Transactions of Nanjing University of Aeronautics & Astronautics
Volume 0,Issue 5,2023 Table of Contents
2023(5):511-521.
DOI: 10.16356/j.1005-1120.2023.05.001
Abstract:
Effects of different broaching machining techniques on fatigue damages are studied. Low cycle fatigue tests at high temperature 650 ℃ in air environment are carried out for specimens with four processing techniques. The roughness characterization method， electron backscatter diffraction （EBSD） and nanoindentation test are used to obtain the surface roughness， residual stress and work hardening， respectively. The wire cut electrical discharge machining （WEDM） specimens exhibit the highest fatigue life of average 80 360 cycle with the lowest surface roughness of 0.226 and residual stress， while specimens machined by blunt tool suffer the lowest fatigue life of average 43 978 cycle， decreasing 45% compared to WEDM ones. SEM results show that fatigue cracks are mainly initiated from the coarse non-recrystallized grains and machining defects. The broaching machining methods and parameters have significant influences on the level of fatigue damages and fatigue lives.
Effects of different broaching machining techniques on fatigue damages are studied. Low cycle fatigue tests at high temperature 650 ℃ in air environment are carried out for specimens with four processing techniques. The roughness characterization method， electron backscatter diffraction （EBSD） and nanoindentation test are used to obtain the surface roughness， residual stress and work hardening， respectively. The wire cut electrical discharge machining （WEDM） specimens exhibit the highest fatigue life of average 80 360 cycle with the lowest surface roughness of 0.226 and residual stress， while specimens machined by blunt tool suffer the lowest fatigue life of average 43 978 cycle， decreasing 45% compared to WEDM ones. SEM results show that fatigue cracks are mainly initiated from the coarse non-recrystallized grains and machining defects. The broaching machining methods and parameters have significant influences on the level of fatigue damages and fatigue lives.
2023(5):522-533.
DOI: 10.16356/j.1005-1120.2023.05.002
Abstract:
A numerical study was performed on the flow and heat transfer characteristics inside a rotating rib-roughened pipe with an axial throughflow， under a fixed axial throughflow Reynolds number Rex 6 400 and a series of rotational Reynolds number Reω ranging from 0 to 3.77× ![]()
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A numerical study was performed on the flow and heat transfer characteristics inside a rotating rib-roughened pipe with an axial throughflow， under a fixed axial throughflow Reynolds number Rex 6 400 and a series of rotational Reynolds number Reω ranging from 0 to 3.77
2023(5):534-546.
DOI: 10.16356/j.1005-1120.2023.05.003
Abstract:
With the increasing unmanned aerial vehicles （UAVs） applications， quite a few major aviation incidents and dangerous symptoms have occurred in the vicinity of the airport and the airspace， and air transportation safety is facing a huge potential threat from unorder flight of UAVs. An important aspect regarding the collision risk between a typical UAV and the windshield of a commercial aircraft is investigated. The damage classification and corresponding impact energy range considering the weakest area on the aircraft windshield are obtained via finite element simulation under the most severe condition. According to the simulation results， the damage severity rank can be classified conservatively. In the absence of intervention from air traffic control， Monte Carlo simulation is performed to obtain the collision probability between a UAV and an aircraft with their independent motions by considering the joint constraints of the minimum horizontal safety separation， the minimum lateral safety separation， and the minimum vertical safety separation. In addition， the collision probability levels are also estimated. Based on various combinations of damage severity classifications and collision probability levels， a more conservative qualitative risk matrix is defined regarding collision between a UAV and an aircraft windshield. In general， the results indicate that the collision risk and damage severity are low when the UAV and the aircraft are flying at the height of less than 120 m and a distance of over 3 600 m on the condition of a typical heading angle and a pitch angle， otherwise these factors become serious. This investigation would provide a theoretical basis and practical reference for the normative design and manufacture of UAVs， the policy formulation by authorities on UAV operation control， as well as the risk assessment of UAVs and manned aircraft operating in the same airspace.
