This paper presents the development of a multilevel optimization framework for the design and selection of departure routes, and the distribution of aircraft movements among these routes, while taking the sequence and separation requirements for aircraft on runways and along selected routes into account. The main aim of the framework is to minimize aircraft noise impact on communities around an airport, and the associated fuel consumption. The proposed framework features two consecutive steps. In the first step, for each given Standard Instrument Departure (SID), multi-objective trajectory optimization is utilized to generate a comprehensive set of possible alternative routes. The obtained set is subsequently used as input for the optimization problem in the second step. In this step, the selection of routes for each SID and the distribution of aircraft movements among these routes are optimized simultaneously. To ensure the feasibility of optimized solutions for an entire operational day, the sequence and separation requirements for aircraft on runways and along selected routes are included in this second phase. In order to address these issues, three novel techniques are developed and added to a previously developed multilevel optimization framework, viz., a runway assignment model, a conflict detection algorithm, and a rerouting technique. The proposed framework is applied to a realistic case study at Amsterdam Airport Schiphol in the Netherlands, in which 599 departure flights and 13 different SIDs are considered. The optimization results show that the proposed model can offer conflict-free solutions, one of which can lead to a reduction in the number of people annoyed of up to 21%, and a reduction in fuel consumption of 8% relative to the reference case solution.

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This paper presents a trajectory optimization study that has been conducted using a recently developed tool for the synthesis and analysis of extended flight formations of long-haul commercial aircraft, with the aim to minimize overall fuel consumption. In extended flight formations, trailing aircraft can attain an appreciable reduction in induced drag and associated reduction in fuel burn by flying in the upwash of the lead aircraft's wake. In the present study, a previously developed multi-phase optimal control (MOC) framework for the synthesis of two-ship flight formations has been extended to include the assembly of three-ship flight formations. Using the extended tool, various numerical experiments have been conducted in relation to the assembly of two-ship and three-ship flight formations in long-haul operations across the North-Atlantic Ocean. Additionally, numerical experiments have been carried out to examine the impact of wind fields on the synthesis and performance of flight formations. Additionally, a parametric investigation has been conducted to assess the sensitivity of the solutions with respect to the degree of the induced drag reduction that might be attained by the trailing aircraft in a formation. The results of the various numerical experiments reveal that formation flight can result in appreciable reductions in fuel burn in comparison to flying solo-particularly when larger formation strings are permitted.

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Runway utilisation is a function of actual yearly runway throughput and annual capacity. The aim of the analysis in this project is to find data driven prediction models based on the features and relevant scenarios that might impact runway utilisation. The Gradient Boosting machine learning method will be assessed on their forecast performance and computational time for predicting the procedural and non-procedural runway exit to be utilised after the landing rollout. The Gradient Boosting method obtained an accuracy of 79% and was used to observe key related precursors of unique data patterns. Tests were conducted using runway and final approach data consisting of 54,679 arrival flights at Vienna airport.

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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.

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At many airports, ground track segments may be shared by different departure routes, and the population living underneath these segments is exposed to all aircraft movements which are sent to these routes. This may cause negative community reaction to authorities and policymakers, leading to objections against the expansion of airport and aircraft operations. To take into account this concern in the design of optimal departure routes, a new multi-objective optimization formulation is developed and solved in this study. Besides two conventional objectives, i.e., annoyance and fuel consumption, a new objective is considered in the problem formulation which aims to split a ground track segment shared by two different departure routes into two different parts corresponding to each route. As a consequence, the number of people exposed to all flights operating on these routes is decreased considerably. An optimization problem with three objectives is formed, and solved by a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The reliability and applicability of the developed model are demonstrated through a case study at Amsterdam Airport Schiphol. The obtained simulation results reveal that the proposed approach can offer solutions which can more effectively balance between the considered objectives.@en

The paper first investigates the influence of daily mobility of population on evaluation of aircraft noise effects. Then, a new air traffic assignment model that considers this activity is proposed. The main objective is to reduce the number of people affected by noise via lowering as much as possible the noise exposure level L _den of individuals or groups of people who commute to the same locations during the day. It is hereby intended to reduce the noise impact upon individuals rather than to reduce the impact in particular – typically densely populated – areas. However, sending aircraft farther away from populated regions to reduce noise impact may increase fuel burn, thus affecting airline costs and sustainability. Therefore, a multi-objective optimization approach is utilized to obtain reasonable solutions that comply with overall air transport sustainability. The method aims at generating a set of solutions that provide proper balance between noise annoyance and fuel consumption. The reliability and applicability of the proposed method are validated through a real case study at Belgrade airport in Serbia. The investigation shows that there is a difference between the number of people annoyed (NPA) evaluated based on the census data and the NPA evaluated based on the mobility data. In addition, these numbers differ significantly across residential locations. The optimal results show that the proposed model can offer a considerable reduction in the NPA, and in some cases, it can gain up to 77%, while maintaining the same level of fuel consumption compared with the reference case.

