SRU physics and engineering majors have the opportunity to be involved in faculty/student research projects in the areas of liquid crystals, nanotechnology, material science, computational physics and astrophysics.
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.
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.
Dr. Sagar Bhandari's Research
Dr. Bhandari runs the “Quantum Lab” at Slippery Rock University. His research is focused on deepening our understanding of quantum materials and devices. Atomically thin materials such as graphene, transition metal dichalcogenides (TMDCs) and topological insulators were previously thought to be unstable and unusable because of thermodynamic instability but have been found to be strong and robust, and excellent candidates for new electronics and photonics. The physics of such atomically thin materials is inherently governed by quantum mechanics. As a result, non-classical phenomena such as coherence, interference, spin and wave like properties of electrons dominate at this scale.
Quantum lab designs and builds low noise electronics to probe electronic properties of quantum materials and devices. Projects include probing the densities of electrons in an atomic layer or a quantum dot, and quantum capacitance voltage profiling for spectroscopic measurements of quantum states. The lab is also building a charge sensor to a measure tiny fraction of an electron charge that allows one to map out the local charge density information in a quantum system. In addition, the quantum lab plans to use these techniques to add or remove electrons into a quantum dot and using the charge sensor, plans to map the energies of these states.
Most of the experiments the lab runs operate at extremely low temperatures (4 Kelvin) and high vacuum. Therefore, these projects are suitable for students with broad range of interests. If you are interested in applying your knowledge in electrical/mechanical engineering to work on a challenging project to build an equipment that runs at cryogenic temperatures and ultra-high vacuum, quantum lab would be the right place to push yourself and learn a great deal of engineering on the way. Also, if you want to apply your mathematics or physics knowledge to dig into exciting physics of quantum mechanical behavior of electrons, quantum lab will help you. So, quantum lab is looking to recruit undergraduate students (from freshmen to seniors) to develop low temperature scanning probe tools, make quantum devices and study exciting new physics in these devices.
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.