Gravitational Collision of Small Nonspherical Particles: Swept Volumes of Prolate and Oblate Spheroids in Calm Air

Ehud Gavze aInstitute of Earth Science, Hebrew University of Jerusalem, Jerusalem, Israel

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Alexander Khain aInstitute of Earth Science, Hebrew University of Jerusalem, Jerusalem, Israel

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Abstract

The aggregation rate of ice crystals depends on their shape and intercrystal relative velocity. Unlike spherical particles, the nonspherical ones can have various orientations relative to the gravitational force in the vertical direction and can approach each other at many different angles. Furthermore, the fall velocity of such particles could deviate from the vertical direction velocity. These properties add to the computational complexity of nonspherical particle collisions. In this study, we derive general mathematical expressions for gravity-induced swept volumes of spheroidal particles. The swept volumes are shown to depend on the particles’ joint orientation distribution and relative velocities. Assuming that the particles are Stokesian prolate and oblate spheroids of different sizes and aspect ratios, the swept volumes were calculated and compared to those of equivalent volume spheres. Most calculated swept volumes were larger than the swept volumes of equivalent spherical particles, sometimes by several orders of magnitude. This was due to both the complex geometry and the side drift, experienced by spheroids falling with their major axes not parallel to gravity. We expect that the collision rate between nonspherical particles is substantially higher than that of equivalent volume spheres because the collision process is nonlinear. These results suggest that the simplistic approach of equivalent spheres might lead to serious errors in the computation of the collision rate.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Alexander Khain, alexander.khain@mail.huji.ac.il

Abstract

The aggregation rate of ice crystals depends on their shape and intercrystal relative velocity. Unlike spherical particles, the nonspherical ones can have various orientations relative to the gravitational force in the vertical direction and can approach each other at many different angles. Furthermore, the fall velocity of such particles could deviate from the vertical direction velocity. These properties add to the computational complexity of nonspherical particle collisions. In this study, we derive general mathematical expressions for gravity-induced swept volumes of spheroidal particles. The swept volumes are shown to depend on the particles’ joint orientation distribution and relative velocities. Assuming that the particles are Stokesian prolate and oblate spheroids of different sizes and aspect ratios, the swept volumes were calculated and compared to those of equivalent volume spheres. Most calculated swept volumes were larger than the swept volumes of equivalent spherical particles, sometimes by several orders of magnitude. This was due to both the complex geometry and the side drift, experienced by spheroids falling with their major axes not parallel to gravity. We expect that the collision rate between nonspherical particles is substantially higher than that of equivalent volume spheres because the collision process is nonlinear. These results suggest that the simplistic approach of equivalent spheres might lead to serious errors in the computation of the collision rate.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Alexander Khain, alexander.khain@mail.huji.ac.il
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  • Abraham, F. F., 1970: Functional dependence of drag coefficient of a sphere on Reynolds number. Phys. Fluids, 13, 21942195, https://doi.org/10.1063/1.1693218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ayala, O., W. W. Grabowski, and L.-P. Wang, 2007: A hybrid approach for simulating turbulent collisions of hydrodynamically-interacting particles. J. Comput. Phys., 225, 5173, https://doi.org/10.1016/j.jcp.2006.11.016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ayala, O., B. Rosa, and L.-P. Wang, 2008: Effects of turbulence on the geometric collision rate of sedimenting droplets. Part 2. Theory and parameterization. New J. Phys., 10, 075016, https://doi.org/10.1088/1367-2630/10/7/075016.

    • Crossref
    • Export Citation
  • Batchelor, G. K., 1967: An Introduction to Fluid Dynamics. Cambridge University Press, 615 pp.

    • Export Citation
  • Benmoshe, N., and A. Khain, 2014: The effects of turbulence on the microphysics of mixed-phase deep convective clouds investigated with a 2-D cloud model with spectral bin microphysics. J. Geophys. Res. Atmos., 119, 207221, https://doi.org/10.1002/2013JD020118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benmoshe, N., M. Pinsky, A. Pokrovsky, and A. Khain, 2012: Turbulent effects on the microphysics and initiation of warm rain in deep convective clouds: 2-D simulations by a spectral mixed-phase microphysics cloud model. J. Geophys. Res., 117, D06220, https://doi.org/10.1029/2011JD016603.

