Education

• Ph.D., Technion-Israel Institute of Technology, Israel, 2003
• M.Sc., Technion-Israel Institute of Technology, Israel, 1999
• Dipl.-Ing. in M.E., St. Petersburg State Polytechnic University, Russia, 1994

Background

Dr. Alexeev came to Georgia Tech at the beginning of 2008 as an assistant professor. His research background is in the area of fluid mechanics. He uses computer simulations to solve engineering problems in complex fluids, multiphase flows, fluid-structure interactions, and soft materials. As a part of his graduate research at Technion, he investigated resonance oscillations in gases and probed how periodic shock waves excited at resonance can enhance agglomeration of small airborne particles, a process which is important in air pollution control technology. He also investigated wave propagation in vibrated granular materials and its effect on fluidization of inelastic granules. During postdoctoral studies at TU Darmstadt, he examined how microstructures on heated walls can be harnessed to control thermocapillary flows in thin liquid films and to enhance heat transport in the fluid. That could be beneficial in many practical applications, especially in microgravity. At the University of Pittsburgh, he studied the motion of micrometer-sized, compliant particles on patterned substrates to develop efficient means of controlling movement of such particles in microfluidic devices. Such substrates are needed to facilitate various biological assays and tissue engineering studies dealing with individual cells.

Research

• Fluid Mechanics and Heat Transfer, Combustion, and Energy Systems: Fluid mechanics of complex fluids, soft materials, multiphase and granular flows, surface-tension-driven flows, aerosol mechanics; computational fluid mechanics: coarse-grained numerical simulations

Dr. Alexeev's research interests concern designing microfluidic systems and devices that can operate in a way that we learn from nature. In fact, nature has already found elegant solutions for extremely complicated problems facing the modern engineering. The goal is to facilitate the engineering of active microfluidic systems that can carry out multiple complex functions. Sensing, acting and logic are functions inherent to biological microorganisms, yet they are highly desired for microscale engineering applications. The main challenge is to identify the relevant physical processes that can be integrated into engineering solutions to replicate these biologically inspired functionalities.

Microorganisms effectively propel themselves through the medium by using rapidly beating flagella or periodic shape changes. The design of polymeric micro-robots that undergo self-propelled motion either autonomously or due to external stimuli are studied. The purpose is to design microdevices that can recognize instructions, interact with each other, and perform specific tasks in microfluidic systems.

All living cells are surrounded by lipid membranes that protect the cytoplasm from the outside environment, though allow essential molecules to pass across the membrane. In a similar fashion, synthetic liposomes and polymersomes are employed in drug delivery systems. Here, a membrane protects the drug inside a vesicle on its way to a treatment site and, then, regulates its release. Coarse grained molecular dynamics simulations are used to study mechanics of such vesicles, their interactions with soft tissues and host fluids, and the release from the vesicles to optimize their application as delivery agents.

Dr. Alexeev uses different numerical approaches to solve engineering problems in fluid mechanics. A hybrid approach is developed that integrates the lattice Boltzmann method for fluid mechanics and the lattice spring method for mechanics of elastic solids to study flows interacting with compliant solid structures. Dissipative particle dynamics is another coarse grained method used to study systems at nanometer scale. He is also interested in numerical schemes for multiphase flows, such as volume of fluid, level set, and immersed boundary methods.