Abstract: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.

In an effort to reduce the negative impact of civil aviation on the human environment, trajectory optimisation techniques have been used to minimise the single event impact of noise and gaseous emissions of departures on communities in the vicinity of airports. For this purpose, the earlier developed trajectory optimisation tool NOISHHH has been adapted to design departure trajectories optimised for environmental criteria, based on area navigation. The new version of NOISHHH combines a noise model, an emissions inventory model, a geographic information system and a dynamic trajectory optimisation algorithm to generate flight paths with minimised environmental impact. Operational constraints have been introduced to ensure that the resulting flight paths are fully compliant with the guidelines and regulations that apply to the design of standard instrument departures and the use of area navigation. To illustrate the capabilities of the new version of NOISHHH, two numerical examples are presented, which are both redesigns of standard instrument departures currently in use at Amsterdam Airport Schiphol.

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 biobjective 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.

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 NO x 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.

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. Nomenclature= specific heat at constant pressure = drag force = emission index of water = t h r u s t f o r c e = fuel mass flow = slope of the critical mixing line = gravitational acceleration = objective weighting factor = pressure = specific combustion heat = radius of the Earth = temperature = true airspeed = wind speed = aircraft weight = altitude = flight path angle = ratio of molecular masses of water and air = propulsive efficiency = l o n g i t u d e = bank angle = latitude = heading angle