The computational fluid dynamics (CFD) technology is analyzed and calculated utilizing the turbulence model and multiphase flow model to explore the performance of internal circulating fluidized beds (ICFB) based on CFD. The three-dimensional simulation method can study the hydrodynamic properties of the ICFB, and the performance of the fluidized bed is optimized. The fluidization performance of the ICFB is improved through the experimental study of the cross-shaped baffle. Then, through the cross-shaped baffle and funnel-shaped baffle placement, the fluidized bed reaches a coupled optimization. The results show that CFD simulation technology can effectively improve the mass transfer efficiency and performance of sewage treatment. The base gap crossshaped baffle can improve the hydraulic conditions of the fluidized bed and reduce the system energy consumption. The cross-shaped baffle and funnel-shaped baffle can perfect the performance of the reactor and effectively strengthen the treatment in the intense aerobic process of industrial sewage.
The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segmentation, digital twins (DTs), and lightweight deep learning, a new phase contrast microscope is introduced through the research of optical microscope. Secondly, the results of DTs method and phase contrast imaging principle are compared in stem cell image segmentation and detection. Finally, a lightweight deep learning model is introduced in the segmentation and tracking of stem cell image to observe the gray value and mean value before and after stem cell image movement and stem cell division. The results show that phase contrast microscope can increase the phase contrast and amplitude difference of stem cell image and solve the problem of stem cell image segmentation to a certain extent. The detection results of DTs method are compared with phase contrast imaging principle. It indicates that not only can DTs method make the image contour more accurate and clearer, but also its accuracy, recall, and F1 score are 0.038, 0.024, and 0.043 higher than those of the phase contrast imaging method. The lightweight deep learning model is applied to the segmentation and tracking of stem cell image. It is found that the gray value and mean value of stem cell image before and after movement and stem cell division do not change significantly. Hence, the application of DTs and lightweight deep learning methods in the segmentation, detection, and tracking of stem cell image has great reference significance for the development of biology and medicine.
The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID. The controllers and hybrid bearing system are controlled based on the RBF neural network based on deep learning. The characteristics of the hybrid bearing system are explored at the end of this study, and the control simulation research is developed based on the Simulink simulation platform. The effects of the PID, IPD, and CIPD controllers based on the RBF neural network are compared, and they are also compared based on the traditional particle swarm optimization (PSO). The results show that the thickness, spread angle, and rotation speed of the elastic foil have great impacts on the bearing system. The proposed CIPD bearing control method based on RBF neural network has the shortest response time and the best control effect. The controller parameter tuning optimization starts to converge after one generation, which is the fastest iteration. It proves that RBF neural network control based on deep learning has high feasibility in hybrid bearing system. Therefore, the results provide an important reference for the application of deep learning in rotating machinery.
The purpose is to accurately predict the performance of foil bearing and achieve accurate results in the design of foil bearing structure. A new type of foil bearing with surface microstructure is used as experimental material. First, the lubrication mechanism of elastic foil gas bearing is analyzed. Then, the numerical solution process of the static bearing capacity and friction torque is analyzed, including the discretization of the governing equation of rarefied gas pressure based on the non-dimensional modified Reynolds equation and the over relaxation iteration method, the grid planning within the calculation range, the static solution of boundary parameters and static solution of the numerical process. Finally, the solution program is analyzed. The experimental data in National Aeronautics and Space Administration (NASA) public literature are compared with the simulation results of this exploration, so as to judge the accuracy of the calculation process. The results show that under the same static load, the difference between the minimum film thickness calculated and the test results is not obvious; when the rotor speed of the bearing is 60000 r/min, the influence of the boundary slip effect increases with the increase of the micro groove depth on the flat foil surface; when the eccentricity or the micro groove depth of the bearing increases, the bearing capacity will be strengthened. When the eccentricity is 6 µm and 14 µm, the viscous friction torque of the new foil bearing increases significantly with the increase of the depth of the foil micro groove, but when the eccentricity is 22 µm, the viscous friction torque does not change with the change of the depth of the foil micro groove. It shows that the bearing capacity and performance of foil bearing are improved.
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