Transactions of Nanjing University of Aeronautics & Astronautics
Volume 37,Issue 4,2020 Table of Contents
2020, 37(4):501-507.
DOI: 10.16356/j.1005-1120.2020.04.001
Abstract:
Air and space is one of the most intense fields of science and technology competition for powerful countries. This paper focuses on the competition to achieve mastery of air and space, and analyzes the impact of fast developing intelligent technologies from six basic contradictions of the war, including hiding and finding, understanding and confusion, network resilience and network degradation, hitting and intercepting, speed of action and decision-making, and shaping the perceptions of key crowd. On this basis, aiming at securing competitive advantage in the future, the development directions of intelligent technologies are proposed for the air and space competition.
Air and space is one of the most intense fields of science and technology competition for powerful countries. This paper focuses on the competition to achieve mastery of air and space, and analyzes the impact of fast developing intelligent technologies from six basic contradictions of the war, including hiding and finding, understanding and confusion, network resilience and network degradation, hitting and intercepting, speed of action and decision-making, and shaping the perceptions of key crowd. On this basis, aiming at securing competitive advantage in the future, the development directions of intelligent technologies are proposed for the air and space competition.
2020, 37(4):508-516.
DOI: 10.16356/j.1005-1120.2020.04.002
Abstract:
Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aero-engine. Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing, an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper. Through analyzing the weakness of convex sparse model, i.e. the tradeoff between noise reduction and feature reconstruction, this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction. Accordingly, a sparsity-assisted deep convolutional variational autoencoders network is proposed, which achieves the intelligent identification of fault state through training denoised normal data. Finally, the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment. The comparison results show that the proposed method is good at abnormal pattern recognition, showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.
Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aero-engine. Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing, an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper. Through analyzing the weakness of convex sparse model, i.e. the tradeoff between noise reduction and feature reconstruction, this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction. Accordingly, a sparsity-assisted deep convolutional variational autoencoders network is proposed, which achieves the intelligent identification of fault state through training denoised normal data. Finally, the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment. The comparison results show that the proposed method is good at abnormal pattern recognition, showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.
2020, 37(4):517-527.
DOI: 10.16356/j.1005-1120.2020.04.003
Abstract:
Inspired by eagle’s visual system, an eagle-vision-based object detection method for unmanned aerial vehicle (UAV) formation in hazy weather is proposed in this paper. To restore the hazy image, the values of atmospheric light and transmission are estimated on the basis of the signal processing mechanism of ON and OFF channels in eagle’s retina. Local features of the dehazed image are calculated according to the color antagonism mechanism and contrast sensitivity function of eagle’s visual system. A center-surround operation is performed to simulate the response of reception field. The final saliency map is generated by the Random Forest algorithm. Experimental results verify that the proposed method is capable to detect UAVs in hazy image and has superior performance over traditional methods.
Inspired by eagle’s visual system, an eagle-vision-based object detection method for unmanned aerial vehicle (UAV) formation in hazy weather is proposed in this paper. To restore the hazy image, the values of atmospheric light and transmission are estimated on the basis of the signal processing mechanism of ON and OFF channels in eagle’s retina. Local features of the dehazed image are calculated according to the color antagonism mechanism and contrast sensitivity function of eagle’s visual system. A center-surround operation is performed to simulate the response of reception field. The final saliency map is generated by the Random Forest algorithm. Experimental results verify that the proposed method is capable to detect UAVs in hazy image and has superior performance over traditional methods.
2020, 37(4):528-538.
DOI: 10.16356/j.1005-1120.2020.04.004
Abstract:
A decentralized task planning algorithm is proposed for heterogeneous unmanned aerial vehicle (UAV) swarm with different capabilities. The algorithm extends the consensus-based bundle algorithm (CBBA) to account for a more realistic and complex environment. The extension of the algorithm includes handling multi-agent task that requires multiple UAVs collaboratively completed in coordination, and consideration of avoiding obstacles in task scenarios. We propose a new consensus algorithm to solve the multi-agent task allocation problem and use the Dubins algorithm to design feasible paths for UAVs to avoid obstacles and consider motion constraints. Experimental results show that the CBBA extension algorithm can converge to a conflict-free and feasible solution for multi-agent task planning problems.
