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We present a tool chain to analyze the internal processes of turbulent flows, measured by Lagrangian Particle Tracking (LPT) and Particle Image Thermometry (PIT). It is based on data assimilation by means of Physics-Informed Neural Networks and a subsequent construction of joint distributions of the kinetic energy, velocity vector field curvature and temperature variance as well as the evaluation of their time derivatives. We apply this tool chain to a measured data set of Rayleigh-Bénard convection (RBC) at a Rayleigh number \(\textrm{Ra} = 3.4 \times 10^7\) and a Prandtl number \(\textrm{Pr} = 10.6\) resulting in a curvature-based energy-spectrum and an investigation of the links between the physical and phase space.

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A Tracer Particle’s Path through the Turbulent Energy Spectrum

  • 会议论文

  • 发表日期：

    2026年

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  • Michael Mommert,
  • Theo Käufer,
  • Christian Cierpka,
  • Claus Wagner

摘要

We present a tool chain to analyze the internal processes of turbulent flows, measured by Lagrangian Particle Tracking (LPT) and Particle Image Thermometry (PIT). It is based on data assimilation by means of Physics-Informed Neural Networks and a subsequent construction of joint distributions of the kinetic energy, velocity vector field curvature and temperature variance as well as the evaluation of their time derivatives. We apply this tool chain to a measured data set of Rayleigh-Bénard convection (RBC) at a Rayleigh number \(\textrm{Ra} = 3.4 \times 10^7\) and a Prandtl number \(\textrm{Pr} = 10.6\) resulting in a curvature-based energy-spectrum and an investigation of the links between the physical and phase space.