Search

The higher the linear speed of the gear is, the more significant the friction heat generation phenomenon will be, which is not conducive to the improvement of gear life and efficiency. Thus, it is necessary to conduct an analysis of the oil injection cooling characteristics of the gear pair under the high pitch line velocity and explore optimal cooling parameters. Based on the Hertz contact theory and gear meshing characteristics, the average contact stress, relative sliding velocity, and friction coefficient of the tooth surface were solved. Based on the heat generation theory, the average heat flux density of the tooth surface at the high pitch line velocity was obtained. Furthermore, the heat flux density of the tooth surface was used as a boundary condition, and a simulation model for the oil injection cooling characteristics of high speed gears with a pitch line velocity of 120 m/s was established. The effects of the injection speed, injection angle, and gear baffle configuration on the cooling characteristics were explored. The results show that increasing the injection speed can improve the cooling effect of the tooth surface to a certain extent. When the injection angle is 60° and the axial injection angle is 20° higher, a lower tooth surface temperature can be obtained. The axial baffle configuration has a better cooling effect on the tooth surface compared to other baffle configurations.

The complex flow field in the high-speed gearbox affects the transmission efficiency and performance of the transmission system by affecting the gear oil injection lubrication characteristics. Therefore, it is necessary to carry out research on the two-phase flow field and lubrication characteristics of high-speed gear oil injection considering the complex flow field of high-speed gear box and the baffle structure. Based on the computational fluid dynamics method, a two-phase flow field analysis model of high-speed helical gear oil injection lubrication is established, and the two-phase flow field distribution characteristics are analyzed. On this basis, the effects of injection parameters such as injection speed, injection angle, axial injection angle, and gear baffle on the injection lubrication characteristics are further studied. The results show that the oil on the surface of high-speed helical gear inclines to one side. By increasing the injection speed or adding the positive axial angle, the oil deflection and the lubrication effect can be improved. The gear baffle plays a role in guiding the oil, and the setting of axial baffle is conducive to increasing the oil on the tooth surface. This study provides a basis for the study of two-phase flow field distribution characteristics of high-speed gearboxes and the optimization design of oil injection lubrication.

[Purpose/Significance] Advances in single-cell sequencing and high-throughput technology have made it possible for plant genomics to accumulate large quantities of data describing multidimensional genomic-wide molecular phenotypes at low cost. As powerful data mining tools, deep learning techniques can be utilized to further predict and interpret the acquired molecular phenotypes. In recent studies, deep learning has been shown to yield significant results in plant genomics and crop breeding research. However, a complete review of deep learning applications in plant genomics is lacking. [Method/Process] The input to deep learning applied to genomics is usually biological sequences and molecular phenotypes as predictor and target variables, respectively. We introduced the workflow from four views: input data pre-processing includes retrieval, coding, and splitting; model construction and training includes the selection of model architecture and hyperparameters; model evaluation and interpretability. Specifically, this paper introduces the background of deep learning approaches, including the latest graph neural networks; then it discusses two prominent issues in the intersection of genomics and deep learning with respect to gene characterization and protein characterization: 1) how to model the flow of information from plant genomic DNA sequences to molecular phenotypes; and 2) how deep learning models can be utilized to identify functional variation in natural populations? Specifically, the paper summarizes the current status of deep learning applications in related fields, which include deep learning and DNA and gene characterization research, interpretability of deep learning in genomics applications, graph neural networks in genomics, deep learning and genomic variation research, deep learning in protein prediction, ALPHAFOLD in protein prediction, deep learning and crop breeding research, and unsupervised learning in genomics and protein characterization. [Results/Conclusions] This article summarizes how traditional deep-learning algorithms, graph deep-learning, generative adversarial networks and interpretable AI are applied in current research in order to address these two problems. Finally, the prospects for deep learning in future plant genomics research and crop improvement are discussed. Overall, deep learning has provided better results than conventional methods in many genomics research directions, and the application of deep learning in genomics has yielded early applications of scientific and economic significance. Deep learning offers two distinct advantages: 1) end-to-end learning, with the ability to integrate multiple pre-processing steps into a single model; and 2) multimodal data processing capabilities that can handle extremely heterogeneous data in genomics. The advancement of deep learning has the potential to expand new research perspectives in genomics and crop breeding, and to facilitate larger-scale association studies in both phenotypic and genotypic genomics as algorithms become more accurate.

Rim electromagnetic direct-driven propeller (REDP) is a propeller used in the underwater vehicle. It has many merits compared with the traditional propeller. It is difficult to utilize the sensor to get the rotor position and speed that are important to control REDP. An observer based on sliding mode method was proposed to estimate the rotor position and speed in the paper. Its stability was proved and some parameters were obtained. The influence of the switching function on the observation performance was analyzed and the sigmoid function was chosen as the switching function of the sliding mode observer. Then the observation position and speed were analyzed under different parameters to get the better observation performance. Finally, the experiment verified the feasibility of sliding mode observer.

