A study of the evolution of nanoparticle dynamics in a homogeneous isotropic turbulence flow via a DNS-TEMOM method

Abstract

In this article, a coupling of the direct numerical simulation (DNS) and the population balance modeling (PBM) is implemented to study the effect of turbulence on nanoparticle dynamics in homogenous isotropic turbulence (HIT). The DNS is implemented based on a pseudo-spectral method and the PBM is implemented using the Taylor-series expansion method of moments. The result verifies that coagulation due to turbulent shear force has a bigger impact on the evolution of number concentration, polydispersity, and average diameter of nanoparticles than Brownian coagulation in the HIT. The Reynolds number plays an important role in determining the number concentration, polydispersity, and average diameter of nanoparticles, and these quantities change more rapidly with an increase of Reynolds number. It is also found that the initial geometric standard deviation slows down the evolution of particle dynamics, but almost has no influence on the polydispersity of nanoparticles.

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

References

  1. [1]

    Fox R O. Quadrature-Based Moment Methods for Multiphase Chemically Reacting Flows[M] Advances in Chemical Engineering. Academic Press, 2018, 52: 1–50.

    Article  Google Scholar 

  2. [2]

    Garrick S C. Growth mechanisms of nanostructured titania in turbulent reacting flows[J]. Journal of Nanotechnology, 2015, 2015..

  3. [3]

    Smoluchowski M. Versuch einer matematischen Theorie der Koagulationskinetic kolloider Losunger [J]. Zeitschrift für Physikalische Chemie, 1917: 92:129–168.

    Google Scholar 

  4. [4]

    S. K. Friedlander, John Wiley & Sons., Smoke, dust, and haze fundamentals of aerosal behavior, [J]. Aiche Journal, 1977, 317.

  5. [5]

    Lin J Z, Huang L Z. Review of some researches on nano- and submicron Brownian particle-laden turbulent flow [J]. Journal of Hydrodynamics Ser B, 2012, 24(6):801–808.

    Article  Google Scholar 

  6. [6]

    Salehi F, Cleary M J, Masri A R. Population balance equation for turbulent polydispersed inertial droplets and particles [J]. Journal of Fluid Mechanics, 2017, 831:719–742.

    MathSciNet  Article  Google Scholar 

  7. [7]

    Song, LIU, and J. Z. Lin. Numerical Simulation of Nanoparticle Coagulation in a Poiseuille Flow Via a Moment Method [J]. Journal of Hydrodynamics Ser B, 2008. 20(1): 1–9

    Article  Google Scholar 

  8. [8]

    L. Sun, J. Z. Lin, and F. B. Bao, Numerical simulation on the deposition of nanoparticles under laminar conditions [J]. Journal of Hydrodynamics Ser B, 2006, 18(6): 676–680

    Article  Google Scholar 

  9. [9]

    S. C. Garrick, Effects of Turbulent Fluctuations on Nanoparticle Coagulation in Shear Flows [J]. Aerosol Science and Technology, 2011, 45(10): 1272–1285.

    Article  Google Scholar 

  10. [10]

    N. Settumba and S. C. Garrick, Direct numerical simulation of nanoparticle coagulation in a temporal mixing layer via a moment method [J]. Journal of Aerosol Science, 2003, 34(2): 149–167.

    Article  Google Scholar 

  11. [11]

    S. E. Miller and S. C. Garrick, Nanoparticle Coagulation in A Planar Jet [J]. Aerosol Sci. Technol, 2004, 38(1): 79–89

    Article  Google Scholar 

  12. [12]

    E. G. Moody and L. R. Collins, Effect of mixing on the nucleation and growth of titania particles [J]. Aerosol Science and Technology, 2003, 37(5): 403–424.

    Article  Google Scholar 

  13. [13]

    S. Liu, T. L. Chan, J. Lin, and M. Yu, Numerical study on fractal-like soot aggregate dynamics of turbulent ethylene-oxygen flame [J]. Fuel, 2019, 56: 115857.

