New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

2021 ◽  
pp. 1-36
Author(s):  
R.I. Dancila ◽  
R.M. Botez

Abstract This study investigates a new aircraft flight trajectory optimisation method, derived from the Non-dominated Sorting Genetic Algorithm II method used for multi-objective optimisations. The new method determines, in parallel, a set of optimal flight plan solutions for a flight. Each solution is optimal (requires minimum fuel) for a Required Time of Arrival constraint from a set of candidate time constraints selected for the final waypoint of the flight section under optimisation. The set of candidate time constraints is chosen so that their bounds are contiguous, i.e. they completely cover a selected time domain. The proposed flight trajectory optimisation method may be applied in future operational paradigms, such as Trajectory-Based Operations/free flight, where aircraft do not need to follow predetermined routes. The intended application of the proposed method is to support Decision Makers in the planning phase when there is a time constraint or a preferred crossing time at the final point of the flight section under optimisation. The Decision Makers can select, from the set of optimal flight plans, the one that best fits their criteria (minimum fuel burn or observes a selected time constraint). If the Air Traffic Management system rejects the flight plan, then they can choose the next best solution from the set without having to perform another optimisation. The method applies for optimisations performed on lateral and/or vertical flight plan components. Seven proposed method variants were evaluated, and ten test runs were performed for each variant. For five variants, the worst results yielded a fuel burn less than 90kg (0.14%) over the ‘global’ optimum. The worst variant yielded a maximum of 321kg (0.56%) over the ‘global’ optimum.

2021 ◽  
Vol 125 (1286) ◽  
pp. 618-671
Author(s):  
R.I. Dancila ◽  
R.M. Botez

AbstractThis paper presents a new flight trajectory optimisation method, based on genetic algorithms, where the selected optimisation criterion is the minimisation of the total cost. The candidate flight trajectories evaluated in the optimisation process are defined as flight plans with two components: a lateral flight plan (the set of geographic points that define the flight trajectory track segments) and a vertical flight plan (the set of data that define the altitude and speed profiles, as well as the points where the altitude and/or speed changes occur). The lateral components of the candidate flight plans are constructed by selecting a set of adjacent nodes from a routing grid. The routing grid nodes are generated based on the orthodromic route between the flight trajectory’s initial and final points, a selected maximum lateral deviation from the orthodromic route and a selected grid node step size along and across the orthodromic route. Two strategies are investigated to handle invalid flight plans (relative to the aircraft’s flight envelope) and to compute their flight performance parameters. A first strategy is to assign a large penalty total cost to invalid flight profiles. The second strategy is to adjust the invalid flight plan parameters (altitude and/or speed) to the nearest limit of the flight envelope, with priority being given to maintaining the planned altitude. The tests performed in this study show that the second strategy is computationally expensive (requiring more than twice the execution time relative to the first strategy) and yields less optimal solutions. The performance of the optimal profiles identified by the proposed optimisation method, using the two strategies regarding invalid flight profile performance evaluation, were compared with the performance data of a reference flight profile, using identical input data: initial aircraft weight, initial and final aircraft geographic positions, altitudes and speed, cost index, and atmospheric data. The initial and final aircraft geographic positions, and the reference flight profile data, were retrieved from the FlightAware web site. This data corresponds to a real flight performed with the aircraft model used in this study. Tests were performed for six Cost Index values. Given the randomness of the genetic algorithms, the convergence to a global optimal solution is not guaranteed (the solution may be non-optimal or a local optima). For a better evaluation of the performance of the proposed method, ten test runs were performed for each Cost Index value. The total cost reduction for the optimal flight plans obtained using the proposed method, relative to the reference flight plan, was between 0.822% and 3.042% for the cases when the invalid flight profiles were corrected, and between 1.598% and 3.97% for the cases where the invalid profiles were assigned a penalty total cost.


2020 ◽  
Vol 314 ◽  
pp. 02002
Author(s):  
Alejandro Murrieta-Mendoza ◽  
Hugo Ruiz ◽  
Ruxandra Mihaela Botez

The increasing of flights around the world has led to various problems for the aeronautical industry such as saturated air space and higher levels of fossil fuel consumption. The way in which en-route flights are handled should be improved in order to increase airways’ capacity. A solution is to make aircraft to arrive at specific waypoints at a time constraint called Required Time of Arrival (RTA). Fossil fuel brings as a consequence the release of polluting particles to the atmosphere such as carbon dioxide and nitrogen oxides. It is thus desirable to compute the most economical trajectory in terms of fuel burn while fulfilling the RTA constraint. This article proposes a horizontal reference trajectory optimization algorithm based on the Particle Swarm Optimization technique in order to reduce fuel burn while fulfilling the RTA constraint. Results showed that for a flight without RTA constraint, up to 4% of fuel can be saved comparing against the trajectory of reference. The algorithm was normally able to meet the RTA constrain. However, aggressive RTA constraints might reduce the optimization levels of fuel compared with flights without RTA constraint.


2017 ◽  
Vol 14 (1) ◽  
pp. 161-176
Author(s):  
Maja Rosic ◽  
Mirjana Simic ◽  
Predrag Pejovic ◽  
Milan Bjelica

Determining an optimal emitting source location based on the time of arrival (TOA) measurements is one of the important problems in Wireless Sensor Networks (WSNs). The nonlinear least-squares (NLS) estimation technique is employed to obtain the location of an emitting source. This optimization problem has been formulated by the minimization of the sum of squared residuals between estimated and measured data as the objective function. This paper presents a hybridization of Genetic Algorithm (GA) for the determination of the global optimum solution with the local search Newton-Raphson (NR) method. The corresponding Cramer-Rao lower bound (CRLB) on the localization errors is derived, which gives a lower bound on the variance of any unbiased estimator. Simulation results under different signal-to-noise-ratio (SNR) conditions show that the proposed hybrid Genetic Algorithm-Newton-Raphson (GA-NR) improves the accuracy and efficiency of the optimal solution compared to the regular GA.