    • Search Google Scholar
    • Export Citation
  • Böhm, H. P., 1989: A general equation for the terminal fall speed of solid hydrometeors. J. Atmos. Sci., 46, 24192427, https://doi.org/10.1175/1520-0469(1989)046<2419:AGEFTT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Böhm, H. P., 1992a: A general hydrodynamic theory for mixed-phase microphysics. Part I: Drag and fall speed of hydrometeors. Atmos. Res., 27, 253274, https://doi.org/10.1016/0169-8095(92)90035-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Böhm, H. P., 1992b: A general hydrodynamic theory for mixed-phase microphysics. Part II: Collision kernels for coalescence. Atmos. Res., 27, 275290, https://doi.org/10.1016/0169-8095(92)90036-A.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Böhm, H. P., 1992c: A general hydrodynamic theory for mixed-phase microphysics. Part III: Riming and aggregation. Atmos. Res., 28, 103123, https://doi.org/10.1016/0169-8095(92)90023-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J.-P., and D. Lamb, 1999: Simulation of cloud microphysical and chemical processes using a multicomponent framework. Part II: Microphysical evolution of a wintertime orographic cloud. J. Atmos. Sci., 56, 22932312, https://doi.org/10.1175/1520-0469(1999)056<2293:SOCMAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chwang, A. T., and T. Y.-T. Wu, 1975: Hydromechanics of low-Reynolds-number flow. Part 2. Singularity method for stokes flows. J. Fluid Mech., 67, 787815, https://doi.org/10.1017/S0022112075000614.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Connolly, P., C. Emersic, and P. Field, 2012: A laboratory investigation into the aggregation efficiency of small ice crystals. Atmos. Chem. Phys., 12, 20552076, https://doi.org/10.5194/acp-12-2055-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunnavan, E. L., 2021: How snow aggregate ellipsoid shape and orientation variability affects fall speed and self-aggregation rates. J. Atmos. Sci., 78, 5173, https://doi.org/10.1175/JAS-D-20-0128.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunnavan, E. L., Z. Jiang, J. Y. Harrington, J. Verlinde, K. Fitch, and T. J. Garrett, 2019: The shape and density evolution of snow aggregates. J. Atmos. Sci., 76, 39193940, https://doi.org/10.1175/JAS-D-19-0066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, J., S. Ghan, M. Ovchinnikov, X. Liu, P. J. Rasch, and A. Korolev, 2011: Representation of Arctic mixed-phase clouds and the Wegener-Bergeron-Findeisen process in climate models: Perspectives from a cloud-resolving study. J. Geophys. Res., 116, D00T07, https://doi.org/10.1029/2010JD015375.

    • Search Google Scholar
    • Export Citation
  • Fan, J., and Coauthors, 2015: Improving representation of convective transport for scale-aware parameterization: 1. Convection and cloud properties simulated with spectral bin and bulk microphysics. J. Geophys. Res. Atmos., 120, 34853509, https://doi.org/10.1002/2014JD022142.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Field, P. R., and A. J. Heymsfield, 2003: Aggregation and scaling of ice crystal size distributions. J. Atmos. Sci., 60, 544560, https://doi.org/10.1175/1520-0469(2003)060<0544:AASOIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Formenton, M., and Coauthors, 2013: Using a cloud electrification model to study relationships between lightning activity and cloud microphysical structure. Nat. Hazards Earth Syst. Sci., 13, 10851104, https://doi.org/10.5194/nhess-13-1085-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fuchs, N., 1964: The Mechanics of Aerosols. Pergamon Press, 408 pp.

  • Gallily, I., and A. Cohen, 1979: On the orderly nature of the motion of nonspherical aerosol particles. II. Inertial collision between a spherical large droplet and an axially symmetrical elongated particle. J. Colloid Interface Sci., 68, 338356, https://doi.org/10.1016/0021-9797(79)90287-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garrett, T. J., S. E. Yuter, C. Fallgatter, K. Shkurko, S. R. Rhodes, and J. L. Endries, 2015: Orientations and aspect ratios of falling snow. Geophys. Res. Lett., 42, 46174622, https://doi.org/10.1002/2015GL064040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gavze, E., M. Pinsky, and A. Khain, 2012: The orientations of prolate ellipsoids in linear shear flows. J. Fluid Mech., 690, 5193, https://doi.org/10.1017/jfm.2011.385.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gavze, E., M. Pinsky, and A. Khain, 2016a: The orientation dynamics of small prolate and oblate spheroids in linear shear flows. Int. J. Multiphase Flow, 83, 103114, https://doi.org/10.1016/j.ijmultiphaseflow.2016.03.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gavze, E., M. Pinsky, and A. Khain, 2016b: Corrigendum to “The orientation dynamics of small prolate and oblate spheroids in linear shear flows” [International Journal of Multiphase Flow 83 (2016) 103–114]. Int. J. Multiphase Flow, 85, 1, https://doi.org/10.1016/j.ijmultiphaseflow.2016.05.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, W. D., 1980: A detailed microphysical model within a two-dimensional dynamic framework: Model description and preliminary results. J. Atmos. Sci., 37, 24862507, https://doi.org/10.1175/1520-0469(1980)037<2486:ADMMWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Happel, J., and H. Brenner, 1973: Low Reynolds Number Hydrodynamics. Noordhoff International Publishing, 553 pp.

