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.

Self-excited oscillation pulse jet technology is widely used to clean sediment from oil storage tanks. Its successful application is dependent on jet performance. As the cleaning requirements of the oil industry increase, it is necessary to optimise the structure of self-excited oscillation pulsed jet nozzles (SOPJNs) to optimise cleaning and energy efficiencies. In this study, the jet performance of a SOPJN is modelled and analysed based on computational fluid dynamics with consideration of a large eddy simulation and homogeneous cavitation. The modelling results are highly consistent with experimental results. The effects of the SOPJN’s inlet diameter, cavity diameter, cavity length, wall reflection angle, and inlet pressure on the jet’s peak velocity, oscillation frequency, and cavitation number were analysed. The results show that the oscillation frequency decreases with the increase of the inlet diameter d₁, cavity diameter D, cavity length L and reflection angle of wall α. Optimisation of the SOPJN inlet diameter, cavity length, and wall reflection angle produced a jet with a high peak velocity and strong cavitation. The optimal nozzle cavity diameter strengthens cavitation, while the peak velocity fluctuates as the cavity diameter increases. The peak velocity increases with the inlet pressure, while the increasing rate of the peak velocity decreases. The results of this study can be used in the design and optimisation of similar nozzle structures for improved pulse jet cleaning.

Coaxial borehole heat exchangers provide a practical method for geothermal energy extraction, but heat transfer efficiency is low. In order to address this problem, three coaxial borehole heat exchangers with vortex generators, based on the enhanced heat transfer theory, are proposed in this paper. The author compared and analyzed the heat transfer performance of three coaxial borehole heat exchangers with vortex generators and those of traditional structures, which explains why the new heat exchanger’s heat transfer mechanism is enhanced. The results demonstrated that the vortex generator can enhance the fluid flow’s turbulent kinetic energy in the coaxial heat exchanger. This generator can also improve the mixing characteristics of the fluid flow and heat transfer. The resultant increase in the inlet flow velocity can decrease the friction coefficient f, increase the Nusselt number and strengthen the coaxial sleeve. As a result, the heat exchange performance of the tubular heat exchanger will also be improved. The thread vortex generator (TVG) heat exchanger outperforms the other three heat exchangers in terms of heat exchange performance, extraction temperature and heat extraction power. The results evidenced that the TVG heat exchanger is better than the smooth tube heat exchanger. The thermal performance coefficient PEC was improved by 1.1 times, and the extraction temperature and heating power were increased by 24.06% and 11.93%, respectively. A solid theoretical foundation is provided by the extracted outcomes for designing and selecting high-efficiency coaxial borehole heat exchangers suitable for geothermal energy extraction.

Fracture toughness, as a crucial mechanical property, is essential in the research of fatigue crack propagation and fatigue life. Considering the enormous data scatter of the fracture toughness in experiments, a data processing method is proposed to calculate the fracture toughness for the specified reliability. Based on the traditional data processing method shown in the relative standards, the two-dimensional, one-sided tolerance factor is introduced to calculate the influence of the reliability. In addition, by comparing the data processing methods of the fracture toughness presented in the ISO12135:2016 and ASTM E1820-2013 standards, the differences between them were mainly the range of the qualified data, the function of the construction line, and the equation of the regression line. Because the construction lines of the two standards are based on two different material constitutive models, if the constitutive model of material is more aligned with that of the ideal elastic plastic material, the ASTM E1820-2013 standard is recommended; otherwise, the ISO12135:2016 standard is recommended. For 30CrNi2MoVA, the ISO 12135:2016 standard was more suitable, and the error of the J_IC obtained by the two standards was about 5%.

Geothermal energy, a renewable energy source with enormous reserves independent of the external environment, is essential for reducing carbon emissions. Spiral fin coaxial borehole heat exchanger (SFCBHE) is vital for geothermal energy extraction. Its heat extraction performance requires further improvements for efficient performance that consider the structural sizes and installation positions of the SFCBHE and the nonlinear coupling with respect to several factors. The heat extraction performance of SFCBHE is optimized using a combination of genetic algorithm–back-propagation neural network (GA–BPNN) and the Q-learning-based marine predator algorithm (QLMPA). This study analyzes and compares the effects of geothermal energy extraction of smooth pipe TY-1, structure before optimization TY-2, and optimized structure TY-3. Following optimization with GA–BPNN–QLMPA, the heat extraction performance of TY-3 is enhanced by 30.8% and 23.6%, respectively. The temperature of maximum extraction is improved by 26.8 K and 24.0 K, respectively. The power of maximum heat extraction is increased by 148.2% and 109.5%, respectively. The optimization method can quickly and accurately determine the heat extraction performance for different structural sizes and installation positions of the SFCBHE. These findings are crucial for developing high-performance SFCBHE and efficiently using geothermal energy.

Circular crossbore as a common geometric construction exists widely in the engineering components. Its fatigue life is heavily decreased by the crack failure around the intersecting line. So, the typical analysis model of crossbores with mode I crack is built, and the influence of four main parameters, such as thickness–diameter ratio Y, crossbores diameter ratio Q, crack length a, and axis ratio of elliptical crack face b/ a, on fracture parameter K_I is presented. The results show that the distribution of K_I is mainly influenced by Q and b/ a, and the influence of b/ a is more obvious. The value of K_I is mainly influenced by Y, Q, and a, while the influence of b/ a is not evident. For elliptical crack face, the K_Imax (maximum value of K_I) located at the short axis leads to higher crack growth rate. K_Imax is a crucial factor of fracture criterion, and the significance of factors on it is also analyzed here. A variance analysis of four factors is performed and their order is Y > Q > a > b/ a. In addition, a calculation function of K_Imax is built based on 294 sets’ data and the error is analyzed by mean absolute percentage error with maximum error being less than 7.2%.