Air Flow over Nanoparticle Clusters


In order to characterize nanoparticle pollutants, particles can be given a known charge and accelerated via a known electric field. The measured time of flight is then directly related to the drag force on the particles caused by the surrounding air. If this drag force can be related to nanoparticle cluster geometry and size then such measured time of flight can be used to characterize nanoparticle pollutants in a given sample of plant emissions. Such experimental and theoretical work is the specialty of Prof. Chris Hogan in the Mechanical Engineering department at the University of Minnesota:

Results and Publications


Figure shows diffusion-velocity (5 m/s) flow over a nanoparticle dimer computed by the DSMC method.

Figure shows computed drag and lift over a 3-particle nano cluster for various flow angles.

Transport of Nanoparticle Aggregates using DSMC:

    Zhang C., Thajudeen T., Larriba C., Schwartzentruber T. E., & Hogan C. J., “Determination of

    the Scalar Friction Factor for Non-spherical Particles and Aggregates Across the Entire Knudsen

    Number Range by Direct Simulation Monte Carlo (DSMC)”, Aerosol Sci. Technol. (2012) 46:


Since the gas flow lies in the transitional regime between continuum and free-molecular, the DSMC method is an ideal simulation tool for this study. This research represents a recent collaboration between our group and Prof. Hogan’s, where we are providing high-fidelity modeling of nonequilibrium flow over complex nanoparticle cluster geometries. The MGDS code developed and maintained in our group easily handles any complex nanoparticle cluster geometry. The main challenges for the DSMC method in this flow regime are the procedures to accurately specify subsonic boundary conditions and the low bulk velocity in comparison with the thermal velocity. For example, a typical flow has a bulk velocity of 5 m/s, however, the average thermal speed of the gas molecules is roughly 300 m/s (corresponding to room temperature). Thus a large number of statistical sampling steps are required for an accurate DSMC prediction. A sample DSMC solution is shown in the Figure below.

Since the flow over such nanoparticles actually lies in a regime where both nonequilibrium effects and real fluctuations are physically important (i.e. these particles are “random-walkers”), it is appropriate to determine the orientationally-averaged drag from multiple steady-state DSMC simulations performed at various flow angles. An example of predicted drag values for flow of a 3-sphere nanoparticle at various flow angles is shown in the Figure below.

Figure shows triangulated geometry and flow solution for complex aggregate geometries.