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Particles on demand method: Theoretical analysis, simplification techniques, and model extensions.

N G Kallikounis1, I V Karlin1

  • 1Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland.

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Summary
This summary is machine-generated.

This study enhances the particles on demand method for fluid dynamics simulations. New strategies improve accuracy and extend the model

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Area of Science:

  • Computational fluid dynamics
  • Kinetic theory
  • Numerical methods

Background:

  • The particles on demand (POD) method offers a novel approach to fluid simulations.
  • Previous formulations utilized conservative finite-volume discretization and were validated against benchmarks.
  • Understanding reference frame transformations is crucial for model accuracy.

Purpose of the Study:

  • To analyze the impact of reference frame transformations on the POD method's accuracy.
  • To develop simplified and generalized versions of the POD scheme.
  • To enhance the model's applicability to a wider range of flow conditions.

Main Methods:

  • Investigated reference frame transformation properties.
  • Introduced strategies for scheme simplification and generalization.
  • Incorporated tunable Prandtl number via quasi-equilibrium relaxation.
  • Adapted multiscale semi-Lagrangian lattice Boltzmann concepts.

Main Results:

  • Proposed simplified and generalized POD schemes.
  • Achieved tunable Prandtl number through quasi-equilibrium relaxation.
  • Demonstrated excellent accuracy and stability in high Mach compressible flow simulations.
  • Extended the operational range of the kinetic model.

Conclusions:

  • The enhanced POD method shows significant improvements in accuracy and stability.
  • The proposed modifications broaden the applicability of kinetic models for compressible flows.
  • This work provides a more versatile and robust tool for computational fluid dynamics research.