With the increasing unmanned aerial vehicles （UAVs） applications， quite a few major aviation incidents and dangerous symptoms have occurred in the vicinity of the airport and the airspace， and air transportation safety is facing a huge potential threat from unorder flight of UAVs. An important aspect regarding the collision risk between a typical UAV and the windshield of a commercial aircraft is investigated. The damage classification and corresponding impact energy range considering the weakest area on the aircraft windshield are obtained via finite element simulation under the most severe condition. According to the simulation results， the damage severity rank can be classified conservatively. In the absence of intervention from air traffic control， Monte Carlo simulation is performed to obtain the collision probability between a UAV and an aircraft with their independent motions by considering the joint constraints of the minimum horizontal safety separation， the minimum lateral safety separation， and the minimum vertical safety separation. In addition， the collision probability levels are also estimated. Based on various combinations of damage severity classifications and collision probability levels， a more conservative qualitative risk matrix is defined regarding collision between a UAV and an aircraft windshield. In general， the results indicate that the collision risk and damage severity are low when the UAV and the aircraft are flying at the height of less than 120 m and a distance of over 3 600 m on the condition of a typical heading angle and a pitch angle， otherwise these factors become serious. This investigation would provide a theoretical basis and practical reference for the normative design and manufacture of UAVs， the policy formulation by authorities on UAV operation control， as well as the risk assessment of UAVs and manned aircraft operating in the same airspace.
2023(5):547-554.
DOI: 10.16356/j.1005-1120.2023.05.004
Abstract:
A machine learning-based monocular gaze-tracking technology for mobile devices is proposed. This non-invasive， convenient， and low-cost gaze-tracking method can capture the gaze points of users on the screen of mobile devices in real time. Combined with the quadrotor’s 3D motion control， the user’s gaze information is converted into the quadrotor’s control signal， solving the limitations of previous control methods， which allows the user to manipulate the quadrotor through visual interaction. A complex quadrotor track is set up to test the feasibility of this method. Subjects are asked to intervene their gaze into the control flow to complete the flight tasks. Flight performance is evaluated by comparing with the joystick-based control method. Experimental results show that the proposed method can improve the smoothness and rationality of the quadrotor motion trajectory， and can introduce diversity， convenience， and intuitiveness to the quadrotor control.
A machine learning-based monocular gaze-tracking technology for mobile devices is proposed. This non-invasive， convenient， and low-cost gaze-tracking method can capture the gaze points of users on the screen of mobile devices in real time. Combined with the quadrotor’s 3D motion control， the user’s gaze information is converted into the quadrotor’s control signal， solving the limitations of previous control methods， which allows the user to manipulate the quadrotor through visual interaction. A complex quadrotor track is set up to test the feasibility of this method. Subjects are asked to intervene their gaze into the control flow to complete the flight tasks. Flight performance is evaluated by comparing with the joystick-based control method. Experimental results show that the proposed method can improve the smoothness and rationality of the quadrotor motion trajectory， and can introduce diversity， convenience， and intuitiveness to the quadrotor control.
2023(5):555-565.
DOI: 10.16356/j.1005-1120.2023.05.005
Abstract:
The macroscopic mechanical properties of atmospheric ice are affected by the mesoscopic pore structure， while traditional approaches to simulating still have certain limitations. To more accurately represent the mesoscopic structure of porous atmospheric ice， a new modeling method based on statistical principles is proposed. Firstly， the statistical information of atmospheric ice pore diameter is obtained by image recognition. Then， the optimal distribution function that matches the real distribution state of pore diameter is identified using a goodness-of-fit test. Next， a novel approach for deriving the geometric size of atmospheric ice models is introduced， and a method for generating random pore position and diameter data is provided. Finally， a pore intersection determination module is added to construct the mesoscopic model of atmospheric ice. The results demonstrate that the quantitative information of the pores in the generated atmospheric ice model is in good agreement with the experimental results， illustrating the accuracy and feasibility of the modeling method. Moreover， the influence of model parameters on porosity accuracy is systematically discussed. When the number of model pores reaches 50， a good balance between model accuracy and cost can be achieved. Thus， this study provides a novel method to characterize the mesoscopic features of atmospheric ice， and lays a foundation for the related simulation.
