Investigation of DPD transport properties in modeling bioparticle motion under the effect of external forces: Low Reynolds number and high Schmidt scenarios

Abstract

We have used a dissipative particle dynamics (DPD) model to study the movement of microparticles in a microfluidic device at extremely low Reynolds number (Re). The particles, immersed in a medium, are transported in the microchannel by a flow force and deflected transversely by an external force along the way. An in-house Fortran code is developed to simulate a two-dimensional fluid flow using DPD at Re ≥ 0.0005, which is two orders of magnitude less than the minimum Re value previously reported in the DPD literature. The DPD flow profile is verified by comparing it with the exact solution of Hagen-Poiseuille flow. A bioparticle based on a rigid spring-bead model is introduced in the DPD fluid, and the employed model is verified via comparing the velocity profile past a stationary infinite cylinder against the profile obtained via the finite element method. Moreover, the drag force and drag coefficient on the stationary cylinder are also computed and compared with the reported literature results. Dielectrophoresis (DEP) is investigated as a case study for the proposed DPD model to compute the trajectories of red blood cells in a microfluidic device. A mapping mechanism to scale the external deflecting force from the physical to DPD domain is performed. We designed and built our own experimental setup with the aim to compare the experimental trajectories of cells in a microfluidic device to validate our DPD model. These experimental results are used to investigate the dependence of the trajectory results on the Reynolds number and the Schmidt number. The numerical results agree well with the experiment results, and it is found that the Schmidt number is not a significant parameter for the current application; Reynolds numbers combined with the DEP-to-drag force ratio are the only important parameters influencing the behavior of particles inside the microchannel.

Multiple Particle Manipulation under Dielectrophoresis Effect: Modeling and Experiments

“…The previously developed in-house FORTAN code to model the 2D fluid flow at low Re in a confined geometry is extended to trace microparticles driven by several external forces. , A computational domain is defined followed by the initialization of the parameters, such as the DPD force strength coefficients, number density, time step, equilibrium temperature, and the total number of iterations. To speed up computation time, a commonly used link-list approach is implemented .…”

“…Adopted from ref , each microparticle is constructed by using DPD particles by employing a bead–spring model. A brief description is given here for the purposes of clarity.…”

“…Another strength of the DPD method is the accurate implementation of its boundary conditions which is considered as a key feature in simulating physical systems. Because of the above-mentioned features, DPD has attracted growing interest in studying flow of RBCs in capillaries, colloids, DNA manipulation, multiphase flow, and electrokinetic phenomena (such as electrophoresis and dielectrophoresis). − …”

Temperature Error Reduction of DPD Fluid by Using Partitioned Runge-Kutta Time Integration Scheme

“…Also, the set of random numbers ζ ij does not change within a time step in accordance with the characteristics of the Wiener process for all the time integration schemes. The simulations were implemented for the time increments, ∆t ranging between 0.005 and 0.1, which includes those used in a number of DPD studies [5,[31][32][33][34]. Also, each of them were performed at least 1000 DPD time (t).…”

“…At this scale (approximately ranging between 10 nm and 10 µm), both hydrodynamics and thermal fluctuations are important, and thus a mesoscopic simulation method should be able to take into account these properties. Dissipative particle dynamics (DPD), which is a particle-based method first developed by Hoogerbrugge and Koleman [1], owns these properties and has been used for various simulations of mesoscopic complex fluid systems, such as colloidal suspensions [2,3], biological systems [4,5], and polymer solutions [6,7].…”