A decentralized task planning algorithm is proposed for heterogeneous unmanned aerial vehicle (UAV) swarm with different capabilities. The algorithm extends the consensus-based bundle algorithm (CBBA) to account for a more realistic and complex environment. The extension of the algorithm includes handling multi-agent task that requires multiple UAVs collaboratively completed in coordination, and consideration of avoiding obstacles in task scenarios. We propose a new consensus algorithm to solve the multi-agent task allocation problem and use the Dubins algorithm to design feasible paths for UAVs to avoid obstacles and consider motion constraints. Experimental results show that the CBBA extension algorithm can converge to a conflict-free and feasible solution for multi-agent task planning problems.
2020, 37(4):539-549.
DOI: 10.16356/j.1005-1120.2020.04.005
Abstract:
Images captured in rainy days suffer from noticeable degradation of scene visibility. Unmanned aerial vehicles (UAVs), as important outdoor image acquisition systems, demand a proper rain removal algorithm to improve visual perception quality of captured images as well as the performance of many subsequent computer vision applications. To deal with rain streaks of different sizes and directions, this paper proposes to employ convolutional kernels of different sizes in a multi-path structure. Split attention is leveraged to enable communication across multiscale paths at feature level, which allows adaptive receptive field to tackle complex situations. We incorporate the multi-path convolution and the split attention operation into the basic residual block without increasing the channels of feature maps. Moreover, every block in our network is unfolded four times to compress the network volume without sacrificing the deraining performance. The performance on various benchmark datasets demonstrates that our method outperforms state-of-the-art deraining algorithms in both numerical and qualitative comparisons.
Images captured in rainy days suffer from noticeable degradation of scene visibility. Unmanned aerial vehicles (UAVs), as important outdoor image acquisition systems, demand a proper rain removal algorithm to improve visual perception quality of captured images as well as the performance of many subsequent computer vision applications. To deal with rain streaks of different sizes and directions, this paper proposes to employ convolutional kernels of different sizes in a multi-path structure. Split attention is leveraged to enable communication across multiscale paths at feature level, which allows adaptive receptive field to tackle complex situations. We incorporate the multi-path convolution and the split attention operation into the basic residual block without increasing the channels of feature maps. Moreover, every block in our network is unfolded four times to compress the network volume without sacrificing the deraining performance. The performance on various benchmark datasets demonstrates that our method outperforms state-of-the-art deraining algorithms in both numerical and qualitative comparisons.
WANG Ershen
,
SONG Yuanshang
,
XU Song
,
GUO Jing
,
HONG Chen
,
QU Pingping
,
PANG Tao
,
ZHANG Jiantong
2020, 37(4):550-561.
DOI: 10.16356/j.1005-1120.2020.04.006
Abstract:
Due to the influence of terrain structure, meteorological conditions and various factors, there are anomalous data in automatic dependent surveillance-broadcast (ADS-B) message. The ADS-B equipment can be used for positioning of general aviation aircraft. Aim to acquire the accurate position information of aircraft and detect anomaly data, the ADS-B anomaly data detection model based on deep learning and difference of Gaussian （DoG） approach is proposed. First, according to the characteristic of ADS-B data, the ADS-B position data are transformed into the coordinate system. And the origin of the coordinate system is set up as the take-off point. Then, based on the kinematic principle, the ADS-B anomaly data can be removed. Moreover, the details of the ADS-B position data can be got by the DoG approach. Finally, the long short-term memory (LSTM) neural network is used to optimize the recurrent neural network (RNN) with severe gradient reduction for processing ADS-B data. The position data of ADS-B are reconstructed by the sequence to sequence (seq2seq) model which is composed of LSTM neural network, and the reconstruction error is used to detect the anomalous data. Based on the real flight data of general aviation aircraft, the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model, and its running time is reduced. Compared with the RNN, the accuracy of anomaly detection is increased by 2.7%. The performance of the proposed model is better than that of the traditional anomaly detection models.