The macroscopic mechanical properties of atmospheric ice are affected by the mesoscopic pore structure， while traditional approaches to simulating still have certain limitations. To more accurately represent the mesoscopic structure of porous atmospheric ice， a new modeling method based on statistical principles is proposed. Firstly， the statistical information of atmospheric ice pore diameter is obtained by image recognition. Then， the optimal distribution function that matches the real distribution state of pore diameter is identified using a goodness-of-fit test. Next， a novel approach for deriving the geometric size of atmospheric ice models is introduced， and a method for generating random pore position and diameter data is provided. Finally， a pore intersection determination module is added to construct the mesoscopic model of atmospheric ice. The results demonstrate that the quantitative information of the pores in the generated atmospheric ice model is in good agreement with the experimental results， illustrating the accuracy and feasibility of the modeling method. Moreover， the influence of model parameters on porosity accuracy is systematically discussed. When the number of model pores reaches 50， a good balance between model accuracy and cost can be achieved. Thus， this study provides a novel method to characterize the mesoscopic features of atmospheric ice， and lays a foundation for the related simulation.
2023(5):566-577.
DOI: 10.16356/j.1005-1120.2023.05.006
Abstract:
In-flight icing is threatening aviation safety. The Lagrangian method is widely used in aircraft icing simulation to solve water collection efficiency， the development of which has been impeded by robustness issues and high computational cost. To resolve these disadvantages， two critical algorithms are employed in this study. The Monte Carlo integral method is applied to calculate collection efficiency， which makes the Lagrangian method unconditionally robust for an arbitrary situation. The backpropagation（BP） neural network is also implanted to make a rapid prediction of droplet impingement. Additionally， these two algorithms are deeply coupled in an asynchronous parallelism that allows un-interfered parallel for each procedure respectively. The current study is implemented in NNW-ICE software platform. The asynchronous solver is evaluated with a 3D GLC-305 airfoil and a jet engine nacelle model. The result shows that the BP network contributes a significant acceleration to the Monte Carlo method， saving about 27% running time to achieve equal accurate result. The study is a first attempt for coupling the neural network art and numerical simulation in aircraft icing， providing strong support for the improvement of Lagrangian method and aircraft icing.
In-flight icing is threatening aviation safety. The Lagrangian method is widely used in aircraft icing simulation to solve water collection efficiency， the development of which has been impeded by robustness issues and high computational cost. To resolve these disadvantages， two critical algorithms are employed in this study. The Monte Carlo integral method is applied to calculate collection efficiency， which makes the Lagrangian method unconditionally robust for an arbitrary situation. The backpropagation（BP） neural network is also implanted to make a rapid prediction of droplet impingement. Additionally， these two algorithms are deeply coupled in an asynchronous parallelism that allows un-interfered parallel for each procedure respectively. The current study is implemented in NNW-ICE software platform. The asynchronous solver is evaluated with a 3D GLC-305 airfoil and a jet engine nacelle model. The result shows that the BP network contributes a significant acceleration to the Monte Carlo method， saving about 27% running time to achieve equal accurate result. The study is a first attempt for coupling the neural network art and numerical simulation in aircraft icing， providing strong support for the improvement of Lagrangian method and aircraft icing.
2023(5):578-594.
