Research

16 pages, 8436 KiB

Open AccessArticle

Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation

by Kai Qu, Guhao Zhao, Yarong Wu and Liang Tong

Appl. Sci. 2023, 13(11), 6493; https://doi.org/10.3390/app13116493 - 26 May 2023

Viewed by 885

With the continuous development of general aviation, the contradiction between the air demand of general aviation low-altitude airspace and civil aviation routes is sharp. The difficulty of airspace planning is complex and changeable, and the existing working mode of simply using computer mapping [...] Read more.

With the continuous development of general aviation, the contradiction between the air demand of general aviation low-altitude airspace and civil aviation routes is sharp. The difficulty of airspace planning is complex and changeable, and the existing working mode of simply using computer mapping and manually finding airspace conflict contradictions can no longer meet the large-scale air use demand. In response to the existing spatial representation model of longitude and latitude grid, which has large grid deformation in high latitude areas, and the problem of slow computation speed of the conflict detection (CD) algorithm that determines whether the airspace boundary coordinates overlap, we propose a grid model that represents airspace with a spherical rhombic discrete grid of positive icosahedron and design a matrix-based digital representation method of airspace, which uses matrix product operation. The matrix product operation is used to quickly determine whether there is a conflict between airspace and airspace and between airspace and routes. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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17 pages, 5131 KiB

Open AccessArticle

A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks

by Hui Ding, Minghua Hu, Qiucheng Xu, Yungang Tian and Jianan Yin

Appl. Sci. 2023, 13(2), 866; https://doi.org/10.3390/app13020866 - 08 Jan 2023

Cited by 2 | Viewed by 1560

Abstract

Air transportation is a huge, complex system with emergence and self-organization, which makes it important for it to be modelled. In this paper, to model the air transportation system with more accuracy, from physical facilities to traffic applications, three-layer networks, including the air [...] Read more.

Air transportation is a huge, complex system with emergence and self-organization, which makes it important for it to be modelled. In this paper, to model the air transportation system with more accuracy, from physical facilities to traffic applications, three-layer networks, including the air route network, the city-pair airline network, and flight operation network, are built, where the air route network is regarded as the physical layer, and city-pair airline network and flight operation network are the application layers. Furthermore, a powerful tool, complex network theory, is applied to discuss the topology characteristics of the three-layer networks. Moreover, considering the path diversity of city-pair airlines, a simulated annealing-based framework is proposed to optimize the routing paths on an air route network for each city-pair airline, such that the traffic congestion of the air route network can be alleviated, where an elaborated method for perturbing solutions, named Selection after Remove (SAR), is adopted. Experimental results show that, compared with the default routing paths, the shortest routing paths, and the random routing paths, the proposed routing-path optimization strategy can reduce the maximum traffic flows of the air route network by 2.4%, 4.6%, and 4.8%, respectively, which shows that the proposed optimization method performs well in alleviating the traffic congestion of the air route network. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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21 pages, 5175 KiB

Open AccessArticle

Quantitative Assessment of Dynamic Stability Characteristics for Jet Transport in Sudden Plunging Motion

by Yonghu Wang, Ran Zheng, Fujun Zheng, Jinglong Hao, Xinyu Huang and Juan Zhao

Appl. Sci. 2022, 12(21), 10920; https://doi.org/10.3390/app122110920 - 27 Oct 2022

Viewed by 1161

Abstract

In this paper, we present a monitoring program of loss control prevention for airlines to enhance aviation safety and operational efficiency. The assessments of dynamic stability characteristics based on the approaches of oscillatory motion and eigenvalue motion modes for jet transport aircraft response [...] Read more.