Due to the influence of terrain structure, meteorological conditions and various factors, there are anomalous data in automatic dependent surveillance-broadcast (ADS-B) message. The ADS-B equipment can be used for positioning of general aviation aircraft. Aim to acquire the accurate position information of aircraft and detect anomaly data, the ADS-B anomaly data detection model based on deep learning and difference of Gaussian （DoG） approach is proposed. First, according to the characteristic of ADS-B data, the ADS-B position data are transformed into the coordinate system. And the origin of the coordinate system is set up as the take-off point. Then, based on the kinematic principle, the ADS-B anomaly data can be removed. Moreover, the details of the ADS-B position data can be got by the DoG approach. Finally, the long short-term memory (LSTM) neural network is used to optimize the recurrent neural network (RNN) with severe gradient reduction for processing ADS-B data. The position data of ADS-B are reconstructed by the sequence to sequence (seq2seq) model which is composed of LSTM neural network, and the reconstruction error is used to detect the anomalous data. Based on the real flight data of general aviation aircraft, the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model, and its running time is reduced. Compared with the RNN, the accuracy of anomaly detection is increased by 2.7%. The performance of the proposed model is better than that of the traditional anomaly detection models.
2020, 37(4):562-573.
DOI: 10.16356/j.1005-1120.2020.04.007
Abstract:
A novel deep neural network compression model for airport object detection has been presented. This novel model aims at disadvantages of deep neural network, i.e. the complexity of the model and the great cost of calculation. According to the requirement of airport object detection, the model obtains temporal and spatial semantic rules from the uncompressed model. These spatial semantic rules are added to the model after parameter compression to assist the detection. The rules can improve the accuracy of the detection model in order to make up for the loss caused by parameter compression. The experiments show that the effect of the novel compression detection model is no worse than that of the uncompressed original model. Even some of the original model false detection can be eliminated through the prior knowledge.
A novel deep neural network compression model for airport object detection has been presented. This novel model aims at disadvantages of deep neural network, i.e. the complexity of the model and the great cost of calculation. According to the requirement of airport object detection, the model obtains temporal and spatial semantic rules from the uncompressed model. These spatial semantic rules are added to the model after parameter compression to assist the detection. The rules can improve the accuracy of the detection model in order to make up for the loss caused by parameter compression. The experiments show that the effect of the novel compression detection model is no worse than that of the uncompressed original model. Even some of the original model false detection can be eliminated through the prior knowledge.
2020, 37(4):574-585.
DOI: 10.16356/j.1005-1120.2020.04.008
Abstract:
Anomalous trajectory detection and traffic flow classification for complicated airspace are of vital importance to safety and efficiency analysis. Some researchers employed density-based unsupervised machine learning method to exploit these trajectories related to air traffic control (ATC) actions. However, the quality of position data and the tiny density difference between traffic flows in the terminal area make it particularly challenging. To alleviate these two challenges, this paper proposes a novel framework which combines robust deep auto-encoder (RDAE) model and density peak (DP) clustering algorithm. Specifically, the RDAE model is utilized to reconstruct denoising trajectory and identify anomaly trajectories in the terminal area by two different regularizations. Then, the nonlinear components captured by the encoder of RDAE are input in the DP algorithm to classify the global traffic flows. An experiment on a terminal airspace at Guangzhou Baiyun Airport (ZGGG) with anomaly label shows that the proposed combination can automatically capture non-conventional spatiotemporal traffic patterns in the aircraft movement. The superiority of RDAE and combination are also demonstrated by visualizing and quantitatively evaluating the experimental results.
Anomalous trajectory detection and traffic flow classification for complicated airspace are of vital importance to safety and efficiency analysis. Some researchers employed density-based unsupervised machine learning method to exploit these trajectories related to air traffic control (ATC) actions. However, the quality of position data and the tiny density difference between traffic flows in the terminal area make it particularly challenging. To alleviate these two challenges, this paper proposes a novel framework which combines robust deep auto-encoder (RDAE) model and density peak (DP) clustering algorithm. Specifically, the RDAE model is utilized to reconstruct denoising trajectory and identify anomaly trajectories in the terminal area by two different regularizations. Then, the nonlinear components captured by the encoder of RDAE are input in the DP algorithm to classify the global traffic flows. An experiment on a terminal airspace at Guangzhou Baiyun Airport (ZGGG) with anomaly label shows that the proposed combination can automatically capture non-conventional spatiotemporal traffic patterns in the aircraft movement. The superiority of RDAE and combination are also demonstrated by visualizing and quantitatively evaluating the experimental results.
2020, 37(4):586-596.
