Parametric study of fluid–solid interaction for single-particle dissipative particle dynamics model

  • Yi Wang
  • Jie OuyangEmail author
  • Yanggui Li
Research Paper


In this paper, a parametric study of fluid–solid interaction for single-particle dissipative particle dynamics (DPD) model is conducted to describe the hydrodynamic interactions in a large range of particle sizes. To successfully reproduce the hydrodynamics for different particle sizes, and overcome the problem that effective radius of solid sphere does not match its real radius, the cut-off radius and conservative force coefficient of single-particle DPD model have been modified. The cut-off radius and conservative force coefficient are related to the drag force and radial distribution function, so that, for each particle size, they can be determined by DPD simulations. Through numerical fitting, two empirical formulas as a function of spherical radius are developed to calculate the cut-off radius and conservative force coefficient. Numerical results indicate that the single-particle DPD model is, indeed, capable of capturing low Reynolds number hydrodynamic interactions for different particle sizes by selecting these model parameters reasonably. Specifically, the model can not only insure that drag force and torque are quantitatively consistent with theoretical results, but also guarantee the effective radius matches well its real radius. In addition, the shear dissipative force is the major part of drag force and should not be ignored. This study will help to improve the application range of single-particle DPD model to make it suitable for different particle sizes and provide parameter guidance for studying fluid–solid interaction using single-particle DPD model.


Fluid–solid interaction Dissipative particle dynamics Mesoscale Drag force Effective radius 



We gratefully acknowledge the anonymous referees who have provided us with valuable comments and suggestions for improving our study. This work is financially supported by the National Basic Research Program of China (973 Program) (Grant no. 2012CB025903), the Major Research Plan of the National Natural Science Foundation of China (Grant no. 91434201), and the National Natural Science Foundation of China (Grant no. 11671321).

Supplementary material

10404_2018_2099_MOESM1_ESM.docx (939 kb)
Supplementary material 1 (DOCX 938 KB)


