The aerodynamic properties of rotor systems operating within low Reynolds number flow field conditions are profoundly influenced by their geometric and flight parameters. Precise estimation of optimal airfoil parameters at different angles of attack is indispensable for enhancing these aerodynamic properties. This study presents a technique for optimizing the airfoil parameters of a Mars helicopter by employing machine learning methods in conjunction with computational fluid dynamics (CFD) simulations, thereby circumventing the need for expensive experiments and simulations. The effectiveness of diverse machine learning algorithms for prediction is evaluated, and the resultant models are utilized for airfoil optimization. Ultimately, the aerodynamic properties of the optimized airfoil are experimentally validated. The experimental findings exhibit agreement with the simulated predictions, indicating the successful optimization of the aerodynamic properties. This research offers valuable insights into the influence of airfoil parameters on the aerodynamic properties of the Mars helicopter, along with guidance for airfoil optimization.

The sparse atmosphere on the surface of Mars provides the necessary flight conditions for Mars unmanned aerial vehicles (UAVs) to perform low-altitude flights. This work presents a comprehensive overview of key technologies in the development of Mars UAVs, with a specific focus on rotary-wing Mars UAVs. It summarizes prototypes of rotary-wing Mars UAVs developed by various global research institutions. It reviews essential technologies in rotary-wing Mars UAV research, including the Mars near-surface atmospheric environment, aerodynamic characteristics, and principles of low-pressure flight control. This work also summarizes various experimental setups and ground test results for rotary-wing Mars UAVs. Furthermore, it discusses the future development trends of rotary-wing Mars UAVs.

Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due to non-uniform materials in the workpiece, making it difficult to accurately monitor tool condition by relying on instantaneous signals. To reduce the impact of transient fluctuations, this paper proposes a novel network based on deep learning to monitor and predict tool wear. Firstly, a CNN model based on residual connection was designed to extract deep features from multi-sensor signals. After that, a temporal model based on an encoder and decoder was built for short-term monitoring and long-term prediction. It captured the instantaneous features and long-term trend features by mining the temporal dependence of the signals. In addition, an encoder and decoder-based temporal model is proposed for smoothing correction to improve the estimation accuracy of the temporal model. To validate the performance of the proposed model, the PHM dataset was used for wear monitoring and prediction and compared with other deep learning models. In addition, CFRP milling experiments were conducted to verify the stability and generalization of the model under different machining conditions. The experimental results show that the model outperformed other deep learning models in terms of MAE, MAPE, and RMSE.

In the nanoindentation testing of metallic materials, mechanical properties often decrease significantly when the indentation position shifts from the central region to the edge due to edge effects, leading to premature edge failure and potential device malfunctions. In this work, molecular dynamic (MD) simulations were conducted to investigate the anisotropic edge effects of nanoindentation on monocrystalline copper with a specific crystal orientation. The results reveal that changes in indentation position strongly influence surface collapse and lateral pile-up behaviors. Notably, edge positions resulted in significant reductions in indentation force and hardness, accompanied by pronounced anisotropy in nanoindentation hardness. Additionally, distinct von Mises stress distributions were observed at different indentation positions, highlighting the crystallographic orientation’s role in modulating edge effects. This study provides new insights into the atomic-scale mechanisms underlying edge effects in metallic materials and their anisotropic characteristics.

The edge effect refers to what occurs when an object undergoes elastic contact with the edge of a material. This is common in practical applications, but the understanding of this phenomenon is not yet mature enough, and understanding the microscopic characteristics of the material regarding this phenomenon is necessary. This article investigates the edge effects of single-crystal silicon at different indentation positions through molecular dynamics simulations. The results indicate that the edge effect of the indentation is influenced by the indentation position and depth. The closer the indentation head is to the edge of the workpiece, the more particles are extruded from the side of the workpiece and the wider the collapse range of the indentation surface. At the same time, the indentation position also affects the distribution of the von Mises stress and phase transition area. When the edge effect occurs, the von Mises stress and phase transition region tend to be concentrated near the workpiece edge. This study demonstrates the atomic-scale deformation mechanism of single-crystal silicon under varying indentation positions.

Obtaining the mechanical properties of boron nitride nanosheets (BNNSs) requires extensive computational atomistic simulations, so it is necessary to predict to reduce time costs. In this work, we obtained the ultimate tensile strength and Young’s modulus of the BNNS material through molecular dynamics (MDs) simulations by taking into account factors, such as the BNNSs’ chirality, layer number, ambient temperature, and strain rate. Subsequently, employing comprehensive training and optimization of the MDs data, we developed multiple ML models to estimate the ultimate tensile strength and Young’s modulus. Among these models, the random forest model was chosen for its accurate prediction of the mechanical properties of the BNNSs, offering significant benefits for performance analysis and the engineering design of two-dimensional nanomaterials resembling BNNSs. Finally, based on the predicted results of the ML models, we propose a predictive model for the mechanical properties of the BNNSs, which serves as a valuable reference for future research endeavors.