The current work presents the collision integral data for N(⁴ S)–N(⁴ S, ² D, ² P) and O(³ P, ¹ D, ¹ S)–O(³ P, ¹ D, ¹ S) interactions in the temperature range of 500–50 000 K. The collision integrals are calculated based on high-quality potential energy curves (PECs) obtained from fitting the high-level a b i n i t i o calculation data in a wide energy range to the neural network (NN) functions. In the construction of PECs, the diabatic PECs are adopted when avoided crossings exist because the diabatic paths are much more likely to be followed for such situations. Moreover, the nonadiabatic transition effects are estimated to be negligible for PECs crossings. The accuracy of traditional analytical formulas to fit PECs are also examined. It is found that the collision integral calculations are sensitive to the accuracy of PECs and the NN based PECs overwhelm the others. The contribution of inelastic excitation exchange processes to the diffusion collision integrals are also computed by using an accurate evaluation of the differences of PECs for gerade and ungerade pairs of excited atoms. Finally, based on the new collision integral data, we calibrate the collision model parameters suitable for the widely used particle simulation methods. The collision integrals and collision models developed in this work can be used to support high-confidence simulations of weakly ionized air plasma problems.

In this paper, the coupling effect of multiphysical fields of droplet migration is deeply studied by constructing a physical model of droplet migration with multiphysical fields. Digital holographic interferometry and particle image velocimetry are used to simultaneously measure the temperature and velocity fields of the mother liquor in the process of droplet migration for the first time. Due to the advancements of measuring, the zero-velocity region is also in the region where the thermal wake appears, four vortexes appear in the droplet migration and the off-axis behavior of double-droplet migration is found. The aim of this work is to analyze the coupling relationship of multiphysical fields, so as to reveal the physical laws of thermocapillary migration of single droplet and multiple droplets with the same phase and heterophase and to study the driving mechanism of the thermocapillary force and the flow of the mother liquor.

The energy transfer and accommodation for the catalytic recombination of atomic oxygen (O) on silica surfaces, a key process to be understood for the accurate prediction of aerothermal heating of hypersonic vehicles, are studied using a combination of the density functional theory (DFT) and reactive molecular dynamics calculations. The key elementary reactions are determined by the DFT calculations, along with the barriers and changes of free energy for each reaction. The energy carried by the recombined oxygen molecules (O2) and its partition into different internal modes for various reactions are obtained by averaging the corresponding molecular information from a significant number of molecular dynamics trajectories. The chemical energy accommodation (CEA) coefficients, for various reactions, internal modes, and surface structures, are then computed based on the free energy changes and energies carried by the recombined O2. Moreover, the detailed energy distributions of O2 are also provided. It is found that CEA for the recombination of O on the silica surfaces depends greatly on the reaction type and internal energy mode but is less profoundly influenced by the surface structures. The results of the present study offer better insights into the mechanisms of chemical energy transfer and accommodation for the catalytic recombination of O on silica surfaces and can help improve the modeling of the relevant gas–surface interactions for more reliable aerothermal heating prediction.

A hybrid stochastic simulation method is developed to study H2–O2 auto-ignition at the microscale. Simulation results show that the discrete and stochastic characteristics of reaction collisions have notable impacts on the ignition process, particularly in the early stages when only a few radicals exist. The statistical properties of ignition delay time, which reflect the accumulated stochasticity during ignition, are obtained and analyzed for different initial temperatures and total molecular numbers. It is found that the average and standard deviation of ignition delay time increase as the total molecular number decreases, with this phenomenon being particularly pronounced near the crossover temperature. When the total molecular number is sufficiently small, the chain initiation reaction becomes crucial to the stochastic properties, as its average firing time exhibits an inverse proportionality to the total molecular number. As the total molecular number increases, the influence of other chain reactions intensifies, causing the power law relation between standard deviation and total molecular number to shift from −1 power to −0.5 power. Owing to different chain reaction paths for high- and low-temperature auto-ignition, the strongest relative fluctuation occurs near the crossover temperature. A theoretical equation for the standard deviation of ignition delay time is obtained based on dimensional analysis, giving excellent agreement with the simulation results in both high- and low-temperature modes.