Simulation of three-dimensional viscous flow in turbomachinery geometries using a solution-adaptive unstructured mesh methodology

This paper presents a numerical method for the simulation of flow in turbomachinery blade rows using a solution-adaptive mesh methodology. The fully three-dimensional, compressible, Reynolds-averaged Navier-Stokes equations with k-ε turbulence modeling (and low Reynolds number damping terms) are solved on an unstructured mesh formed from tetrahedral finite volumes. At stages in the solution, mesh refinement is carried out based on flagging cell faces with either a fractional variation of a chosen variable (like Mach number) greater than a given threshold or with a mean value of the chosen variable within a given range. Several solutions are presented, including that for the highly three-dimensional flow associated with the corner stall and secondary flow in a transonic compressor cascade, to demonstrate the potential of the new method.

Laser microstructuring of Si surfaces for low-threshold field-electron emission

Dense arrays of high aspect ratio Si micro-pyramids have been formed by cumulative high intensity laser irradiation of doped Si wafers in an SF6 environment. A comparative study using nanosecond (XeCl, 308 nm) and femtosecond (Ti: Sapphire, 800 nm and KrF, 248 nm) laser pulses has been performed in this work. The influence of pulse duration and ambient gas pressure (SF6) is also presented. Scanning electron microscopy has shown that upon laser irradiation conical features appear on the Si surface in a rather homogenous distribution and with a spontaneous self alignment into arrays. Their lowest tip diameter is 800 nm; while their height reaches up to 90 mum. Secondary tip decoration appears on the surface of the formed spikes. Areas of 2 X 2 mm(2) covered with Si cones have been tested as cold cathode field emitters. After several conditioning cycles, the field emission threshold for the studied Si tips is as low as 2 V/mum, with an emission current of 10(-3) A/cm(2) at 4 V/mum. Even though these structures have smaller aspect ratios than good quality carbon nanotubes, their field emission properties are similar. The simple and direct formation of field emission Si arrays over small pre-selected areas by laser irradiation could lead to a novel approach for the development of electron sources. (C) 2003 Elsevier B.V. All rights reserved.

Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Instability in threshold voltage and subthreshold behavior in Hf-In-Zn-O thin film transistors induced by bias-and light-stress

Electrical bias and light stressing followed by natural recovery of amorphous hafnium-indium-zinc-oxide (HIZO) thin film transistors with a silicon oxide/nitride dielectric stack reveals defect density changes, charge trapping and persistent photoconductivity (PPC). In the absence of light, the polarity of bias stress controls the magnitude and direction of the threshold voltage shift (Δ VT), while under light stress, VT consistently shifts negatively. In all cases, there was no significant change in field-effect mobility. Light stress gives rise to a PPC with wavelength-dependent recovery on time scale of days. We observe that the PPC becomes more pronounced at shorter wavelengths. © 2010 American Institute of Physics.