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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.

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The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been recognized as a promising method for solving multi-objective optimization problems (MOPs), receiving a lot of attention from researchers in recent years. However, its performance in handling MOPs with complicated Pareto fronts (PFs) is still limited, especially for real-world applications whose PFs are often complex featuring, e.g., a long tail or a sharp peak. To deal with this problem, an improved MOEA/D (named iMOEA/D) that mainly focuses on bi-objective optimization problems (BOPs) is therefore proposed in this paper. To demonstrate the capabilities of iMOEA/D, it is applied to design optimization problems of truss structures. In iMOEA/D, the set of the weight vectors defined in MOEA/D is numbered and divided into two subsets: one set with odd-weight vectors and the other with even-weight vectors. Then, a two-phase search strategy based on the MOEA/D framework is proposed to optimize their corresponding populations. Furthermore, in order to enhance the total performance of iMOEA/D, some recent developments for MOEA/D, including an adaptive replacement strategy and a stopping criterion, are also incorporated. The reliability, efficiency and applicability of iMOEA/D are investigated through seven existing benchmark test functions with complex PFs and three optimal design problems of truss structures. The obtained results reveal that iMOEA/D generally outperforms MOEA/D and NSGA-II in both benchmark test functions and real-world applications.

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In this paper a tool is developed that optimizes the trajectories of multiple airliners that seek to join in formation to minimize overall fuel consumption or direct operating cost. The developed optimization framework relies on optimal control theory to solve the multiple-phase optimization problem associated to flight formation assembly. A reduced-order point-mass formulation is employed for modelling of the aircraft dynamics within an extended flight formation, and of the solo flight legs that connect the flight formation to the origin and destination airports. When in formation, a discount factor is applied to simulate a reduction in the induced drag of the trailing aircraft. Using the developed tool a case study has been conducted pertaining to the assembly of two-aircraft formation flights across the North-Atlantic. Results are presented to illustrate the synthesis of the formation trajectories and to demonstrate the potential for reducing fuel and operating cost. The results of the various numerical experiments show that formation flight can lead to significant reductions in fuel consumption compared to flying solo, even when the original trip times are maintained. Additionally, the results clearly reveal how the performance and the characteristics of the flight formation mission—notably the location of rendezvous and splitting points—are affected when one aircraft seeking to join the formation suffers a departure delay.@en

During arrival and departure procedures at an airport, aircraft generate noise. High levels of noise during these terminal procedures can have a negative impact on the communities located near the airport. We assess the impact of the noise generated by a standard instrument departure of a twin-engine narrow body mid-range aircraft over the communities nearby Amsterdam Airport Schiphol, the Netherlands. During the departure procedure, we consider a stochastic flight trajectory that is subject to lateral position errors. We estimate, using Monte Carlo simulation, the distribution of the sound exposure level and of the number of awakenings generated by a departure. We also identify the residential areas where the number of awakenings are overestimated or underestimated, when comparing a stochastic and a deterministic departure approach. Lastly, we approximate the distribution of the noise level for a generic aircraft departure, which can be further employed for further optimization of departure procedures. @en

Recently, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) has emerged as a potential method for solving multi-objective optimization problems (MOPs) and attracted much attention from researchers. In MOEA/D, the MOPs are decomposed into a number of scalar optimization sub-problems, and these sub-problems are optimized concurrently by only utilizing the information from their neighboring sub-problems. Thanks to these advantages, MOEA/D has demonstrated to be more efficient than the non-dominated sorting genetic algorithm II (NSGA-II) and other methods. However, its applications to practical problems are still limited, especially in the domain of aerospace engineering. Therefore, this paper aims to present a new application of MOEA/D for the optimal design of noise abatement aircraft terminal routes. First, in order to optimize aircraft noise for aircraft terminal routes while taking into account the interests of various stakeholders, bi-objective optimization problems including noise and fuel consumption are formulated, in which both the ground track and vertical profile of a terminal route are optimized simultaneously. Then, MOEA/D is applied to solve these problems. Furthermore, to ensure the design space of vertical profiles is always feasible during the optimization process, a trajectory parameterization technique recently proposed is also used. This technique aims at reducing the number of model evaluations of MOEA/D and hence the computational cost will decrease significantly. The efficiency and reliability of the developed method are evaluated through case studies for departure and arrival routes at Rotterdam The Hague Airport in the Netherlands.@en

