The blades of vertical-axis turbines (VAT) operate in curved and nearly circular flow. Compared to a uniform flow, the curved streamlines alter the aerodynamic coefficients of the airfoil. As a result, measuring and predicting the aerodynamic coefficients of an airfoil in curved flow poses a challenge. In this work, we first present a methodology to properly determine the aerodynamic coefficients (lift, drag, and moment) of a NACA 0015 airfoil attached at the quarter-chord in steady curved flow at Rec=6×106 using blade-resolved CFD with a “key-hole mesh domain”. By varying the airfoil's angle of attack and the airfoil's chord to turbine's radius ratio (c/R), the aerodynamic coefficient curves are obtained and presented. The results show that the curvature effects on the aerodynamic coefficients are significant. It is observed that the drag coefficient is closely related to the moment coefficient of the airfoil and the moment coefficient about the airfoil's rotation axis. Furthermore, the Coriolis effect is shown to be responsible for reducing the drag coefficient values compared to those in a uniform flow. The findings of this investigation will help develop improved models based on actuator lines (ALM) for the prediction of VAT's performance.

To address the issue of reduced motion accuracy in tracked sandblasting robots caused by nonlinear slippage on steel grit-covered hard surfaces, a hierarchical control framework combining model and data learning is proposed to enhance dual-motor coordination precision and terrain disturbance suppression capability. The inner layer employs a slip-mode control-cross-coupling (SMC-CCC) algorithm, combined with a novel composite approach law, to simultaneously optimize motor tracking error and synchronization error. The outer layer employs a coupled simulation of multi-body dynamics and discrete element method (MBD-DEM) to generate high-fidelity data, which trains a radial basis function neural network (RBFNN) to predict and compensate in real time for slip disturbances caused by track-ground interactions. The standard deviation of dual PMSM synchronous error decreased to 1.12, while the standard deviation of tracking error dropped to 7.86. After RBFNN compensation, vehicle lateral deviation was reduced to 70.08 mm—a 92.28% decrease compared to the uncontrolled condition—with slip rate stabilizing between 20% and 25%. In summary, the proposed hierarchical framework effectively addresses motion instability issues beneath steel grit surfaces: SMC-CCC ensures motor coordination precision, while RBFNN achieves adaptive compensation for nonlinear slippage based on MBD-DEM data, providing a viable solution for high-precision motion control in complex terrain.

This research paper focuses on analyzing the tractive effort coefficient of a Non-Pneumatic (NP) tire model using the Finite Element Method (FEM). The NP tire is modelled with four layers representing tread, under-tread, spokes, and rim. The material behavior of the tire is defined using various material models, including the Mooney-Rivlin formulation for rubber components. The NP tire model is verified under both static and dynamic conditions through vertical stiffness, footprint, and first-mode vibration tests. Subsequently, the NP tire is positioned on a rigid road surface with a constant friction coefficient of 0.8 to simulate asphalt. The NP tire–road interaction characteristics are analyzed, and the resulting contact forces and moments are determined. The tractive effort is then evaluated under different operating conditions, including variations in vertical load and applied torque. This study provides valuable insights into the factors influencing the performance of driven NP tires.

4D printing has sparked a transformative shift in hydrogel technology by enabling the fabrication of intricate, shape-changing structures with precise temporal control. This innovation holds promise for applications ranging from advanced tissue engineering to responsive drug delivery systems. This study focuses on the manufacturing of poly(N-isopropylacrylamide) (pNIPAM)-based hydrogels, renowned for their thermo-responsive behaviour. We investigate the influence of composition and parameters on the fabrication of responsive hydrogel structures. We investigate the impact of the amount of photoinitiator (Irgacure 2959), crosslinker (MBA), light intensity, and time of light exposure on the fabrication process, which utilizes an adapted 3D digital light processing (DLP) printer. We identified the minimum amounts for the analyzed parameters, the effects of excessive quantities, and possible variations to be taken according to different final objectives. Our results offer valuable insights into the design of customizable 4D printable hydrogels, enabling the precise engineering of responsive structures for various applications.

Currently, a low-altitude economic strategy is proposed. The lubrication state of gear systems critically influences the reliability of Oil-powered heavy-duty Unmanned Aerial Vehicles (UAVs). Simultaneous numerical estimation of grease-lubricated gear lubrication states and non-Gaussian tooth surface roughness distributions remains underexplored. In this study, we developed a distribution model for gear surface roughness during machining, which couples the mixed-thermal elastohydrodynamic model for grease-lubricated spiral bevel gears developed by our research group. The research results demonstrate that the ratio of the average oil film thickness to the peak roughness is the dominant factor governing the influence of roughness distribution on film thickness. An increase in torque at low speeds significantly mitigates the effects of varying roughness distributions. Additionally, surface roughness with high kurtosis and positive skewness effectively reduces tooth surface temperature rise.

