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Motion of a nano-spheroid in a cylindrical vessel flow: Brownian and hydrodynamic interactions

Published online by Cambridge University Press:  18 May 2017

N. Ramakrishnan
Affiliation:
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19204, USA
Y. Wang
Affiliation:
Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19204, USA
D. M. Eckmann
Affiliation:
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19204, USA Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA 19204, USA
P. S. Ayyaswamy
Affiliation:
Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19204, USA
R. Radhakrishnan*
Affiliation:
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19204, USA Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19204, USA Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19204, USA
*
Email address for correspondence: rradhak@seas.upenn.edu

Abstract

We study the motion of a buoyant or a nearly neutrally buoyant nano-sized spheroid in a fluid filled tube without or with an imposed pressure gradient (weak Poiseuille flow). The fluctuating hydrodynamics approach and the deterministic method are both employed. We ensure that the fluctuation–dissipation relation and the principle of thermal equipartition of energy are both satisfied. The major focus is on the effect of the confining boundary. Results for the velocity and the angular velocity autocorrelations (VACF and AVACF), the diffusivities and the drag and the lift forces as functions of the shape, the aspect ratio, the inclination angle and the proximity to the wall are presented. For the parameters considered, the boundary modifies the VACF and AVACF such that three distinct regimes are discernible – an initial exponential decay followed by an algebraic decay culminating in a second exponential decay. The first is due to the thermal noise, the algebraic regime is due both to the thermal noise and the hydrodynamic correlations, while the second exponential decay shows the effect of momentum reflection from the confining wall. Our predictions display excellent comparison with published results for the algebraic regime (the only regime for which earlier results exist). We also discuss the role of the off-diagonal elements of the mobility and the diffusivity tensors that enable the quantifications of the degree of lift and margination of the nanocarrier. Our study covers a range of parameters that are of wide applicability in nanotechnology, microrheology and in targeted drug delivery.

Type
Papers
Copyright
© 2017 Cambridge University Press 

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Footnotes

Present address: Center for Applied Mathematics, Tianjin University, Tianjin, China, 300072.

§

These authors contributed equally.

