Macro-Fiber Composite Actuators for Flow Control of a Variable Camber Airfoil

This research employs solid-state actuators for delay of flow separation seen in airfoils at low Reynolds numbers. The flow control technique investigated here is aimed for a variable camber airfoil that employs two active surfaces and a single four-bar (box) mechanism as the internal structure. To reduce separation, periodic excitation to the flow around the leading edge of the airfoil is induced by a total of nine piezocomposite actuated clamped-free unimorph benders distributed in the spanwise direction. An electromechanical model is employed to design an actuator capable of high deformations at the desired frequency for lift improvement at post-stall angles. The optimum spanwise distribution of excitation for increasing lift coefficient is identified experimentally in the wind tunnel. A 3D (non-uniform) excitation distribution achieved higher lift enhancement in the post-stall region with lower power consumption when compared to the 2D (uniform) excitation distribution. A lift coefficient increase of 18.4% is achieved with the identified non-uniform excitation mode at the bender resonance frequency of 125 Hz, the flow velocity of 5 m/s and at the reduced frequency of 3.78. The maximum lift (Clmax) is increased 5.2% from the baseline. The total power consumption of the flow control technique is 639 mW(RMS).

Experimental analysis of wheel/workpiece dynamic interactions in grinding

The aim of this work is to study the wheel/workpiece dynamic interactions in high-speed grinding using vitrified CBN wheel and DTG (difficult to grind) work materials. This problem is typical in the grinding of engine valve heads. The influence of tangential force per abrasive grain was investigated as an important control variable for the determination of G ratio. Experiments were carried out to observe the influence of vibrations in the wheel wear. The measurements of acoustic emission (AE) and vibration signals helped in identifying the correlation between the dynamic interactions (produced by forced random excitation) and the wheel wear. The wheel regenerative chatter phenomenon was observed by using the wheel mapping technique. (c) 2008 CIRP.

Simulation of the Barkhausen Noise using random field Ising model with long-range interaction

We present a method to simulate the Magnetic Barkhausen Noise using the Random Field Ising Model with magnetic long-range interaction. The method allows calculating the magnetic flux density behavior in particular sections of the lattice reticule. The results show an internal demagnetizing effect that proceeds from the magnetic long-range interactions. This demagnetizing effect induces the appearing of a magnetic pattern in the region of magnetic avalanches. When compared with the traditional method, the proposed numerical procedure neatly reduces computational costs of simulation. (c) 2008 Published by Elsevier B.V.

Batch distillation: Better at constant or variable reflux?

Due to its outstanding flexibility, batch distillation is still widely used in many separation processes. In the present work, a comparison between constant and variable reflux operations is studied. Firstly, a mathematical model is developed and then validated through comparison between predicted and experimental results accomplished in a lab-scale apparatus. Therefore, case studies are performed through mathematical simulations. It is noted that the most economical form of batch distillation is at constant overhead product composition, keeping the flow rate of vapor from the top of the column constant. (C) 2010 Elsevier B.V. All rights reserved.

Methodological analysis of gamma tomography system for large random packed columns

Gamma ray tomography experiments have been carried out to detect spatial patterns in the porosity in a 0.27 m diameter column packed with steel Rashig rings of different sizes: 12.6, 37.9, and 76 mm. using a first generation CT system (Chen et al., 1998). A fast Fourier transform tomographic reconstruction algorithm has been used to calculate the spatial variation over the column cross section. Cross-sectional gas porosity and solid holdup distribution were determinate. The values of cross-sectional average gas porosity were epsilon=0.849, 0.938 and 0.966 for the 12.6, 37.9, and 76 mm rings, respectively. Radial holdup variation within the packed bed has been determined. The variation of the circumferentially averaged gas holdup in the radial direction indicates that the porosity in the column wall region is a somewhat higher than that in the bulk region, due to the effect of the column wall. (C) 2009 Elsevier Ltd. All rights reserved.

