Modeling of Foam Flow in Porous Media for Subsurface Environmental Remediation

Among numerous foam applications in a wide range of disciplines, foam flow in porous media has been spotlighted for improved/enhanced oil recovery processes in petroleum-bearing geological formations and shallow subsurface in-situ NAPL (non-aqueous phase liquid) environmental remediation in contaminated soils and aquifers. In those applications, foams are known to reduce the mobility of gas phase by increasing effective gas viscosity and improve sweep efficiency by mitigating subsurface heterogeneity. This study investigates how surfactant/foam process works fundamentally for environmental remediation purpose by using MoC (Method of Characteristics) based foam modeling and simulation techniques. It consists of two main parts: Part 1, developing foam model using three-phase fractional flow theory accounting for foam flow rheology such as foam strength and stability at different phase saturations; and Part 2, extending the model to investigate the mechanisms of surfactant/foam displacement in multi-layer systems. Part 1 investigates six scenarios such as different levels of foam strength (i.e., gas mobility reduction factors), different initial conditions (i.e., initially oil/water or oil/water/gas present), foam stability affected by water saturation (Sw), oil saturation (So), and both together, and uniform vs. non-uniform initial saturations. The process is analyzed by using ternary diagrams, fractional flow curves, effluent histories, saturation profiles, time-distance diagrams, and pressure and recovery histories. The results show that the three-phase fractional flow analysis presented in this study is robust enough to analyze foam-oil displacements in various conditions, as validated by an in-house numerical simulator built in this study. The use of numerical simulation seems crucial when the foam modeling becomes complicated and faces multiple possible solutions. Part 2 first shows how to interpret theoretically the injection of surfactant preflush and following foams into a single-layer system at pre-specified rock and fluid properties, and then extends the knowledge gained into multi-layer systems where the properties vary in different layers. The results in general show that the mechanisms of foam displacement strongly depend on foam properties such as gas-phase mobility reduction factors (MRF), limiting water saturation (Sw*), critical oil saturation (So*), and so on as well as petrophysical properties of individual layers such as porosity (φ), permeability (k), relative permeability and so on. The overall sweep efficiency in a multi-layer system is very difficult to predict because of the complexity, but the mathematical framework presented in this study is shown to be still reliable. The in-house foam simulator is also extended to compare with modeling results

Backward elastic light scattering of malaria infected red blood cells

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98695/1/ApplPhysLett_99_073704.pd

The switching of rotaxane-based motors

Linear molecular motors have recently attracted considerable interest. In this paper we use molecular dynamics simulations to investigate the structural and energetic properties of neutral and oxidized [2]rotaxane motors. We first consider a neutral structure to identify the stable configuration of a bistable [2]rotaxane whose ring component is on the tetrathiafulvalene (TTF) recognition site, followed by a study of the dynamic switching process of an oxidized [2]rotaxane. The study shows that for a neutral structure the ring component stays at the TTF station in both free and constrained situations. When the station is oxidized, the ring is pushed to the other station and the dynamic switching process finishes in about 10 ns. By comparing the results for the cases of free and fixed ends, the simulations show that structural deformation plays an important role during the switching process and can significantly affect the displacement output of molecular motors.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90788/1/0957-4484_22_20_205501.pd

Enhanced Deep Residual Networks for Single Image Super-Resolution

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.Comment: To appear in CVPR 2017 workshop. Best paper award of the NTIRE2017 workshop, and the winners of the NTIRE2017 Challenge on Single Image Super-Resolutio