In this paper, we present a monitoring program of loss control prevention for airlines to enhance aviation safety and operational efficiency. The assessments of dynamic stability characteristics based on the approaches of oscillatory motion and eigenvalue motion modes for jet transport aircraft response to sudden plunging motions are demonstrated. A twin-jet transport aircraft encountering severe clear-air turbulence in transonic flight during the descending phase was examined as the study case. The flight results in sudden plunging motions with abrupt changes in attitude and gravitational acceleration (i.e., the normal load factor) are provided. Development of the required thrust and aerodynamic models with the flight data mining and the fuzzy logic modeling techniques was carried out. The oscillatory derivatives extracted from these aerodynamic models were then used in the study of variations in stability characteristics during the sudden plunging motion. The fuzzy logic aerodynamic models were utilized to estimate the nonlinear unsteady aerodynamics while performing numerical integration of flight dynamic equations. The eigenvalues of all motion modes were obtained during time integration. The positive real part of the eigenvalues is to indicate unstable motion. The dynamic stability characteristics during sudden plunging motion are easily judged by the values in positive or negative. The present quantitative assessment method is an innovation to examine possible mitigation concepts of accident prevention and promote the understanding of aerodynamic responses of the jet transport aircraft. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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20 pages, 5821 KiB

Open AccessArticle

Prediction of Flight Delays at Beijing Capital International Airport Based on Ensemble Methods

by Xunuo Wang, Zhan Wang, Lili Wan and Yong Tian

Appl. Sci. 2022, 12(20), 10621; https://doi.org/10.3390/app122010621 - 20 Oct 2022

Cited by 3 | Viewed by 1833

Abstract

Predicting flight delays plays a critical role in reducing financial losses and increasing passenger satisfaction. Due to their ability to combine multiple algorithms, ensemble methods have demonstrated strong predictive performance in many research fields. In this paper, ensemble methods are adopted to predict [...] Read more.

Predicting flight delays plays a critical role in reducing financial losses and increasing passenger satisfaction. Due to their ability to combine multiple algorithms, ensemble methods have demonstrated strong predictive performance in many research fields. In this paper, ensemble methods are adopted to predict flight delays. First, based on the current studies, two novel explanatory variables, named arrival/departure pressure and cruise pressure, are proposed as factors affecting flight delays. Second, we introduce the ensemble methods and select representative algorithms for the prediction problem. In addition to the ensemble methods, classical algorithms are also used to predict flight delays. Finally, the actual operational data of Beijing Capital International Airport were utilized to conduct a case study. The results show that the stacking method has better prediction performance than other baseline methods. The mean absolute error (MAE) of the stacking method was about 12.58 min on the test dataset. Furthermore, we tested the effect of the two explanatory variables proposed in this paper, and the results show that the MAE was reduced by about 20% by using the stacking method. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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25 pages, 7199 KiB

Open AccessArticle

An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality

by Xianghua Tan, Yan Liu, Dandan Liu, Dan Zhu, Weili Zeng and Huawei Wang

Appl. Sci. 2022, 12(20), 10433; https://doi.org/10.3390/app122010433 - 16 Oct 2022

Cited by 5 | Viewed by 1258

Abstract

The airport network is a highly dynamic and complex network connected by air routes, and it is difficult to study the impact of delays at one airport on another airport by means of human intervention. Due to the delay propagation law contained in [...] Read more.

The airport network is a highly dynamic and complex network connected by air routes, and it is difficult to study the impact of delays at one airport on another airport by means of human intervention. Due to the delay propagation law contained in the delay time series, some studies have used Granger causality and transfer entropy to explore whether there is a causal relationship between any two airports. However, no research has yet established a delay causal network from the perspective of the airport network as a whole. To this end, an attention mechanism is introduced into the deep convolutional network architecture, and a deep temporal convolution prediction model considering the attention mechanism is proposed, so as to establish the relationship between different airport delay time series under the same network architecture. According to the attention factor score, the delay propagation causality between airports is preliminarily screened, and the direct causality is verified based on a t-test and propagation delay analysis. Taking China’s civil airport network as an example, the method proposed in this paper can not only discover the causal relationship of delays between airports but also characterize the strength of the relationship. Further analysis found that each airport is affected by an average of six airports, and airports with small delays are more likely to be affected by other airports. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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20 pages, 3126 KiB

Open AccessArticle

Conflict Risk Assessment between Non-Cooperative Drones and Manned Aircraft in Airport Terminal Areas

by Renwei Zhu, Zhao Yang and Jun Chen

Appl. Sci. 2022, 12(20), 10377; https://doi.org/10.3390/app122010377 - 14 Oct 2022

Cited by 2 | Viewed by 1282

Abstract

Recent years have seen an increase in events of drone incursion into airport terminal areas, leading to safety concerns and disruptions to airline operations. It is of great importance to identify the potential conflict, especially for those non-cooperative drones, as their intentions are [...] Read more.