DOI: 10.16356/j.1005-1120.2023.05.007
Abstract:
This paper presents a real-time control method based on deep neural networks （DNNs） for the fuel-optimal rendezvous problem. A backward generation optimal examples method for the fuel-optimal rendezvous problem is proposed， which iterates through the dichotomy method based on the existing backward generation idea while satisfying the two integration cutoff conditions of the backward integration. We construct a DNNs structure suitable for the variable-specific-impulse model and divide the output control of networks into the thrust output and the specific impulse output. For the specific impulse output， a method is proposed that learns the optimal specific impulse first and then limits it according to its actual upper and lower limits. We propose the enhanced fault-tolerant deep neural networks （EFT-DNNs） to improve the robustness when approaching rendezvous. The effectiveness and efficiency of the proposed method are verified by simulations of the Earth-Apophis asteroid and Earth-Mars missions.
This paper presents a real-time control method based on deep neural networks （DNNs） for the fuel-optimal rendezvous problem. A backward generation optimal examples method for the fuel-optimal rendezvous problem is proposed， which iterates through the dichotomy method based on the existing backward generation idea while satisfying the two integration cutoff conditions of the backward integration. We construct a DNNs structure suitable for the variable-specific-impulse model and divide the output control of networks into the thrust output and the specific impulse output. For the specific impulse output， a method is proposed that learns the optimal specific impulse first and then limits it according to its actual upper and lower limits. We propose the enhanced fault-tolerant deep neural networks （EFT-DNNs） to improve the robustness when approaching rendezvous. The effectiveness and efficiency of the proposed method are verified by simulations of the Earth-Apophis asteroid and Earth-Mars missions.
8 Traffic Flow Prediction Model Based on Multivariate Time Series and Pattern Mining in Terminal Area
2023(5):595-606.
DOI: 10.16356/j.1005-1120.2023.05.008
Abstract:
To improve the accuracy of traffic flow prediction under different weather scenarios in the terminal area， a terminal area traffic flow prediction model fusing multivariate time series and pattern mining （MTSPM） is proposed. Firstly， a multivariate time series-based traffic flow prediction model for terminal areas is presented where the traffic demand， weather， and strategy of terminal areas are fused to optimize the traffic flow prediction by a deep learning model CNN-GRUA， here CNN is the convolutional neural network and GRUA denotes the gated recurrent unit （GRU） model with attention mechanism. Secondly， a time series bag-of-pattern （BOP） representation based on trend segmentation symbolization， TSSBOP， is designed for univariate time series prediction model to mine the intrinsic patterns in the traffic flow series through trend-based segmentation， symbolization， and pattern representation. Finally， the final traffic flow prediction values are obtained by weighted fusion based on the prediction accuracy on the validation set of the two models. The comparison experiments on the historical data set of the Guangzhou terminal area show that the proposed time series representation TSSBOP can effectively mine the patterns in the original time series， and the proposed traffic flow prediction model MTSPM can significantly enhance the performance of traffic flow prediction under different weather scenarios in the terminal area.
To improve the accuracy of traffic flow prediction under different weather scenarios in the terminal area， a terminal area traffic flow prediction model fusing multivariate time series and pattern mining （MTSPM） is proposed. Firstly， a multivariate time series-based traffic flow prediction model for terminal areas is presented where the traffic demand， weather， and strategy of terminal areas are fused to optimize the traffic flow prediction by a deep learning model CNN-GRUA， here CNN is the convolutional neural network and GRUA denotes the gated recurrent unit （GRU） model with attention mechanism. Secondly， a time series bag-of-pattern （BOP） representation based on trend segmentation symbolization， TSSBOP， is designed for univariate time series prediction model to mine the intrinsic patterns in the traffic flow series through trend-based segmentation， symbolization， and pattern representation. Finally， the final traffic flow prediction values are obtained by weighted fusion based on the prediction accuracy on the validation set of the two models. The comparison experiments on the historical data set of the Guangzhou terminal area show that the proposed time series representation TSSBOP can effectively mine the patterns in the original time series， and the proposed traffic flow prediction model MTSPM can significantly enhance the performance of traffic flow prediction under different weather scenarios in the terminal area.
2023(5):607-617.
