Machine Learning-Assisted High-Temperature Nonequilibrium Kinetics of Air Components: From Microscale to Macroscale.

Area of Science:

• Chemical Kinetics
• Computational Chemistry
• Aerospace Engineering

Background:

• High-temperature air kinetics require state-to-state (StS) accuracy for modeling extreme conditions.
• Traditional methods for generating microscopic state-specific data are computationally expensive.
• Existing macroscopic thermochemical models need revision with detailed microscopic data.

Purpose of the Study:

• To review machine learning (ML) applications in high-temperature nonequilibrium kinetics.
• To highlight ML's role in generating accurate state-specific kinetic data efficiently.
• To promote ML-driven simulations for aerospace and plasma applications.

Main Methods:

• Utilizing machine learning for electronic structure calculations.
• Applying ML to fit potential energy surfaces.
• Employing ML for kinetic database construction.
• Reviewing ML advancements in non-adiabatic dynamics and scattering.

Main Results:

• Machine learning significantly reduces the computational cost of obtaining state-specific kinetic data.
• ML enables the generation of accurate datasets for high-temperature air components.
• ML-augmented models improve simulation accuracy in extreme nonequilibrium environments.
• ML facilitates the replacement or enhancement of traditional engineering models.

Conclusions:

• Machine learning is a transformative tool for high-temperature nonequilibrium kinetics.
• ML-powered simulations offer enhanced accuracy for atmospheric entry, hypersonic flight, and combustion.
• Further research is needed to address challenges in non-adiabatic dynamics and complex systems.
• Adoption of ML workflows will accelerate future dynamics and kinetics research.