    • Export Citation
  • Harrington, J. Y., K. Sulia, and H. Morrison, 2013: A method for adaptive habit prediction in bulk microphysical models. Part I: Theoretical development. J. Atmos. Sci., 70, 349364, https://doi.org/10.1175/JAS-D-12-040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hashino, T., and G. Tripoli, 2007: The Spectral Ice Habit Prediction System (SHIPS). Part I: Model description and simulation of the vapor deposition process. J. Atmos. Sci., 64, 22102237, https://doi.org/10.1175/JAS3963.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hogan, R. J., L. Tian, P. R. Brown, C. D. Westbrook, A. J. Heymsfield, and J. D. Eastment, 2012: Radar scattering from ice aggregates using the horizontally aligned oblate spheroid approximation. J. Appl. Meteor. Climatol., 51, 655671, https://doi.org/10.1175/JAMC-D-11-074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holmstedt, E., H. O. Åkerstedt, T. S. Lundström, and S. M. Högberg, 2016: Modeling transport and deposition efficiency of oblate and prolate nano- and micro-particles in a virtual model of the human airway. J. Fluids Eng., 138, 081203, https://doi.org/10.1115/1.4032934.

    • Crossref
    • Export Citation
  • Hosler, C. L., D. Jensen, and L. Goldshlak, 1957: On the aggregation of ice crystals to form snow. J. Atmos. Sci., 14, 415420, https://doi.org/10.1175/1520-0469(1957)014<0415:OTAOIC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jensen, A. A., and J. Y. Harrington, 2015: Modeling ice crystal aspect ratio evolution during riming: A single-particle growth model. J. Atmos. Sci., 72, 25692590, https://doi.org/10.1175/JAS-D-14-0297.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, Z., J. Verlinde, E. E. Clothiaux, K. Aydin, and C. Schmitt, 2019: Shapes and fall orientations of ice particle aggregates. J. Atmos. Sci., 76, 19031916, https://doi.org/10.1175/JAS-D-18-0251.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jucha, J., A. Naso, E. Lévêque, and A. Pumir, 2018: Settling and collision between small ice crystals in turbulent flows. Phys. Rev. Fluids, 3, 014604, https://doi.org/10.1103/PhysRevFluids.3.014604.

    • Crossref
    • Export Citation
  • Kajikawa, M., and A. J. Heymsfield, 1989: Aggregation of ice crystals in cirrus. J. Atmos. Sci., 46, 31083121, https://doi.org/10.1175/1520-0469(1989)046<3108:AOICIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khain, A. P., and M. Pinsky, 1995: Drop inertia and its contribution to turbulent coalescence in convective clouds. Part I: Drop fall in the flow with random horizontal velocity. J. Atmos. Sci., 52, 196206, https://doi.org/10.1175/1520-0469(1995)052<0196:DIAICT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khain, A. P., and M. Pinsky, 2018: Physical Processes in Clouds and Cloud Modeling. Cambridge University Press, 626 pp.

    • Crossref
    • Export Citation
  • Kim, S., and S. Karrila, 1991: Microhydrodynamics: Principles and Selected Applications. Butterworth-Heinemann, 507 pp.

    • Crossref
    • Export Citation
  • Klett, J. D., 1995: Orientation model for particles in turbulence. J. Atmos. Sci., 52, 22762285, https://doi.org/10.1175/1520-0469(1995)052<2276:OMFPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamb, H., 1945: Hydrodynamics. Dover Publications, 738 pp.