DOI: 10.16356/j.1005-1120.2023.05.009
Abstract:
A cascading virtual-real fusion approach is proposed to recognize various aircraft sheet metal parts （SMPs） with remarkable similarities. The SMPs are identified and numbered by cascading “Rough” “Fine”， and “Preferred” Through the virtual-real fusion approach of virtual workbench modelling and physical workbench actual recognition. The “Rough” SMP set is identified by gathering the main direction image of the sheet metal item on the real workbench and obtaining an eight-dimensional （8D） shape-description vector from the image. This leads to the discovery of a candidate SMP set. Then， template matching is conducted on the candidate SMP set based on the image’s grey information， and “Fine” matching is obtained. A quantitative index of recognition reliability is proposed to subsequently initiate the “Preferred” recognition process， which is accomplished with an augmented reality 3D projection. The effectiveness and superiority of the propsed method are verified by real experiments， and the best accuracy rate of 96.9% is achieved in testing parts. With the help of 3D projection， the accuracy of man-machine combination is 100%.
A cascading virtual-real fusion approach is proposed to recognize various aircraft sheet metal parts （SMPs） with remarkable similarities. The SMPs are identified and numbered by cascading “Rough” “Fine”， and “Preferred” Through the virtual-real fusion approach of virtual workbench modelling and physical workbench actual recognition. The “Rough” SMP set is identified by gathering the main direction image of the sheet metal item on the real workbench and obtaining an eight-dimensional （8D） shape-description vector from the image. This leads to the discovery of a candidate SMP set. Then， template matching is conducted on the candidate SMP set based on the image’s grey information， and “Fine” matching is obtained. A quantitative index of recognition reliability is proposed to subsequently initiate the “Preferred” recognition process， which is accomplished with an augmented reality 3D projection. The effectiveness and superiority of the propsed method are verified by real experiments， and the best accuracy rate of 96.9% is achieved in testing parts. With the help of 3D projection， the accuracy of man-machine combination is 100%.
2023(5):618-626.
DOI: 10.16356/j.1005-1120.2023.05.010
Abstract:
The microhardness and texture of cold rolled AlFeMn alloy sheets were investigated by means of hardness tester， transmission electron microscopy（TEM）， and X-ray diffraction（XRD）. It was found that the hardness of the rolled AlFeMn alloy sheet increased with the increase of cold rolling reduction， reaching the peak at 82% cold rolling reduction， and then decreasing. Simultaneously， the dislocation density of the rolled sheets decreased obviously， accompanied by the polygonization of sub-structures. It showed that there was a transition from work hardening to work softening during the cold rolling of AlFeMn alloy. At low and medium strains （≤78% cold rolling reduction）， the content of copper and S orientation increased slightly with the increase of cold rolling reduction， while the content of Brass orientation remained almost unchanged. When the cold rolling reduction reached 82%， the content of copper， S and Brass increased sharply， and then decreased sharply when the cold rolling reduction exceeded 84%. The content of cube orientation decreased during the work hardening and increased slightly at the end of work softening. The results showed that the occurrence of work softening was accompanied by changes in grain orientation.
The microhardness and texture of cold rolled AlFeMn alloy sheets were investigated by means of hardness tester， transmission electron microscopy（TEM）， and X-ray diffraction（XRD）. It was found that the hardness of the rolled AlFeMn alloy sheet increased with the increase of cold rolling reduction， reaching the peak at 82% cold rolling reduction， and then decreasing. Simultaneously， the dislocation density of the rolled sheets decreased obviously， accompanied by the polygonization of sub-structures. It showed that there was a transition from work hardening to work softening during the cold rolling of AlFeMn alloy. At low and medium strains （≤78% cold rolling reduction）， the content of copper and S orientation increased slightly with the increase of cold rolling reduction， while the content of Brass orientation remained almost unchanged. When the cold rolling reduction reached 82%， the content of copper， S and Brass increased sharply， and then decreased sharply when the cold rolling reduction exceeded 84%. The content of cube orientation decreased during the work hardening and increased slightly at the end of work softening. The results showed that the occurrence of work softening was accompanied by changes in grain orientation.
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