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. The hierarchical attribute has two advantages: (i) It allows the simplification of the training phase via transfer learning, starting from the slowest temporal scales; (ii) It accelerates the prediction step by enabling adaptivity as the surrogate's evaluation is limited to the necessary leaf nodes based on the local degree of non-equilibrium of the gas. The model is applied to the study of chemical kinetics relevant for application to hypersonic flight, and it is tested here on pure oxygen gas mixtures. In 0-[Formula: see text] scenarios, the proposed ML framework can adaptively predict the dynamics of almost thirty species with a maximum relative error of 4.5% for a wide range of initial conditions. Furthermore, when employed in 1-[Formula: see text] shock simulations, the approach shows accuracy ranging from 1% to 4.5% and a speedup of one order of magnitude compared to conventional implicit schemes employed in an operator-splitting integration framework. Given the results presented in the paper, this work lays the foundation for constructing an efficient ML-based surrogate coupled with reactive Navier-Stokes solvers for accurately characterizing non-equilibrium phenomena in multi-dimensional computational fluid dynamics simulations.

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. The QCT emulator is constructed using three independent components: two deep operator networks (DeepONets) for inelastic and exchange processes and a feed-forward neural network (FNN) for the dissociation reactions. SurQCT is tested on the O2 + O system, showing a computational speed-up of 85%. Furthermore, we carry out a StS master equation analysis of an isochoric, isothermal heat bath simulation at various temperatures to study how the predicted rate coefficients impact the accuracy of multiple quantities of interest (QoIs) at the kinetics level (e.g., global quasi-steady state (QSS) dissociation rate coefficients and energy relaxation times). For all these QoIs, the master equation analysis relying on SurQCT data shows an accuracy within 15% across the entire temperature regime.

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

This work constructs a rovibrational state-to-state model for the O2 + O2 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. This procedure makes the problem tractable, reducing the computational cost to a comparable scale with the O2 + O system. The constructed rovibrational-specific kinetic database covers the temperature range of 7500-20 000 K. The reaction rate coefficients included in the database are parameterized in the function of kinetic and internal temperatures. Analysis of the energy transfer and dissociation process in isochoric and isothermal conditions reveals that significant departure from the equilibrium Boltzmann distribution occurs during the energy transfer and dissociation phase. Comparing the population distribution of the O2 molecules against the O2 + O case demonstrates a more significant extent of nonequilibrium characterized by a more diffuse distribution whereby the vibrational strands are more clearly identifiable. This is partly due to less efficient mixing of the rovibrational states, which results in more diffuse rovibrational distributions in the quasi-steady-state distribution of O2 + O2. A master equation analysis for the combined O2 + O and O2 + O2 system reveals that the O2 + O2 system governs the early stage of energy transfer, whereas the O2 + O system takes control of the dissociation dynamics. The findings of the present work will provide a strong physical foundation that can be exploited to construct an improved reduced-order model for oxygen chemistry.

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), 1A', 3A', and 3A″, are constructed using multireference configuration interaction (MRCI) calculations for a comprehensive arrangement of the nuclei. Quasi-classical scattering calculations provide elementary reaction rate constants resulting from the interaction between the CN, CH, and NH molecules with H, N, and C atoms, respectively. The rovibrational collisional model developed comprises 50 million bound-bound and free-bound collisional processes. This model is used to study the dynamics of energy transfer and dissociation in an isochoric and isothermal chemical reactor via the solution of the master equation for a wide temperature range from 1000 to 10,000 K. This study unravels the dynamics of dissociation of the molecules in the HCN system, which the PESs primarily control via the formation of short-lived intermediates that shortcut the dissociation pathway. The exchange processes in CH and NH enhance the dissociation by over 80%. The importance of exchange processes is also highlighted in comparing the quasi-steady state and thermal dissociation rates with state-of-the-art rate models and experimental fits.

Rovibrational-Specific QCT and Master Equation Study on N2(X1Σg+) + O(3P) and NO(X2Π) + N(4S) Systems in High-Energy Collisions.

This work presents a detailed investigation of the energy-transfer and dissociation mechanisms in N2(X1Σg+) + O(3P) and NO(X2Π) + N(4S) 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 N2 and NO have dominant contributions to the NO formation and the corresponding extinction of N2 through the exchange process. For the considered temperature range, it is found that nearly 50% of the dissociation processes for N2 and NO molecules occur in the quasi-steady-state (QSS) regime, while for the Zel'dovich reaction, the distribution of the reactants does not reach the QSS conditions. Furthermore, using the QSS approximation to model the Zel'dovich mechanism leads to overestimating NO production by more than a factor of 4 in the high-temperature range. The breakdown of this well-known approximation has profound consequences for the approaches that heavily rely on the validity of QSS assumption in hypersonic applications. Finally, the investigation of the rovibrational state population dynamics reveals substantial similarities among different chemical systems for the energy-transfer and the dissociation processes, providing promising physical foundations for the use of reduced-order strategies in other chemical systems without significant loss of accuracy.

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

Comparisons are made between potential energy surfaces (PES) for N2 + N and N2 + N2 collisions and between rate coefficients for N2 dissociation that were computed using the quasiclassical trajectory method (QCT) on these PESs. For N2 + N we compare the Laganà's empirical LEPS surface with one from NASA Ames Research Center based on ab initio quantum chemistry calculations. For N2 + N2 we compare two ab initio PESs (from NASA Ames and from the University of Minnesota). These use different methods for computing the ground state electronic energy for N4, but give similar results. Thermal N2 dissociation rate coefficients, for the 10,000K-30,000K temperature range, have been computed using each PES and the results are in excellent agreement. Quasi-stationary state (QSS) rate coefficients using both PESs have been computed at these temperatures using the Direct Molecular Simulation of Schwartzentruber and coworkers. The QSS rate coefficients are up to a factor of 5 lower than the thermal ones and the thermal and QSS values bracket the results of shock-tube experiments. We conclude that the combination of ab initio quantum chemistry PESs and QCT calculations provides an attractive approach for the determination of accurate high-temperature rate coefficients for use in aerothermodynamics modeling.