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As the most vulnerable part of the aircraft,the fuel tank is damaged by hydrodynamics ram effect when attacked by high-speed projectiles,which will directly threaten the safety of the aircraft.The numerical research on the hydrodynamics ram effect is of great significance.In this paper,CEL(coupled Eulerian Lagrangian)method is used to simulate the hydrodynamics ram effect.Firstly,the accuracy of the numerical model is verified by comparing with the existing experimental results;Then,the effects of impact velocity and liquid filling rate on the hydrodynamics ram effect are analyzed.Finally,in order to reduce the damage power of hydrodynamics ram effect,the design of protective structure is numerically analyzed.The results show that the damage power of hydrodynamics ram effect comes from shock wave.The greater the kinetic energy of projectile and the higher the liquid filling rate,the greater the damage of oil tank.The protective baffle can reduce the damage caused by shock wave to the oil tank structure to a certain extent,and the air-interlayer protective structure is the best for the protective effect.

The correct wearing of safety helmets and reflective vests is of great significance in construction sites, offices, and civil engineering sites. Aiming to address the issues of low detection accuracy and high algorithm complexity caused by complex background environments in the small target detection of safety helmets and reflective clothing using existing algorithms, an improved algorithm based on YOLOv8n is proposed. Firstly, the SE module is utilized to reduce interference in complex environments. Next, the IOU function is modified to speed up calculations. Then, a lightweight universal upsampling operator (CARAFE) is employed to obtain a larger receptive field. Finally, the Bidirectional Feature Pyramid Network is used to replace the Concat module of the original head layer. Based on these four modifications made to the model, this article names the new model SDCB-YOLO, derived from the initial letters of the four respective modules. The experimental results show that the mAP of the SDCB-YOLO model on the test set reached 97.1%, which is 4.6% higher than YOLOv5s and 3.5% higher than YOLOv8n. Additionally, the model boasts a parameter count of 3,094,304, a computational load of 8.4 GFLOPs, and a model size of 6.13 MB. Compared to YOLOv5s, with a parameter count of 7,030,417, a computational cost of 16.0 GFLOPs, and a model size of 13.79 MB, the SDCB-YOLO model is significantly smaller. When compared to YOLOv8n, with a parameter count of 3,011,628, a computational complexity of 8.2 GFLOPs, and a model size of 6.11 MB, the SDCB-YOLO model's parameters and model size are only slightly increased, while maintaining a comparable computational load. Therefore, the improved detection algorithm presented in this article not only ensures the lightweight nature of the model but also significantly enhances its detection accuracy. [ABSTRACT FROM AUTHOR]

The number of the collision accidents between Unmanned Aerial Vehicle (UAV) and people on the ground increase in recent years. It is necessary to clarify human injury characteristics brought by the impact of UAV and promote its management. This study focuses on head and neck injury risk resulted from the impact of the UAV with a relatively large mass, and the difference brought by the UAV with a small mass. First, the model of the Y3-dummy (Hybrid III 50th) is developed. Second, simulations are conducted to analyse head and neck injury characteristics from Y3's impact under different velocities, angles, and locations. With increasing the impact velocity, HIC and Nij increase with an exponential function and a power function, respectively. When the vertical impact velocity exceeds 5 m/s, both head and neck have a high probability of injury, while for the horizontal impact, the corresponding velocity is 9 m/s. As the impact angle increases, the changes of HIC satisfy the form of the sum of two exponential functions, and Nij changes in the form of the sum of a power function and a quadratic polynomial function. When the impact angle is close to 0°, the head injury risk is relatively high, while for 58°, the neck is more likely to be injury. The injuries induced by the top of mainframe and the front of the long arm are more serious. Finally, the differences in head and neck injury characteristics under different velocities and locations for UAVs with different masses are compared. [ABSTRACT FROM AUTHOR]

In order to study the uniaxial compression properties of closed-cell metal foam, three different modeling methods were used in this paper to establish 2D random cell, 3D Kelvin and 3D voronoi metal foam model. Based on the LS-DYNA software, the differences of the compressive mechanical properties of the closed-cell metal foam under three different models were analyzed. The deformation mode and the stress strain response of the metal foam under three models were studied. The results show that 2D random cell model is more suitable for the study of the compression regularity of metal foam, and the 3D model is more suitable for the prediction and optimization of the compression response of metal foam. The 2D random cell model is restricted by its plane geometry, it can only simulate the closed-cell metal foam with low porosity in most cases. The Kelvin model and Voronoi model can simulate the closed- cell metal foam with high porosity. Compared with the Kelvin model and 2D random cell model, the voronoi model is more realistic and reasonable, and it is able to better characterize the compressive mechanical properties of closed-cell metal foam.