Research

SRU Engineering majors have the opportunity to be involved in faculty/student research projects.

Dr. Kazemi's Research

Dr. Kazemi's research is currently focusing on developing methodologies to enhance accuracy and speed of flash calculations in compositional reservoir simulators. Molecular dynamics simulations along with artificial neural networks are being used to develop a fast and accurate model for the phase behavior of petroleum mixtures. Another area of his research is on enhanced oil recovery of shale oil reservoirs by investigating them on a molecular scale. His research is not limited to that and he is doing experimental measurements of interfacial tension which is especially important for enhanced oil recovery processes. The goal is to develop molecular simulations models that match very well with experimental measurements to reduce the cost and time.

Kudrashou Group

Dr. Kudrashou's research focuses on studying and improving techniques of Enhanced Oil/Gas Recovery (Chemical and Thermal methods) and Well Stimulation (Hydraulic Fracturing and Acidizing). For the past seven years, I have been working on research projects in this area. Some of the data for this work is collected by conducting hands-on experiments in laboratories using standard and custom-built equipment; some of the results are obtained by modeling the reservoir processes using computer simulations; and some data comes from field implementations and case trials.

Normally, oil/gas well life cycle consists of the following stages: (1) growing production rate (newly-drilled well); (2) stabilized or plateaued production (developed well) ; (3) declining production (mature well). Hydrocarbon recovery efficiency can be improved by introducing Enhanced Recovery Methods in the later stages of the well life cycle (to prevent sharp production decline). For example, we can apply thermal methods, such as hot water or steam injection to reduce the viscosity of oil, which results in increased oil production. Alternatively, for some cases we can design chemical treatment methods, such as polymer flooding. In this method viscous polymer propagates from injecting to producing wells to effectively "push" the remaining oil in a "piston-like" displacement. These are just a few examples out of multiple known and developing Enhanced Recovery techniques.

Well Stimulation projects include design and optimization of hydraulic fracturing and acidizing treatments. These well interventions are used to increase oil/gas production rate by removing formation damage (materials that block pores and pathways through which we produce hydrocarbons) or creating new flow channels to stimulate the production.

Mentioned projects require a multidisciplinary research approach that includes learning about rock properties; experimentally testing various fluids, chemical additives, and their interaction; creating simulation models to evaluate methods' effectiveness, and so on. Well-designed treatments for a well or reservoir are in high demand in the industry because increased hydrocarbon production translates into higher revenue of a project.

Dr. Limon's Research

Dr. Limon’s research group focuses on improving systems, process and product design using data-driven decision making and optimization techniques. The purpose of his research group is to develop an accurate model in the presence of operational and environmental uncertainty to proactively control and maintain the product as well as systems. Several aspects of this research are reliability modeling, uncertainty analysis, prognostic estimation, design optimal experiments, statistical modeling, and analytics of sensor data. Our experimental and analytical studies are applicable but not limited to automobiles, aircraft, flexible hybrid electronics, mobile construction equipment, energy systems, medical equipment, food processing industries, logistics and maintenance services.

In this era of rapid technological change, product testing and evaluation during the development stages become very essential in particularly for assuring quality, compliance, and market competition. In the last five years, our research team developed a framework to shorten the product test and evaluation time effectively by utilizing the accelerated degradation test sensor data. A prognostic model is also developed to maintain high-valued engineering systems by reducing downtime. To better understand the interaction phenomena of components in complex engineering systems and their root cause of failures, a cognitive map-based framework is developed. Very recently, we are working on the optimal maintenance scheduling of wind turbines considering adverse weather, multidimensional sensor data analytics of machinery, and newly developed flexible electronics’ lifetime estimation. Besides these current projects, the aim is to extend our work to investigate and develop a prognostic model for electric batteries and infrastructure & distribution centers' reliability in the context of Industry 4.0.

System Modeling in Healthcare:

Dr. Zhang’s Research

Dr. Zhang’s research focuses on social network analysis, optimization, modeling and simulation in healthcare. For the past several years, I have been done research on systems modeling on prevention of adolescent obesity using social network analysis and computer simulation. I have also used data analysis, optimization and simulation to help healthcare organizations and industries to improve quality and operational efficiency.

