Research Interests: Fluid Mechanics & Thermal Sciences Professor Vanka is working in a number of areas related to computational fluid dynamics. These include transport processes in chemical vapor deposition (CVD) reactors, large eddy simulations in continuous casting of steel, microscale mixing, particle transport in turbulent flows and heat transfer in wavy passages. His most recent
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“Research Interests: Engineering-economic modeling and analysis of electric power systems; carbon capture and sequestration technologies; climate change mitigation strategies; energy and environmental technology innovation and the role of government policies; integrated assessments.”
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“Research interests: Micro/nanofluidics and BioMEMS/NEMS, ultra-sensitive single molecule biosensors; Micro/nanosystems for engineering synthetic ex vivo stem cell microenvironments; Mechanobiology and mechanotransduction during stem cell differentiation and molecular mechanisms of how stem cells sense physicochemical cues of extracellular matrix.”
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“The goal of my research program is to use methods from control theory, movement analysis, design, and dynamic systems modeling to investigate issues related to musculoskeletal biomechanics and rehabilitation engineering. In particular, my research focuses on investigating and improving movement control and function through two main areas: locomotion biomechanics and assistive device development. My research
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The ERISE Laboratory is engaged in the development of innovative systemic approaches to facilitating secure and reliable energy delivery solutions. These systemic approaches integrate system architectures, protection, control and communication
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“Research Interests: Rheology, Non-Newtonian Fluid Dynamics, Hydrodynamic Instabilities, Extensional Rheometry”
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Our research area is regional ocean data assimilation. By definition, this includes numerical modeling, uncertainty quantification and assimilation schemes, and applications to ocean dynamics. In contemporary ocean science and engineering, modeling systems that integrate understanding of complex phenomena and utilize efficient numerics are paramount. Advances in this area require that we quantify and predict uncertainties
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