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A high-fidelity computational fluid dynamics and rigid-body dynamics coupling platform suitable for virtual flight simulation of dual-spin projectile is developed. Based on this platform, the influence of rotating canards on the trajectory following stability of the dual-spin projectile is studied. To accurately characterize the differential rotation effect of the dual-spin projectile’s fore and aft bodies, a slidingmesh algorithm is introduced into the in-house unstructured mixed grid flow field numerical simulation program HUND3D. By conducting axisymmetric rotational unsteady simulations of the dual-spin projectile and investigating the flow characteristics and aerodynamic properties under different canard rotation speeds, the dynamics mechanism of the trajectory following process is analyzed. Through the coupled solution of unsteady Reynolds-averaged Navier-Stokes equations and seven-degree-of-freedom rigid-body dynamics equations, virtual flight simulations of the dual-spin projectile with different curvature trajectories are realized. The dynamic mechanism of the trajectory tracking process is analyzed, and strategies for improving trajectory following stability by controlling the canard rotation speed based on the analysis of aerodynamic characteristics are studied. The research results indicate that the aerodynamic disturbances generated by canard rotation can significantly affect the lateral force and yawing moment of the projectile. By controlling the rotation speed of the fore canards to produce a yawing moment favorable for trajectory tracking, the trajectory following stability can be improved to a certain extent.

Chiral amines are valuable building blocks in the manufacture of agrochemicals, pharmaceuticals, natural products, fine chemicals, functional materials among others. Compared to asymmetric hydrogenation of imines/enamides and biocatalytic/organocatalytic asymmetric reductive amination, transition metal catalyzed direct asymmetric hydrogenative amination reactions (DAHA) using molecular dihydrogen as the reductant are highly economical, atom and/or step‐efficient and thus attractive for researchers from both industry and academia. In previous reviews, catalysts/methods development, applications in synthesis of pharmaceuticals and [N] source were summarized and discussed. In this review, we summarize and discuss the ketone substrates scope upon utilizing the state‐of‐the‐art transition metal catalysts. The substrate limitations, potential applications and key mechanisms are also critically concluded. [ABSTRACT FROM AUTHOR]

The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network (GGRU), one-dimensional convolutional neural network (1DCNN), and adaptive batch normalization (AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network's feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model's domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University, aero-engine bearings, Xi'an Jiaotong University, and the Changxing Sumyoung Technology. The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability. [ABSTRACT FROM AUTHOR]