2016 ◽  
Vol 7 (5) ◽  
pp. 577-582 ◽  
Author(s):  
Anrieta Dudoit ◽  
Jonas Stankūnas

EUROCONTROL aims at improving the design and use of the European routes. Inefficiencies in the design of airspace and use of the air route network are considered to be a major causal factor of flight inefficiencies in Europe. The European ATM system is the sum total of a large number of separate Air Navigation Service Providers (ANSP) whereas the US system is operated by a single ANSP. Airspace fragmentation following National Borders makes flight routes inefficient due to non requested air routes, flight time, excessive fuel burn, CO and NOx emissions. That is the reason why airspace and the fixed route network should be reorganised to satisfy airspace operator needs and maintain required safety levels.The focus of the paper is to show the differences between planned flights and actual trajectories in terms of flight distance, duration and fuel burn. In connection with this, an overview of these indicators in Europe and the USA was made. EUROCONTROL siekia pagerinti Europos maršrutų planus ir jų naudojimą. Neefektyvus oro erdvės planų ir oro maršrutinio tinklo naudojimas laikomas viena pagrindinių Europos skrydžių neefektyvumo priežasčių. Europos oro eismo valdymo (angl. ATM) sistema sudaryta iš daugelio atskirų oro navigacijos paslaugų teikėjų (angl. ANSP), o JAV sistema valdoma vieno oro navigacijos paslaugų teikėjo. Oro erdvės susiskirstymas pagal valstybių ribas daro skrydžio maršrutus neefektyvius dėl nepareikalautų oro maršrutų, skrydžio laiko, per didelio kuro sunaudojimo, CO ir NOx išsiskyrimo. Štai kodėl reikėtų pertvarkyti oro erdvę ir fiksuotų maršrutų tinklą, norint patenkinti oro erdvės operatorių poreikius ir išlaikyti reikalingą saugumo lygį. Šio straipsnio tikslas – parodyti skirtumus tarp suplanuotų skrydžių ir realių trajektorijų, įvertinant skrydžio atstumą, trukmę ir kuro sunaudojimą. Be to, buvo padaryta šių rodiklių apžvalga Europos ir JAV mastu.


2021 ◽  
Author(s):  
Ryoji Onagawa ◽  
Kazutoshi Kudo

Abstract In goal-directed behavior, individuals are often required to plan and execute a movement with multiple competing reach targets simultaneously. The time constraint assigned to the target is an important factor that affect the initial movement planning, but the adjustments made to the starting behavior considering the time constraints specific to each target have not yet been clarified. The current study examined how humans adjusted their motor planning for double potential targets with independent time constraints under a go-before-you-know situation. The results revealed that the initial movements were modulated depending on the time constraints for potential targets. However, under tight time constraints, the performance in the double-target condition was lower than the single-target condition, which was a control condition implemented to estimate performance when one target is ignored. These results indicate that the initial movement for multiple potential targets with independent time constraints can be modified, but the planning is suboptimal.


2018 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Thais Spiegel ◽  
Ana Carolina P V Silva

In the study of decision-making, the classical view of behavioral appropriateness or rationality was challenged by neuro and psychological reasons. The “bounded rationality” theory proposed that cognitive limitations lead decision-makers to construct simplified models for dealing with the world. Doctors' decisions, for example, are made under uncertain conditions, as without knowing precisely whether a diagnosis is correct or whether a treatment will actually cure a patient, and often under time constraints. Using cognitive heuristics are neither good nor bad per se, if applied in situations to which they have been adapted to be helpful. Therefore, this text contextualizes the human decision-making perspective to find descriptions that adhere more closely to the human decision-making process. Then, based on a literature review of cognition during decision-making, particularly in healthcare context, it addresses a model that identifies the roles of attention, categorization, memory, emotion, and their inter-relations, during the decision-making process.


2020 ◽  
Vol 92 (7) ◽  
pp. 1063-1072
Author(s):  
Muhaned Gilani ◽  
Durmuş Sinan Körpe

Purpose This paper aims to minimize aircraft fuel consumption during the cruise phase when the flight is subjected to a specific time of arrival for different weights and distances. Design/methodology/approach The approach adopted herein uses sequential quadratic programming algorithm from MATLAB optimization toolbox, which includes a mathematical model of a jet airliner based on the Base of Aircraft Data as a function evaluator, to find out the impact of meet-time of arrival constraints on fuel consumption. The cruising speeds at predefined segments and the altitude are defined as the design variables. Findings The algorithm determines the optimum cruise altitudes and speeds for minimum fuel consumption in the case of no time constraints, also, for different time constraints where the flight time shall be reduced by increasing speed and lowering the altitude in most of the investigated cases. Practical implications The algorithm computes the optimum speed and the altitude according to different flight scenarios with the meet-time of arrival constraints for minimum fuel consumption which affects the direct operating cost of the flight. The algorithm might greatly help in decision-making for the meet-time of arrival operations. Originality/value Developing an algorithm to optimize the speed and the altitude of an aircraft based on weight and range for minimization of fuel consumption. It is a pioneer study in the literature that deals with the effect of meet-time constraints on fuel consumption.


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