Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning

Abstract

We investigate the utility of deep learning for modeling the clustering of particles that are aerodynamically coupled to turbulent fluids. Using a Lagrangian particle module within the Athena++ hydrodynamics code, we simulate the dynamics of particles in the Epstein drag regime within a periodic domain of isotropic forced hydrodynamic turbulence. This setup is an idealized model relevant to the collisional growth of micron to mm-sized dust particles in early stage planet formation. The simulation data are used to train a U-Net deep learning model to predict gridded three-dimensional representations of the particle density and velocity fields, given as input the corresponding fluid fields. The trained model qualitatively captures the filamentary structure of clustered particles in a highly non-linear regime. We assess model fidelity by calculating metrics of the density field (the radial distribution function) and of the velocity field (the relative velocity and the relative radial velocity between particles). Although trained only on the spatial fields, the model predicts these statistical quantities with errors that are typically <10%. Our results suggest that, given appropriately expanded training data, deep learning could complement direct numerical simulations in predicting particle clustering within turbulent flows.

20 pages, 13 figures; Accepted to ApJ on 2023 October 30

References in corpus (10)

• Particle Stirring in Turbulent Gas Disks: Including Orbital Oscillations
• The Athena++ Adaptive Mesh Refinement Framework: Design and Magnetohydrodynamic Solvers
• Closed-form expressions for particle relative velocities induced by turbulence
• Initial mass function of planetesimals formed by the streaming instability
• Thresholds for Particle Clumping by the Streaming Instability
• Streaming Instability for Particle-Size Distributions
• Streaming Instability with Multiple Dust Species: II. Turbulence and Dust-Gas Dynamics at Nonlinear Saturation
• A Multi-Fluid Dust Module in Athena++: Algorithms and Numerical Tests
• Stratified and vertically-shearing streaming instabilities in protoplanetary disks
• TNT: Vision Transformer for Turbulence Simulations

Cited by in corpus (1)

• The First Star-by-star $N$-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model