Analyzing commercial aircraft fuel consumption during descent: A case study using an improved K-means clustering algorithm

Qing, Zhu; Pei, Jun; Liu, Xinbao; Zhou, Zhiping

doi:10.1016/j.jclepro.2019.02.235

Cited by 30 publications

(11 citation statements)

References 38 publications

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“…Prats et al [23] presented a non-linear multi-objective optimal control methodology to design noise abatement procedures aimed at reducing the global annoyance perceived by the population living around the airports. Zhu et al [24] generated the optimal descent trajectory based on the verification that there existed a certain relationship between fuel consumption and flight altitude as well as between aircraft weight and vacuum speed during descent. Rodríguez-Díaz et al [25] developed a multi-objective optimization model under the Constrained Position Shifting (CPS) constraint to minimize noise pollution, fuel consumption, and delays.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Xie

et al. 2021

Sustainability

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Investigating potential ways to improve fuel efficiency of aircraft operations is crucial for the development of the global air traffic management (ATM) performance target. The implementation of trajectory-based operations (TBOs) will play a major role in enhancing the predictability of air traffic and flight efficiency. TBO also provides new means for aircraft to save energy and reduce emissions. By comprehensively considering aircraft dynamics, available route limitations, sector capacity constraints, and air traffic control restrictions on altitude and speed, a “runway-to-runway” four-dimensional trajectory multi-objective planning method under loose-to-tight heterogeneous constraints is proposed in this paper. Taking the Shanghai–Beijing city pair as an example, the upper bounds of the Pareto front describing potential fuel consumption reduction under the influence of flight time were determined under different airspace rigidities, such as different ideal and realistic operating environments, as well as fixed and optional routes. In the congestion-free scenario with fixed route, the upper bounds on fuel consumption reduction range from 3.36% to 13.38% under different benchmarks. In the capacity-constrained scenario, the trade-off solutions of trajectory optimization are compressed due to limited available entry time slots of congested sectors. The results show that more flexible route options improve fuel-saving potentials up to 8.99%. In addition, the sensitivity analysis further illustrated the pattern of how optimal solutions evolved with congested locations and severity. The outcome of this paper would provide a preliminary framework for predicting and evaluating fuel efficiency improvement potentials in TBOs, which is meaningful for setting performance targets of green ATM systems.

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Section: Introductionmentioning

confidence: 99%

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

Liu

Xie

et al. 2021

Sustainability

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“…Clustering is a common exploratory tool for pattern recognition in large samples in various fields of science, such as electro-electronics [35], medicine [36], cleaner-production research [37], management [38], and ecology [39]. According to Kaufmann and Rousseeuw [40], the choice of a clustering algorithm depends both on the type of data available and on the particular purpose to which they are put.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Dependence of GHG Emissions on the Support and Taxes in the EU Countries

2021

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The reduction of GHG emissions is one of the priorities of the EU countries. The majority of studies show that financial support and environmental taxes are one of the most effective measures for the mitigation of the negative consequences of climate change. The EU countries employ different environmental support measures and environmental taxes to reduce GHG emissions. There is a shortage of new studies on these measures. The aim of the present study is to compare the effectiveness of the environmental support measures of the EU countries with the effectiveness of environmental taxes in relation to the reduction of GHG emissions. This study is characterized by the broad scope of its data analysis and its systematic approach to the EU’s environmental policy measures. An empirical study was performed for the EU countries with the aim of addressing this research problem and substantiating theoretical insights. A total of 27 EU member states from 2009 to 2018 were selected as research samples. The research is based on a cause-and-effect relationship, where the factors affecting environmental pollution (environmental taxes and subsidies) are the cause, and GHG emissions are the effect. Statistical research methods were used in the empirical study: descriptive statistics, the Shapiro–Wilk test, one-way analysis of variance (ANOVA), simple regression and cluster analysis. The results show that the older member countries of the EU, which had directed the financial measures of environmental policy towards a reduction in energy consumption, managed to achieve a greater reduction in GHG emissions compared to the countries which had not applied those measures. The Central and Eastern European countries are characterized by lower environmental taxes and lower expenditure allocated to environmental protection. The countries with a higher GDP per capita have greater GHG emissions that the countries with lower GDP per capita. This is associated with greater consumption, waste, and energy consumption. The study conducted gives rise to a discussion regarding data sufficiency in the assessment and forecasting of GHG emissions and their environmental consequences.

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“…Clustering included in the category of unsupervised learning, whose goal is to partition the data that does not have a label into the same group. Data belonging to the same cluster will be close to each other and will be far from the data about different groups [3]. Various distance criteria can be used to evaluate how close the data is.…”

Section: A Clusteringmentioning

confidence: 99%

Application of K-Means Algorithm for Costumer Grouping

Nugraha¹

2020

IJCTE

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Sales fluctuations are risks that must be faced by business people, PT. Gunung Hijau Success experienced this in 2016, with GAP being quite high. By giving rewards to loyal customers, it is expected to stabilize sales in the next period. So the company needs customer grouping based on customer loyalty to reward. The application of data mining can be used as an analysis to determine the loyal customer inventory according to the total purchase. In the data mining method, the clustering algorithm is one of the most popular to use where the data belonging to the same cluster will be close to each other and will be far from the data about another cluster. The results obtained in the form of customer information with criteria not loyal, loyal, and very loyal based on sales data in 2016. Also, customer criteria information from the clustering process can be used as a reference to determine the reward for customers.

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Analyzing commercial aircraft fuel consumption during descent: A case study using an improved K-means clustering algorithm

Cited by 30 publications

References 38 publications

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

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

Assessment of the Dependence of GHG Emissions on the Support and Taxes in the EU Countries

Application of K-Means Algorithm for Costumer Grouping

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