By combining statistical analysis and computer simulation, we are able to explore strategies to improve the efficiency and performance of systems which include manufacturing, services, logistics and supply chain, transportation, etc. In addition, optimization and operations research can help us to find the best/better ways to use our limited resources. So that the growth of the organization could be sustainable. Social network analysis is another great system modeling and analysis technology. For example, one of my previous research studied how adolescence make friends based on similarity between them, same age, gender, and hobbies, etc., and how their friendship impact on their eating behavior and body weight.

Computational Physics

The SRU computational physics research group is run by Dr. Herat and Dr. Valera. Dr. Herat's research focuses on the application of physical models and simulation techniques to material science, biological systems, and medical applications. He is currently working on a cross-disciplinary medical imaging research project. This investigation focuses on fMRI neuroimaging analysis to investigate brain activation in different cohorts of individuals. Dr. Herat frequently seeks to involve student research assistants in his research project. He often has research grant money to pay student stipends.

Mathematical modeling and computations are essential components of modern research in physics. Physicists have very precise mathematical theories describing how various systems behave. Unfortunately, very often the equations that describe these theories are so complex that solving them by hand is not realistic. This is where the computational physicists come in. With the use of contemporary computing technologies, they perform these complex and vast calculations (or simulations) that cannot be done using the traditional techniques. Areas of applications include condensed-matter physics, astrophysics, elementary particle physics, and medical physics.

Herat Group

Dr. Herat's research specialty is computational physics. In particular his research agenda focuses on the application of physical models and simulation techniques to material science, biological systems, and medical applications. He frequently involves undergraduate research assistants in his research projects, and currently mentors two students. Dr. Herat often has research grant money available to pay student stipends. Two of his recent projects are briefly described below:

A collaborative computational biophysics research project. The primary objective of this research effort is to
numerically investigate the dynamics of protein aggregation. Proteins are essential parts of organisms and play a key role in a wide variety of biological processes. However, proteins also have a propensity to misfold and aggregate, which not only alter their original biological function but also can be harmful to the organism. We are looking at a particular type of misfolding of proteins into hedgehog shaped structure (or Spherulites) that is known to cause number of neurodegenerative diseases. It is our hope that shedding light into this malformation of proteins could one day lead to cures for some of the most debilitating deceases, including Alzheimer's, Parkinson's, and Creutzfeldt-Jakob's decease.

He is also currently working on a cross-disciplinary medical imaging research project. This investigation focuses on fMRI neuroimaging .Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique that measures brain activity during a specific experiment over a time period by detecting associated changes in the oxygenation of blood flow. fMRI studies can be used to produce brain activation maps showing which parts of the brain are active while people are thinking, feeling or performing cognitive tasks.

The existing statistical methods used in fMRI analysis have several limitations. There are certain characteristics of fMRI studies that contribute to these limitations. First, fMRI sample sizes are small. Another compounding characteristic is that the data is highly correlated. As a result, it is challenging to observe statistically significant brain activation. The currently used analysis technique does not lend itself to adequately handle these limitations. This study will evaluate a more novel analysis technique known as Partial Least Squares Regression (PLSR) to be used as the statistical tool in fMRI analysis. This technique uses a rigorous mathematical procedure called Eigen Decomposition, which directly addresses the issues posed by the small sample sizes and the highly correlated data encountered in these studies. Establishing PLSR as a reliable and valid method of analysis for fMRI data will advance the field of neuroimaging.

Valera Group

Dr. Valera's research focuses on studying Soft Condensed Matter Systems using computational techniques. For the past six years I have been involved in work using Molecular Dynamics to simulate systems such as colloids and water.

Colloid research is a field of critical importance for the development of a wide range of products and industrial processes. Examples of colloidal suspensions include inks, paints, lubricants, cosmetics and milk, products that are ubiquitous in everyday life. One of the most important features is that several of the properties of these systems can be changed by exposing them to external factors, such as electric or magnetic fields. In this way we can control or "tune" the physical behavior of the system and this could lead to a new set colloidal structures. In addition, the tunability of the structures allows for the possibility of creating advance materials whose functions and properties can be switched on and off at will. These features of colloidal suspensions give rise to a range of interesting materials that includes ferrofluids. Using computer simulations, we are able to study ferrofluids under different constraints. We look for properties such as glassy states and/or organized behavior.

Engineering