway capacity constraints are studied. The influence of factors, such as the number of departure aircraft, the gate position of the aircraft, and the configuration of airport arrival and departure runways, on the aircraft taxiing time for departure is analyzed. Based on a multivariate linear regression equation, a time prediction model of aircraft departure taxiing time is established. The fuel consumption and pollutant emissions of aircraft are calculated. The experimental results show that, without reducing the utilization rate of the runway and the departure rate of flights, implementing a reasonable pushback number for control of departing aircraft during busy hours can reduce the departure taxiing time of aircraft by nearly 32%, effectively reducing the fuel consumption and pollutant emissions during taxiing on the airport surface.

Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system

In this paper, an efficient trajectory planning system is proposed to solve the integration of arrivals and departures on parallel runways with a novel route network system. Our first effort is made in designing an advanced Point Merge (PM) route network named Multi-Level Point Merge (ML-PM) to meet the requirements of parallel runway operations. Then, more efforts are paid on finding a complete and efficient framework capable of dynamically modelling the integration of arrival and departure trajectories on parallel runways, modelling the conflict detection and resolution in presence of curved trajectory and radius-to-fix merging process. After that, a suitable mathematical optimization formulation is built up. Receding Horizon Control (RHC) and Simulated Annealing (SA) algorithms are proposed to search the near-optimal solution for the large scale trajectories in routine dense operations. Taking Beijing Capital International Airport (BCIA) as a study case, the experimental results show that our system shows good performances on the management of arrivals and departures. It can automatically solve all the potential conflicts in presence of dense traffic flows. With its unique ML-PM route network, it can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, an economical climbing for departure aircraft, an easier runway allocation together with trajectory control solutions. It shows a good and dynamic sequencing efficiency in Terminal Manoeuvring Area (TMA). In mixed ML-PM mode, under tested conditions, our proposed system can increase throughput at BCIA around 26%, compared with baseline. The methodology defined here could be easily applied to airports worldwide.

Integrated design and allocation of optimal aircraft departure routes

This paper presents a new multi-objective optimization formulation for the design and allocation of optimal aircraft departure routes. In the considered problem – besides two conventional objectives based on cumulative noise criteria and fuel burn – a new objective considering the flight frequency is introduced. Moreover, to take advantage of the combination of designing new routes and allocating flights to these routes, two different routes are considered simultaneously, and the distribution of flights over these two routes is addressed in parallel. Then, a new version of the so-called MOEA/D optimization algorithm is developed to solve the formulated optimization problem. Two different case studies, one at Rotterdam The Hague Airport and one at Amsterdam Airport Schiphol in The Netherlands, are carried out to evaluate the reliability and applicability of the proposed approach. The obtained results reveal that the proposed approach can provide solutions which can balance more effectively the concerned metrics such as the number of annoyed people, fuel burn, number of people exposed to certain noise levels, and number of aircraft movements which people are subjected to.

Multi-objective optimisation of aircraft departure trajectories

A multi-objective trajectory optimisation has been developed to minimise multiple environmental impacts (noise and exhaust emissions) from commercial airplanes. Three different non-gradient algorithms are used. First, a parameterization method is established for airplane departure trajectories, with path constraints considered where necessary. Second, a method to parameterize movement in the lateral plane based on a Bézier curve has been proposed to decrease the number of free parameters. The environmental impacts on target areas have been simulated by a comprehensive flight mechanics program. Finally, two posterior selection strategies based on preference function and monetisation approaches are used to evaluate the resulting Pareto solution set. A case study for a departure of an Airbus A320-211 with population distribution of residential communities around Manchester Airport (ICAO code: EGCC) is carried out with three different optimisers. We demonstrate that this simulation framework is able to solve trajectory optimisation problems with multiple simultaneous environmental objectives.

A metric for the real-time evaluation of the aircraft boarding progress

Future 4D aircraft trajectories demand the comprehensive consideration of environmental, economic, and operational constraints. A reliable prediction of all aircraft-related processes along the specific trajectories is essential for punctual operations. The uncertainties in the airborne phase only have minor impacts on the punctuality of a flight. The necessary change to an air-to-air perspective, with a specific focus on the ground operations, will provide key elements for complying with the challenging future requirements of a comprehensive 4D aircraft trajectory over the day of operations. A major task of the ground operations is to ensure a reliable and predictable departure time, which is an operational milestone for both the current and the destination airport. These mutual interdependencies between airports result in system-wide, far-reaching effects (reactionary delays). The ground trajectory of an aircraft primarily consists of the handling processes at the stand (deboarding, catering, fueling, cleaning, boarding, unloading, and loading), which are defined as the aircraft turnaround. To provide a reliable prediction of the turnaround, the critical path of processes has to be managed in a sustainable manner. The turnaround processes are mainly controlled by the ground handling, airport or airline staff, except the aircraft boarding, which is driven by the passengers’ experience and willingness or ability to follow the proposed procedures.Addressing the fact that boarding is on the critical path of the aircraft 4D trajectory and not controlled by the operators, this paper provides a scientific approach for a real-time evaluation of the boarding progress using the capabilities of a future connected cabin (e.g. sensor environment). A calibrated microscopic approach is used to model the distinct passenger behavior, where the individual movement is defined as a one-dimensional, stochastic, and time/space discrete transition process. The simulation environment is capable of covering a broad range of behaviors, boarding strategies and operational constraints and allows the integration of infrastructural changes and future technologies. The paper provides a set of indicators for depicting the real-time status of the boarding progress as a fundamental basis for the prediction of the boarding time. In this context, the aircraft seats are used as a sensor network with the capability to detect the seat status: free or occupied. The seat status is the basis for the calculation of an aircraft-wide interference potential as the major indicator for the boarding time. In combination with an integrated airline/airport information management (e.g. sequence of boarding passengers), the boarding progress will be transformed from a black box to a transparent progress with the operator’s real-time ability to react to significant deviations from the planned progress. Thus, the research results provide a fundamental contribution towards the derivation of the crucial aircraft departure time.

Development of a Pushback Time Assignment Algorithm Considering Uncertainty

Pushback time assignment is a promising method to reduce the fuel burn of departure aircraft on the ground at congested airports. Departure aircraft can wait at the gate with engines off instead of waiting in a departure queue. However, airport operations include considerable uncertainty, which can result in unexpected situations such as takeoff time delays. This paper proposes a new algorithm to assign the pushback time considering uncertainty. To avoid takeoff delay by uncertainty, useful information sources are identified and the optimal pushback time is calculated based on the obtained information. The problem is formulated as a combinatorial optimization problem, and a tabu search technique is applied to solve it. The simulation results show that the proposed algorithm reduces takeoff delay caused by uncertainty by 60–80%, while maintaining taxi-out time savings.

가중선형회귀를 통한 순항항공기의 궤적예측

The departure flow management is the planning tool to optimize the schedule of the departure aircraft and allows them to join smoothly into the overhead traffic flow. To that end, the arrival time prediction to the merge point for the cruising aircraft is necessary to determined. This paper proposes a trajectory prediction model for the cruising aircraft based on the machine learning approach. The proposed method includes the trajectory vectored from the procedural route and is applied to the historical data to evaluate the prediction performances.