References

Adhikari, R., Stratford, K., Cates, M. E. & Wagner, A. J. 2005 Fluctuating lattice Boltzmann. Eur. Phys. Lett. 71 (3), 473479.Google Scholar
Atzberger, P. J. 2011 Journal of Computational Physics. J. Comput. Phys. 230 (8), 28212837.Google Scholar
Ayyaswamy, P. S., Muzykantov, V., Eckmann, D. M. & Radhakrishnan, R. 2013 Nanocarrier hydrodynamics and binding in targeted drug delivery: challenges in numerical modeling and experimental validation. J. Nanotechnol. Eng. Med. 4 (1), 011001.Google ScholarPubMed
Balakrishnan, V. 2008 Elements of Nonequilibrium Statistical Mechanics. CRC Press.Google Scholar
Champion, J. A. & Mitragotri, S. 2006 Role of target geometry in phagocytosis. Proc. Natl Acad. Sci. USA 103 (13), 49304934.Google Scholar
Chou, J. C. K. 1992 Quaternion kinematic and dynamic differential equations. IEEE Trans. Robotics Automation 8 (1), 5364.CrossRefGoogle Scholar
Cichocki, B. & Felderhof, B. U. 1995 Long-time rotational motion of a rigid body immersed in a viscous fluid. Physica A 213 (4), 465473.Google Scholar
Cichocki, B. & Felderhof, B. U. 1996 Comment on ‘Long-time tails in angular momentum correlations’ [J. Chem. Phys. 103, 1582 (1995)]. J. Chem. Phys. 104 (18), 7363.Google Scholar
Cichocki, B. & Felderhof, B. U. 1997 Comment on ‘Long-time behavior of the angular velocity autocorrelation function’ [J. Chem. Phys. 105, 9695 (1996)]. J. Chem. Phys. 107 (1), 291.Google Scholar
Cicuta, P. & Donald, A. M. 2007 Microrheology: a review of the method and applications. Soft Matt. 3 (12), 1449.CrossRefGoogle ScholarPubMed
Clift, R. R., Grace, J. R. & Weber, M. E. 1978 Bubbles, Drops, and Particles. Academic.Google Scholar
Dasgupta, S., Auth, T. & Gompper, G. 2013 Wrapping of ellipsoidal nano-particles by fluid membranes. Soft Matt. 9 (22), 5473.CrossRefGoogle Scholar
Ding, E. J. & Aidun, C. K. 2000 The dynamics and scaling law for particles suspended in shear flow with inertia. J. Fluid Mech. 423 (0), 317344.Google Scholar
Donev, A., Vanden-Eijnden, E., Garcia, A. & Bell, J. 2010 On the accuracy of finite-volume schemes for fluctuating hydrodynamics. Commun. Appl. Maths Comput. Sci. 5 (2), 149197.Google Scholar
Dünweg, B. & Ladd, A. J. C. 2009 Lattice Boltzmann simulations of soft matter systems. In Advanced Computer Simulation Approaches for Soft Matter Sciences III (ed. Holm, C. & Kremer, K.), pp. 89166. Springer.Google Scholar
Español, P., Anero, J. G. & Zúñiga, I. 2009 Microscopic derivation of discrete hydrodynamics. J. Chem. Phys. 131 (24), 244117.Google Scholar
George, P. L. 1991 Automatic Mesh Generation: Application to Finite Element Methods. Wiley.Google Scholar
Glowinski, R., Pan, T. W., Hesla, T. I., Joseph, D. D. & Périaux, J. 2001 A fictitious domain approach to the direct numerical simulation of incompressible viscous flow past moving rigid bodies: application to particulate flow. J. Comput. Phys. 169 (2), 363426.Google Scholar
Gómez-González, M. & del Álamo, J. C. 2016 Two-point particle tracking microrheology of nematic complex fluids. Soft Matt. 12, 57585779.CrossRefGoogle ScholarPubMed
Hauge, E. H. & Martin-Löf, A. 1973 Fluctuating hydrodynamics and Brownian motion. J. Stat. Phys. 7 (3), 259281.Google Scholar
Henry, A. & Chen, G. 2008 High thermal conductivity of single polyethylene chains using molecular dynamics simulations. Phys. Rev. Lett. 101 (2), 235502.Google Scholar
Hocquart, R. & Hinch, E. J. 1983 The long-time tail of the angular-velocity autocorrelation function for a rigid Brownian particle of arbitrary centrally symmetric shape. J. Fluid Mech. 137, 217220.Google Scholar
Hsu, R. & Ganatos, P. 1989 The motion of a rigid body in a viscous fluid bounded by a plane wall. J. Fluid Mech. 207, 2972.Google Scholar
Hu, H. H., Patankar, N. A. & Zhu, M. Y. 2001 Direct numerical simulations of fluid–solid systems using the arbitrary Lagrangian–Eulerian technique. J. Comput. Phys. 169 (2), 427462.CrossRefGoogle Scholar
Huang, H., Yang, X. & Lu, X.-y. 2014 Sedimentation of an ellipsoidal particle in narrow tubes. Phys. Fluids 26 (5), 053302.Google Scholar
Iwashita, T., Nakayama, Y. & Yamamoto, R. 2008 A numerical model for Brownian particles fluctuating in incompressible fluids. J. Phys. Soc. Japan (7), 074007.Google Scholar
Janjua, M., Nudurupati, S., Singh, P. & Aubry, N. 2011 Electric field-induced self-assembly of micro- and nanoparticles of various shapes at two-fluid interfaces. Electrophoresis 32 (5), 518526.Google Scholar
Koenig, S. H. 1975 Brownian motion of an ellipsoid: a correction to Perrin’s results. Biopolymers 14, 24212423.CrossRefGoogle Scholar
Korotkin, A. I. 2008 Added Masses of Ship Structures, 1st edn. Springer.Google Scholar
Kubo, R. 1966 The fluctuation-dissipation theorem. Rep. Prog. Phys. 29 (1), 255284.Google Scholar
Kuipers, J. B. 1999 Quaternions and Rotation Sequences. Princeton University Press.Google Scholar
Ladd, A. J. C. 1993 Short-time motion of colloidal particles: numerical simulation via a fluctuating lattice-Boltzmann equation. Phys. Rev. Lett. 70 (9), 13391342.Google Scholar
Ladd, A. J. C. 1994a Numerical simulations of particulate suspensions via a discretized Boltzmann equation. Part 1. Theoretical foundation. J. Fluid Mech. 271, 285309.Google Scholar
