Scattering kernels are of paramount importance in modeling gas-surface interactions for rarefied gas flows. However, most existing empirical models need one or several accommodation coefficients (ACs) to be determined before applications. In this paper, an unsupervised machine learning technique, known as the Gaussian mixture (GM) model, is applied to establish a new scattering kernel based on the simulated data collected by molecular dynamics (MD) simulations. The main work is devoted to the scattering of diatomic molecules under thermal non-equilibrium conditions. Correspondingly, different MD simulations on the scattering process of nitrogen molecules from a platinum surface have been performed, involving rotational and translational excitation. Here we evaluate the performance of the GM and Cercignani-Lampis-Lord (CLL) models against the MD approach, by comparing the velocity correlation distributions and the relevant outgoing velocity PDFs, as well as the computed ACs. The presented comparisons have demonstrated the superiority of the GM model in matching with MD results. Therefore, in the case of diatomic gases, the GM model can be employed as a promising strategy to derive the generalized boundary conditions.
Attacks using penetration-explosion warheads can cause the backside of concrete targets to collapse. Current research on collapse damage mainly focuses on the damage caused by external explosions and the damage of targets with prefabricated holes under internal blasts. However, the penetration effect also affects the destructive effect of the explosion-induced collapse, so experiments on prefabricated borehole charges cannot fully reflect real implosions. To study the effect of the initial penetration damage of imploding concrete structures with finite thickness on their collapse, in this study, field experiments are carried out on concrete targets with different initial penetration damage. The collapse damage of the targets is analyzed, and the dimensional analysis method is used to fit the experimental data and obtain the rules and prediction methods of the effect of the initial penetration damage on the relative collapse thickness. The collapse depth of the concrete targets is found to decrease with the increase in the impact energy factor; this increase is found to reduce gradually until stabilization. The conclusion has been verified and analyzed in depth through numerical simulations. The results of this study can provide a basis for subsequent simulations of the actual penetration and explosion effects and a reference for the optimal protection design of concrete structures and the optimal damage design of penetration-explosion warheads.
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