    Article  Google Scholar 

  14. [14]

    Q. Zhang et al., Investigating particle emissions and aerosol dynamics from a consumer fused deposition modeling 3D printer with a lognormal moment aerosol model [J]. Aerosol Science and Technology, 2018, 52(10): 1099–1111.

    Article  Google Scholar 

  15. [15]

    P. G. Saffman and J. S. Turner, On the collision of drops in turbulent clouds [J]. Journal of Fluid Mechanics., 1956, 101: 16.

    Article  Google Scholar 

  16. [16]

    S. Park, F. Kruis, K. Lee, and H. Fissan, Evolution of particle size distribution due to turbulent coagulation [J]. Journal of Aerosol Science, 2000, 3: 572–573.

    Article  Google Scholar 

  17. [17]

    J. Lin, X. Pan, Z. Yin, and X. Ku, Solution of general dynamic equation for nanoparticles in turbulent flow considering fluctuating coagulation [J]. Applied Mathematics and Mechanics, 2016, 37(10): 1275–1288.

    MathSciNet  Article  Google Scholar 

  18. [18]

    B. Kong and R. O. Fox, A moment-based kinetic theory model for polydisperse gas-particle flows [J]. Powder Technol, 2019, 04: 031.

    Google Scholar 

  19. [19]

    M. Yu and J. Lin, Taylor series expansion scheme applied for solving population balance equation [J]. Reviews in Chemical Engineering, 2018, 34(4): 561–594.

    Article  Google Scholar 

  20. [20]

    S. E. Pratsinis, Simultaneous nucleation, condensation, and coagulation in aerosol reactors [J]. Journal of Colloid and Interface Science, 1988, 124(2): 416–427.

    Article  Google Scholar 

  21. [21]

    M. M.. Williams, On the modified gamma distribution for representing the size spectra of coagulating aerosol particles [J]. Journal of Colloid and Interface Science, 1985. 103(2): 516–527.

    Article  Google Scholar 

  22. [22]

    McGraw, Robert. Description of Aerosol Dynamics by the Quadrature Method of Moments [J]. Aerosol Science and Technology, 1997, 27(2):255–265.

    Article  Google Scholar 

  23. [23]

    D. L. Marchisio and R. O. Fox, Solution of population balance equations using the direct quadrature method of moments [J]. Journal of Aerosol Science, 2005, 36(1):43–73.

    Article  Google Scholar 

  24. [25]

    J. C. Barrett and J. S. Jheeta, Improving the accuracy of the moments method for solving the aerosol general dynamic equation [J]. Journal of Aerosol Science, 1996, 27(8): 1135–1142.

    Article  Google Scholar 

  25. [25]

    M. Frenklach, Method of moments with interpolative closure [J], Chemical Engineering Science, 2002, 57(12): 2229–2239.

    Article  Google Scholar 

  26. [26]

    M. Yu, J. Lin, and T. Chan, A New Moment Method for Solving the Coagulation Equation for Particles in Brownian Motion [J]. Aerosol Science and Technology, 2008. 42(9): 705–713.

    Article  Google Scholar 

  27. [27]

    R. S. Rogallo, Numerical experiments in homogeneous turbulence [R], NASA STI/Recon Technical Report N, 1981, 31508.

  28. [28]

    Yu Mingzhou Jiang Ying Zhang Kai., The study on micro-scale particle coagulation due to turbulent shear mechanism using TEMOM model [J]. Chinese Journal of Theoretical and Applied Mechani, 2011, 43: 447–453.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ming-zhou Yu.

Additional information

Project supported by the Zhejiang Provincial Natural Science Foundation of China (grant number, LR16A020002) and the National Natural Science Foundation of China (grant number, 11872353 and 91852102).

Biography: Hong-ye Ma (1995-), Male, Master

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ma, H., Yu, M. & Jin, H. A study of the evolution of nanoparticle dynamics in a homogeneous isotropic turbulence flow via a DNS-TEMOM method. J Hydrodyn (2020). https://doi.org/10.1007/s42241-020-0033-1

Download citation

Key words

  • nanoparticle dynamics
  • Turbulence
  • pseudo-spectral method
  • Taylor-series expansion method of moments