Predicting Fuel Consumption Reduction Potentials Based on 4D Trajectory Optimization with Heterogeneous Constraints

Liu, Fangzi; Li, Zihong; Xie, Hua; Yang, Lei; Hu, Minghua

doi:10.3390/su13137043

Cited by 6 publications

(6 citation statements)

References 42 publications

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“…With the simplification of the problem, the search space of the decision variables becomes smaller, and both four algorithms can meet the searching needs well. The solution results and the running time of NSGA2 and NSGA3 in Scenario2 are close to the performance shown in experiments by other scholars [39], [44].…”

Section: ) Scenario2: Multi-objective Optimization For Single Aircraf...supporting

confidence: 84%

“…The index of IGD [44] reflects the degree of population convergence and the diversity of solutions in the population. The smaller the IGD, the more single type of solutions in the population.…”

Section: B Analysis Of Algorithm 1) Analysis Of Algorithm Solution Pe...mentioning

confidence: 99%

“…Finally, the index of MPFE [44], [45] is used to reflect the distance of the non-dominated solution set to the Pareto frontier. Usually, the smaller the MPFE is, the better the nondominated solution is.…”

Section: B Analysis Of Algorithm 1) Analysis Of Algorithm Solution Pe...mentioning

confidence: 99%

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Multiobjective Four-Dimensional Trajectory Synergetic Optimization Based on Congestion Prediction and NSGA3-SA

et al. 2022

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Synergetic trajectory planning of flights is one of the important goals of trajectory-based operation (TBO), and it is also a method to further improve the utilization of airspace resources with the increasing number of flights in recent years. In order to plan the four-dimensional trajectory (4DT) pretactically and comprehensively, match the flight traffic with airspace capacity, reduce congestion, potential conflicts, and fuel consumption thus improving the efficiency of flights, this paper conducts a method for synergetic trajectory planning in the en-route phase from the perspective of airlines. Firstly, the aircraft performance model, aircraft fuel consumption model, and atmospheric model are constructed according to the base of aircraft data (BADA3.11), and an airspace congestion prediction model is constructed based on the historical flow data of airspace. Secondly, a multi-objective synergetic trajectory planning model is established, and a solution method based on the non-dominated sorting genetic algorithm and simulated annealing algorithm (NSGA3-SA) for the problem of synergetic trajectory planning is designed. The simulation shows that the optimization model and the solution algorithm of NSGA3-SA can reduce fuel consumption by about 4.5% compared to the original flight plans and has a good effect on reducing congestion and avoiding conflicts. The running time of the NSGA3-SA can meet the operational requirements of the pre-tactical trajectory planning. The multi-objective optimization model and the solution algorithm proposed in this paper have great value for the research of flight plan optimization.

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Section: ) Scenario2: Multi-objective Optimization For Single Aircraf...supporting

confidence: 84%

Section: B Analysis Of Algorithm 1) Analysis Of Algorithm Solution Pe...mentioning

confidence: 99%

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Multiobjective Four-Dimensional Trajectory Synergetic Optimization Based on Congestion Prediction and NSGA3-SA

et al. 2022

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“…Mendoza et al (2020) proposed a model that minimizes flight costs and fuel consumption and provides an alternative route using the Floyd–Warshall methodology. Liu et al (2021) explored potential ways to increase the fuel efficiency of flight operations. The purposeful planning method discusses airspace dynamics and ATC factors.…”

Section: Literature Reviewmentioning

confidence: 99%

An algorithmic application with flexible airspace approach

Kökhan

Gökdalay

2023

AEAT

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Purpose The purpose of this study is to develop an operational level decision support system model for air traffic controllers (ATCos) within the framework of the Flexible Use of Airspace (FUA) concept to enable more efficient use of airspace capacity. This study produces a systematic solution to the route selection process so that the ATCo can determine the most efficient route with an operational decision support system model using Dijkstra’s Shortest Path Algorithm. Design/methodology/approach In this study, a new decision support system model for ATCos in decision-making positions was recommended and used. ATCos use this model as a main model for determining the shortest and safest route for aircraft as an operational-level decision support system. Dijkstra Algorithm, used in the model, is defined step by step and then explained with the pseudocode. Findings It has been determined that when the FUA concept and DSS are used while the ATCo chooses a route, significant fuel, time and capacity savings are achieved in flight operations. Emissions resulting from the negative environmental effects of air transportation are reduced, and significant capacity increase can be achieved. The operational level decision support system developed in the study was tested with 55 scenarios on the Ankara–Izmir flight route compared to the existing fixed route. The results for the proposed most efficient route were achieved at 11.22% distance (nm), 9.36%-time (min) savings and 837.71 kg CO2 emission savings. Originality/value As far as the literature is reviewed, most studies aimed at increasing airspace efficiency produce solutions that try to improve rather than replace the normal process. Considering the literature positioning of this study compared to other studies, the proposed model provides a new systematic solution to the problems that cause human-induced route inefficiency within the framework of the FUA concept.

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“…A comprehensive literature review (see for example, [2,3,[11][12][13][14][15] from several academic publications and websites pertinent to factors that affect fuel consumption and reduction of carbon pollution in aircrafts was undertaken. Recent research [12] has confirmed that fuel is the highest cost component in aviation operations, even higher than labor costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Affecting the Rate of Fuel Consumption in Aircrafts

2021

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The cost of fuel and its availability are among the most major concerns for aircrafts and the aviation industry overall. Environmental difficulties with chemical pollutant emissions emitted by aviation machines are also connected to fuel consumption. As a result, it is crucial to examine factors that affect the overall fuel usage and consumption in the airport-based aviation industry. Several variables were investigated related to the total fuel consumed, such as dry operating weight (DOW) (KG), zero-fuel weight (ZFW), take-off weight (TOW), air distance (AIR DIST) (KM), and ground distance (GDN DIST). Analysis of the correlation between total fuel consumed as well as the extra fuel and selected variables was conducted. The results showed that the most positively associated factors with the total used fuel were the air distance (r2 = 0.86, p < 0.01), ground distance (r2 = 0.78, p < 0.01), TOW (r2 = 0.68, p < 0.01), and flight time (r2 = 0.68, p < 0.01). There was also a strong positive association between the average fuel flow (FF) and actual TOW (r2 = 0.74, p < 0.01) as well as ZFW (r2 = 0.61, p < 0.01). The generalized linear model (GLM) was utilized to assess the predictions of total energy usage after evaluating important outliers, stability of the homogeneity of variance, and the normalization of the parameter estimation. The results of multiple linear regression revealed that the most significant predictors of the total consumed fuel were the actual ZFW (p < 0.01), actual TOW (p < 0.01), and actual average FF (p < 0.05). The results interestingly confirmed that wind speed has some consequences and effects on arrival fuel usage. The result reflects that thermal and hydrodynamic economies impact on the flying fuel economy. The research has various implications for both scholars and practitioners of aviation industry.

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Predicting Fuel Consumption Reduction Potentials Based on 4D Trajectory Optimization with Heterogeneous Constraints

Cited by 6 publications

References 42 publications

Multiobjective Four-Dimensional Trajectory Synergetic Optimization Based on Congestion Prediction and NSGA3-SA

Multiobjective Four-Dimensional Trajectory Synergetic Optimization Based on Congestion Prediction and NSGA3-SA

An algorithmic application with flexible airspace approach

Factors Affecting the Rate of Fuel Consumption in Aircrafts

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