Recently, Morabito(2010) has studied the water spray phenomena in planing hulls and presented new analytical equations. However, these equations have not been used for detailed parametric studies of water spray around...Recently, Morabito(2010) has studied the water spray phenomena in planing hulls and presented new analytical equations. However, these equations have not been used for detailed parametric studies of water spray around planing hulls. In this paper, a straight forward analysis is conducted to apply these analytical equations for finding the spray geometry profile by developing a computer program based on presented computational process. The obtained results of the developed computer program are compared against existing data in the literature and favorable accuracy is achieved. Parametric studies have been conducted for different physical parameters. Positions of spray apex are computed and three dimensional profiles of spray are examined. It is concluded that spray height increases by an increase in the speed coefficient or the deadrise angle. Ultimately, a computational process is added to Savitsky's method and variations of spray apex are computed for different velocities. It is shown that vertical, lateral, and longitudinal positions of spray increase as the craft speed increases. On the other hand, two new angles are defined in top view and it is concluded that they have direct relation with the trim angle. However, they show inverse relation with the deadrise angle.展开更多
For traffic object detection in foggy environment based on convolutional neural network(CNN),data sets in fog-free environment are generally used to train the network directly.As a result,the network cannot learn the ...For traffic object detection in foggy environment based on convolutional neural network(CNN),data sets in fog-free environment are generally used to train the network directly.As a result,the network cannot learn the object characteristics in the foggy environment in the training set,and the detection effect is not good.To improve the traffic object detection in foggy environment,we propose a method of generating foggy images on fog-free images from the perspective of data set construction.First,taking the KITTI objection detection data set as an original fog-free image,we generate the depth image of the original image by using improved Monodepth unsupervised depth estimation method.Then,a geometric prior depth template is constructed to fuse the image entropy taken as weight with the depth image.After that,a foggy image is acquired from the depth image based on the atmospheric scattering model.Finally,we take two typical object-detection frameworks,that is,the two-stage object-detection Fster region-based convolutional neural network(Faster-RCNN)and the one-stage object-detection network YOLOv4,to train the original data set,the foggy data set and the mixed data set,respectively.According to the test results on RESIDE-RTTS data set in the outdoor natural foggy environment,the model under the training on the mixed data set shows the best effect.The mean average precision(mAP)values are increased by 5.6%and by 5.0%under the YOLOv4 model and the Faster-RCNN network,respectively.It is proved that the proposed method can effectively improve object identification ability foggy environment.展开更多
The measurement of droplet velocities in Diesel sprays close to the nozzle is important because of the complexity of in-nozzle flow, spray break-up and evaporation. However, the measurement of droplet velocities in th...The measurement of droplet velocities in Diesel sprays close to the nozzle is important because of the complexity of in-nozzle flow, spray break-up and evaporation. However, the measurement of droplet velocities in the dense region of Diesel sprays is very difficult or impossible by means of widely used laser diagnostic techniques, in particular under engine-like high-pressure and high-temperature conditions. The limitations of phase Doppler anemometry （PDA） and particle image velocimetry （PIV） prevent the application to the ultra-dense region of the spray. It was demonstrated that these problems can be greatly reduced by the laser flow tagging （LFT） technique. It was also demonstrated recently that LFT measurements can be conducted in clustered Diesel jets with improved spatial resolution and increased number of simultaneous measurements in the near-nozzle region. In the present work, the nozzle design, the temperature and pressure of the ambient air, and the fuel rail pressure are varied, in order to investigate the influence on the near-nozzle jet velocity and the underlying physical mechanisms.展开更多
文摘Recently, Morabito(2010) has studied the water spray phenomena in planing hulls and presented new analytical equations. However, these equations have not been used for detailed parametric studies of water spray around planing hulls. In this paper, a straight forward analysis is conducted to apply these analytical equations for finding the spray geometry profile by developing a computer program based on presented computational process. The obtained results of the developed computer program are compared against existing data in the literature and favorable accuracy is achieved. Parametric studies have been conducted for different physical parameters. Positions of spray apex are computed and three dimensional profiles of spray are examined. It is concluded that spray height increases by an increase in the speed coefficient or the deadrise angle. Ultimately, a computational process is added to Savitsky's method and variations of spray apex are computed for different velocities. It is shown that vertical, lateral, and longitudinal positions of spray increase as the craft speed increases. On the other hand, two new angles are defined in top view and it is concluded that they have direct relation with the trim angle. However, they show inverse relation with the deadrise angle.
文摘For traffic object detection in foggy environment based on convolutional neural network(CNN),data sets in fog-free environment are generally used to train the network directly.As a result,the network cannot learn the object characteristics in the foggy environment in the training set,and the detection effect is not good.To improve the traffic object detection in foggy environment,we propose a method of generating foggy images on fog-free images from the perspective of data set construction.First,taking the KITTI objection detection data set as an original fog-free image,we generate the depth image of the original image by using improved Monodepth unsupervised depth estimation method.Then,a geometric prior depth template is constructed to fuse the image entropy taken as weight with the depth image.After that,a foggy image is acquired from the depth image based on the atmospheric scattering model.Finally,we take two typical object-detection frameworks,that is,the two-stage object-detection Fster region-based convolutional neural network(Faster-RCNN)and the one-stage object-detection network YOLOv4,to train the original data set,the foggy data set and the mixed data set,respectively.According to the test results on RESIDE-RTTS data set in the outdoor natural foggy environment,the model under the training on the mixed data set shows the best effect.The mean average precision(mAP)values are increased by 5.6%and by 5.0%under the YOLOv4 model and the Faster-RCNN network,respectively.It is proved that the proposed method can effectively improve object identification ability foggy environment.
文摘The measurement of droplet velocities in Diesel sprays close to the nozzle is important because of the complexity of in-nozzle flow, spray break-up and evaporation. However, the measurement of droplet velocities in the dense region of Diesel sprays is very difficult or impossible by means of widely used laser diagnostic techniques, in particular under engine-like high-pressure and high-temperature conditions. The limitations of phase Doppler anemometry （PDA） and particle image velocimetry （PIV） prevent the application to the ultra-dense region of the spray. It was demonstrated that these problems can be greatly reduced by the laser flow tagging （LFT） technique. It was also demonstrated recently that LFT measurements can be conducted in clustered Diesel jets with improved spatial resolution and increased number of simultaneous measurements in the near-nozzle region. In the present work, the nozzle design, the temperature and pressure of the ambient air, and the fuel rail pressure are varied, in order to investigate the influence on the near-nozzle jet velocity and the underlying physical mechanisms.
