Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows.

This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance with the underlying physics. The framework combines dimensionality reduction and neural operators through a hierarchical and adaptive deep learning strategy to learn the solution of multi-scale coarse-grained governing equations for chemical kinetics. The proposed surrogate's architecture is structured as a tree, with leaf nodes representing separate neural operator blocks where physics is embedded in the form of multiple soft and hard constraints.

Efficient quasi-classical trajectory calculations by means of neural operator architectures.

An accurate description of non-equilibrium chemistry relies on rovibrational state-to-state (StS) kinetics data, which can be obtained through the quasi-classical trajectory (QCT) method for high-energy collisions. However, these calculations still represent one of the major computational bottlenecks in predictive simulations of non-equilibrium reacting gases. This work addresses this limitation by proposing SurQCT, a novel machine learning-based surrogate for efficiently and accurately predicting StS chemical reaction rate coefficients.

Rovibrational internal energy transfer and dissociation of high-temperature oxygen mixture.

This work constructs a rovibrational state-to-state model for the O + O system leveraging high-fidelity potential energy surfaces and quasi-classical trajectory calculations. The model is used to investigate internal energy transfer and nonequilibrium reactive processes in a dissociating environment using a master equation approach, whereby the kinetics of each internal rovibrational state is explicitly computed. To cope with the exponentially large number of elementary processes that characterize reactive bimolecular collisions, the internal states of the collision partner are assumed to follow a Boltzmann distribution at a prescribed internal temperature.

Comprehensive Study of HCN: Potential Energy Surfaces, State-to-State Kinetics, and Master Equation Analysis.

Understanding the kinetics of the HCN system is critical to several disciplines in science and engineering, including interstellar chemistry, atmospheric reentry, and combustion, to name a few. This paper constructs a rovibrational state-specific kinetic mechanism for the HCN system, leveraging electronic structure calculations, classical scattering dynamics, and state-to-state kinetics. To this aim, three accurate potential energy surfaces (PESs), ', ', and ″, are constructed using multireference configuration interaction (MRCI) calculations for a comprehensive arrangement of the nuclei.

Rovibrational-Specific QCT and Master Equation Study on N(XΣ) + O(P) and NO(XΠ) + N(S) Systems in High-Energy Collisions.

This work presents a detailed investigation of the energy-transfer and dissociation mechanisms in N(XΣ) + O(P) and NO(XΠ) + N(S) systems using rovibrational-specific quasiclassical trajectory (QCT) and master equation analyses. The complete set of state-to-state kinetic data, obtained via QCT, allows for an in-depth investigation of the Zel'dovich mechanism leading to the formation of NO molecules at microscopic and macroscopic scales. The master equation analysis demonstrates that the low-lying vibrational states of N and NO have dominant contributions to the NO formation and the corresponding extinction of N through the exchange process.

Comparison of potential energy surfaces and computed rate coefficients for N dissociation.

Comparisons are made between potential energy surfaces (PES) for N + N and N + N collisions and between rate coefficients for N dissociation that were computed using the quasiclassical trajectory method (QCT) on these PESs. For N + N we compare the Laganà's empirical LEPS surface with one from NASA Ames Research Center based on quantum chemistry calculations. For N + N we compare two PESs (from NASA Ames and from the University of Minnesota).