DOI: 10.16356/j.1005-1120.2020.04.009
Abstract:
In the non-contact measurement using the linear structured light (LSL), the extraction precision of the light stripe center directly affects the measurement accuracy of the whole detection system. To solve the problem that general algorithms cannot accurately extract the center of the light stripe with the uneven width and unstable grey-value distribution, an adaptive optimization method is proposed. In this method, the stripe region is firstly segmented, and the widths of the laser stripe are calculated by boundary detection. The initial stripe center points are computed by the quadratic weighted grayscale centroid method based on the self-adaptive stripe width. After that, these center points are optimized according to the determined slope threshold. The sub-pixel coordinates of these center points are recalculated. Detailed analysis is also performed in line with the proposed evaluation index of the extraction algorithm. The experimental results show that the mean square error of extracted center points is only 0.1 pixel, meaning that the accuracy of laser stripe center extraction is improved significantly by the method. Furthermore, the method can run effectively at a relatively low computational time cost, and can demonstrate great robustness as well.
In the non-contact measurement using the linear structured light (LSL), the extraction precision of the light stripe center directly affects the measurement accuracy of the whole detection system. To solve the problem that general algorithms cannot accurately extract the center of the light stripe with the uneven width and unstable grey-value distribution, an adaptive optimization method is proposed. In this method, the stripe region is firstly segmented, and the widths of the laser stripe are calculated by boundary detection. The initial stripe center points are computed by the quadratic weighted grayscale centroid method based on the self-adaptive stripe width. After that, these center points are optimized according to the determined slope threshold. The sub-pixel coordinates of these center points are recalculated. Detailed analysis is also performed in line with the proposed evaluation index of the extraction algorithm. The experimental results show that the mean square error of extracted center points is only 0.1 pixel, meaning that the accuracy of laser stripe center extraction is improved significantly by the method. Furthermore, the method can run effectively at a relatively low computational time cost, and can demonstrate great robustness as well.
2020, 37(4):597-606.
DOI: 10.16356/j.1005-1120.2020.04.010
Abstract:
Focusing on controlling the press-assembly quality of high-precision servo mechanism, an intelligent early warning method based on outlier data detection and linear regression is proposed. Linear regression is used to deal with the relationship between assembly quality and press-assembly process, then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control. To preprocess the raw dataset of displacement-force in the press-assembly process, an improved local outlier factor based on area density and P weight ( LAOPW) is designed to eliminate the outliers which will result in inaccuracy of the mathematical model. A weighted distance based on information entropy is used to measure distance, and the reachable distance is replaced with P weight. Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor (LOF) algorithm, and the detection accuracy is improved by about 2% compared with the local outlier factor based on area density (LAOF) algorithm. The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.
Focusing on controlling the press-assembly quality of high-precision servo mechanism, an intelligent early warning method based on outlier data detection and linear regression is proposed. Linear regression is used to deal with the relationship between assembly quality and press-assembly process, then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control. To preprocess the raw dataset of displacement-force in the press-assembly process, an improved local outlier factor based on area density and P weight ( LAOPW) is designed to eliminate the outliers which will result in inaccuracy of the mathematical model. A weighted distance based on information entropy is used to measure distance, and the reachable distance is replaced with P weight. Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor (LOF) algorithm, and the detection accuracy is improved by about 2% compared with the local outlier factor based on area density (LAOF) algorithm. The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.
2020, 37(4):607-614.
DOI: 10.16356/j.1005-1120.2020.04.011
Abstract:
To adjust the samples of filtering adaptively, an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence (KLD)-sampling (KLGPF) is proposed in this paper. During the process of sampling, the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function. KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly. Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly. Compared with the particle filter algorithm using KLD-sampling (KLPF), the speed of KLGPF increases by 28% under the same conditions.
To adjust the samples of filtering adaptively, an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence (KLD)-sampling (KLGPF) is proposed in this paper. During the process of sampling, the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function. KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly. Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly. Compared with the particle filter algorithm using KLD-sampling (KLPF), the speed of KLGPF increases by 28% under the same conditions.
2020, 37(4):615-629.
DOI: 10.16356/j.1005-1120.2020.04.012
Abstract:
Air traffic controllers face challenging initiatives due to uncertainty in air traffic. One way to support their initiatives is to identify similar operation scenes. Based on the operation characteristics of typical busy area control airspace, an complexity measurement indicator system is established. We find that operation in area sector is characterized by aggregation and continuity, and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components. Using principle components, discrete features and time series features are constructed. Based on Gaussian kernel function, Euclidean distance and dynamic time warping (DTW) are used to measure the similarity of the features. Then the matrices of similarity are input in Spectral Clustering. The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system. Finally, actual vertical operation decisions for area sector and results of identification are compared, which are visualized by metric multidimensional scaling (MDS) plots. We find that identification results can well reflect the operation at peak hours, but controllers make different decisions under the similar conditions before dawn. The compliance rate of busy operation mode and division decisions at peak hours is 96.7%. The results also show subjectivity of actual operation and objectivity of identification. In most scenes, we observe that similar air traffic activities provide regularity for initiatives, validating the potential of this approach for initiatives and other artificial intelligence support.