Probing Individual Quantum Dots: Noise in Self-Assembled Systems

In this work we explore the noise characteristics in lithographically-defined two terminal devices containing self-assembled InAs/InP quantum dots. The experimental ensemble of InAs dots show random telegraph noise (RTN) with tuneable relative amplitude-up to 150%-in well defined temperature and source-drain applied voltage ranges. Our numerical simulation indicates that the RTN signature correlates with a very low number of quantum dots acting as effective charge storage centres in the structure for a given applied voltage. The modulation in relative amplitude variation can thus be associated to the altered electrostatic potential profile around such centres and enhanced carrier scattering provided by a charged dot.

Threshold voltages of SOI MuGFETs

The multiple-gate field-effect transistor (MuGFET) is a device with a gate folded on different sides of the channel region. They are one of the most promising technological solutions to create high-performance ultra-scaled SOI CMOS. In this work, the behavior of the threshold voltage in double-gate, triple-gate and quadruple-gate SOI transistors with different channel doping concentrations is studied through three-dimensional numerical simulation. The results indicated that for double-gate transistors, one or two threshold voltages can be observed, depending on the channel doping concentration. However, in triple-gate and quadruple-gate it is possible to observe up to four threshold voltages due to the corner effect and the different doping concentration between the top and bottom of the Fin. (C) 2008 Elsevier Ltd. All rights reserved.

Exploring the random genesis of co-occurrence networks

Using the network random generation models from Gustedt (2009)[23], we simulate and analyze several characteristics (such as the number of components, the degree distribution and the clustering coefficient) of the generated networks. This is done for a variety of distributions (fixed value, Bernoulli, Poisson, binomial) that are used to control the parameters of the generation process. These parameters are in particular the size of newly appearing sets of objects, the number of contexts in which new elements appear initially, the number of objects that are shared with `parent` contexts, and, the time period inside which a context may serve as a parent context (aging). The results show that these models allow to fine-tune the generation process such that the graphs adopt properties as can be found in real world graphs. (C) 2011 Elsevier B.V. All rights reserved.

Economic design of an X chart for short-run production

The aim of this paper is to present an economical design of an X chart for a short-run production. The process mean starts equal to mu(0) (in-control, State I) and in a random time it shifts to mu(1) > mu(0) (out-of-control, State II). The monitoring procedure consists of inspecting a single item at every m produced ones. If the measurement of the quality characteristic does not meet the control limits, the process is stopped, adjusted, and additional (r - 1) items are inspected retrospectively. The probabilistic model was developed considering only shifts in the process mean. A direct search technique is applied to find the optimum parameters which minimizes the expected cost function. Numerical examples illustrate the proposed procedure. (C) 2009 Elsevier B.V. All rights reserved.

On the use of correlated beta random variables with animal population modelling

We give reasons why demographic parameters such as survival and reproduction rates are often modelled well in stochastic population simulation using beta distributions. In practice, it is frequently expected that these parameters will be correlated, for example with survival rates for all age classes tending to be high or low in the same year. We therefore discuss a method for producing correlated beta random variables by transforming correlated normal random variables, and show how it can be applied in practice by means of a simple example. We also note how the same approach can be used to produce correlated uniform triangular, and exponential random variables. (C) 2008 Elsevier B.V. All rights reserved.

Biological characteristics and thermal requirements of a Brazilian strain of the parasitoid Trichogramma pretiosum reared on eggs of Pseudoplusia includens and Anticarsia gemmatalis

