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

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This study introduces a machine learning (ML) framework to speed up non-equilibrium reacting flow simulations. The hierarchical deep learning model accurately predicts chemical kinetics for hypersonic flight applications.

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Area of Science:

  • Computational fluid dynamics
  • Chemical kinetics
  • Machine learning

Background:

  • Simulating non-equilibrium reacting flows is computationally intensive.
  • Accurate modeling of chemical kinetics is crucial for applications like hypersonic flight.

Purpose of the Study:

  • To develop a machine learning (ML)-based surrogate model to enhance computational efficiency in non-equilibrium reacting flow simulations.
  • To ensure the ML model adheres to fundamental physical principles.

Main Methods:

  • A hierarchical and adaptive deep learning strategy combining dimensionality reduction and neural operators.
  • Physics-informed neural operator blocks with soft and hard constraints.
  • Transfer learning for simplified training and adaptive prediction for accelerated evaluations.

Main Results:

  • Accurate prediction of chemical kinetics for nearly thirty species in 0-D scenarios with a maximum relative error of 4.5%.
  • Achieved 1-4.5% accuracy in 1-D shock simulations, with a tenfold speedup over conventional methods.
  • Demonstrated adaptive prediction capabilities based on local non-equilibrium conditions.

Conclusions:

  • The proposed ML framework provides a foundation for efficient, physics-compliant surrogates for reactive Navier-Stokes solvers.
  • Enables accurate characterization of non-equilibrium phenomena in complex multi-dimensional simulations.
  • Offers significant speedup for simulating hypersonic flow chemical kinetics.