Air traffic controllers face challenging initiatives due to uncertainty in air traffic. One way to support their initiatives is to identify similar operation scenes. Based on the operation characteristics of typical busy area control airspace, an complexity measurement indicator system is established. We find that operation in area sector is characterized by aggregation and continuity, and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components. Using principle components, discrete features and time series features are constructed. Based on Gaussian kernel function, Euclidean distance and dynamic time warping (DTW) are used to measure the similarity of the features. Then the matrices of similarity are input in Spectral Clustering. The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system. Finally, actual vertical operation decisions for area sector and results of identification are compared, which are visualized by metric multidimensional scaling (MDS) plots. We find that identification results can well reflect the operation at peak hours, but controllers make different decisions under the similar conditions before dawn. The compliance rate of busy operation mode and division decisions at peak hours is 96.7%. The results also show subjectivity of actual operation and objectivity of identification. In most scenes, we observe that similar air traffic activities provide regularity for initiatives, validating the potential of this approach for initiatives and other artificial intelligence support.
2020, 37(4):630-637.
DOI: 10.16356/j.1005-1120.2020.04.013
Abstract:
Community division is an important method to study the characteristics of complex networks. The widely used fast-Newman(FN) algorithm only considers the topology division of the network at the static layer, and dynamic traffic flow demand is ignored. The result of the division is only structurally optimal. To improve the accuracy of community division, based on the static topology of air route network, the concept of network traffic contribution degree is put forward. The concept of operational research is introduced to optimize the network adjacency matrix to form an improved community division algorithm. The air route network in East China is selected as the object of algorithm comparison experiment, including 352 waypoints and 928 segments. The results show that the improved algorithm has a more ideal effect on the division of the community structure. The proportion of the number of nodes included in the large community has increased by 21.3%, and the modularity value has increased from 0.756 to 0.806， in which the modularity value is in the range of [-0.5,1）. The research results can provide theoretical and technical support for the optimization of flight schedules and the rational use of air route resources.
Community division is an important method to study the characteristics of complex networks. The widely used fast-Newman(FN) algorithm only considers the topology division of the network at the static layer, and dynamic traffic flow demand is ignored. The result of the division is only structurally optimal. To improve the accuracy of community division, based on the static topology of air route network, the concept of network traffic contribution degree is put forward. The concept of operational research is introduced to optimize the network adjacency matrix to form an improved community division algorithm. The air route network in East China is selected as the object of algorithm comparison experiment, including 352 waypoints and 928 segments. The results show that the improved algorithm has a more ideal effect on the division of the community structure. The proportion of the number of nodes included in the large community has increased by 21.3%, and the modularity value has increased from 0.756 to 0.806， in which the modularity value is in the range of [-0.5,1）. The research results can provide theoretical and technical support for the optimization of flight schedules and the rational use of air route resources.
2020, 37(4):638-645.
DOI: 10.16356/j.1005-1120.2020.04.014
Abstract:
The paper aims to schedule check-in staff with hierarchical skills as well as day and night shifts in weekly rotation. That shift ensures staff work at day in a week and at night for the next week. The existing approaches do not deal with the shift constraint. To address this, the proposed algorithm firstly guarantees the day and night shifts by designing a data copy tactic, and then introduces two algorithms to generate staff assignment in a polynomial time. The first algorithm is to yield an initial solution efficiently, whereas the second incrementally updates that solution to cut off working hours. The key idea of the two algorithms is to utilize a block Gibbs sampling with replacement to simultaneously exchange multiple staff assignment. Experimental results indicate that the proposed algorithm reduces at least 15.6 total working hours than the baselines.