Recent years have seen an increase in events of drone incursion into airport terminal areas, leading to safety concerns and disruptions to airline operations. It is of great importance to identify the potential conflict, especially for those non-cooperative drones, as their intentions are always unknown. For the safe operation of air traffic, this paper proposes a conflict risk assessment method between non-cooperative drones and manned aircraft in the terminal area. First, the trajectory data of manned aircraft and drones are obtained. Real-time cylindrical protection zones are established around manned aircraft according to the separation interval for safe operation between the drone and the manned aircraft at different altitudes. Secondly, trajectory predictions for the manned aircraft and the drone are conducted, respectively. A quartile regression bidirectional gate recurrent unit neural network is proposed in this research for the trajectory prediction of the drones. The model integrates the bidirectional gated recurrent unit structure and the quartile regression structure. The performance indicators confirm the superiority of the proposed model. Based on the trajectory prediction results, it is then determined whether there is a conflict risk between the drone and manned aircraft by comparing the position distribution of the drone as well as the real-time cylindrical protection zone of the manned aircraft. The conflict probability between the drone and the manned aircraft is then calculated. The prediction accuracy of conflict probability is estimated by Monte Carlo simulation methods. The collision probability prediction accuracy of manned aircraft and drones at different flight stages and altitudes ranges from 73% to 97%, which shows the reliability of the proposed method. Finally, the collision probability between the drone and the manned aircraft at the closest encountering point and the estimated time to reach the closest encountering point are calculated. This paper predicts the conflict risk between the drone and manned aircraft, thus providing theoretical support for the safe operation of air transport in low-altitude environments. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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19 pages, 5062 KiB

Open AccessArticle

A High-Precision Method for Evaluating Sector Capacity in Bad Weather Based on an Improved WITI Model

by Shiyu Huang, Lin Xu, Yuzhi Zhou, Yujie Qiao and Zhiyuan Shen

Appl. Sci. 2022, 12(19), 10114; https://doi.org/10.3390/app121910114 - 08 Oct 2022

Viewed by 1010

Abstract

The rapid development of the civil aviation industry has increased the pressure on airspace resources in China. The traditional sector capacity assessment method does not take into account the impact of bad weather, resulting in flight plans often deviating markedly from the predicted [...] Read more.

The rapid development of the civil aviation industry has increased the pressure on airspace resources in China. The traditional sector capacity assessment method does not take into account the impact of bad weather, resulting in flight plans often deviating markedly from the predicted plans, causing flight delays and affecting the punctuality rate of flights. To solve this issue, we propose a novel evaluation method based on an improved Weather-Impacted Traffic Index (WITI) model to calculate sector capacity. The WITI model is optimized in order to calculate the weather-influence coefficients under different types of bad weather. These coefficients were also considered in a controller workload model. Finally, the model was trained using a deep-neural-network algorithm, which is combined with a linear regression algorithm to calculate sector capacity under different bad weather conditions. The novel approach leads to the output results being within a specified error range, which greatly improves their accuracy. This method was applied to the actual case data of Yinchuan Hedong International Airport to consider different types of bad weather and quantify their severity, which more specifically assesses the sector capacity under the condition of bad weather. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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15 pages, 3669 KiB

Open AccessArticle

Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model

by Jinghan Du, Minghua Hu, Jianan Yin and Weining Zhang

Appl. Sci. 2022, 12(19), 9849; https://doi.org/10.3390/app12199849 - 30 Sep 2022

Cited by 1 | Viewed by 1593

Abstract

Gate allocation has always been a fundamental but critical issue in the daily operation of airports, which is related to service quality and schedule efficiency. In order to obtain reasonable and efficient gate allocation results, in this paper, a multi-commodity network flow model [...] Read more.

