Turbulent dispersed multiphase flows are common in many engineering and environmental applications. The stochastic nature of both the carrier-phase turbulence and the dispersed-phase distribution makes the problem of turbulent dispersed multiphase flow far more complex than its single-phase counterpart. In this article we first review the current state-of-the-art experimental and computational techniques for turbulent dispersed multiphase flows, their strengths and limitations, and opportunities for the future. The review then focuses on three important aspects of turbulent dispersed multiphase flows: the preferential concentration of particles, droplets, and bubbles; the effect of turbulence on the coupling between the dispersed and carrier phases; and modulation of carrier-phase turbulence due to the presence of particles and bubbles.

Turbulent Dispersed Multiphase Flow

UNLABELLED A large number of infectious diseases are believed to be transmitted between people via large droplets and by airborne routes. An understanding of evaporation and dispersion of droplets and droplet nuclei is not only significant for developing effective engineering control methods for infectious diseases but also for exploring the basic transmission mechanisms of the infectious diseases. How far droplets can move is related to how far droplet-borne diseases can transmit. A simple physical model is developed and used here to investigate the evaporation and movement of droplets expelled during respiratory activities; in particular, the well-known Wells evaporation-falling curve of droplets is revisited considering the effect of relative humidity, air speed, and respiratory jets. Our simple model considers the movement of exhaled air, as well as the evaporation and movement of a single droplet. Exhaled air is treated as a steady-state non-isothermal (warm) jet horizontally issuing into stagnant surrounding air. A droplet is assumed to evaporate and move in this non-isothermal jet. Calculations are performed for both pure water droplets and droplets of sodium chloride (physiological saline) solution (0.9% w/v). We calculate the droplet lifetimes and how droplet size changes, as well as how far the droplets travel in different relative humidities. Our results indicate that a droplet's size predominately dictates its evaporation and movement after being expelled. The sizes of the largest droplets that would totally evaporate before falling 2 m away are determined under different conditions. The maximum horizontal distances that droplets can reach during different respiratory activities are also obtained. Our study is useful for developing effective prevention measures for controlling infectious diseases in hospitals and in the community at large. PRACTICAL IMPLICATIONS Our study reveals that for respiratory exhalation flows, the sizes of the largest droplets that would totally evaporate before falling 2 m away are between 60 and 100 microm, and these expelled large droplets are carried more than 6 m away by exhaled air at a velocity of 50 m/s (sneezing), more than 2 m away at a velocity of 10 m/s (coughing) and less than 1 m away at a velocity of 1 m/s (breathing). These findings are useful for developing effective engineering control methods for infectious diseases, and also for exploring the basic transmission mechanisms of the infectious diseases. There is a need to examine the air distribution systems in hospital wards for controlling both airborne and droplet-borne transmitted diseases.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1600-0668.2007.00469.x

How far droplets can move in indoor environments--revisiting the Wells evaporation-falling curve.

A random flow generation (RFG) technique is presented, which can be used for initial/ inlet boundary generation in LES (Large-Eddy-Simulations) or particle tracking in LES/ RANS (Reynolds-Averaged Navier-Stokes) computations of turbulent flows. The technique is based on previous methods of synthesizing divergence-free vector fields from a sample of Fourier harmonics and allows to generate non-homogeneous anisotropic flow field representing turbulent velocity fluctuations. It was validated on the cases of boundary layer and flat plate flows. Applications of the technique to LES and particle tracking are considered

Random Flow Generation Technique for Large Eddy Simulations and Particle-Dynamics Modeling

The approach of combining computational fluid dynamics (CFD) for continuum fluid and the discrete element method (DEM) for discrete particles has been increasingly used to study the fundamentals of coupled particle–fluid flows. Different CFD–DEM models have been used. However, the origin and the applicability of these models are not clearly understood. In this paper, the origin of different model formulations is discussed first. It shows that, in connection with the continuum approach, three sets of formulations exist in the CFD–DEM approach: an original format set I, and subsequent derivations of set II and set III, respectively, corresponding to the so-called model A and model B in the literature. A comparison and the applicability of the three models are assessed theoretically and then verified from the study of three representative particle–fluid flow systems: fluidization, pneumatic conveying and hydrocyclones. It is demonstrated that sets I and II are essentially the same, with small differences resulting from different mathematical or numerical treatments of a few terms in the original equation. Set III is however a simplified version of set I. The testing cases show that all the three models are applicable to gas fluidization and, to a large extent, pneumatic conveying. However, the application of set III is conditional, as demonstrated in the case of hydrocyclones. Strictly speaking, set III is only valid when fluid flow is steady and uniform. Set II and, in particular, set I, which is somehow forgotten in the literature, are recommended for the future CFD–DEM modelling of complex particle–fluid flow.