In this paper, a semi-analytical model for forced vibration of the thin-walled hard-coating cylindrical shell with arbitrary uniform circular perforations based on the energy superposition principle is proposed, in which the structural damping variation induced by arbitrary perforations is considered by the developed equivalent Rayleigh damping. The first-order shear deformation theory, second-kind Chebyshev polynomials, and artificial spring technique are employed to derive the governing equations used for forced vibration of the composite shell. Referring to the experimental natural frequencies and resonance response amplitudes of the perforated shell with NiCoCrAlY hard coating, the developed equivalent Rayleigh damping coefficients within a wider frequency band of interest are identified by the pattern search algorithm. The comparation between the experimental and semi-analytical amplitude-frequency curves of resonance exhibits the reliability and effectiveness of the semi-analytical model. Furthermore, a systematic analysis is conducted on the influence of perforation number and radius on the forced vibration characteristics of the shell, which can provide valuable theoretical support for vibration reduction design of complex perforated shell structures in aviation power equipment.

In recent years, many diagnosis methods in few-shot fault diagnosis have achieved remarkable results on known faults with limited samples. But in actual industrial conditions, new fault types often emerge during long-term equipment operation. These methods require retraining with new samples, thus failing to meet rapid diagnosis needs. To address this, this paper proposes MAML-CNN-GRU, a novel few-shot model based on the Model-Agnostic Meta-Learning (MAML) framework. Firstly, the CNN-GRU hybrid model can effectively extract the spatiotemporal features from original time-series fault signals. Specifically, it uses CNN to extract local spatial features and GRU to capture long-term sequence dependencies. Secondly, leveraging MAML's meta-training mechanism, the initial parameters of CNN-GRU can be optimized through multiple fault diagnosis tasks. As a result, it gains the ability to learn cross-task general features. Then, a reasonable meta-task generation strategy enables rapid identification of novel fault types or variable condition faults even with limited samples. Ultimately, results from CWRU and XJTU bearing datasets illustrate this method’s outstanding performance in diagnosing novel faults under diverse operating conditions. For unknown or compound faults, this method achieves 98.91% and 96.96% diagnosis accuracy in 3-way 5-shot and 5-way 5-shot tasks respectively. Evidently, these results validate the method’s effectiveness.

This study addresses the challenges in bearing remaining useful life (RUL) prediction, including small sample size and incomplete labels. A novel RUL prediction method is proposed. The method is based on adaptive degradation point detection and a hybrid deep learning framework. First, to determine the initial degradation time, the original vibration signals are combined with a physical bearing model. The Aquila Optimizer optimization algorithm is used to quantify the real-time crack values. An improved adaptive dynamic 3 σ criterion is applied together with the real-time crack values to identify the first prediction time. Second, to reduce the need for manually defined labels in RUL prediction, a feature model combining Gaussian mixture model and fuzzy entropy is developed. This model constructs an unsupervised health indicators (HI). Finally, a convolutional neural network-Bidirectional GRU-Attention model is proposed to predict the HI. The model integrates time-series modeling with an attention mechanism. The bearing HI is predicted sequentially. When the predicted HI exceeds the life threshold, the RUL is calculated. Experimental results on multiple datasets and comparisons with other methods show that the proposed method achieves the highest prediction accuracy, exceeding 93.59%. The results demonstrate that the method is reliable and practical for bearing RUL prediction.

With the advancement of the oil and gas industry, ultra-deep wells, extended-reach wells, and long-distance horizontal wells are increasingly prevalent. The drilling condition is very complicated. Therefore, this paper develops the rotary coupled controller (RCC) for directional wells to improve the efficiency of directional drilling. The structure and working principle of RCC are introduced, and the advantages of sliding directional drilling and rotary steerable drilling are combined. According to the working characteristics of RCC, the drilling string dynamic model of horizontal well is established. The results indicate that, compared with the dynamic model with or without RCC, the bit angular velocity fluctuates periodically in the range of 0–16.2 rad/s without RCC. The bit movement is unstable and always lags behind the upper drill string. After using RCC, the angular velocity initially fluctuates briefly before stabilizing at approximately 31.2 rad/s. The torque result of drill string system is always in dynamic friction state, indicating that RCC can reduce friction and suppress stick–slip vibration. This tool serves as a valuable reference for achieving high-precision and high-efficiency directional well drilling.

The use of exoskeleton robots is increasing due to the rising number of musculoskeletal injuries. However, their effectiveness depends heavily on the design of control systems. Designing robust controllers is challenging due to uncertainties in human–robot systems. Among various control strategies, model predictive control (MPC) is a powerful approach due to its ability to handle constraints and optimize performance. Previous studies used linearization-based methods to implement robust MPC on exoskeletons, but these can degrade performance due to nonlinearities in the robot’s dynamics. To address this gap, this paper proposes a robust nonlinear model predictive control (NMPC) method, called multi-stage NMPC, to control a two-degree-of-freedom exoskeleton by solving a nonlinear optimization problem. This method uses multiple scenarios to model system uncertainties. The study focuses on minimizing human–robot interaction forces during the swing phase, particularly when the robot carries unknown loads. Simulations and experimental tests show the proposed method significantly improves robustness, outperforming nonrobust NMPC. It achieves lower tracking errors and interaction forces under various uncertainties. For instance, when a 2 kg unknown payload combined with external disturbances, the root mean square values of thigh and shank interaction forces for multi-stage NMPC are 77% and 94% lower, respectively, compared to nonrobust NMPC.