Gate allocation has always been a fundamental but critical issue in the daily operation of airports, which is related to service quality and schedule efficiency. In order to obtain reasonable and efficient gate allocation results, in this paper, a multi-commodity network flow model is proposed to describe the gate allocation process in flight flow, based on which a multi-objective optimization model is constructed. It not only comprehensively considers the flight information of aircraft arrivals and departures, but also integrates the broader interests of passengers, airlines, and airports. To solve it, a linear weighting technique is applied. In addition, K-means cluster analysis is used to explore different weight combinations, and on this basis, the idle time of the gate is introduced as a performance evaluation index to guide the selection of the final weight. By analyzing the optimization results of actual operation data, the proposed model significantly balances the interests of multiple parties and the number of flights at each gate and has a relatively high gate-utilization rate. It can provide rich decision support and a reasonable allocation scheme for airport management to a certain extent. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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16 pages, 18614 KiB

Open AccessArticle

Root Cause Failure Analysis of Deep-Groove Ball Bearing Used in a Governor

by Xueqin Hou, Yujian Liu, Tianyu Li, Changkui Liu, Zheng Zhang and Chunhu Tao

Appl. Sci. 2022, 12(19), 9658; https://doi.org/10.3390/app12199658 - 26 Sep 2022

Cited by 4 | Viewed by 2652

Abstract

Premature failure of a deep-groove ball bearing used in an aeroengine governor took place during service. In this paper, the failure mode and root cause of the bearing were studied by macroscopic and microscopic examination, metallographic analysis, hardness test, calculations of contact stress [...] Read more.

Premature failure of a deep-groove ball bearing used in an aeroengine governor took place during service. In this paper, the failure mode and root cause of the bearing were studied by macroscopic and microscopic examination, metallographic analysis, hardness test, calculations of contact stress and L₁₀ life, flatness measurement and comparative experiment. The results show that the failure modes of the inner ring raceway and steel balls are contact fatigue spalling, the failure modes of the outer ring raceway are wear and contact fatigue spalling, and the failure mode of the cage is fatigue fracture. The root cause and direct cause of the bearing failure were the unqualified machining process of the spring end face and the high unbalanced axial load, respectively. The unqualified machining process induced high points of the spring end face, which caused misalignment of the outer ring and inner ring and thereby resulted in the high unbalanced axial load. The characteristic damages induced by high axial load were climbing with the morphology of metal extrusion and accumulation at the border of the raceway for the inner and outer ring, and multiple fatigue fractures with the characteristic of multi origins for the cage. The unqualified machining process can be prevented by adopting the refined grinding process and adding detection requirements of flatness. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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12 pages, 2339 KiB

Open AccessArticle

Helicopter Takeoff and Landing Point Location in Cities for Emergency Services

by Bin Hu, Xingyuan Chen and Songchen Han

Appl. Sci. 2022, 12(19), 9570; https://doi.org/10.3390/app12199570 - 23 Sep 2022

Viewed by 1227

Abstract

In low-altitude rescue, civilian helicopters, relying on their speed, efficiency and flexibility, play at center stage. Due to the terrain restrictions of the disaster area, it is difficult for helicopters to carry out safe and efficient rescue in cities. In order to facilitate [...] Read more.

