Comparison of potential energy surfaces and computed rate coefficients for N2 dissociation

Formation Mechanisms of Electronically Excited Nitrogen Molecules from N + N2 and N + N + N Collisions Revealed by Full-Dimensional Potential Energy Surfaces

This work reports six new full-dimensional adiabatic potential energy surfaces (PESs) of the N3 system (four 4A″ states and two 2A″ states) at the MRCI + Q/AVQZ level of theory that correlated to N2(X1Σg+) + N(4S), N2(X1Σg+) + N(2D), N2(A3Σu+) + N(4S), N2(B3Πg) + N(4S), N2(W3Δu) + N(4S), and N(4S) + N(4S) + N(4S) channels. The neural networks with a proper account of the nuclear permutation invariant symmetry of N3 were employed to fit the PESs based on about 4000 ab initio points. The accuracy of the PESs was validated by excellent agreement on the equilibrium bond length, vertical excitation energy, and dissociation energy with experimental values. Two possible mechanisms of the formation of N2(A) were found. One is that the collision occurs between N2(X) and N(4S) in the 14A″ state, followed by a nonadiabatic transition through the conical intersection with the 24A″ PES, resulting in the formation of the N2(A) + N(4S) product. The other takes place in the collision among three N(4S) atoms in the adiabatic 24A″ state, and then, N2(A) + N(4S) is formed. This is the first systematical research of the N3 system focusing on the formation of the excited states of N2 via both adiabatic and nonadiabatic pathways.

Stochastic determination of thermal reaction rate coefficients for air plasmas

This work deals with the stochastic inference of gas-phase chemical reaction rates in high temperature air flows from plasma wind tunnel experimental data. First, a Bayesian approach is developed to include not only measurements but also additional information related to how the experiment is performed. To cope with the resulting computationally demanding likelihood, we use the Morris screening method to find the reactions that influence the solution to the stochastic inverse problem from a mechanism comprising 21 different reactions for an air mixture with seven species: O2, N2, NO, NO+, O, N, e-. A set of six reactions, mainly involving nitrogen dissociation and exchange, are the ones identified to impact the solution the most. As such, they are assumed to be uncertain and estimated along with the boundary conditions of the experiment and the catalytic recombination parameters of the materials involved in the testing. The remaining 15 reactions are set to their nominal values. The posterior distribution is then propagated through the proposed boundary layer model to produce the posterior predictive distributions of the temperature and mass fraction profiles along the boundary layer stagnation line. It is identified that NO concentrations have the largest increase in uncertainty levels compared to cases where the inference problem is carried out for fixed chemical model parameter values. This allows us to inform a new experimental campaign targeting the reduction of uncertainties affecting the chemical models.

First-principles predictions for shear viscosity of air components at high temperature

The direct molecular simulation (DMS) method is used to obtain shear viscosity data for non-reacting air and its components by simulating isothermal, plane Poiseuille subsonic flows. Shear viscosity is estimated at several temperatures, from 273 K to 10 000 K, by fitting the DMS velocity profiles using the analytic solution of the Navier-Stokes equations for this simple canonical flow. The ab initio potential energy surfaces (PESs) that describe the various atomic-level interactions are the only input in the simulations. Molecules involved in a collision within the flow can occupy any rovibrational state that is allowed by the effective diatomic potential. For molecular nitrogen, oxygen, and air at standard condition molar composition, the DMS shear viscosity predictions are in excellent agreement with the experimental data that are available up to about 2000 K. The results for pure molecular nitrogen and pure molecular oxygen also agree very well with previously published quasi-classical trajectory (QCT) calculations based on the same PESs. It is further shown that the ab initio shear viscosity data are generally lower than the corresponding values used in popular computational fluid dynamics codes, over a wide temperature range. Finally, Wilke's mixing rule is demonstrated to accurately predict the DMS air viscosity results from the pure molecular components data up to 4000 K.

Constructing feed-forward artificial neural networks to fit potential energy surfaces for molecular simulation of high-temperature gas flows

Kinetic rates for thermochemical nonequilibrium models are generally computed from quasiclassical trajectory (QCT) calculations on accurate ab initio potential energy surfaces (PES). In this article, we use a feed-forward artificial neural network (ANN) to fit existing single-point energies for N_{2}+N_{2} interactions [Bender et al., J. Chem. Phys. 143, 054304 (2015)JCPSA60021-960610.1063/1.4927571] to construct a PES suitable for molecular simulation of high-temperature gas flows. We then perform detailed comparisons with a widely used N_{4} PES that was built using the permutation invariant polynomials (PIP) method. Specific physical considerations in the construction of the ANN for this application are detailed. Translation, rotation, and permutation invariance are precisely satisfied by mapping the interatomic distances onto a set of permutation invariant inputs, known as fundamental invariants (FI) that generate the permutation invariant polynomial ring. The diatomic energy is imposed by decomposing the total potential energy into a sum of a two-body and a many-body energy contribution. To obtain the correct dynamical behavior with the most basic, yet computationally efficient ANN, spurious long-distance interactions must be removed to avoid incorrect physical behavior at the dissociation threshold. We use a simple apodization function to smoothly taper off to zero any residual many-body interaction at large separations. Both accuracy and performance of the FI-ANN PES are assessed. QCT calculations are used to compute dissociation probabilities and vibrational energy distributions at various equilibrium temperatures. Excellent agreement with the results obtained from the PIP PES is found. For our test case, the ANN PES is also significantly more computationally efficient than the PIP PES at comparable root-mean-square error levels.

State-to-State Master Equation and Direct Molecular Simulation Study of Energy Transfer and Dissociation for the N2-N System

We present a detailed comparison of two high-fidelity approaches for simulating non-equilibrium chemical processes in gases: the state-to-state master equation (StS-ME) and the direct molecular simulation (DMS) methods. The former is a deterministic method, which relies on the pre-computed kinetic database for the N₂-N system based on the NASA Ames ab initio potential energy surface (PES) to describe the evolution of the molecules' internal energy states through a system of master equations. The latter is a stochastic interpretation of molecular dynamics relying exclusively on the same ab initio PES. It directly tracks the microscopic gas state through a particle ensemble undergoing a sequence of collisions. We study a mixture of nitrogen molecules and atoms forced into strong thermochemical non-equilibrium by sudden exposure of rovibrationally cold gas to a high-temperature heat bath. We observe excellent agreement between the DMS and StS-ME predictions for the transfer rates of translational into rotational and vibrational energy, as well as of dissociation rates across a wide range of temperatures. Both methods agree down to the microscopic scale, where they predict the same non-Boltzmann population distributions during quasi-steady-state dissociation. Beyond establishing the equivalence of both methods, this cross-validation helped in reinterpreting the NASA Ames kinetic database and resolve discrepancies observed in prior studies. The close agreement found between the StS-ME and DMS methods, whose sole model inputs are the PESs, lends confidence to their use as benchmark tools for studying high-temperature air chemistry.