  1. Allen MP, Tildesley DJ (1989) Computer simulation of liquids. Oxford University Press, New YorkzbMATHGoogle Scholar
  2. Backer JA, Lowe CP, Hoefsloot HCJ, Iedema PD (2005) Poiseuille flow to measure the viscosity of particle model fluids. J Chem Phys 122(15):154503CrossRefGoogle Scholar
  3. Boek ES, Schoot PVD (1998) Resolution effects in dissipative particle dynamics simulations. Int J Mod Phys C 9(08):1307–1318CrossRefGoogle Scholar
  4. Bolintineanu DS, Grest GS, Lechman JB, Pierce F, Plimpton SJ, Schunk PR (2014) Particle dynamics modeling methods for colloid suspensions. Comput Part Mech 1(3):321–356CrossRefGoogle Scholar
  5. Chen S, Phan-Thien N, Khoo BC, Fan XJ (2006) Flow around spheres by dissipative particle dynamics. Phys Fluids 18(10):103605MathSciNetCrossRefGoogle Scholar
  6. Dzwinel W, Yuen DA (2000) A two-level, discrete-particle approach for simulating ordered colloidal structures. J Colloid Interf Sci 225(1):179–190CrossRefGoogle Scholar
  7. Ermak DL, McCammon JA (1978) Brownian dynamics with hydrodynamic interactions. J Chem Phys 69(4):1352–1360CrossRefGoogle Scholar
  8. Español P (1997) Fluid particle dynamics: a synthesis of dissipative particle dynamics and smoothed particle dynamics. Europhys Lett 39(6):605CrossRefGoogle Scholar
  9. Español P (1998) Fluid particle model. Phys Rev E 57(3):2930–2948CrossRefGoogle Scholar
  10. Fan XJ, Phan-Thien N, Chen S, Wu XH, Ng TY (2006) Simulating flow of DNA suspension using dissipative particle dynamics. Phys Fluids 18(6):063102CrossRefGoogle Scholar
  11. Fedosov DA, Pivkin IV, Karniadakis GE (2008) Velocity limit in DPD simulations of wall-bounded flows. J Comput Phys 227(4):2540–2559MathSciNetCrossRefGoogle Scholar
  12. Fedosov DA, Caswell B, Karniadakis GE (2010) A multiscale red blood cell model with accurate mechanics, rheology, and dynamics. Biophys J 98(10):2215–2225CrossRefGoogle Scholar
  13. Fedosov DA, Caswell B, Suresh S, Karniadakis GE (2011) Quantifying the biophysical characteristics of Plasmodium-falciparum-parasitized red blood cells in microcirculation. Proc Natl Acad Sci USA 108(1):35–39CrossRefGoogle Scholar
  14. Frisch U, Hasslacher B, Pomeau Y (1986) Lattice-gas automata for the Navier–Stokes equation. Phys Rev Lett 56(14):1505CrossRefGoogle Scholar
  15. Groot RD, Warren PB (1997) Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation. J Chem Phys 107(11):4423–4435CrossRefGoogle Scholar
  16. Happel J, Brenner H (1991) Low Reynolds number hydrodynamics. Kluwer Academic, DordrechtzbMATHGoogle Scholar
  17. Hoogerbrugge PJ, Koelman JMVA (1992) Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics. Europhys Lett 19(3):155–160CrossRefGoogle Scholar
  18. Izvekov S, Rice BM (2015) On the importance of shear dissipative forces in coarse-grained dynamics of molecular liquids. Phys Chem Chem Phys 17(16):10795–10804CrossRefGoogle Scholar
  19. Jiang W, Huang J, Wang Y, Laradji M (2007) Hydrodynamic interaction in polymer solutions simulated with dissipative particle dynamics. J Chem Phys 126(4):044901CrossRefGoogle Scholar
  20. Jiang C, Ouyang J, Liu Q, Li W, Zhuang X (2016) Studying the viscosity of methane fluid for different resolution levels models using Poiseuille flow in a nano-channel. Microfluid Nanofluid 20(12):157CrossRefGoogle Scholar
  21. Jiang C, Ouyang J, Li W, Wang X, Wang L (2017) The effects of wall roughness on the methane flow in nano-channels using non-equilibrium multiscale molecular dynamics simulation. Microfluid Nanofluid 21(5):92CrossRefGoogle Scholar
  22. Keaveny EE (2014) Fluctuating force-coupling method for simulations of colloidal suspensions. J Comput Phys 269:61–79MathSciNetCrossRefGoogle Scholar
  23. Khani S, Jamali S, Boromand A, Hore MJ, Maia J (2015) Polymer-mediated nanorod self- assembly predicted by dissipative particle dynamics simulations. Soft Matter 11(34):6881–6892CrossRefGoogle Scholar
  24. Kim JM, Phillips RJ (2004) Dissipative particle dynamics simulation of flow around spheres and cylinders at finite Reynolds numbers. Chem Eng Sci 59(20):4155–4168CrossRefGoogle Scholar
  25. Li Z, Drazer G (2008) Hydrodynamic interactions in dissipative particle dynamics. Phys Fluids 20(10):103601CrossRefGoogle Scholar
  26. Li Y, Geng X, Ouyang J, Zang D, Zhuang X (2015) A hybrid multiscale dissipative particle dynamics method coupling particle and continuum for complex fluid. Microfluid Nanofluid 19(4):941–952CrossRefGoogle Scholar
