Data-Inspired and Physics-Driven Model Reduction for Dissociation: Application to the O₂ + O System.

Area of Science:

• Physical Chemistry
• Chemical Kinetics
• Computational Chemistry

Background:

• Nonequilibrium dissociation of O2 molecules by O atoms is crucial in high-temperature environments.
• Conventional vibrational-specific models struggle to accurately capture dissociation dependencies.
• Accurate modeling requires considering rovibrational states and their energy deficits relative to the centrifugal barrier.

Purpose of the Study:

• To develop and validate a new physics-based coarse-grained model for O2 dissociation.
• To compare the performance of the new model against vibrational-specific and state-to-state master equation approaches.
• To investigate the influence of internal energy deficit and rotational equilibrium on dissociation probability.

Main Methods:

• Quasi-classical trajectory (QCT) calculations incorporating nine adiabatic electronic states of O3.
• Master equation formulation and dimensionality reduction techniques.
• Development of a hybrid approach combining rovibrationally resolved excitation with coarse-grained dissociation.

Main Results:

• Dissociation probability is primarily governed by the internal energy deficit compared to the centrifugal barrier.
• Vibrational-specific models are inadequate due to their inability to characterize this energy-dependent dissociation.
• The new reduced-order model, even with twenty groups, significantly outperforms vibrational-specific models, improving accuracy by up to 2000% at 20,000 K.

Conclusions:

• The proposed coarse-graining strategy, based on energy deficit, provides a more accurate representation of O2 dissociation kinetics.
• The inadequacy of vibrational-specific models stems from their treatment of dissociation, not energy transfer.
• This reduced-order model offers a computationally efficient and highly accurate alternative for simulating high-temperature chemical kinetics.