The gear modification is an important technique to improve gear transmission performance. A new topological modification method with circular tooth profilein gear cutter and tooth drum-shaped is put forward. Based on the gear meshing theory and the tooth contact analysis（TCA） technology,the modification parameters for helical gear are designed,and error sensitivity is studied,that is,the effects of helix angle and pressure angle errors on the transmission error and contact trace. The study shows thats gear with the proposed topological modification is insensitive to the helix angle and pressure angle errors,and it has good performance without edge contact. The result provides a theoretical foundation for the modification of helical gear.

High-speed gears are crucial transmission components found in airplanes and other systems, and they are maintained primarily through oil injection. However, due to their high operating speeds and the influence of oil injection settings, gear surface lubrication efficacy is frequently insufficient, compromising the transmission system's precision, durability, and safety. Currently, the parameter choices for oil injection in high-speed gears mostly rely on empirical judgment, which results in significant time and resource expenses. This study focuses on one pair of internal meshing gears within a specific aircraft gearbox, establishing an oil injection lubrication model for high-speed internal meshing gears using the computational fluid dynamics (CFD) approach. This research provides insights and references for optimizing oil injection parameters and improving lubrication efficiency in high-speed internal meshing gear systems by examining the dynamic characteristics of internal meshing wind resistance in addition to the effects of injection tube position, angle, and speed on lubrication performance. [ABSTRACT FROM AUTHOR]

The transient lubrication conditions of rolling bearings are different in gearboxes and bearing testers. It has been observed that samples of qualified rolling bearings tested in rolling bearing testers often fail and do not meet lifespan requirements when employed in other lubrication conditions. This may be caused by different factors affecting the bearing in testing and applying lubrication. Needle roller bearings were selected for this study to investigate the causes of this phenomenon in terms of lubrication. Based on the computational fluid dynamics (CFD) method, fluid domain models for the same type of rolling bearings with different lubrication conditions were established. The transient flow fields of rolling bearings with oil jet lubrication in a tester and splash lubrication in a planetary gearbox were simulated. The air–oil transient distribution of rolling bearings in two kinds of lubrication was analyzed. The results indicate that the rotational speed significantly affected the oil jet lubrication of the needle roller bearing. The average oil volume fraction rose by 0.2 with the increase in the bearing speed from 1200 r/min to 6000 r/min and by 0.06 with the increase in the oil jet velocity from 8 m/s to 16 m/s. The splash lubrication of the bearings in the planetary gearbox was directly related to the immersion depth of the rolling bearings in the initial position. Meanwhile, the splash lubrication of the bearings was also affected by other factors, including the initial layout of the planetary gears. The increase in speed from 1200 r/min to 6000 r/min made the average oil volume fraction of splash lubrication decrease by 4.4%. The average oil volume fraction of the bearings with splash lubrication was better than that with oil jet lubrication by an average of 41.9% when the bearing speed was in the low-speed stage, ranging from 1200 r/min to 3600 r/min. On the contrary, the bearings with oil jet lubrication were better than those with splash lubrication by an average of 31.8% when the bearing speed was in the high-speed stage, ranging from 4800 r/min to 6000 r/min. [ABSTRACT FROM AUTHOR]

The vector form intrinsic finite element (VFIFE) method is springing up as a new numerical method in strong non-linear structural analysis for its good convergence, but has been constricted in static or transient analysis. To overwhelm its disadvantages, a new damping model was proposed: the value of damping force is proportional to relative velocity instead of absolute velocity, which could avoid inaccuracy in high-speed dynamic analysis. The accuracy and efficiency of the proposed method proved under low speed; dynamic characteristics and vibration rules have been verified under high speed. Simulation results showed that the modified VFIFE method could obtain numerical solutions with good efficiency and accuracy. Based on this modified method, high-speed vibration rules of spiral bevel gear pair under different loads have been concluded. The proposed method also provides a new way to solve high-speed rotor system dynamic problems. [ABSTRACT FROM AUTHOR]

The vector form intrinsic finite element method has been proposed to deal with large deformation and complex non-linear problems in structural analysis. However, the study of method is mainly limited in plane element. This article expands the application range of the vector form intrinsic finite element method by deriving the formula of eight-node hexahedral element. A new method to determine pure nodal deformation was proposed and described in detail, which could simplify the computation comparing to the traditional vector form intrinsic finite element method. Compared with the finite element method and other methods in three different numerical examples, the numerical results of this method show a good reliability and accuracy in large deformation problems. This method lays the foundation for solving three-dimensional solid problems by vector form intrinsic finite element method in structural engineering fields. [ABSTRACT FROM AUTHOR]