Modeling the Flow of Fluids Through Microfluidic Devices
Researchers observe significant differences in seemingly identical microfluidics experiments, and demonstrate that stochastic modeling approaches can be used to account for the variability in flow.
The reproducibility of how fluids flow through microfluidic cells has not been well studied, and yet, the use of microfluidic devices to study a variety of fluid flow processes has been steadily increasing. Now, scientists have performed a set of well-controlled drainage and imbibition experiments using six identically manufactured microfluidic cells to study the reproducibility of multiphase flow experiments. The result: a variability (upwards of 200%) among the cells and within each cell, confirming that multiphase flow experiments should be considered as a stochastic process. Researchers proposed a stochastic model with randomly varying injection rate, which was able to reproduce both the average behavior and variability observed in the experiments.
The collected data set reveals variability in pore-scale multiphase flow, which was explained by the proposed numerical model. Both the data and the model can provide an improved understanding of the multiphase flow physics of microfluidic devices, and this information can be very helpful for studying important environmental challenges such as subsurface contaminant remediation.
Department of Energy (DOE) sites, such as the Hanford Site, have a history of contaminants discharged into the ground. They mix, separate, and flow at varying speeds depending on the subsurface composition, temperature, moisture, and pressure. Researchers want to predict the flow of these various contaminants to devise more effective remediation solutions. Recent advances in numerical methods allow simulations of multiphase flow at pore, field, and regional scales, but researchers need to be able to validate the numerical results. The traditional approach to model validation is through comparison with experiments. Microfluidic devices and pore-scale numerical models are commonly used to study multiphase flow in biological, geological, and man-made porous materials. The thin plastic devices, each resembling a miniaturized slice of Swiss cheese, help researchers understand the physics of how water, particulates, contaminants, and other constituents flow in the subsurface. In this study, researchers used microfluidic cells to understand the physics of multiphase flow in porous media. Six identical cells were manufactured, and a precise pump was used to inject the liquids into the device. The flow in 30 experiments (five experiments for each of the six cell replicas) varied by close to 200%. The findings were surprising because they revealed significant variability in pore-scale multiphase flow cell experiments due to cell manufacturing defects and fluctuations in the pump injection rate. “It’s extremely difficult to replicate multiphase flow experiments in a lab,” said lead researcher Alexandre Tartakovsky, a scientist at the Pacific Northwest National Laboratory. Miniscule differences in manufacturing of the cell devices and small fluctuation in the pump injection rate can cause large variations in the experimental results. Such variations are virtually uncontrollable and can wreak havoc on results. Researchers proposed a stochastic model with randomly varying injection rate, which was able to reproduce both the average behavior and variability observed in the experiments. The standard deterministic models, on the other hand, cannot explain variability and give a poor estimate of the average behavior.
Pacific Northwest National Laboratory
This research was partially supported by the Subsurface Biogeochemical Research (SBR) program of the Office of Biological and Environmental Research, within the U.S. Department of Energy (DOE) Office of Science, through the SBR Scientific Focus Area at Pacific Northwest National Laboratory (PNNL) and by the National Science Foundation (NSF). A. Tartakovsky was partially supported by the DOE Office of Advanced Scientific Computing (ASCR) as part of the Early Career Award “New Dimension Reduction Methods and Scalable Algorithms for Multiscale Nonlinear Phenomena.”