The wheels have a considerable influence on the aerodynamic properties and can contribute up to 25%of the total drag on modern vehicles.In this study,the effect of the wheel spoke structure on the aerodynamic performa...The wheels have a considerable influence on the aerodynamic properties and can contribute up to 25%of the total drag on modern vehicles.In this study,the effect of the wheel spoke structure on the aerodynamic performance of the isolated wheel is investigated.Subsequently,the 35°Ahmed body with an optimized spoke structure is used to analyze the flow behavior and the mechanism of drag reduction.The Fluent software is employed for this investigation,with an inlet velocity of 40 m/s.The accuracy of the numerical study is validated by comparing it with experimental results obtained from the classical Ahmed model.To gain a clearer understanding of the effects of the wheel spoke parameters on the aerodynamics of both the wheel and Ahmedmodel,and five design variables are proposed:the fillet angleα,the inside arc radius R1,the outside radius R2,and the same length of the chord L1 and L2.These variables characterize the wheel spoke structure.The Optimal Latin Hypercube designmethod is utilized to conduct the experimental design.Based on the simulation results of various wheel spoke designs,the Kriging model and the adaptive simulated annealing algorithm is selected to optimize the design parameters.The objective is to achieve the best combination for maximum drag reduction.It is indicated that the optimized spoke structure resulted in amaximum drag reduction of 5.7%and 4.7%for the drag coefficient of the isolated wheel and Ahmed body,respectively.The drag reduction is primarily attributed to changes in the flow state around the wheel,which suppressed separation bubbles.Additionally,it influenced the boundary layer thickness around the car body and reduced the turbulent kinetic energy in the wake flow.These effects collectively contributed to the observed drag reduction.展开更多
Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurre...Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents,thereby providing a guarantee for the safety of drivers.However,detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds,varying target scales,and different resolutions.Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios,this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5.The algorithm integrates Attention-based Intra-scale Feature Interaction(AIFI)into the backbone network,enabling it to focus on enhancing feature interactions within the same scale through the attention mechanism.By emphasizing important features,this approach improves detection accuracy,thereby enhancing performance in complex backgrounds.Additionally,a Triple Feature Encoding(TFE)module has been added to the neck network.This module utilizes multi-scale features,encoding and fusing them to create a more detailed and comprehensive feature representation,enhancing object detection and localization,and enabling the algorithm to fully understand the image.Finally,the shape-IoU(Intersection over Union)loss function is adopted to replace the original IoU for more precise bounding box regression.Comparative evaluation of the improved YOLOv5 distraction detection algorithm against the original YOLOv5 algorithm shows an average accuracy improvement of 1.8%,indicating significant advantages in solving distracted driving problems.展开更多
基金funding of the National Natural Science Foundation of China (Nos.52072156,51605198)Postdoctoral Foundation of China (2020M682269).
文摘The wheels have a considerable influence on the aerodynamic properties and can contribute up to 25%of the total drag on modern vehicles.In this study,the effect of the wheel spoke structure on the aerodynamic performance of the isolated wheel is investigated.Subsequently,the 35°Ahmed body with an optimized spoke structure is used to analyze the flow behavior and the mechanism of drag reduction.The Fluent software is employed for this investigation,with an inlet velocity of 40 m/s.The accuracy of the numerical study is validated by comparing it with experimental results obtained from the classical Ahmed model.To gain a clearer understanding of the effects of the wheel spoke parameters on the aerodynamics of both the wheel and Ahmedmodel,and five design variables are proposed:the fillet angleα,the inside arc radius R1,the outside radius R2,and the same length of the chord L1 and L2.These variables characterize the wheel spoke structure.The Optimal Latin Hypercube designmethod is utilized to conduct the experimental design.Based on the simulation results of various wheel spoke designs,the Kriging model and the adaptive simulated annealing algorithm is selected to optimize the design parameters.The objective is to achieve the best combination for maximum drag reduction.It is indicated that the optimized spoke structure resulted in amaximum drag reduction of 5.7%and 4.7%for the drag coefficient of the isolated wheel and Ahmed body,respectively.The drag reduction is primarily attributed to changes in the flow state around the wheel,which suppressed separation bubbles.Additionally,it influenced the boundary layer thickness around the car body and reduced the turbulent kinetic energy in the wake flow.These effects collectively contributed to the observed drag reduction.
基金supported by the National Natural Science Foundation of China(62072158,U2004163)the Key Research and Development Special Projects of Henan Province(231111221500)Science and Technology Project of Henan Province(232102210158,242102210197).
文摘Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents,thereby providing a guarantee for the safety of drivers.However,detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds,varying target scales,and different resolutions.Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios,this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5.The algorithm integrates Attention-based Intra-scale Feature Interaction(AIFI)into the backbone network,enabling it to focus on enhancing feature interactions within the same scale through the attention mechanism.By emphasizing important features,this approach improves detection accuracy,thereby enhancing performance in complex backgrounds.Additionally,a Triple Feature Encoding(TFE)module has been added to the neck network.This module utilizes multi-scale features,encoding and fusing them to create a more detailed and comprehensive feature representation,enhancing object detection and localization,and enabling the algorithm to fully understand the image.Finally,the shape-IoU(Intersection over Union)loss function is adopted to replace the original IoU for more precise bounding box regression.Comparative evaluation of the improved YOLOv5 distraction detection algorithm against the original YOLOv5 algorithm shows an average accuracy improvement of 1.8%,indicating significant advantages in solving distracted driving problems.
