Increasing the coverage rate of ultrasonic path to flow field is helpful to improve measurement results' accuracy of transit-time ultrasonic flowmeters while asymmetric flow or turbulence exists. In this paper, a 3D isosceles triangular ultrasonic path is presented considering the width of the ultrasonic signal and the maximum coverage rate of the ultrasonic path to flow field. To evaluate the feasibility and effectiveness of the path proposed, computational fluid dynamics simulations of three pipeline configurations, including straight pipeline, single elbow, and contracted pipeline upstream ultrasonic flowmeter 3D _pipe (diameter of pipeline, D _pipe ), 5D _pipe , or 8D _pipe long are performed. In particular, inlet velocity of flow ranges from 0.1 to 10 m/s. Meanwhile, control simulation of U-type ultrasonic path is discussed to verify the effect of coverage rate of ultrasonic path to flow field on measurement results. It is shown that the proposed 3D isosceles triangular ultrasonic path, whose coverage rate is 54%, does well in improving the stability and accuracy of measurement results while pipeline configuration upstream flowmeter changes.

A defect detection approach based on the BiFormer + MPDIoU’s YOLOv8 (BM-YOLOv8) model is proposed which addresses the challenges of low accuracy and low efficiency in detecting tiny defects on the inner-wall outer surface of automotive Anti-lock Brake Systems (ABS) brake master cylinder. This method constructs an imaging model based on process parameters such as speed and inspection accuracy required during the production of automotive ABS brake master cylinder. On this basis, it employs the dynamic sparse self-attention mechanism of the BiFormer to build a network for self-attention feature extraction and fusion. It also utilizes the Minimum Point Distance Intersection over Union (MPDIoU) to optimize the bounding box regression loss function, allowing for precise detection of defects on the inner-wall outer surface of automotive ABS brake master cylinder. Both qualitative and quantitative studies demonstrated that the BM-YOLOv8 method achieves a defect identification rate of 98.8% for the inner-wall outer surface defects of automotive ABS brake master cylinder. More than 25 images per second can be detected in this process. The performance of this method meets the accuracy and real-time requirements for defect detection on the inner-wall outer surface of automotive ABS brake master cylinder.

This study proposes a novel defect detection method to address the low accuracy and insufficient efficiency encountered during surface defect detection on aeroengine turbine blades (ATBs). The proposed approach employs the LDconv model to adjust the size and shape of convolutional kernels dynamically, integrates the deformable attention mechanism (DAT) to capture minute defect features effectively, and uses Focaler-CIoU to optimize the bounding box loss function of the detection network. Our approaches collectively provide precise detection of surface defects on ATBs. The results show that the proposed method achieves a mean average precision (mAP_0.5) of 96.2%, an F-measure of 96.7%, and an identification rate (I_r) of 98.8%, while maintaining a detection speed of over 25 images per second. The proposed method meets the stringent requirements for accuracy and real-time performance in ATB surface defect detection.