A new strain of the parasitoid Trichogramma pretiosum, was collected in Rio Verde County, State of Goias, Central Brazil, and designated as T. pretiosum RV. This strain was then found to be the most effective one among several different strains of T. pretiosum tested in a parasitoid selection assay. Therefore, its biological characteristics and thermal requirements were studied, aiming at allowing its multiplication under controlled environmental conditions in the laboratory. The parasitoid was reared on eggs of Pseudoplusia includens and Anticarsia gemmatalis at different constant temperatures within an 18-32 degrees C temperature range. The number of annual generations of the parasitoid was also estimated at those temperatures. Results have shown that T. pretiosum RV developmental time, from egg to adult, was influenced by all temperatures tested within the range, varying from 6.8 to 20.3 days and 6.0 to 17.0 days on eggs of P. includens and A. gemmatalis, respectively. The emergence of T. pretiosum RV from eggs of A. gemmatalis was higher than 94% at all temperatures tested. When this variable was evaluated on eggs of P. includens, however, the figures were higher than that within the 18-30 degrees C range (more than 98%), and were also statistically higher than the emergence observed at 32 degrees C (90.2%). The sex ratio of the parasitoids emerged from eggs of A. gemmatalis decreased from 0.55 to 0.29 at 18-32 degrees C, respectively. However, for those emerged from eggs of P. includens, the sex ratio was similar (0.73, 0.72 and 0.71) at 20, 28 and 32 degrees C, respectively. The lower temperature threshold (Tb) and thermal constant (K) were 10.65 degrees C and 151.25 degree-days when the parasitoid was reared on eggs of P. includens; and 11.64 degrees C and 127.60 degree-days when reared on eggs of A. gemmatalis. The number of generations per month increased from 1.45 to 4.23 and from 1.49 to 4.79 when the parasitoid was reared on eggs of P. includens and A. gemmatalis, respectively, following the increases in the temperature. (C) 2009 Elsevier Inc. All rights reserved.

Spatial portability of numerical models of leaf wetness duration based on empirical approaches

Leaf wetness duration (LWD) models based on empirical approaches offer practical advantages over physically based models in agricultural applications, but their spatial portability is questionable because they may be biased to the climatic conditions under which they were developed. In our study, spatial portability of three LWD models with empirical characteristics - a RH threshold model, a decision tree model with wind speed correction, and a fuzzy logic model - was evaluated using weather data collected in Brazil, Canada, Costa Rica, Italy and the USA. The fuzzy logic model was more accurate than the other models in estimating LWD measured by painted leaf wetness sensors. The fraction of correct estimates for the fuzzy logic model was greater (0.87) than for the other models (0.85-0.86) across 28 sites where painted sensors were installed, and the degree of agreement k statistic between the model and painted sensors was greater for the fuzzy logic model (0.71) than that for the other models (0.64-0.66). Values of the k statistic for the fuzzy logic model were also less variable across sites than those of the other models. When model estimates were compared with measurements from unpainted leaf wetness sensors, the fuzzy logic model had less mean absolute error (2.5 h day(-1)) than other models (2.6-2.7 h day(-1)) after the model was calibrated for the unpainted sensors. The results suggest that the fuzzy logic model has greater spatial portability than the other models evaluated and merits further validation in comparison with physical models under a wider range of climate conditions. (C) 2010 Elsevier B.V. All rights reserved.

Does using stepwise variable selection to build sequential path analysis models make sense?

Causal inference methods - mainly path analysis and structural equation modeling - offer plant physiologists information about cause-and-effect relationships among plant traits. Recently, an unusual approach to causal inference through stepwise variable selection has been proposed and used in various works on plant physiology. The approach should not be considered correct from a biological point of view. Here, it is explained why stepwise variable selection should not be used for causal inference, and shown what strange conclusions can be drawn based upon the former analysis when one aims to interpret cause-and-effect relationships among plant traits.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

A total of 152,145 weekly test-day milk yield records from 7317 first lactations of Holstein cows distributed in 93 herds in southeastern Brazil were analyzed. Test-day milk yields were classified into 44 weekly classes of DIM. The contemporary groups were defined as herd-year-week of test-day. The model included direct additive genetic, permanent environmental and residual effects as random and fixed effects of contemporary group and age of cow at calving as covariable, linear and quadratic effects. Mean trends were modeled by a cubic regression on orthogonal polynomials of DIM. Additive genetic and permanent environmental random effects were estimated by random regression on orthogonal Legendre polynomials. Residual variances were modeled using third to seventh-order variance functions or a step function with 1, 6,13,17 and 44 variance classes. Results from Akaike`s and Schwarz`s Bayesian information criterion suggested that a model considering a 7th-order Legendre polynomial for additive effect, a 12th-order polynomial for permanent environment effect and a step function with 6 classes for residual variances, fitted best. However, a parsimonious model, with a 6th-order Legendre polynomial for additive effects and a 7th-order polynomial for permanent environmental effects, yielded very similar genetic parameter estimates. (C) 2008 Elsevier B.V. All rights reserved.