Ladd, A. J. C. 1994b Numerical simulations of particulate suspensions via a discretized Boltzmann equation. Part 2. Numerical results. J. Fluid Mech. 271, 311339.Google Scholar
Landau, L. D. & Lifshitz, E. M. 1987 Fluid Mechanics, 2nd edn. Course of Theoretical Physics, vol. 6. Butterworth-Heinemann.Google Scholar
Leal, G. L. 2007 Advanced Transport Phenomena: Fluid Mechanics and Convective Transport Processes. Cambridge University Press.Google Scholar
Liu, Y., Shah, S. & Tan, J. 2012 Computational modeling of nanoparticle targeted drug delivery. Rev. Nanosci. Nanotech. 1 (1), 6683.Google Scholar
Lowe, C. P., Frenkel, D. & Masters, A. J. 1995 Long-time tails in angular-momentum correlations. J. Chem. Phys. 103 (4), 15821587.CrossRefGoogle Scholar
Masters, A. J. 1996 Long-time behavior of the angular velocity autocorrelation function. J. Chem. Phys. 105 (21), 96959697.Google Scholar
Masters, A. J. 1997 Response to ‘Comment on “Long time behavior of the angular velocity autocorrelation function”’ [J. Chem. Phys. 107, 291 (1997)]. J. Chem. Phys. 107 (1), 292293.Google Scholar
Mazumdar, J. 2015 Biofluid Mechanics. World Scientific.Google Scholar
Nie, D. & Lin, J. 2009 A fluctuating lattice-Boltzmann model for direct numerical simulation of particle Brownian motion. Particuology 7, 501506.CrossRefGoogle Scholar
Onsager, L. 1931a Reciprocal relations in irreversible processes. I. Phys. Rev. 37, 405426.Google Scholar
Onsager, L. 1931b Reciprocal relations in irreversible processes. II. Phys. Rev. 38, 22652279.Google Scholar
Ouchene, R., Khalij, M., Taniere, A. & Arcen, B. 2015 Drag, lift and torque coefficients for ellipsoidal particles: from low to moderate particle Reynolds numbers. Comput. Fluids 113, 5364.CrossRefGoogle Scholar
Pagonabarraga, I., Hagen, M. H. J., Lowe, C. P. & Frenkel, D. 1998 Algebraic decay of velocity fluctuations near a wall. Phys. Rev. E 58 (6), 72887295.Google Scholar
Patankar, N. A. 2002 Direct numerical simulation of moving charged, flexible bodies with thermal fluctuations. In Technical Proceedings of the 2002 International Conference on Modeling and Simulation of Microsystems, pp. 3235; ISBN-10: 0970827571; ISBN-13: 978-0970827579.Google Scholar
Perrin, F. 1934 Mouvement brownien d’un ellipsoide – I. Dispersion diélectrique pour des molécules ellipsoidales. J. Phys. Radium 5 (10), 497511.Google Scholar
Perrin, F. 1936 Mouvement Brownien d’un ellipsoide (II). Rotation libre et dépolarisation des fluorescences. Translation et diffusion de molécules ellipsoidales. J. Phys. Radium 7 (1), 111.Google Scholar
Radhakrishnan, R., Yu, H.-Y., Eckmann, D. M. & Ayyaswamy, P. S. 2017 Computational models for nanoscale fluid dynamics and transport inspired by nonequilibrium thermodynamics 1. Trans. ASME J. Heat Transfer 139 (3), 033001.Google Scholar
Shah, S., Liu, Y., Hu, W. & Gao, J. 2011 Modeling particle shape-dependent dynamics in nanomedicine. J. Nanosci. Nanotech. 11 (2), 919928.Google Scholar
Sharma, N. & Patankar, N. A. 2004 Direct numerical simulation of the Brownian motion of particles by using fluctuating hydrodynamic equations. J. Comput. Phys. 201, 466486.Google Scholar
Squires, T. M. & Mason, T. G. 2010a Fluid mechanics of microrheology. Annu. Rev. Fluid Mech. 42 (1), 413438.Google Scholar
Squires, T. M. & Mason, T. G. 2010b Tensorial generalized Stokes–Einstein relation for anisotropic probe microrheology. Rheol. Acta 49, 11651177.Google Scholar
Sugihara-Seki, M. 1996 The motion of an ellipsoid in tube flow at low Reynolds number. J. Fluid Mech. 324, 287308.Google Scholar
Swaminathan, T. N., Mukundakrishnan, K. & Hu, H. H. 2006 Sedimentation of an ellipsoid inside an infinitely long tube at low and intermediate Reynolds numbers. J. Fluid Mech. 551, 357385.Google Scholar
Uma, B., Swaminathan, T. N., Radhakrishnan, R., Eckmann, D. M. & Ayyaswamy, P. S. 2011 Nanoparticle Brownian motion and hydrodynamic interactions in the presence of flow fields. Phys. Fluids 23 (7), 073602.CrossRefGoogle ScholarPubMed
Vitoshkin, H., Yu, H.-Y., Eckmann, D. M., Ayyaswamy, P. S. & Radhakrishnan, R. 2016 Nanoparticle stochastic motion in the inertial regime and hydrodynamic interactions close to a cylindrical wall. Phys. Rev. Fluids 1 (5), 054104.CrossRefGoogle ScholarPubMed
Waigh, T. A. 2005 Microrheology of complex fluids. Rep. Prog. Phys. 68 (3), 685742.CrossRefGoogle Scholar
Wakiya, S. 1957 Viscous flows past a spheroid. J. Phys. Soc. Japan 12 (1), 11301141.Google Scholar
Xia, Z., Connington, K. W., Rapaka, S., Yue, P., Feng, J. J. & Chen, S. 2009 Flow patterns in the sedimentation of an elliptical particle. J. Fluid Mech. 625, 249.Google Scholar
Xu, Q. & Michaelides, E. E. 1996 A numerical study of the flow over ellipsoidal objects inside a cylindrical tube. Intl J. Numer. Meth. Fluids 22 (1), 10751088.Google Scholar
Yu, H.-Y., Eckmann, D. M., Ayyaswamy, P. S. & Radhakrishnan, R. 2015 Composite generalized Langevin equation for Brownian motion in different hydrodynamic and adhesion regimes. Phys. Rev. E 91 (5), 052303.Google Scholar
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