A CFD Based Residence Time Distribution Model for Aerosol Flows

A residence time distribution (RTD) describes the distribution of times that particles spend within a flow system and can be represented as a normalized probability density function. It is a fundamental descriptor of aerosol transport in systems where time-dependent processes occur, including sampling lines, measurement systems, reactors, and inhalation flows. Accurate RTD characterization is therefore essential for interpreting transient signals and predicting time-dependent processes such as chemical reactions, phase change, and particle transformation.

In aerosol flow systems, ideal plug flow corresponds to a delta function RTD (i.e., with zero variance); however, practical systems deviate from this limit due to wall effects, flow recirculation, and diffusion, which broaden the RTD. In aerosol instrumentation, this broadening leads to smearing of concentration signals and reduced temporal response and measurement accuracy.

In this work, a Computational Fluid Dynamics (CFD) framework is developed in OpenFOAM to predict RTDs in laminar and turbulent internal flows relevant to aerosol instrumentation. The approach first resolves the gas-phase flow field by solving the Navier–Stokes equations (or appropriate turbulence models), after which particle transport is modeled through coupled advection–diffusion equations. Key deposition mechanisms, including Brownian diffusion, are incorporated to capture particle–wall interactions that broaden the RTD. Model verification is performed through mesh independence studies and comparison against analytical solutions, including Poiseuille flow and well-established particle deposition models.

Preliminary results demonstrate the sensitivity of RTD shape to flow structure and geometry, highlighting the role of recirculation zones, velocity profile and diffusive properties in broadening the distribution and limiting instrument temporal response. Experimental validation using a fast-response condensation particle counter confirms the model’s ability to predict RTDs under controlled flow conditions. The validated model provides a robust, physics-based tool for design and optimization of internal aerosol flows and achieve next-generation fast response instruments.