Film cooling is a cooling technique in which a protective layer is generated by the coolant along the wall to shield the wall surface against high temperature flow. When the fluids of the coolant and high-temperature gas are of different qualities, it is often accompanied by the mass and energy transfer. During the film cooling process, the lower-temperature gas is transported to the surface through the tangential gap on the wall which requires thermal protection. And then the film is formed along the direction of the high-temperature flow, causing the solid wall isolated from the high temperature gas, thereby it can prevent the wall surface from being touched and damaged by hot stream. Since the film cooling sets high standards for hole shape, the structure of injection, the length of the coolant supply tube, the angle of incidence, the blowing ratio, the thickness of the boundary layer, ribs and the Reynolds number, etc., the analysis and optimization of these factors have been particular significance on improving cooling efficiency. This paper introduces the dominant factors which influence film cooling effectiveness, besides, analyzes the methods to achieve the best cooling results. The impact of the vortex structures on enhancing film cooling performance is explored. Moreover, a new train of thought is proposed to improve the cooling efficiency under the joint action of the vortex generator and the synthetic jet.

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot’s work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

The behaviors of vortex rings for free jet and synthetic jet with various Reynolds numbers and Strouhal numbers are numerically studied by the k-ε model. The positions of the leading vortex of free jet are investigated under the three different conditions, and the effect of Strouhal number on the vortex ring is analyzed in detail. The results show that different Reynolds numbers lead to different positions for the shedding of the vortex rings. During the movement of the vortex rings, the symmetry of the vortex ring in synthetic jet retains in good state, and the vortex rings do not break up with well-arranged distributions. The distance between two adjacent vortex rings near the synthetic jet exit has the close link between the Strouhal number yet is independent of the Reynolds numbers.

Spiking Neural Networks (SNNs) not only better handle sequential data but also have significant energy efficiency advantages compared to traditional Artificial Neural Networks (ANNs). However, in SNNs, since the output spikes of neurons are discrete, the model may face information loss, especially when the membrane potential is quantized into binary spikes, where quantization errors can lead to model precision loss and information loss. To address these challenges, this study proposes a research method that starts from the quantization mechanism of spiking neurons. Based on the YOLO V5 model, an error feedback and fine-grained quantization Spiking Neural Network model, EQ-LIF-YOLO, is designed. The neurons in the EQ-LIF-YOLO model retain the basic characteristics of traditional LIF neurons while introducing an error feedback mechanism and a fine-grained quantization strategy. The error feedback mechanism constructs an error accumulation module within the neuron to dynamically adjust the quantization threshold of the membrane potential, compensating for errors introduced during quantization and thus improving quantization precision. The fine-grained quantization strategy performs higher-resolution quantization on the membrane potential, allowing it to be fine-tuned at each time step, thereby reducing information loss caused by coarse quantization. Experimental results show that the EQ-LIF-YOLO model achieves significant advantages in both accuracy and energy consumption on the DOTA1.5 and COCO datasets.