Investigation on the multiphase sink vortex Ekman pumping effects by CFD-DEM coupling method

Li, Lin; Qi, Huan; Yin, Zichao; Li, Daifeng; Zhu, Zhiliang; Tangwarodomnukun, Viboon; Tan, Dapeng

doi:10.1016/j.powtec.2019.06.036

Cited by 68 publications

(35 citation statements)

References 59 publications

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“…The discrete pressure interpolation was adopted to avoid tremendous change of internal pressure and high swirl by the Pressure Staggering Option (PRESTO). The second order upwind scheme was applied to the functions of momentum, turbulent kinetic energy, and turbulent dissipation rate . The stability of VOF model is greatly affected by the size of the cell grid and time step, so the appropriate time step can be selected according to the linear numerical stability conditions given by Hirt and Nichols …”

Section: Model Descriptionsmentioning

confidence: 99%

Physical and Mathematical Simulation of Surface‐Free Vortex Formation and Vortex Prevention Design during the End of Casting in Tundish

Ruan

Yao

Shen

et al. 2020

steel research int.

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Herein, the formation of the vortex and slag entrainment in tundish is studied by using mathematical and physical simulation. This article aims at contributing to the understanding of the free surface vortex formation in continuous casting tundish at the end of casting. A 3D multiphase mathematical model using the renormalization group (RNG) k–ε turbulence model is developed to predict the vortex formation and slag layer behavior on the scale of the 1:4 water model of a two‐strand slab tundish. The Eulerian volume of fluid model is adopted to track the steel–slag–air free surface and vortex, and the effect of Coriolis force is considered in this simulation. An improved vortex prevention design is also proposed by changing the position of the diversion hole on the dam of the tundish, which is compared with the original scheme using numerical and physical simulation. The predicted results of the vortex structure and the critical height of the vortex formation agree well with the water modeling results. It is demonstrated that the critical height of the vortex formation is reduced significantly by regulating the position of the diversion hole on the dam near the outlet of the tundish.

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Section: Model Descriptionsmentioning

confidence: 99%

Physical and Mathematical Simulation of Surface‐Free Vortex Formation and Vortex Prevention Design during the End of Casting in Tundish

Ruan

Yao

Shen

et al. 2020

steel research int.

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show abstract

“…An integrated piezoelectric sensor was used to acquire the acceleration signal of each component of the gearbox during the sawing process. Based on the layout of the shaft system inside the gearbox, the sensor was arranged closer to the bearing [52][53][54], as shown in Figure 14. The sensor signal was transmitted to the LMS data acquisition front end via a coaxial cable, and the vibration signal was monitored and recorded in real-time.…”

Section: Experimental Data Acquisitionmentioning

confidence: 99%

“…In the figure, "s" indicates that the frequency, damping ratio, and vibration mode are stable; "d" indicates that the frequency and the damping ratio are stable; "v" indicates that the frequency and the vibration type are stable; ʺfʺ indicates that the frequency is stable, and "o" represents a new An integrated piezoelectric sensor was used to acquire the acceleration signal of each component of the gearbox during the sawing process. Based on the layout of the shaft system inside the gearbox, the sensor was arranged closer to the bearing [52][53][54], as shown in Figure 14. The sensor signal was transmitted to the LMS data acquisition front end via a coaxial cable, and the vibration signal was monitored and recorded in real-time.…”

Section: Experimental Data Acquisitionmentioning

confidence: 99%

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Dynamic Performance Optimization of Circular Sawing Machine Gearbox

Wang

Wen

et al. 2019

Applied Sciences

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To optimize the rigidity and dynamic mechanical properties of a sawing machine and improve its processing quality and stability, a design method for the sawing machine’s gearbox was proposed. First, a lightweight design of the gearbox was realized by topology optimization. Second, the sensitivity of different design variables of the new gearbox was determined via sensitivity analysis of the objective function. Finally, multi-objective optimization was used to obtain the optimal solution for the gearbox. Considering the complexity of the internal structure of the gearbox assembly and the accuracy of the numerical calculation process, a modeling method with mass points was proposed. A comparison between the numerical calculation results and the operation mode analysis revealed that the former was accurate and can be applied to the verification of the optimized gearbox. By optimizing the vibration signals before and after, and the analysis of the end face quality of the workpiece, the results revealed that the optimized gearbox has a significantly reduced amplitude under various operating conditions. In addition, the vibration stability was improved, and the end face quality of the workpiece was significantly enhanced compared to that before optimization. This study serves as a theoretical reference for multi-body dynamics modeling and optimization of machine tools, and also outlines technical solutions for high-speed stable cutting with sawing machines.

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“…The improved Fast R-CNN was used to identify hazards and compared with traditional neural network methods; Santur, et al [23] used a 3D laser to acquire the defect image of arail, and then conducted deep learning to achieve the high-precision and rapid detection of lateral defects such as fracture, scour and abrasion on Machine vision detection is a recognition technology based on computer vision to achieve object detection which replaces traditional manual detection technology. Since the last century, machine vision technology has been widely used in many fields [1][2][3][4][5][6][7][8] of defect detection and quality control, such as mechanics, chemistry, material science, agriculture, tanning, textile, printing, electronics, and so on. In recent years, the application of deep learning technology using neural networks in the field of machine vision has made the recognition ability of machine vision reach new heights.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning

Qingsheng

Tan

et al. 2019

Applied Sciences

Self Cite

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Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.

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Investigation on the multiphase sink vortex Ekman pumping effects by CFD-DEM coupling method

Cited by 68 publications

References 59 publications

Physical and Mathematical Simulation of Surface‐Free Vortex Formation and Vortex Prevention Design during the End of Casting in Tundish

Physical and Mathematical Simulation of Surface‐Free Vortex Formation and Vortex Prevention Design during the End of Casting in Tundish

Dynamic Performance Optimization of Circular Sawing Machine Gearbox

Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning

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