Skip to main content
Log in

Aggregation Kinetics in Sedimentation: Effect of Diffusion of Particles

  • MATHEMATICAL PHYSICS
  • Published:
Computational Mathematics and Mathematical Physics Aims and scope Submit manuscript

Abstract

The aggregation kinetics of settling particles is studied theoretically and numerically using the advection–diffusion equation. Agglomeration caused by these mechanisms (diffusion and advection) is important for both small particles (e.g., primary ash or soot particles in the atmosphere) and large particles of identical or close size, where the spatial inhomogeneity is less pronounced. Analytical results can be obtained for small and large Péclet numbers, which determine the relative importance of diffusion and advection. For small numbers (spatial inhomogeneity is mainly due to diffusion), an expression for the aggregation rate is obtained using an expansion in terms of Péclet numbers. For large Péclet numbers, when advection is the main source of spatial inhomogeneity, the aggregation rate is derived from ballistic coefficients. Combining these results yields a rational approximation for the whole range of Péclet numbers. The aggregation rates are also estimated by numerically solving the advection–diffusion equation. The numerical results agree well with the analytical theory for a wide range of Péclet numbers (extending over four orders of magnitude).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.

REFERENCES

  1. B. Vowinckel, J. Withers, P. Luzzatto-Fegiz, and E. Meiburg, “Settling of cohesive sediment: Particle-resolved simulations,” J. Fluid Mech. 858, 5–44 (2019).

    Article  MathSciNet  MATH  Google Scholar 

  2. A. Fischer, A. Chatterjee, and T. Speck, “Aggregation and sedimentation of active Brownian particles at constant affinity,” J. Chem. Phys. 150 (6), 064910 (2019).

  3. Y.-J. Yang, A. V. Kelkar, D. S. Corti, and E. I. Franses, “Effect of interparticle interactions on agglomeration and sedimentation rates of colloidal silica microspheres,” Langmuir 32 (20), 5111–5123 (2016).

    Article  Google Scholar 

  4. Ch. Hongsheng, L. Wenwei, Ch. Zhiwei, and Zh. Zhong, “A numerical study on the sedimentation of adhesive particles in viscous fluids using LBM-LES-DEM,” Powder Technol. 391, 467–478 (2021).

    Article  Google Scholar 

  5. J. K. Whitmer and E. Luijten, “Sedimentation of aggregating colloids,” J. Chem. Phys. 134, 034510 (2011).

  6. M. Pinsky, A. Khain, and M. Shapiro, “Collision efficiency of drops in a wide range of Reynolds numbers: Effects of pressure on spectrum evolution,” J. Atmos. Sci. 58, 742–766 (2001).

    Article  Google Scholar 

  7. T. V. Khodzher et al., “Study of aerosol nano-and submicron particle compositions in the atmosphere of Lake Baikal during natural fire events and their interaction with water surface,” Water Air Soil Pollution 232, 266 (2021).

    Article  Google Scholar 

  8. G. Zhamsueva et al., “Studies of the dispersed composition of atmospheric aerosol and its relationship with small gas impurities in the near-water layer of Lake Baikal based on the results of ship measurements in the summer of 2020,” Atmosphere 13, 139 (2022).

    Article  Google Scholar 

  9. H. A. K. Shahad, “An experimental investigation of soot particle size inside the combustion chamber of a diesel engine,” Energy Conversation Manage. 29, 141–149 (1989).

    Article  Google Scholar 

  10. P. L. Krapivsky, A. Redner, and E. Ben-Naim, A Kinetic View of Statistical Physics (Cambridge Univ. Press, Cambridge, 2010).

    Book  MATH  Google Scholar 

  11. F. Leyvraz, “Scaling theory and exactly solved models in the kinetics of irreversible aggregation,” Phys. Rep. 383, 95–212 (2003).

    Article  Google Scholar 

  12. M. V. Smoluchowski, “Attempt for a mathematical theory of kinetic coagulation of colloid solutions,” Z. Phys. Chem. 92, 265–271 (1917).

    Google Scholar 

  13. K. Higashitani, R. Ogawa, G. Hosokawa, and Y. Matsuno, “Kinetic theory of shear coagulation for particles in a viscous fluid,” J. Chem. Eng. Jpn. 15 (4), 299–304 (1982).

    Article  Google Scholar 

  14. P. F. Saffman and N. F. Turner, “On the collision of drops in turbulent clouds,” J. Fluid Mech. 1 (1), 16–30 (1956).

    Article  MATH  Google Scholar 

  15. G. Falkovich, A. Fouxon, and M. Stepanov, “Acceleration of rain initiation by cloud turbulence,” Nature 419, 151 (2002).

    Article  Google Scholar 

  16. G. Falkovich, M. G. Stepanov, and M. Vucelja, “Rain initiation time in turbulent warm clouds,” J. Appl. Meteor. Climatol. 45, 591 (2006).

    Article  Google Scholar 

  17. T. G. M. van de Ven and S. G. Mason, “The microrheology of colloidal dispersions: VIII. Effect of shear on perikinetic doublet formation,” Colloid Polym. Sci. 255 (8), 794–804 (1977).

    Article  Google Scholar 

  18. D. H. Melik and H. S. Fogler, “Effect of gravity on Brownian flocculation,” J. Colloid Interface Sci. 101 (1), 84–97 (1984).

    Article  Google Scholar 

  19. D. L. Feke and W. R. Scjowalter, “The effect of Brownian diffusion on shear-induced coagulation of colloidal dispersions,” J. Fluid Mech. 133, 17–35 (1983).

    Article  MATH  Google Scholar 

  20. N. van Kampen, Stochastic Processes in Physics and Chemistry (Elsevier, Amsterdam, 1992).

    MATH  Google Scholar 

  21. A. N. Tikhonov and A. A. Samarsky, Equations of Mathematical Physics (Dover, New York, 2013).

    Google Scholar 

  22. I. Andrianov and A. Shatrov, “Padé approximants, their properties, and applications to hydrodynamic problems,” Symmetry 13, 1869 (2021).

    Article  Google Scholar 

  23. C. Brezinski, History of Continued Fractions and Padé Approximants (Springer, Berlin, 1991).

    Book  MATH  Google Scholar 

  24. C. C. Reed and J. L. Anderson, “Hindered settling of a suspension at low Reynolds number,” AIChE J. 26 (5), 816–827 (1980).

    Article  MathSciNet  Google Scholar 

  25. A. A. Samarskii and P. N. Vabishchevich, Computational Heat Transfer (Wiley, New York, 1995).

    Google Scholar 

  26. T. A. Davis, “Algorithm 832,” ACM Trans. Math. Software 30 (2), 196–199 (2004)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Funding

The work by Brilliantov, Zagidullin, and Smirnov (derivation of the equations, formulation of the problem, and the numerical computations) was supported by the Russian Science Foundation (project no. 21-11-00363) and the work by Matveev (construction of the rational approximation) was supported by the Russian Science Foundation (project no. 19-11-00338).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to N. V. Brilliantov, R. R. Zagidullin, S. A. Matveev or A. P. Smirnov.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by I. Ruzanova

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brilliantov, N.V., Zagidullin, R.R., Matveev, S.A. et al. Aggregation Kinetics in Sedimentation: Effect of Diffusion of Particles. Comput. Math. and Math. Phys. 63, 596–605 (2023). https://doi.org/10.1134/S096554252304005X

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S096554252304005X

Keywords:

Navigation