Direct numerical simulation of mixed convection in turbulent channel flow: on the Reynolds number dependency of momentum and heat transfer under unstable stratification
Proceedings of the 8th International Conference on Computational Heat and Mass Transfer, ICCHMT 2015, , , 2015
Abstract: Direct numerical simulations of unstably stratified turbulent channel flow have been performed in order to investigate the Reynolds number effect on mixed convection. Six different cases are considered with friction Reynolds number Re_\tau= 180 and 395 and friction Richardson number Ri_\tau= 0, 100 and 1000. It is shown that both friction coefficient and Nusselt number increase under unstable stratification for a sufficiently large Richardson number. At low Richardson number, the friction coefficient can either increase or decrease depending on the Reynolds number. The drag reduction is associated with an increase of mean velocity due to an enhanced dissipation of Reynolds shear stress by pressure strain in the buffer region. The breakdown of the Reynolds analogy is demonstrated as the turbulent Prandtl number exhibits a non-constant behavior due to buoyancy.
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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).