The paper aims to schedule check-in staff with hierarchical skills as well as day and night shifts in weekly rotation. That shift ensures staff work at day in a week and at night for the next week. The existing approaches do not deal with the shift constraint. To address this, the proposed algorithm firstly guarantees the day and night shifts by designing a data copy tactic, and then introduces two algorithms to generate staff assignment in a polynomial time. The first algorithm is to yield an initial solution efficiently, whereas the second incrementally updates that solution to cut off working hours. The key idea of the two algorithms is to utilize a block Gibbs sampling with replacement to simultaneously exchange multiple staff assignment. Experimental results indicate that the proposed algorithm reduces at least 15.6 total working hours than the baselines.
2020, 37(4):646-654.
DOI: 10.16356/j.1005-1120.2020.04.015
Abstract:
An aircraft cabin is a narrow, closed-space environment. To keep the air quality in cabin healthy for passengers, especially during an epidemic such as SARS-CoV-2 (or 2019-nCoV) in 2020, a novel aircraft air conditioning system, called the ultra-high-temperature instantaneous sterilization air conditioning system (UHT-ACS), is proposed. Based on the proposed system, a simulation of the UHT-ACS is analysed in various flight states. In the UHT-ACS, the mixing air temperature of return and bleed air can reach temperature up to 148.8 ℃, which is high enough to kill bacilli and viruses in 2―8 s. The supply air temperature of the UHT-ACS in a mixing cavity is about 12 ℃ in cooling mode, both on the ground and in the air. The supply air temperature is about 42 ℃ in heating mode. Compared with the air conditioning systems (ACS) of traditional aircraft, the supply air temperatures of the UHT-ACS in the mixing cavity are in good agreement with those of a traditional ACS with 60% fresh air and 40% return air. Furthermore, the air temperature at the turbine outlet of the UHT-ACS is higher than that of a traditional ACS, which will help to reduce the risk of icing at the outlet. Therefore, the UHT-ACS can operate normally in various flight states.
An aircraft cabin is a narrow, closed-space environment. To keep the air quality in cabin healthy for passengers, especially during an epidemic such as SARS-CoV-2 (or 2019-nCoV) in 2020, a novel aircraft air conditioning system, called the ultra-high-temperature instantaneous sterilization air conditioning system (UHT-ACS), is proposed. Based on the proposed system, a simulation of the UHT-ACS is analysed in various flight states. In the UHT-ACS, the mixing air temperature of return and bleed air can reach temperature up to 148.8 ℃, which is high enough to kill bacilli and viruses in 2―8 s. The supply air temperature of the UHT-ACS in a mixing cavity is about 12 ℃ in cooling mode, both on the ground and in the air. The supply air temperature is about 42 ℃ in heating mode. Compared with the air conditioning systems (ACS) of traditional aircraft, the supply air temperatures of the UHT-ACS in the mixing cavity are in good agreement with those of a traditional ACS with 60% fresh air and 40% return air. Furthermore, the air temperature at the turbine outlet of the UHT-ACS is higher than that of a traditional ACS, which will help to reduce the risk of icing at the outlet. Therefore, the UHT-ACS can operate normally in various flight states.
2020, 37(4):655-662.
DOI: 10.16356/j.1005-1120.2020.04.016
Abstract:
Three types of composite stringers were impacted from two different directions. Relationships between impact energy and visible defect length were found. The critical impact energy corresponding to barely visible impact damage (BVID) of each stringer was determined. Specimens with BVID were then compressed to obtain the residual strength. Experimental results showed that for all types of stringers, the critical energy of in-plane impact is always much lower than out-plane ones. In-plane impact causes much more decrement of stringers’ bearing capacity than out-plane impact. For both impact directions, I-stringers own the highest defect detectability, T-stringers come second. Meanwhile, T-stringers own the better residual strength ratio than I-stringers and J-stringers. Synthetic considering impact defect detectability and residual bearing capacity after impact, T-stringers own the best compression-after-impact (CAI) behaviors.
Three types of composite stringers were impacted from two different directions. Relationships between impact energy and visible defect length were found. The critical impact energy corresponding to barely visible impact damage (BVID) of each stringer was determined. Specimens with BVID were then compressed to obtain the residual strength. Experimental results showed that for all types of stringers, the critical energy of in-plane impact is always much lower than out-plane ones. In-plane impact causes much more decrement of stringers’ bearing capacity than out-plane impact. For both impact directions, I-stringers own the highest defect detectability, T-stringers come second. Meanwhile, T-stringers own the better residual strength ratio than I-stringers and J-stringers. Synthetic considering impact defect detectability and residual bearing capacity after impact, T-stringers own the best compression-after-impact (CAI) behaviors.
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