Abstract: Laminar flow mixing remains an active area of research within the microfluidics community. Traditional mixing methods often rely upon turbulent flow, which is generally not present on the micro-scale and so alternative approaches must be sought. This work studies enhanced laminar mixing for use in a proposed monopropellant microthruster based upon homogeneous catalysis in a flow with Re < 10. The enhancement is realized through the introduction of an inert gas at a channel junction, which can lead to the formation of discrete liquid slugs. These slugs contain the monopropellant and the catalyst and have an internal recirculation that is found to enhance mixing. The focus of this study is on the numerical investigation of this process with the goal of minimizing the mixing length and characterizing the dependence of mixing on inlet conditions. The slug formation process is found to decrease the minimum mixing length by a factor of up to 7.2, with much of the benefit of the multiphase flow occurring shortly after slug formation. As minimizing the dimensions of the microthruster is a key design consideration, this reduction in mixing length demonstrates the value of the enhanced laminar mixing for the proposed micropropulsion application.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).