Lightweight sandwich structures have been receiving significant attention. By studying and imitating the structure of biomaterials, its application in the design of sandwich structures has also been found to be feasible. With inspiration from the arrangement of fish scales, a 3D re-entrant honeycomb was designed. In addition, a honeycomb stacking method is proposed. The resultant novel re-entrant honeycomb was utilized as the core of the sandwich structure in order to increase the impact resistance of the sandwich structure under impact loads. The honeycomb core is created using 3D printing. By using low-velocity impact experiments, the mechanical properties of the sandwich structure with Carbon-Fiber-Reinforced Polymer (CFRP) face sheets under different impact energies were studied. To further investigate the effect of the structural parameters on the structural, mechanical properties, a simulation model was developed. Simulation methods examined the effect of structural variables on peak contact force, contact time, and energy absorption. Compared to traditional re-entrant honeycomb, the impact resistance of the improved structure is more significant. Under the same impact energy, the upper face sheet of the re-entrant honeycomb sandwich structure sustains less damage and deformation. The improved structure reduces the upper face sheet damage depth by an average of 12% compared to the traditional structure. In addition, increasing the thickness of the face sheet will enhance the impact resistance of the sandwich panel, but an excessively thick face sheet may decrease the structure’s energy absorption properties. Increasing the concave angle can effectively increase the energy absorption properties of the sandwich structure while preserving its original impact resistance. The research results show the advantages of the re-entrant honeycomb sandwich structure, which has certain significance for the study of the sandwich structure.

Carbon fiber reinforced polymer (CFRP) laminates, as unique multifunctional materials, are widely applied in various aircraft, such as airliners, fighter planes, and space shuttles. To ensure aircraft safety during the production and application of CFRP laminates, it is necessary to improve the accuracy of nonlinear Lamb wave nondestructive testing to assess the damage in CFRP laminates caused by impact, high temperature, friction, corrosion, etc. In this study, the accuracy of nonlinear ultrasonic nondestructive testing was found to highly depend on the cycle number, output level and gain of the nonlinear ultrasonic detection system. Based on a single-factor experiment that considered the cycle number, output level, and gain of the amplifier as independent variables, a regression analysis was carried out on the fundamental wave amplitude value (A₁) and second harmonic amplitude value (A₂). Two response surface surrogate models were established to improve the accuracy of nonlinear Lamb wave nondestructive testing and to optimize the detection system parameters. The response surface models were verified via an analysis of variance (ANOVA), significance tests and an error statistical analysis. The results revealed the significant influence of these three factors on A₁ and A₂. Optimization of the response surface was achieved at eight cycles, an output level of 42 and a gain of 32 dB. Moreover, the nonlinear ultrasonic detection system achieved good operational stability, high accuracy and reliability under the above optimal parameter conditions. This approach provides scientific guidance for the accurate assessment of CFRP laminate damage.

Carbon fiber-reinforced polymer (CFRP) laminates, a key composite material, are widely used in aircraft structures and are susceptible to low-velocity impact (LVI) damage from bird strikes, lightning strikes, hail impacts and other situations. Therefore, finding a method that repairs the damaged structure and detects the effect of these repairs under LVI is a very important goal. In this work, the repair effect of LVI damage in CFRP laminates repaired with patches of various sizes is investigated via experimental and numerical nonlinear Lamb wave analyses. An integrated numerical procedure that combines LVI with nonlinear Lamb wave detection is developed to predict the nonlinear Lamb wave behavior in LVI-damaged patch-repaired CFRP laminates. The CFRP laminate damage in the nonlinear Lamb wave simulation is evaluated based on relative acoustic nonlinearity parameters (RANPs). As a result, the integrated numerical procedure is validated with drop-weight impact tests and RAM-5000 SNAP nonlinear ultrasonic detection system. An optimal patch design is established via interpolation to optimize the absorbed energy, delamination surface area, second RANP and third RANP with different patch repair sizes. These parameters exhibit consistent curve fitting trends, indicating that they can be used as important indicators of impact damage. The optimal circular patch design with a radius of 2.5 r has better impact resistance behavior and repair performance.

Real-time monitoring of the battery thermal status is important to ensure the effectiveness of battery thermal management system (BTMS), which can effectively avoid thermal runaway. In the study of BTMS, driver behavior is one of the factors affecting the performance of the battery thermal status, and it is often neglected in battery temperature studies. Therefore, it is necessary to predict the dynamic heat generation of the battery in actual driving cycles. In this work, a thermal equivalent circuit model (TECM) and an artificial neural network (ANN) thermal model based on the driving data, which can predict the thermal behavior of the battery in real-world driving cycles, are proposed and established by MATLAB/Simulink tool. Driving behaviors analysis of different drivers are simulated by PI control as input, and battery temperature is used as output response. The results show that aggressive driving behavior leads to an increase in battery temperature of nearly 1.2 K per second, and the average prediction error of TECM model and ANN model is 0.13 K and 0.11 K, respectively. This indicates that both models can accurately estimate the real-time battery temperature. However, the computational speed of the ANN thermal model is only 0.2 s, which is more efficient for battery thermal management.