Using dissipative particle dynamics for modeling surfactants

Oil recovery is an industrial process that injects aqueous solutions into an oil reservoir to pump out crude oil and promote the oil production. The aqueous solution contains surfactants for reducing the interfacial tension (IFT) between aqueous phase and oil. The critical micelle concentration (CMC) is the concentration of surfactant above which micelles form and the interfacial tension reaches a plateau. Our research seeks to measure IFT and CMC for surfactants using dissipative particle dynamics (DPD) technique, which is a coarse-grained method based on the molecular dynamics. We first study how IFT is influenced by the surfactant concentration. Furthermore, another simulation is performed, in which an oil drop passes through porous media in the presence of surfactants under various capillary numbers. Using the simulation of emulsion flow through porous media, we get qualitatively similar velocity profile and average velocity as the results from computational fluid dynamics. In addition, the porous media simulation indicates that the presence of surfactants would slightly reduce the average velocity of the oil drop

Analytical Solution Of Microbes Interacting With Surfaces

Nowadays, there is a rising interest in studying the behavior of microbes and their interactions with flow and surfaces. In order to explore the velocity field, pressure and forces around the microbes, the solution of Stokes equations, which is called a Stokeslet, is used. This solution represents a singular velocity field due to a concentrated external force acting on fluid at a single point. This singularity could cause the expression of velocity not integrable. We use the Regularized Stokeslet Method and Method of Images to deal with this problem. The expression of force is replaced by a radially symmetric function, which distributes the force on a certain area rather than at a single point. We perform different numerical simulations to validate the code against analytical solution for flow around a sphere and a swimmer. The numerical results match well with the exact solutions. It can be concluded that the analytical solutions of microbes interacting with surfaces can be well simulated using the method of Regularized Stokeslet

Pore Scale Transport of Miscible and Immiscible Fluids in Porous Media

The separation of harmful or valuable substances entrapped in porous media has applications in processes such as enhanced oil recovery, diffusion in tissue, and aquifer remediation. In this study the motion and removal rate of immiscible and miscible solutions have been analyzed to gain understanding of solvent effectiveness as it is diluted due to diffusion or mixing within porous materials. The extraction of oil using water, a surfactant solution of 4% CTAB in water, and a foam produced form the surfactant solution is observed using two dimensional flows between parallel slides containing cylindrical obstacles. The fluid motion is visualized. The foam proved to be the most effective solution at removing oil. The formation of large air bubble during foam propagation indicated that foam is not capable of holding its structure. The dissolution of two miscible fluids (glycerol and water) is visualized in square and round capillary tubes of various diameters. The capillaries are filled with solute before being immersed in a bath in which the solute concentration within the solvent is increased. The observation of the miscible liquid-liquid interfaces in a tube help us quantify the effective diffusion process

Movement and Distribution of Bacteria near Surfaces

Bacteria are found everywhere in nature, including within human/animal bodies, biomedical devices, industrial equipment, oceans and lakes. They can be found in planktonic state within a bulk liquid phase or attached to surfaces with the potential to form biofilms. In this study we are interested in the movement and distribution of bacteria near surfaces. The concentrations and fluid interactions of bacteria were characterized at various distances from a surface. Psuedomonas putida F1 was observed in a suspension near a surface. Bacteria movements were visualized with an inverted microscope at 40x magnification. P. putida F1 exhibited greater density in close proximity to the surface when compared to the bulk. Additionally, the ability to move in a direction normal to the surface was significantly reduced

Bacterial Motility and its Role in Biofilm Formation

Bacterial biofilms are known to cause millions of dollars in damage in the medical industry per year via infection of central venous catheters, urinary catheters, and mechanical heart valves. Unfortunately, there are some characteristics of biofilm formation that are yet to be fully understood. Recently much work has been done to investigate the motility characteristics of bacteria with hopes of better understanding the phenomena of biofilm formation. Still, one of the least understood stages is bacterial attachment or adhesion, a process designed to anchor bacteria in an advantageous environment. Providing a better understanding of bacterial motility near solid interfaces will serve to advance knowledge of hydrodynamic interactions at play in one of the early stages of biofilm formation, bacterial attachment. In this study, multiple bacteria strains: HCB 437, HCB 1262, HCB 1736, and Putida Pseudomonas are placed in lab created-microfluidics chambers. Using phase-contrast microscopic cinematography, the motion paths of individual bacterium are analyzed for evidence of significant hydrodynamic interaction with their surroundings. Preliminary results have indicated that the locomotive behavior of individual bacteria, as well as their collective motion in a constrained environment, is greatly altered compared to the behavior seen in the bulk fluid. These differences could be vital in the initial stages of biofilm formation and highlight the need for further research to more accurately reflect the environments that bacteria encounter

Inverse resolution of spatially varying diffusion coefficient using Physics-Informed neural networks

Resolving the diffusion coefficient is a key element in many biological and engineering systems, including pharmacological drug transport and fluid mechanics analyses. Additionally, these systems often have spatial variation in the diffusion coefficient which must be determined, such as for injectable drug-eluting implants into heterogeneous tissues. Unfortunately, obtaining the diffusion coefficient from images in such cases is an inverse problem with only discrete data points. The development of a robust method that can work with such noisy and ill-posed datasets to accurately determine spatially-varying diffusion coefficients is of great value across a large range of disciplines. Here, we developed an inverse solver that uses physics informed neural networks (PINNs) to calculate spatially-varying diffusion coefficients from numerical and experimental image data in varying biological and engineering applications. The residual of the transient diffusion equation for a concentration field is minimized to find the diffusion coefficient. The robustness of the method as an inverse solver was tested using both numerical and experimental datasets. The predictions show good agreement with both the numerical and experimental benchmarks; an error of less than 6.31% was obtained against all numerical benchmarks, while the diffusion coefficient calculated in experimental datasets matches the appropriate ranges of other reported literature values. Our work demonstrates the potential of using PINNs to resolve spatially-varying diffusion coefficients, which may aid a wide-range of applications, such as enabling better-designed drug-eluting implants for regenerative medicine or oncology fields