System-level simulation of liquid filling in microfluidic chips

Abstract

Liquid filling in microfluidic channels is a complex process that depends on a variety of geometric, operating, and material parameters such as microchannel geometry, flow velocity/pressure, liquid surface tension, and contact angle of channel surface. Accurate analysis of the filling process can provide key insights into the filling time, air bubble trapping, and dead zone formation, and help evaluate tradeoffs among the various design parameters and lead to optimal chip design. However, efficient modeling of liquid filling in complex microfluidic networks continues to be a significant challenge. High-fidelity computational methods, such as the volume of fluid method, are prohibitively expensive from a computational standpoint. Analytical models, on the other hand, are primarily applicable to idealized geometries and, hence, are unable to accurately capture chip level behavior of complex microfluidic systems. This paper presents a parametrized dynamic model for the system-level analysis of liquid filling in three-dimensional ͑3D͒ microfluidic networks. In our approach, a complex microfluidic network is deconstructed into a set of commonly used components, such as reservoirs, microchannels, and junctions. The components are then assembled according to their spatial layout and operating rationale to achieve a rapid system-level model. A dynamic model based on the transient momentum equation is developed to track the liquid front in the microchannels. The principle of mass conservation at the junction is used to link the fluidic parameters in the microchannels emanating from the junction. Assembly of these component models yields a set of differential and algebraic equations, which upon integration provides temporal information of the liquid filling process, particularly liquid front propagation ͑i.e., the arrival time͒. The models are used to simulate the transient liquid filling process in a variety of microfluidic constructs and in a multiplexer, representing a complex microfluidic network. The accuracy ͑relative error less than 7%͒ and orders-of-magnitude speedup ͑30 000X-4 000 000X͒ of our system-level models are verified by comparison against 3D high-fidelity numerical studies. Our findings clearly establish the utility of our models and simulation methodology for fast, reliable analysis of liquid filling to guide the design optimization of complex microfluidic networks.

Splitter Microchannel Network for Equal Plasma Flow Division on Compact Disk Microfluidic Chip

“…Owing to the two-phase nature of plasma flow and the microchannel geometry, the optimization process is most conveniently achieved using numerical methods. 22,23) Accordingly, in the present study, the optimal geometry parameters of the two splitter microchannel networks were determined via a series of CFD simulations performed using commercial COMSOL finite element analysis software. Figures 3(a) and 3(b) show the COMSOL models of the U-and Y-shaped splitter microchannels, respectively.…”

Maximization of the capillary pump efficiency in microfluidics

“…It divides the development of microfluidics over the past 40 years into three waves, dictated by the fabrication technology as mechanical micromaching, cleanroom fabrication and rapid prototyping [14]. In order to further understand the processes of liquid flows, powerful commercial software, such as CFD-ACE + , ANSYS Fluent and COMSOL Multiphysics, are usually employed to the study of details of liquid motion in the microfluidic channels [15][16][17][18]. However, due to the long time involved in simulation of micro fluid motion using the commercial software, mixed methodology simulations have been developed and are being employed for the integrated system with very fast speed and acceptable accuracy [18,19].…”

Modeling of capillary-driven microfluidic networks using electric circuit analogy

“…Development of complex capillary-driven microfluidic networks is challenging without the use of suitable modeling techniques. Computational fluid dynamics (CFD) has been used to model capillary-driven flows in detail using the volume-of-fluid method (VOF) [6][7][8][9]. However, CFD using VOF requires high computational power and is practically intractable for large capillary-driven systems where a fine mesh is often needed to resolve an interface that is moving through the computational domain.…”

“…However, CFD using VOF requires high computational power and is practically intractable for large capillary-driven systems where a fine mesh is often needed to resolve an interface that is moving through the computational domain. Simplified modeling techniques using electric circuit analogy have been shown to offer both a high level of accuracy in predicting the interface position as a function of time and computational efficiency (several orders of magnitude faster than predictions from CFD using VOF) [7,8,[10][11][12][13]. However, the proposed models using electric circuit analogy are primarily developed for single-phase flow and simplified systems.…”