On the modeling of low-Reynolds-number turbulence

A full Reynolds-stress closure that is capable of describing the flow all the way to the wall was formulated for turbulent flow through circular pipe. Since viscosity does not appear explicitly in the pressure redistribution terms, conventional high-number models for these terms are found to be applicable. However, the models for turbulent diffusion and viscous dissipation have to be modified to account for viscous diffusion near a wall. Two redistribution and two diffusion models are investigated for their effects on the model calculations. Wall correction to pressure redistribution modeling is also examined. Diffusion effects on calculated turbulent properties are further investigated by simplifying the transport equations to algebraic equations for Reynolds stress. Two approximations are explored. These are the equilibrium and nonequilibrium turbulence assumptions. Finally, the two-equation closure is also used to calculate the flow in question and the results compared with all the other model calculations. Fully developed pipe flows at two moderate Reynolds numbers are used to validate these model calculations

Viscoelastic Behavior of Solid 4^4He

Over the last five years several experimental groups have reported anomalies in the temperature dependence of the period and amplitude of a torsional oscillator containing solid $^4$He. We model these experiments by assuming that $^4$He is a viscoelastic solid--a solid with frequency dependent internal friction. We find that while our model can provide a quantitative account of the dissipation observed in the torsional oscillator experiments, it only accounts for about 10% of the observed period shift, leaving open the possibility that the remaining period shift is due to the onset of superfluidity in the sample.Comment: 4 pages, 3 figure

Silicon solar cell process development, fabrication, and analysis

Two large cast ingots were evaluated. Solar cell performance versus substrate position within the ingots was obtained and the results are presented. Dendritic web samples were analyzed in terms of structural defects, and efforts were made to correlate the data with the performance of solar cells made from the webs

Rich time series classification using temporal logic

© 2017 MIT Press Journals. All rights reserved. Time series classification is an important task in robotics that is often solved using supervised machine learning. However, classifier models are typically not 'readable' in the sense that humans cannot intuitively learn useful information about the relationship between inputs and outputs. In this paper, we address the problem of rich time series classification where we propose a novel framework for finding a temporal logic classifier specified in a human-readable form. The classifier is represented as a signal temporal logic (STL) formula that is expressive in capturing spatial, temporal and logical relations from a continuous-valued dataset over time. In the framework, we first find a set of representative logical formulas from the raw dataset, and then construct an STL classifier using a treebased clustering algorithm. We show that the framework runs in polynomial time and validate it using simulated examples where our framework is significantly more efficient than the closest existing framework (up to 920 times faster)