A new spin-averaged potential energy surface (PES) for non-reactive O₂(${}^3\mathrm{\Sigma }_g^{-}$ ) + O₂(${}^3\mathrm{\Sigma }_g^{-}$ ) collisions is presented. The potential is formulated analytically according to the nature of the principal interaction components, with the main van der Waals contribution described through the improved Lennard-Jones model. All the parameters involved in the formulation, having a physical meaning, have been modulated in restricted variation ranges, exploiting a combined analysis of experimental and ab initio reference data. The new PES is shown to be able to reproduce a wealth of different physical properties, ranging from the second virial coefficients to transport properties (shear viscosity and thermal conductivity) and rate coefficients for inelastic scattering collisions. Rate coefficients for the vibrational inelastic processes of O₂, including both vibration-to-vibration (V–V) and vibration-to-translation/rotation (V–T/R) energy exchanges, were then calculated on this PES using a mixed quantum–classical method. The effective formulation of the potential and its combination with an efficient, yet accurate, nuclear dynamics treatment allowed for the determination of a large database of V–V and V–T/R energy transfer rate coefficients in a wide temperature range.

Vibration-to-translation and vibration-to-vibration rate coefficients for N₂–N₂ inelastic scattering are calculated on an improved potential including high temperature regimes.

A stagnation streamline model incorporating quantum-state-resolved chemistry is proposed to study hypersonic nonequilibrium flows along the stagnation streamline. This model is developed by reducing the full Navier–Stokes equations to the stagnation streamline with proper approximations for equation closure. The thermochemical nonequilibrium is described by either the state-to-state approach for detailed analysis or conventional two-temperature models for comparison purpose. The model is validated against various data, and nearly identical results are obtained as compared with those from full field computational fluid dynamics data. In addition, the calculated distributions agree well with the measurement data of a shock tube experiment for the dissociation and vibrational relaxation of O₂, including the distributions of species mole fractions and vibrational temperature of the first excited state of O₂ molecules. Furthermore, the results with the state-resolved chemistry show that the flow within a shock layer exhibits a strong thermochemical nonequilibrium behavior, which is beyond the capability of commonly used two-temperature models to correctly evaluate the dissociation rate and the associated reaction energy. The present model is also employed to calculate the nonequilibrium re-entry flow along the stagnation streamline for a five-species air mixture as an example to demonstrate the model capability. It is found that both species and internal energy are in a nonequilibrium state, especially the vibrational distributions are strongly deviated from the Boltzmann distribution right behind the bow shock and near the wall surface. The results demonstrate that the proposed stagnation streamline model is very useful to understand thermochemical nonequilibrium phenomena in hypersonic flows.

The state-of-the-art high-fidelity state-to-state (StS) model is performed to investigate the hypersonic shock wave/laminar boundary layer interaction over a 25°–55° double cone. This work aims to clarify whether the shortcomings of thermochemical models are the underlying source for the discrepancies between the simulations and experiments. A high-enthalpy nitrogen flow with a Mach number of 11.54 and a unit Reynolds number of 4.394×105/m is considered. We first find that the StS and widely used two-temperature models yield two different shock reflection patterns (i.e., the regular reflection and Mach reflection, respectively). However, the surface pressure and heat flux distributions predicted by the two models are generally consistent, which are not influenced by the differences in the shock patterns, dissociation rates, and non-Boltzmann vibrational distributions in the flowfields. Moreover, the StS model fails to match the experiments in spite of fairly limited improvement. Our findings indicate that the shortcomings of thermochemical models are not the main reason for the discrepancies in the simulations and experiments for the high-enthalpy nitrogen double-cone flow.

The large vibrational deactivation, experimentally found in low-energy O + N ₂ collisions, is quantitatively reproduced only using a vibronic mechanism.

In this study, complete (i.e., including all vibrational quantum numbers in an N₂ vibrational ladder) data sets of vibration-to-vibration and vibration-to-translation rate coefficients for N₂-N₂ collisions are explicitly computed along with transport properties (shear and bulk viscosity, thermal conductivity, and self-diffusion) in the temperature range 100-9000 K. To reach this goal, we improved a mixed quantum-classical (MQC) dynamics approach by lifting the constraint of a Morse treatment of the vibrational wave function and intramolecular potential and permitting the use of more realistic and flexible representations. The new formulation has also allowed us to separately analyze the role of intra- and intermolecular potentials on the calculated rates and properties. Ab initio intramolecular potentials are indispensable for highly excited vibrational states, though the Morse potential still gives reasonable values up to v = 20. An accurate description of the long-range interaction and the van der Waals well is a requisite for the correct reproduction of qualitative and quantitative rate coefficients, particularly at low temperatures, making physically meaningful analytical representations still the best choice compared to currently available ab initio potential energy surfaces. These settings were used to directly compute the MQC rates corresponding to a large number of initial vibrational quantum numbers, and the missing intermediate values were predicted using a machine learning technique (i.e., the Gaussian process regression approach). The obtained values are reliable in the wide temperature range employed and are therefore valuable data for many communities dealing with nonlocal thermal equilibrium conditions in different environments.