In low-altitude rescue, civilian helicopters, relying on their speed, efficiency and flexibility, play at center stage. Due to the terrain restrictions of the disaster area, it is difficult for helicopters to carry out safe and efficient rescue in cities. In order to facilitate emergency rescue, fixed helicopter takeoff and landing points for rescue missions must be selected strategically and wisely. However, the traditional method is to analyze the satellite data and conduct field surveys manually, which is rather subjective. A scientific, simple and efficient method of location selection is in urgent need. This paper analyzes the normativeness of the location of the helicopter’s take-off and landing point and establishes a model of the slope and undulation of the landing site. It utilizes ArcGIS software to build layers and selects for the terrain element models that meet the specifications. It also studies the rescue radius commonly used in the world and then maximizes the location range of the take-off and landing point based on the greedy algorithm. Considering the construction cost, the final optimized site selection result is obtained. The results show that the use of GIS space technology can effectively select suitable take-off and landing points and gain valuable time for low-altitude rescue. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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21 pages, 4727 KiB

Open AccessArticle

Air Traffic Trajectory Operation Mode Mining Based on Clustering

by Xinmin Tang, Yusheng Liu and Kefan Chen

Appl. Sci. 2022, 12(12), 5944; https://doi.org/10.3390/app12125944 - 10 Jun 2022

Cited by 1 | Viewed by 1482

Abstract

When processing track sequences, it is time-consuming and difficult to separate clusters with substantial density variations to deal with the problem of classic clustering methods mining common flight patterns in airspace. To overcome these issues, this research proposes a clustering-based technique for mining [...] Read more.

When processing track sequences, it is time-consuming and difficult to separate clusters with substantial density variations to deal with the problem of classic clustering methods mining common flight patterns in airspace. To overcome these issues, this research proposes a clustering-based technique for mining air traffic trajectory operation patterns. The track data are first decoded and rebuilt using a motion-based track training approach; next, a compression based on a deep autoencoder (OFAE) is provided, allowing the model to deal with the high-dimensional trajectory vector containing derived information. The compressed trajectory data are made as compact and dense as feasible using the L21 norm constraint, which reduces the operation time and improves the performance of the clustering process. The compressed trajectory is then analyzed using a fast-clustering algorithm based on density peaks (DPCA). To save time, a more refined distance measurement technique is added into the model in order to achieve the usual aircraft operation mode in the terminal area. The accuracy of trajectory prediction can be improved by using the generated unitized and high-class similarity trajectory data. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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20 pages, 11354 KiB

Open AccessArticle

Time-Varying Pilot’s Intention Identification Based on ESAX-CSA-ELM Classification Method in Complex Environment

by Haibo Wang, Ting Pan, Haiqing Si, Hongjia Zhang, Lei Shang and Haibo Liu

Appl. Sci. 2022, 12(10), 4858; https://doi.org/10.3390/app12104858 - 11 May 2022

Viewed by 1440

Abstract

Dynamic and accurate identification of pilot intention is an important prerequisite for more accurate identification of control behavior, automatic flight early warning, and human–aircraft shared autonomy. Meanwhile, it is also the basic requirement of microscopic research on flight safety. In response to these [...] Read more.

Dynamic and accurate identification of pilot intention is an important prerequisite for more accurate identification of control behavior, automatic flight early warning, and human–aircraft shared autonomy. Meanwhile, it is also the basic requirement of microscopic research on flight safety. In response to these demands, the airfield traffic pattern flight simulation experiment was carried out to obtain the pilot’s physiological data, such as electrocardiogram, respiration, and skin electricity, under different intentions. The extended symbol aggregation approximation theory (ESAX) and the intelligent icon method were utilized to analyze and extract the characteristics of the pilot’s intention. Furthermore, combined with the crow search algorithm (CSA) and extreme learning machine (ELM), a CSA-ELM pilot intention identification model was constructed and it is applied to climb, descend, level flight, and other situations in airfield traffic pattern missions to effectively identify whether the pilot has an intention. The rationality and validity of the identification model were verified through experiments with interactive computer simulations. In addition, compared with the traditional machine learning method, the accuracy of the identification method proposed in this paper is improved by about 10%. The above shows that the research results in this paper can provide support for improving the flight safety early-warning system and the pilot’s micro-behavior evaluation system. Full article

(This article belongs to the Special Issue Analysis, Optimization, and Control of Air Traffic System)

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