A combination of large eddy simulation and physics-informed machine learning to predict pore-scale flow behaviours in fibrous porous media: A case study of transient flow passing through a surgical mask

A predictive method using physics-informed machine learning (PIML) and large eddy simulation (LES) is developed to capture the transient flow field through microscale porous media (PSPM). An image processing technique extracts the 3D geometry of the internal layers of the mask from 2D microscopy images, and then the fluid flow is first simulated numerically. The subsequently developed PIML method successfully predicts the transient flow patterns inside the porous medium. For the first time, 3D maps of time-dependent pressure, velocity, and vorticity are predicted across the fibrous porous medium. The results show that, compared to conventional computational fluid dynamics, the PIML method can reduce the computational cost by over 20 times. Further, the LES model can replicate the fine fluctuations caused by the flow passage through the porous medium. Therefore, the developed methodology allows for transient flow predictions in highly complex configurations at a substantially reduced cost. The results indicate that the PIML method can reduce the total computational time (including training and prediction) by 22.5 and 20.7 times over the standard numerical simulation, based on speeds of 0.1 and 0.5 m/s, respectively. Several factors including the inherent differences between CPUs and GPUs, algorithms and software, appear to influence this improvement

Research on geometric dimension measurement system of shaft parts based on machine vision

Abstract Computer vision measurement systems have become more and more widely used in industrial production processes. Traditional manual measurement methods cannot guarantee product quality. Therefore, it is of great significance to improve the technology level of the manufacturing industry to study the automatic measurement system for the dimension of shaft parts with low cost, high precision, and high efficiency. A geometric part measurement system for shaft parts based on machine vision is presented in this paper. It uses the CCD camera to get the image. First, it preprocesses the collected images. In view of the influence of the noise and other factors, the wavelet denoising is used to denoise the image. Then, an improved single pixel edge detection method is proposed based on the Canny detection operator to extract the edge contour of the part image. Finally, the geometrical quantity algorithm is applied to the measurement research, and the measured data are obtained and analyzed. The experimental results show that the repeatability error of the system is less than 0.01 mm