The dynamic yaw motion of the wind turbine will affect the overall aerodynamic performance of the impeller and the corresponding wake flow, but the current research on this issue is inadequate. Thus, it is very necessary to study the complicated near-wake aerodynamic behaviors during the yaw process and the closely related blade aerodynamic characteristics. This work utilized the multi-relaxation time lattice Boltzmann (MRT-LBM) model to investigate the integral aerodynamic performance characteristics of the specified impeller and the dynamic changes in the near wake under a sine yawing process, in which the normalized result is adopted to facilitate data comparison and understanding. Moreover, considering the complexity of the wake flows, the large eddy simulation (LES) and wall-adapting local eddy-viscosity (WALE) model are also used in this investigation. The related results indicate that the degree of stability of tip spiral wake in the dynamic yaw condition is inversely related to the absolute value of the change rate of yaw angular speed. When the wind turbine returns to the position with the yaw angle of 0 (deg) around, the linearized migration of tip vortex is changed, and the speed loss in the wake center is reduced at about the normalized velocity of 0.27, and another transverse expansion appeared. The directional inducing downstream of the impeller sweep surface for tip vortex is clearly reflected on the entering side and the exiting side. Additionally, the features of the static pressure on the blade surface and the overall aerodynamic effects of the impeller are also discussed, respectively.

In the grinding process of complex-shaped cast workpieces, discrepancies between the workpiece’s contours and their corresponding three-dimensional models frequently lead to deviations in the machining trajectory, resulting in instances of under-grinding or over-grinding. Addressing this challenge, this study introduces an advanced robotic grinding force automatic tracking technique, leveraging a combination of deep neural networks and genetic algorithms. Harnessing the capability of force sensing, our method dynamically recalibrates the grinding path, epitomizing truly flexible grinding. Initially, in line with the prerequisites for force and pose tracking, an impedance control strategy was developed, integrating pose deviations with force dynamics. Subsequently, to enhance steady-state force tracking, we employed a genetic algorithm to compensate for force discrepancies caused by positional errors. This was built upon the foundational concepts of the three-dimensional model, impedance control, and environmental parameter estimation, leading to an optimized grinding trajectory. Following tracking tests, it was observed that the grinding’s normal force was consistently controlled within the bracket of 20 ± 2.5 N. To further substantiate our methodology, a specialized experimental platform was established for grinding complex-shaped castings. Optimized strategies were employed under anticipated forces of 5 N, 10 N, and 15 N for the grinding tests. The results indicated that the contact forces during the grinding process remained stable at 5 ± 1 N, 10 ± 1.5 N, and 15 ± 2 N. When juxtaposed with conventional teaching grinding methods, our approach manifested a reduction in grinding forces by 71.4%, 70%, and 75%, respectively. Post-grinding, the workpieces presented a pronounced enhancement in surface texture, exhibiting a marked increase in surface uniformity. Surface roughness metrics, originally recorded at 17.5 μm, 17.1 μm, and 18.7 μm, saw significant reductions to 1.5 μm, 1.6 μm, and 1.4 μm, respectively, indicating reductions by 76%, 73%, and 78%. Such outcomes not only meet the surface finishing standards for complex-shaped castings but also offer an efficacious strategy for robot-assisted flexible grinding.

The behavior of the tip wake of a wind turbine is one of the hot issues in the wind power field. This problem can partially be tackled using Computational Fluid Dynamics (CFD). However, this approach lacks the ability to provide insights into the spatial structure of important high-order flows. Therefore, with the horizontal axis wind turbine as the main focus, in this work, firstly, we conduct CFD simulations of the wind turbine in order to obtain a data-driven basis relating to multiple working conditions for further analysis. Then, these data are studied using an extended Proper Orthogonal Decomposition (POD) algorithm. The quantitative results indicate that the tip vortex in the wake has a complicated spatio-temporal morphological configuration in the higher-order extended POD space. The radial velocity modes obtained are effective and credible, and such reconstructed flow of the tip vortex becomes clearer with the increase of the reconstruction orders. Interestingly, the changes of relatively high-order correlation coefficients are essentially affected by the periodic fusion of tip and central eddies in the wake.