Guochao Fan

An inhomogeneous thin internal stratum sometimes exists between two dissimilar materials, which is usually caused by non-uniform thermal distribution, interaction of different media, diffusion impurity or material degeneration and damage. In this paper, it is considered as a functional graded (FG) piezoelectric material in surface acoustic wave devices, and we investigate its effect on Love wave propagation within the framework of the linear piezoelectric theory. Correspondingly, the power series technique is presented and applied to solve the dynamic governing equations, i.e., two-dimensional partial differential equations with variable coefficients, with the convergence and correctness being proved. In this method, the material coefficients can change in random functions along the thickness direction, which reveals the generality of this method to some extent. As the numerical case, the elastic coefficient, piezoelectric coefficient, dielectric permittivity, and mass density change in the linear form but with different graded parameters, and the influence of material inhomogeneity on the Love wave propagation is systematically investigated, including the phase velocity, electromechanical coupling factor, and displacement distribution. In addition, the FG piezoelectric material caused by piezoelectric damage and material bonding is discussed. Numerical results demonstrated that both piezoelectric damaged and material bonding can make the higher modes appear earlier for the electrically open case, decrease the initial phase velocity, and limit the existing region of the fundamental Love mode for the electrically shorted case. The qualitative conclusions and quantitative results can provide a theoretical guide for the structural design of surface wave devices and sensors.

The calculation of flow fields and aerodynamic heat under hypersonic flow conditions faces challenges such as low computational efficiency and stringent grid requirements. In contrast, the physics-informed neural network (PINN) model exhibits advantages such as adhering to physical laws, being mesh-free, and having high computational efficiency. Given that neural network models tend to smooth out the data distribution at locations with large gradients and strong discontinuities in hypersonic flow fields, this paper develops a PINN model with high-frequency positional encoding (PE-PINN). This model maps coordinate information to a high-dimensional space, enhancing the network's capability to capture high-gradient features. Furthermore, in the process of modeling two-dimensional flow fields, the PE-PINN model incorporates a shock wave attention module to capture shock wave positions and weighted enhancement of features in shock wave regions, allowing the network to better capture areas with large gradient data distributions in hypersonic flow fields. Compared with the traditional PINN model, the PE-PINN maintains the same prediction efficiency while improving the accuracy of shock wave position characterization from 91.2% to 99%. It successfully simulates the large gradient temperature distribution near the wall, achieving aerothermal prediction with 65.96% and 72.34% efficiency improvements compared to traditional numerical simulations using the PINN and PE-PINN models. This provides an effective method for rapid iterative calculations of fluid–structure interaction in the thermal protection systems of hypersonic vehicles.

To further improve the efficiency of the unified gas-kinetic wave-particle (UGKWP) method in hypersonic rarefied non-equilibrium flows, particularly the particle simulation process, we presented the first application of the three-dimensional UGKWP method to multiple graphics processing unit (GPU) devices in this study. The wave and particle evolution components of the method are addressed using cell and particle paralleling strategies, respectively, enabling the primary loop of the GPU-based UPKWP (GPU-UGKWP) to be executed entirely by the compute unified device architecture threads on GPU devices. Concurrently, communication issues between central processing unit (CPU) nodes are resolved by employing the message passing interface model. Additionally, we introduce a tailored memory management scheme for the GPU-UGKWP method, facilitating efficient access to the particle array. Performance comparisons reveal that, relative to a single Intel Xeon Gold 6148 CPU core, the Nvidia Tesla P100 achieves a total speedup of 34 using one GPU device, and 226 with eight GPU devices, and a single Nvidia Titan V GPU device attains a speedup of 62. The speedup outcomes on multiple CPU cores and GPU devices demonstrate that the GPU-based algorithm is better suited for computationally demanding tasks, particularly in particle-dominated simulations. As evidenced by the reduced calculation time for a hypersonic technology vehicle simulation performed on the P100 cluster, GPU devices significantly outperform their CPU counterparts.

Recently, the unified gas-kinetic wave-particle (UGKWP) method was proposed to describe rarefied flows efficiently in all Knudsen numbers. However, the initial UGKWP method with the Bhatnagar–Gross–Krook (BGK) model has its own limitations due to the unit Prandtl number. To further improve its performance in non-equilibrium flows, the UGKWP method is extended for the ellipsoidal statistical model (ESBGK) and Shakhov model (SBGK) in this paper. The numerical results of shock structures of the above BGK-type models show that the SBGK model fits the reference data best except the asymmetry parameter, and the ESBGK model comes the second. Meanwhile, in order to overcome the deficiency of negative probability density function (PDF) in SBGK, the UGKWP method with positive-preserving Shakhov PDF is also constructed. It weakens the early rising of temperature phenomenon that existed in the SBGK model, and improves the performance in predicting the stress and heat flux evidently at high Mach numbers.