Air traffic assignment to departure and arrival routes has a major impact on the population noise exposure in the vicinity of the airport. In some cases, by choosing the suitable air traffic assignment it is possible to avoid overflying populated areas and reduce number of people affected by noise. However, such an approach almost always leads to an increase in route length, and therefore an increase in fuel consumption and CO2 emissions. Although aircraft noise and fuel consumption reduction are conflicting goals, they both represent pivotal aspects of air transport sustainable development. In this paper, the methods of multi-criteria optimisation are applied, which are generally used when it is necessary to make an optimal decision that requires a compromise (trade-off) solution between two or more conflicting goals. This research aims to develop a mathematical model and to propose an algorithm for air traffic assignment to departure and arrival routes that will, through the Pareto optimality concept, find the approximation of a set of nondominated solutions that minimize population noise exposure and fuel consumption. The approach was demonstrated on Belgrade airport to show the benefits of the proposed model on a real data example. Since all Pareto optimal solutions are considered equally good, from all obtained air traffic assignments, the three representative solutions were compared to the actual air traffic assignment (Base case). The obtained results indicate that the proposed approach can provide solutions which offer a good trade-off between the concerned metrics.@en

In this paper a tool is developed that optimizes the trajectories of multiple airliners that seek to join in formation to minimize overall fuel consumption or Direct Operating Cost (DOC). The developed optimization framework relies on optimal control theory to solve the multiple-phase optimization problem associated to flight formation assembly. A reduced-order point-mass formulation is employed for modelling of the aircraft dynamics within an extended flight formation, and of the solo flight legs that connect the flight formation to the origin and destination airports. When in formation, a discount factor is applied to simulate a reduction in the induced drag of the trailing aircraft. Using the developed tool a case study has been conducted pertaining to the assembly of two-aircraft formation flights across the North-Atlantic. Results are presented to illustrate the synthesis of the formation trajectories and to demonstrate the potential for reducing fuel and operating cost. The results of the various numerical experiments show that formation flight can lead to significant reductions in fuel consumption compared to flying solo, even when the original trip times are maintained. Also, the results clearly reveal how the performance and the characteristics of the flight formation mission - notably the location of rendezvous and splitting points - are affected when one aircraft seeking to join the formation suffers a departure delay.@en

This paper discusses the development of a software suite that aims to optimize helicopter trajectories with respect to the noise impact on the ground. The software suite has been developed around an advanced gradient-based optimization algorithm based on optimal control theory. The helicopter trajectories are modeled using an eight-degree-of-freedom flight mechanics model. To determine the noise impact on the ground, a helicopter noise model developed in this work consists of three modules, namely, a helicopter source noise model, a propagation model, and a noise impact model. To determine the noise levels on the ground, the source noise levels are determined from a database of aeroacoustically determined noise levels for varying flight conditions and projected on a hemisphere centered at the main rotor hub. The noise propagation model included in the suite is capable of determining the propagation loss between the source and the receiver in nonstandard atmospheric conditions, and it yields the total noise level in individual receiver locations. Finally, these noise levels can be quantified into a single noise impact criterion, which can be used as an optimization criterion in the optimal control formulation. To exemplify the capabilities of the suite, a hypothetical city center approach procedure is optimized for the noise impact in communities surrounding the helispot.
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This paper investigates different methodologies for the evaluation of the acoustic disturbance emitted by helicopter’s main rotors during unsteady maneuvers. Nowadays, the simulation of noise emitted by helicopters is of great interest to designers, both for the assessment of the acoustic impact of helicopter flight on communities and for the identification of optimal-noise trajectories. Typically, the numerical predictions consist of the atmospheric propagation of a near-field noise model, extracted from an appropriate database determined through steady-state flight simulations/measurements (quasi-steady approach). In this work, three techniques for maneuvering helicopter noise predictions are compared: one considers a fully unsteady solution process, whereas the others are based on quasi-steady approaches. These methods are based on a three-step solution procedure: first, the main rotor aeroelastic response is evaluated by a nonlinear beam-like rotor blade model coupled with a boundary element method for potential flow aerodynamics; then, the aeroacoustic near field is evaluated through the 1A Farassat formulation; finally, the noise is propagated to the ground by a ray tracing model. Only the main rotor component is examined, although tail rotor contribution might be included as well. The numerical investigation examines the differences among the noise predictions provided by the three techniques, focusing on the assessment of the reliability of the results obtained through the two quasi-steady approaches as compared with those from the fully unsteady aeroacoustic solver.

