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Influence of Wall Heat Transfer on Supersonic MicroNozzle Performance

Journal of Spacecraft and Rockets, 49, 450-460, 2012


Status: Published

Citations:

Cite: [bibtex]


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Abstract: A numerical model to characterize the influence of wall heat transfer on performance of a microelectromechanical systems (MEMS)-based supersonic nozzle is reported. Owing to the large surface-area-to-volume ratio and inherently low Reynolds numbers of a MEATS device, wall phenomena, such as viscous forces and heat transfer, play critical roles in shaping performance characteristics of the micronozzle. Viscous subsonic layers inhibit flow and can grow sufficiently large on the nozzle expander walls, potentially merging to cause the flow to be subsonic at the nozzle exit, and result in reduced efficiency and performance. Heat flux from the flow into the surrounding substrate can mitigate subsonic layer growth and improve overall thrust production. In this study, subsonic layer growth is quantified to characterize the impact on performance of micronozzles with a flowfield that is subject to wall heat transfer. Both two- and three-dimensional (3-D) simulations are performed for varying expander half-angles (15 deg, 30 deg, and 45 deg) and varying throat Reynolds numbers (30-800), whereas the depth of the 3-D nozzle is varied (25-300 mu m). Simulation results and nozzle efficiencies are compared with inviscid theory, previous adiabatic results, and existing numerical and experimental data. It is found that heat loss to the substrate will further accelerate the supersonic core flow via Rayleigh flow theory and can reduce subsonic layer growth. These effects can combine to alter the micronozzle expansion angle, which maximizes thrust production and specific impulse efficiency.



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Joshua Bongard - Department of Computer Science, Associate Professor

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.


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Chris Danforth -Department of Mathematics and Statistics, Flint Professor of Mathematical, Natural, and Technical Sciences

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.

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Laurent Hébert-Dufresne - Assistant Professor, Computer Science

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.

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Paul Hines - School of Engineering, Associate Professor

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.

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James Bagrow - Assistant Professor, Department of Mathematics and Statistics

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).

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