Discrete particle simulation of particle–fluid flow: model formulations and their applicability

A drug product combines pharmacologic activity with pharmaceutical properties. Desirable performance characteristics are physical and chemical stability, ease of processing, accurate and reproducible delivery to the target organ, and availability at the site of action. For the dry powder inhaler (DPI), these goals can be met with a suitable powder formulation, an efficient metering system, and a carefully selected device. This review focuses on the DPI formulation and development process. Most DPI formulations consist of micronized drug blended with larger carrier particles, which enhance flow, reduce aggregation, and aid in dispersion. A combination of intrinsic physicochemical properties, particle size, shape, surface area, and morphology affects the forces of interaction and aerodynamic properties, which in turn determine fluidization, dispersion, delivery to the lungs, and deposition in the peripheral airways. When a DPI is actuated, the formulation is fluidized and enters the patient's airways. Under the influence of inspiratory airflow, the drug particles separate from the carrier particles and are carried deep into the lungs, while the larger carrier particles impact on the oropharyngeal surfaces and are cleared. If the cohesive forces acting on the powder are too strong, the shear of the airflow may not be sufficient to separate the drug from the carrier particles, which results in low deposition efficiency. Advances in understanding of aerosol and solid state physics and interfacial chemistry are moving formulation development from an empirical activity to a fundamental scientific foundation.

http://rc.rcjournal.com/content/respcare/50/9/1209.full.pdf

Dry powder inhaler formulation.

Introduction Industrial Applications Energy Conversion and Propulsion Fire Suppression and Control Summary Properties of Dispersed Phase Flows Concept of a Continuum Density and Volume Fraction Particle or Droplet Spacing Response Times Stokes Number Dilute versus Dense Flows Phase Coupling Properties of an Equilibrium Mixture Summary Exercises Size Distribution Discrete Size Distributions Continuous Size Distributions Statistical Parameters Frequently Used Size Distributions Summary Exercises Particle-Fluid Interaction Single-Particle Equations Mass Coupling Linearmomentumcoupling Energy Coupling Summary Exercises Particle-Particle Interaction Particle-Particle Interaction Particle-Wall Interaction Summary Exercises Continuous Phase Equations Averaging Procedures Volume Averaging Property Flux Through a Particle Cloud Volume-Averaged Conservation Equations Equation Summary Summary Exercises Turbulence Review of Turbulence in Single-Phase Flow Turbulence Modulation by Particles Review of Modulation Models Basic Test Case for Turbulence Models Volume-Averaged Turbulence Models Application to Experimental Results Summary Exercises Droplet-Particle Cloud Equations Discrete Element Method (DEM) Discrete Parcel Method (DPM) Two-Fluid Model PDF Models Summary Numerical Modeling Complete Numerical Simulation DNS Models LES Models VANS Numerical Models Summary Experimental Methods Sampling Integral Methods Local Measurement Techniques Summary Exercises Appendix A: Single-Particle Equations Appendix B: Volume Averaging Appendix C: Volume-Averaged Equations Appendix D: Turbulence Equations 425 Appendix E: Brownian Motion References Nomenclature Index

Multiphase Flows with Droplets and Particles

A dissipation transport equation for the carrier phase turbulence in particle-laden flow is derived from fundamental principles. The equation is obtained by volume averaging, which inherently includes the effects of the particle surfaces. Three additional terms appear that reveal the effect of the particles; these terms are evaluated using Stokes drag law. Two of the terms reduce to zero and only one term remains which is identified as the production of dissipation due to the particles. The dissipation equation is then applied to cases where particles generate homogeneous turbulence, and experimental data are used to evaluate the empirical coefficients. The ratio of the coefficient of the production of dissipation (due to the presence of particles) to the coefficient of the dissipation of dissipation is found to correlate well with the relative Reynolds number. V C 2009 American Institute of Chemical Engineers AIChE J, 55: 1416–1425, 2009

http://www.mstf.wsu.edu/articles/pros_AIChE3.pdf

John D. Schwarzkopf

Papers

Multiphase Flows with Droplets and Particles

A Turbulence Dissipation Model for Particle Laden Flow

Droplet-Particle Cloud Equations

Direct numerical simulation of stationary particles in homogeneous turbulence decay: Application of the k–ε model

Properties of Dispersed Phase Flows