A Lagrangian Stochastic Model for Heavy Particle Deposition

Area of Science:

• Fluid Dynamics
• Particle Transport
• Turbulence Modeling

Background:

• Accurate modeling of heavy particle deposition in turbulent flows is crucial for various industrial and environmental applications.
• Existing Lagrangian stochastic models often fail to capture particle behavior in specific flow regimes, particularly near walls.
• Understanding particle concentration dynamics in the viscous sublayer is essential for predicting deposition rates.

Purpose of the Study:

• To develop and validate a novel Lagrangian stochastic model for heavy particle deposition.
• To investigate the prediction capabilities of the model for different particle inertia regimes.
• To assess the model's ability to predict particle concentration build-up in the viscous sublayer.

Main Methods:

• A Lagrangian stochastic model was developed, treating heavy particles as tracers in a virtual fluid.
• Particle velocity statistics were derived from the particle momentum equation.
• The model was validated against experimental data and direct numerical simulations for various flow regimes.

Main Results:

• The model shows excellent agreement with experimental data for the inertia-moderated and diffusion-impaction regimes (tau+p > 5).
• The model successfully predicts a build-up of particle concentration in the viscous sublayer, consistent with direct numerical simulations.
• Current Lagrangian stochastic models and diffusion/free-flight models do not predict this viscous sublayer build-up.

Conclusions:

• The proposed Lagrangian stochastic model provides a more accurate representation of heavy particle deposition, especially near surfaces.
• The model's ability to predict particle concentration build-up in the viscous sublayer offers improved insights into deposition mechanisms.
• This model advances the understanding and prediction of particle transport in turbulent flows.