Abstract: In this study, numerical computations are performed that examine the thrust production and efficiency of supersonic micronozzles with bell-shaped expanders. The bell geometry is favored on the macroscale for its flow alignment. To date, concerns over microfabrication challenges of the contoured geometry have limited its consideration for microscale applications. Three different bell expander configurations are examined (100% full bell, 80%, and 60%) for two-dimensional and three-dimensional duct configurations of varying depths (25-200 mu m), and a decomposed H2O2 monopropellant is used as the working fluid, and the associated throat Reynolds numbers range from 15 to 800. Owing to the inherently low Reynolds numbers on the microscale, substantial viscous subsonic layers develop on the walls of the nozzle expander, retard the bulk flow, and reduce the nozzle performance. The thrust production and specific impulse efficiency are computed for the various flow scenarios and nozzle geometries to delineate the impact of viscous forces on the nozzle performance. Results are also compared to the inviscid theory and to two-dimensional and three-dimensional results for 30deg linear nozzle configurations. It is found that the flow alignment of the bell nozzle comes at the expense of increased viscous losses, and, on the microscale, a 30deg linear nozzle offers a higher efficiency for Re<320 in two-dimensional micronozzles and over the majority of Reynolds numbers in three-dimensional simulations. The simulation results indicate that a short micronozzle outperforms a longer nozzle at a given Reynolds number, and this result is supported by existing micronozzle studies in the literature.
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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).