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In order to further improve the microplastic deformation ability and forming performance of metal sheets, a pulse current pretreatment-assisted micro-forming method was proposed. Firstly, a 304 stainless steel sheet was pretreated with current (0–25 A), and the microstructure changes in the sheet under the action of current were analyzed. Then, a current-assisted bulging experiment was carried out from three aspects as follows: current size, mold structure size, and material properties, to explore the influence of different process parameters on the micro-bulging of the sheet. Finally, the forming quality was analyzed and evaluated from the two perspectives of bulging depth and wall thickness uniformity. The research results show that when the current intensity increases from 0 to 25 A, the fibrous distribution in the thickness direction of the sheet is alleviated, the structure is more uniform, the bulging depth shows an increasing trend, and the thinning rate and wall thickness uniformity are improved. When the current intensity reaches 25 A, the bulging depth increases from the original 463 μm to 503 μm, and the thinning rate drops from the most serious 48.52% to 19.4%. At the same time, as the mold size increases, the single-channel aspect ratio (W/H) also increases accordingly. When the mold groove width (W) is 2 mm, the ratio reaches 0.4, the sheet deforms significantly, and the filling effect is better. In addition, the larger the roundness of the convex and concave molds, the more uniform the wall thickness distribution of the bulging parts. Under the same experimental conditions, the bulging depth of the 304 stainless steel sheet is higher than that of TC4 titanium alloy, and it is less prone to springback and is more conducive to plastic deformation.

This study analyzes the status-quo of the proved oil/gas initially-in-place and its variation trend, the proved undeveloped oil/gas initially-in-place, and the remaining proved technically recoverable reserves (TRR) of oil/gas in China as of 2020 based on statistics. As shown by the results, the proved oil initially-in-place (OIIP), the proved undeveloped OIIP, and the remaining proved TRR of oil in China are mainly distributed in the Bohai Bay, Ordos and Songliao Basins, and those of free gas are mainly in the Ordos, Sichuan, and Tarim Basins. From 2011 to 2020, the largest increment in the proved OIIP, the proved undeveloped OIIP and the remaining proved TRR of oil occurred in the Ordos Basin, followed by the Bohai Bay Basin, while that in the proved gas initially-in-place (GIIP), the proved undeveloped GIIP, and the remaining proved TRR of gas occurred in the Ordos Basin, followed by the Sichuan Basin. In addition, a comprehensive analysis reveals that the petroliferous basins in China with the potential of reserve addition and production growth include the Ordos Basin, the Bohai Bay Basin, the Sichuan Basin, and the Tarim Basin.

As the exploration of ultra-deep layers and the development of geothermal resources continue, obtaining more accurate downhole parameters becomes increasingly important. Flow measurement, in particular, is a complex parameter that presents significant challenges. In order to enhance the accuracy of the turbine flow logging tool, this research focuses on optimizing the turbine flow metering structure through a range of methods. A calculation model related to the measurement process is established, and the flow field characteristics in the flowmeter are analyzed using CFD software. The sensitivity of various geometric parameters to the meter coefficient is also analyzed, and the significance of 11 influencing factors is classified and optimized using the Plackett–Burman climbing test design. The Box–Behnken design method is then used to conduct an experimental design for the significant influencing factors, and the results show that the regression model fits the actual situation well. The response surface method is used to optimize the structural parameters, and an orthogonal experimental design is carried out for the selected non-significant influencing parameters to obtain the optimal structure combination. After optimization based on the response surface method, the stability of turbine flow measurement accuracy is improved by 34.5%.

This study presents a two-step fault diagnosis scheme combined with statistical classification and random forests-based classification for rolling element bearings. Considering the inequality of features sensitivity in different diagnosis steps, the proposed method utilizes permutation entropy and variational mode decomposition to depict vibration signals under single scale and multiscale. In the first step, the permutation entropy features on the single scale of original signals are extracted and the statistical classification model based on Chebyshev’s inequality is constructed to detect the faults with a preliminary acquaintance of the bearing condition. In the second step, vibration signals with fault conditions are firstly decomposed into a collection of intrinsic mode functions by using variational mode decomposition and then multiscale permutation entropy features derived from each mono-component are extracted to identify the specific fault types. In order to improve the classification ability of the characteristic data, the out-of-bag estimation of random forests is firstly employed to reelect and refine the original multiscale permutation entropy features. Then the refined features are considered as the input data to train the random forests-based classification model. Finally, the condition data of bearings with different fault conditions are employed to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively identify the working conditions and fault types of rolling element bearings.