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In an effort to allow to increase the number of aircraft and airport operations while mitigating their negative impacts (e.g., noise and pollutant emission) on near-airport communities, the optimal design of new departure routes with less noise and fuel consumption becomes more important. In this paper, a multi-objective evolutionary algorithm based on decomposition (MOEA/D), which recently emerged as a potential method for solving multi-objective optimization problems (MOPs), is developed for this kind of problem. First, to minimize aircraft noise for departure routes while taking into account the interests of various stakeholders, bi-objective optimization problems involving noise and fuel consumption are formulated where both the ground track and vertical profile of a departure route are optimized simultaneously. Second, in order to make the design space of vertical profiles feasible during the optimization process, a trajectory parameterization technique recently proposed is employed. Furthermore, some modifications to MOEA/D that are aimed at significantly reducing the computational cost are also introduced. Two different examples of departure routes at Schiphol Airport in the Netherlands are shown to demonstrate the applicability and reliability of the proposed method. The simulation results reveal that the proposed method is an effective and efficient approach for solving this kind of problem.@en

This paper presents one of the main objective of WP1 of Clean Sky GRC5 MANOEUVRES project, which consists in the correlation of ground noise data measured during flight tests, with numerical predictions obtained by a numerical process aimed at the analysis of the acoustic field emitted by helicopter rotors in arbitrary unsteady manoeuvring flight. Two of the helicopter trajectories analysed by the dedicated GRC5 flight test campaign are considered. Noise measurements obtained by microphones located on the ground at several positions along and aside the ground projection of the vehicle fly-over trajectory are used for correlation. The numerical simulation starts with the aeromechanic identification of the flown trajectory, followed by the corresponding prediction of aerodynamic loads, rotor noise radiation and far field atmospheric propagation.@en

In this study, a genetic optimization algorithm is applied to the design of environmentally friendly aircraft departure trajectories. The environmental optimization has been primarily focused on noise abatement and local NOx emissions, whilst taking fuel burn into account as an economical criterion. In support of this study, a novel parameterization approach has been conceived for discretizing the lateral and vertical flight profiles, which reduces the need to include nonlinear side constraints in the multiparameter optimization problem formulation, while still permitting to comply with the complex set of operational requirements pertaining to departure procedures. The resulting formulation avoids infeasible solutions and hence significantly reduces the number of model evaluations required in the genetic optimization process. The efficiency of the developed approach is demonstrated in a case study involving the design of a noise abatement departure procedure at Amsterdam Airport Schiphol in The Netherlands.@en

One of the main factors that limit public acceptance of rotorcraft is the noise emitted over densely populated areas, which prevents a wider diffusion of these vehicles capable of performing flight operations otherwise unattainable. It is the result of complex phenomena generated aerodynamically, through the main and tail rotors, as well as mechanically, by the engine and transmission system. Significant research effort is currently made for reducing rotorcraft acoustic impact, both by helicopter manufacturers and by research institutes and academia.@en

It is widely believed that the initiation of cloud formation due to condensation trails formed in cruise flight has a net positive effect on global warming due to the radiative forcing of the cloud coverage. This paper introduces a methodology to optimize 3-dimensional long-hail aircraft trajectories in a wind field with the aim of minimizing the flight time in which the formation of persistent condensation trails may take place, whilst taking into account the effects on flight time and total fuel burn. For this purpose, an advanced optimization algorithm based on optimal control theory was combined with a point-mass aircraft model, an atmospheric model based on historic weather data and a model to predict the formation of persistent condensation trails. An example scenario of a long-haul flight between Amsterdan and Washington D.C. is presented indicating significant potential for the reduction of radiative forcing at relatively small cost in terms of fuel and flight time. @en