Due to the rapid development of powder technology around the world, powder materials are being widely used in various fields, including metallurgy, the chemical industry, and petroleum. The turbo air classifier, as a powder production equipment, is one of the most important mechanical facilities in the industry today. In order to investigate the production efficiency of ultrafine powder and improve the classification performance in a turbo air classifier, two process parameters were optimized by analyzing the influence of the rotor cage speed and air velocity on the flow field. Numerical simulations using the ANSYS-Fluent Software, as well as material classification experiments, were implemented to verify the optimal process parameters. The simulation results provide many optimal process parameters. Several sets of the optimal process parameters were selected, and the product particle size distribution was used as the inspection index to conduct a material grading experiment. The experimental results demonstrate that the process parameters of the turbo air classifier with better classification efficiency for the products of barite and iron-ore powder were an 1800 rpm rotor cage speed and 8 m/s air inlet velocity. This research study provides theoretical guidance and engineering application value for air classifiers.

In this paper, three types of air guide vanes are designed: direct-type, L-type, and logarithmic spiral type, respectively. ANSYS-FLUENT 20.0 is used to numerically simulate the internal flow field of turbo air classifier by novel different structures. The numerical results show that the guide vane structures have a good effect on the flow field stability of the annular function zone in the classifying chamber. The distribution of tangential velocity and radial velocity verified the logarithmic spiral guide vane, and makes the airflow flow along the rotor cage circumferentially uniformly. In addition, the turbulent dissipation rate and energy loss decreases in the rotor cage region, which also shows that the guide vane is beneficial to improve classification performance. The tromp curve of the numerical simulation shows that the logarithmic spiral guide vane reduced the cutting size by 6.3% and 23.7% at two different process parameters, and is obviously better than other guide vane structures in improving the classification sharpness index (K). Finally, the reliability of numerical simulation is verified by material experiment. The research results have certain theoretical significance and guidance for the structural design of the guide vanes of the turbo air classifier.

Fracturing pumpheads are typical pressure vessels that experience frequent fatigue failure under the effect of notches in their cross-bore. To enhance the fatigue life of fracturing pumpheads, the study of the notch effect is indispensable and important to establish a reliable mathematical model to predict their fatigue life. In the present paper, two novel fatigue life prediction models are proposed for notched specimens. In these models, two new geometric fatigue failure regions are defined to improve the weight function. Finally, the elaborated novel stress-field intensity approach was applied to three different types of notched specimens. Experiment results indicate that the new SFI approach achieves 47.82%, 39.48%, and 31.85% higher prediction accuracy than the traditional SFI approach, respectively. It was found that the modified SFI approach provided better predictions than the traditional SFI approach and the TCD method. The II-th novel SFI approach had the highest accuracy, and the I-th novel SFI approach was more suitable for sharply notched specimens.

The non-axisymmetric exciting guided wave can detect the thinning section of the elbow, and the time domain energy value of the signal collected at the outer arch position of the receiving end displays a downward trend as the remaining thickness of the erosion area decreases. To address the difficulty in detecting the erosion degree of the elbow with high accuracy, this paper uses the linear frequency modulation (LFM) signal to excite a non-axisymmetric guided wave that propagates in the 90° elbow and collects signals through four PZT receivers. To predict the erosion degree, the corresponding relationship between the energy value of the four signals after fractional Fourier filtering and the degree of elbow erosion is established through the particle swarm optimization (PSO)–least squares support vector machine (LSSVM) algorithm. The results show that the method proposed has an average accuracy rate of 98.1864%, 94.7167%, 99.119%, and 99.9593% for predicting the erosion degree of four elbow samples, and 94.0039%. and 81.2976% for two new erosion degrees, which are higher than the nonlinear regression model, LSSVM algorithm, and BP neural network algorithm. This study has guiding significance for real-time monitoring of elbow erosion.