  27. Lin NY, Guy BM, Hermes M, Ness C, Sun J, Poon WC, Cohen I (2015) Hydrodynamic and contact contributions to continuous shear thickening in colloidal suspensions. Phys Rev Lett 115(22):228304CrossRefGoogle Scholar
  28. Liu H, Qian HJ, Zhao Y, Lu ZY (2007a) Dissipative particle dynamics simulation study on the binary mixture phase separation coupled with polymerization. J Chem Phys 127(14):144903CrossRefGoogle Scholar
  29. Liu M, Meakin P, Huang H (2007b) Dissipative particle dynamics simulation of fluid motion through an unsaturated fracture and fracture junction. J Comput Phys 222(1):110–130CrossRefGoogle Scholar
  30. Liu M, Meakin P, Huang H (2007c) Dissipative particle dynamics simulation of multiphase fluid flow in microchannels and microchannel networks. Phys Fluids 19(3):033302CrossRefGoogle Scholar
  31. Liu H, Jiang S, Chen Z, Liu M, Chang J, Wang Y, Tong Z (2015a) Mesoscale study of particle sedimentation with inertia effect using dissipative particle dynamics. Microfluid Nanofluid 18(5–6):1309–1315CrossRefGoogle Scholar
  32. Liu M, Liu G, Zhou L, Chang J (2015b) Dissipative particle dynamics (DPD): an overview and recent developments. Arch Comput Methods Eng 22(4):529–556MathSciNetCrossRefGoogle Scholar
  33. Lu Z-Y, Wang Y-L (2013) An introduction to dissipative particle dynamics. Methods Mol Biol 924:617–633CrossRefGoogle Scholar
  34. Mai-Duy N, Pan D, Phan-Thien N, Khoo BC (2013) Dissipative particle dynamics modeling of low Reynolds number incompressible flows. J Rheol 57(2):585–604CrossRefGoogle Scholar
  35. Mai-Duy N, Phan-Thien N, Khoo BC (2015) Investigation of particles size effects in dissipative particle dynamics (DPD) modelling of colloidal suspensions. Comput Phys Commun 189:37–46CrossRefGoogle Scholar
  36. Masubuchi Y, Langeloth M, Böhm MC, Inoue T, Müller-Plathe F (2016) A multichain slip-spring dissipative particle dynamics simulation method for entangled polymer solutions. Macromolecules 49(23):9186–9191CrossRefGoogle Scholar
  37. Mehboudi A, Noruzitabar M, Mehboudi M (2014) Simulation of mixed electroosmotic/pressure-driven flows by utilizing dissipative particle dynamics. Microfluid Nanofluid 17(1):199–215CrossRefGoogle Scholar
  38. Moshfegh A, Jabbarzadeh A (2016) Fully explicit dissipative particle dynamics simulation of electroosmotic flow in nanochannels. Microfluid Nanofluid 20(4):67CrossRefGoogle Scholar
  39. Pan W, Pivkin IV, Karniadakis GE (2008) Single-particle hydrodynamics in DPD: a new formulation. Europhys Lett 84(1):10012MathSciNetCrossRefGoogle Scholar
  40. Pan W, Caswell B, Karniadakis GE (2010) Rheology, microstructure and migration in brownian colloidal suspensions. Langmuir 26(1):133–142CrossRefGoogle Scholar
  41. Phan-Thien N, Mai-Duy N, Khoo BC (2014) A spring model for suspended particles in dissipative particle dynamics. J Rheol 58(4):839–867CrossRefGoogle Scholar
  42. Pryamitsyn V, Ganesan V (2005) A coarse-grained explicit solvent simulation of rheology of colloidal suspensions. J Chem Phys 122(10):104906CrossRefGoogle Scholar
  43. Ranjith SK, Patnaik B, Vedantam S (2013) No-slip boundary condition in finite-size dissipative particle dynamics. J Comput Phys 232(1):174–188MathSciNetCrossRefGoogle Scholar
  44. Reichl LE (1980) A modern course in statistical physics. University of Texas Press, AustinzbMATHGoogle Scholar
  45. Schmidt JR, Skinner JL (2004) Brownian Motion of a Rough Sphere and the Stokes-Einstein Law. J Phys Chem B 108(21):6767–6771CrossRefGoogle Scholar
  46. Shan XW, Chen HD (1993) Lattice Boltzmann model for simulating flows with multiple phases and components. Phys Rev E 47(3):1815CrossRefGoogle Scholar
  47. Yang L, Yin H (2014) Parametric study of particle sedimentation by dissipative particle dynamics simulation. Phys Rev E 90(3):033311CrossRefGoogle Scholar
  48. Yong X, Kuksenok O, Balazs AC (2015) Modeling free radical polymerization using dissipative particle dynamics. Polymer 72:217–225CrossRefGoogle Scholar
  49. Zhao T, Wang X, Jiang L, Larson RG (2014) Dissipative particle dynamics simulation of dilute polymer solutions—inertial effects and hydrodynamic interactions. J Rheol 58(4):1039–1058CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Applied MathematicsNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Key Laboratory of Space Applied Physics and Chemistry of Ministry of Education, School of ScienceNorthwestern Polytechnical UniversityXi’anChina

Personalised recommendations