    • Export Citation
  • Lee, H., and J.-J. Baik, 2016: Effects of turbulence-induced collision enhancement on heavy precipitation: The 21 September 2010 case over the Korean Peninsula. J. Geophys. Res. Atmos., 121, 12 31912 342, https://doi.org/10.1002/2016JD025168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitchell, D. L., 1988: Evolution of snow-size spectra in cyclonic storms. Part I: Snow growth by vapor deposition and aggregation. J. Atmos. Sci., 45, 34313451, https://doi.org/10.1175/1520-0469(1988)045<3431:EOSSSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naso, A., J. Jucha, E. Lévêque, and A. Pumir, 2018: Collision rate of ice crystals with water droplets in turbulent flows. J. Fluid Mech., 845, 615641, https://doi.org/10.1017/jfm.2018.238.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phillips, V. T. J., A. Khain, N. Benmoshe, E. Ilotoviz, and A. Ryzhkov, 2015: Theory of time-dependent freezing. Part II: Scheme for freezing raindrops and simulations by a cloud model with spectral bin microphysics. J. Atmos. Sci., 72, 262286, https://doi.org/10.1175/JAS-D-13-0376.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinsky, M., A. Khain, and M. Shapiro, 2000: Stochastic effects of cloud droplet hydrodynamic interaction in a turbulent flow. Atmos. Res., 53, 131169, https://doi.org/10.1016/S0169-8095(99)00048-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinsky, M., A. Khain, and M. Shapiro, 2007: Collisions of cloud droplets in a turbulent flow. Part IV: Droplet hydrodynamic interaction. J. Atmos. Sci., 64, 24622482, https://doi.org/10.1175/JAS3952.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinsky, M., A. Khain, and H. Krugliak, 2008: Collisions of cloud droplets in a turbulent flow. Part V: Application of detailed tables of turbulent collision rate enhancement to simulation of droplet spectra evolution. J. Atmos. Sci., 65, 357374, https://doi.org/10.1175/2007JAS2358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pruppacher, H., and J. Klett, 1997: Microphysics of Clouds and Precipitation. 2nd ed. Kluwer Academic, 954 pp.

  • Rangno, A. L., 2008: Fragmentation of freezing drops in shallow maritime frontal clouds. J. Atmos. Sci., 65, 14551466, https://doi.org/10.1175/2007JAS2295.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A., M. Pinsky, A. Pokrovsky, and A. Khain, 2011: Polarimetric radar observation operator for a cloud model with spectral microphysics. J. Appl. Meteor. Climatol., 50, 873894, https://doi.org/10.1175/2010JAMC2363.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seifert, A., L. Nuijens, and B. Stevens, 2010: Turbulence effects on warm-rain autoconversion in precipitating shallow convection. Quart. J. Roy. Meteor. Soc., 136, 17531762, https://doi.org/10.1002/qj.684.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shpund, J., and Coauthors, 2019: Simulating a mesoscale convective system using WRF with a new spectral bin microphysics: 1: Hail vs graupel. J. Geophys. Res. Atmos., 124, 14 07214 101, https://doi.org/10.1029/2019JD030576.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siewert, C., R. Kunnen, M. Meinke, and W. Schröder, 2014a: Orientation statistics and settling velocity of ellipsoids in decaying turbulence. Atmos. Res., 142, 4556, https://doi.org/10.1016/j.atmosres.2013.08.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siewert, C., R. Kunnen, and W. Schröder, 2014b: Collision rates of small ellipsoids settling in turbulence. J. Fluid Mech., 758, 686701, https://doi.org/10.1017/jfm.2014.554.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stein, T. H., C. D. Westbrook, and J. Nicol, 2015: Fractal geometry of aggregate snowflakes revealed by triple-wavelength radar measurements. Geophys. Res. Lett., 42, 176183, https://doi.org/10.1002/2014GL062170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sulia, K. J., Z. J. Lebo, V. M. Przybylo, and C. G. Schmitt, 2021: A new method for ice–ice aggregation in the adaptive habit model. J. Atmos. Sci., 78, 133154, https://doi.org/10.1175/JAS-D-20-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vickers, G., 1996: The projected areas of ellipsoids and cylinders. Powder Technol., 86, 195200, https://doi.org/10.1016/0032-5910(95)03049-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L.-P., O. Ayala, B. Rosa, and W. W. Grabowski, 2008: Turbulent collision efficiency of heavy particles relevant to cloud droplets. New J. Phys., 10, 075013, https://doi.org/10.1088/1367-2630/10/7/075013.

    • Crossref
    • Export Citation
  • Wang, P. K., 2002: Ice Microdynamics. Academic Press, 273 pp.

    • Export Citation
  • Wang, P. K., and W. Ji, 1997: Numerical simulation of three-dimensional unsteady flow past ice crystals. J. Atmos. Sci., 54, 22612274, https://doi.org/10.1175/1520-0469(1997)054<2261:NSOTDU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, P. K., and W. Ji, 2000: Collision efficiencies of ice crystals at low–intermediate Reynolds numbers colliding with supercooled cloud droplets: A numerical study. J. Atmos. Sci., 57, 10011009, https://doi.org/10.1175/1520-0469(2000)057<1001:CEOICA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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