Transient analysis of volume packing effects on turbofan engine

AbstractAn integrated turbofan model, capable of coupling engine performance prediction with air-system calculation, is established. Efforts are focused on predicting volume packing effects on turbofan engine both in acceleration and in deceleration process. Results show that the volumes in secondary air-system exert more impacts on transient loads, compared with those in main gas path. Due to disk cavities in air-system, the percentages of coolant flow for the turbines are significantly reduced in acceleration manoeuvre, which result in potential adverse transient thermal load on the engine

Research of Space Positioning Method Based on Sound Field HBT Interference

Based on Hanbury Brown-Twiss (HBT) interference in the sound field, a space positioning method is presented to realize the long-distance and high-precision positioning of sound sources in media. Firstly, theoretical model of HBT interference positioning is established. Location of the sound source can be acquired by analyzing the correlation function of the output signals. Then, sound source localization under different signal-to-noise ratios (SNR) shows that by this method, the sound source can be accurately found with six sensors (two arrays) even the SNR is low to 0.04. Positioning experiment in air is carried out, and the experimental results show that the sound source can be accurately located at 42 meters, and the positioning error is low to 0.1 meters. Thus the validity and accuracy of the HBT interference space location principle is demonstrated. It provides new ideas for the research of long-range target location in sound propagation media (air, water, etc.)

Research of Space Positioning Method Based on Sound Field HBT Interference

Based on Hanbury Brown-Twiss (HBT) interference in the sound field, a space positioning method is presented to realize the long-distance and high-precision positioning of sound sources in media. Firstly, theoretical model of HBT interference positioning is established. Location of the sound source can be acquired by analyzing the correlation function of the output signals. Then, sound source localization under different signal-to-noise ratios (SNR) shows that by this method, the sound source can be accurately found with six sensors (two arrays) even the SNR is low to 0.04. Positioning experiment in air is carried out, and the experimental results show that the sound source can be accurately located at 42 meters, and the positioning error is low to 0.1 meters. Thus the validity and accuracy of the HBT interference space location principle is demonstrated. It provides new ideas for the research of long-range target location in sound propagation media (air, water, etc.)

Real-Time Detection of Drones Using Channel and Layer Pruning, Based on the YOLOv3-SPP3 Deep Learning Algorithm

Achieving a real-time and accurate detection of drones in natural environments is essential for the interception of drones intruding into high-security areas. However, a rapid and accurate detection of drones is difficult because of their small size and fast speed. In this paper a drone detection method as proposed by pruning the convolutional channel and residual structures of YOLOv3-SPP3. First, the k-means algorithm was used to cluster label the boxes. Second, the channel and shortcut layer pruning algorithm was used to prune the model. Third, the model was fine tuned to achieve a real-time detection of drones. The experimental results obtained by using the Ubuntu server under the Python 3.6 environment show that the YOLOv3-SPP3 algorithm is better than YOLOV3, Tiny-YOLOv3, CenterNet, SSD300, and faster R-CNN. There is significant compression in the size, the maximum compression factor is 20.1 times, the maximum detection speed is increased by 10.2 times, the maximum map value is increased by 15.2%, and the maximum precision is increased by 16.54%. The proposed algorithm achieves the mAP score of 95.15% and the detection speed of 112 f/s, which can meet the requirements of the real-time detection of UAVs

Nonuniform Clearance Effects on Pressure Distribution and Leakage Flow in the Straight-through Labyrinth Seals

Straight-through labyrinth seal is a simple and reliable noncontact seal structure widely used in aeroengines. During the actual operation of aeroengines, the labyrinth seal clearance might experience a nonuniform variation in the flow direction due to asymmetric structure and uneven temperature. In order to characterize the degree of nonuniformity, the nonuniformity coefficient is defined in current paper. The effect of nonuniform causes (rotor deformation or stator deformation), nonuniform type (convergent clearance or divergent clearance), and nonuniformity coefficient (nonuniformity degree) is carefully studied by numerical simulation. Comparative analysis has shown that there is no obvious difference in flow coefficient (less than 0.8% in current studies) between two nonuniform causes. As for nonuniform type, the nonuniformity impact of divergent type on the flow coefficient is more significant than that of convergent type, as a result of stronger axial inertia. Pressure distributions of teeth cavities indicate that the total pressure drop of the divergent type is more obvious than that of the convergent type when the pressure ratio is the same. With the same dimensionless minimum tip clearance, clearance nonuniformity would result in the increase of leakage flow of the straight-through labyrinth, particularly for the condition with small pressure ratio, large circumferential Mach number, and small dimensionless minimum tip clearance. When the nonuniformity coefficient is within the range of -0.1 to 0.1, the variation curves of nonuniformity impact factor versus the nonuniformity coefficient almost coincide (the maximum deviation is no more than 1.2%) under different operation conditions (Reynolds number, pressure ratio, circumferential Mach number, and dimensionless minimum tip clearance). Current work proves it that the effect of seal clearance nonuniformity on the leakage flow requires special attention for the refined design of aeroengines

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features

It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%