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.

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.

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.

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.

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.

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.

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.

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.

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.

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.