Deposition Patterns of Inertial Particles Mediated by Turbulent Boundary Layer Interactions

ORAL

Abstract

Particle deposition through turbulent boundary layers onto substrates is a fundamental process relevant to diverse applications. Despite decades of research, key questions remain about how inertial particles deposit, redistribute, and accumulate over time, particularly under the influence of electrostatic forces and turbulent flow structures. To address these challenges, we perform controlled deposition experiments in a vertical electrostatic turbulent channel, employing diagnostics that capture both the dynamics of charged particles in the flow and the evolving deposition patterns on a transparent wall. The deposition process unfolds in three distinct regimes. In the initial stage, sparse deposits nucleate preferentially along low-speed streaks, guided by coherent structures and electrostatic attraction. This is followed by a lateral growth phase, where deposits expand as particles are intercepted along their edges. In the final regime, neighboring deposits merge to form continuous, band-like structures separated by persistent clean zones. These gaps result from aerodynamic shadowing, in which existing deposits modify the near-wall flow and deflect incoming particles, thereby suppressing downstream deposition. By resolving the spatiotemporal dynamics of deposition and uncovering the complex interplay between turbulence, particle inertia, and electrostatic forces, this work sheds new light on long-standing questions about particle transport and surface patterning in realistic turbulent flows.

*This work is supported by the Office of Naval Research (ONR) under Grant NO. N00014-21-1-2620. This work is also partially supported by an Early Stage Innovation grant from NASA's Space Technology Research Grants Program under Grant NO. 80NSSC21K0222.

Presenters

  • Rui Ni

    • Johns Hopkins University

Authors

  • Matt T Gorman

    • Johns Hopkins University
  • Miguel X. Diaz-Lopez

    • Johns Hopkins University
  • Rui Ni

    • Johns Hopkins University