Grain Floatation During Equiaxed Solidification of an Al-Cu Alloy in a Side-Cooled Cavity: Part II-Numerical Studies

In this article, a single-phase, one-domain macroscopic model is developed for studying binary alloy solidification with moving equiaxed solid phase, along with the associated transport phenomena. In this model, issues such as thermosolutal convection, motion of solid phase relative to liquid and viscosity variations of the solid-liquid mixture with solid fraction in the mobile zone are taken into account. Using the model, the associated transport phenomena during solidification of Al-Cu alloys in a rectangular cavity are predicted. The results for temperature variation, segregation patterns, and eutectic fraction distribution are compared with data from in-house experiments. The model predictions compare well with the experimental results. To highlight the influence of solid phase movement on convection and final macrosegregation, the results of the current model are also compared with those obtained from the conventional solidification model with stationary solid phase. By including the independent movement of the solid phase into the fluid transport model, better predictions of macrosegregation, microstructure, and even shrinkage locations were obtained. Mechanical property prediction models based on microstructure will benefit from the improved accuracy of this model.

Solubilities of resorcinol and pyrocatechol and their mixture in supercritical carbon dioxide

The equilibrium solubilities of dihydroxy benzene isomers (resorcinol and pyrocatechol) and its mixture were experimentally determined at different temperatures (308, 318, 328, and 338 K) in the pressure range of 9.8-16.2 MPa. In the ternary system, the solubilities of pyrocatechol increased while the solubilities of resorcinol decreased relative to their binary solubilities. A new association model was developed based on the concept of formation of solvate complex molecules to correlate the solubility of the solid for mixed solids in supercritical carbon dioxide (SCCO(2)). The model equation relates the solubility of solute in terms of the cosolute composition, temperature, pressure and density of SCCO(2). The proposed model correlated the solubilities of sixteen solid systems taken from the literature and current experimental data with an average absolute relative deviation (AARD) of around 4%. (C) 2011 Elsevier B.V. All rights reserved.

Constitutive Response of Passivated Copper Films: Experiments and Analyses

The constitutive behavior of passivated copper films is studied. Stresses in copper films of thickness ranging from 1000 nm to 40 nm, passivated with silicon oxide on a quartz or silicon substrate, were measured using the curvature method. The thermal cycling spans a temperature range from - 196 to 600°C. It is seen that the strong relaxation at high temperatures normally found in unpassivated films is nonexistent for passivated films. The copper film did not show any rate-dependent effect over a range of heating/cooling rate from 5 to 25°C/min. Further analyses showed that significant strain hardening exists during the course of thermal loading. In particular, the measured stress- temperature response can only be fitted with a kinematic hardening model, if a simple constitutive law within the continuum plasticity framework is to be used. The analytic procedures for extracting the film properties are presented. Implications to stress modeling of copper interconnects in actual devices are discussed.

A two Roll Mill as a Rheomer for Pastes

Previous work involving the squeeze-film flow of a model paste substance, a mixture of clay particles and mineral oil commonly known as ‘Plasticine’, has suggested that it behaves as a simple Herschel-Bulkley fluid which exhibits little strain history. However, tensile measurements, which are naturally limited to small strains by the onset of necking, indicate that this material shows strain hardening. A two roll-mill is employed here to investigate the influence of larger extensional strains. The data are analysed using an available first order engineering plasticity solution. The results confirm that this material exhibits both extensional strain and strain rate hardening. This observed strain hardening effect, which is not observed in the squeeze-film experiments, is attributed, in part, to the more homogeneous deformation fields induced during rolling and tensile extension.

Studies of the wetting characteristics of liquid iron on dense alumina by the X-ray sessile drop technique

In the present work, the reaction between a molten iron drop and dense alumina was studied using the X-ray sessile-drop method under different oxygen partial pressures in the gas atmosphere. The changes in contact angles between the iron drop and the alumina substrate were followed as functions of temperature and varying partial pressures of oxygen in the temperature range 1823 to 1873 K both in static and dynamic modes. The results of the contact angle measurements with pure iron in contact with dense alumina in extremely well-purified argon as well as under different oxygen partial pressures in the gas atmosphere showed good agreement with earlier measurements reported in the literature. In the dynamic mode, when argon was replaced by a CO-CO2-Ar mixture with a well-defined PO, in the gas, the contact angle showed an initial decrease followed by a period of nearly constant contact angle. At the end of this period, the length of which was a function of the P-O2 imposed, a further steep decrease in the contact angle was noticed. An intermediate layer of FeAl2O4 was detected in the scanning electron microscope (SEM) analysis of the reacted substrates. An interesting observation in the present experiments is that the iron drop moved away from the site of the reaction once the product layer covered the interface. The results are analyzed on the basis of the various forces acting on the drop.

Subspace Based Speech Enhancement Using Gaussian Mixture Model

Traditional subspace based speech enhancement (SSE)methods use linear minimum mean square error (LMMSE) estimation that is optimal if the Karhunen Loeve transform (KLT) coefficients of speech and noise are Gaussian distributed. In this paper, we investigate the use of Gaussian mixture (GM) density for modeling the non-Gaussian statistics of the clean speech KLT coefficients. Using Gaussian mixture model (GMM), the optimum minimum mean square error (MMSE) estimator is found to be nonlinear and the traditional LMMSE estimator is shown to be a special case. Experimental results show that the proposed method provides better enhancement performance than the traditional subspace based methods.Index Terms: Subspace based speech enhancement, Gaussian mixture density, MMSE estimation.

Swam Intelligent Based Inverse Model for Laboratory Double Reservoir Diffusion Experiments

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are, in general, estimated by fitting the theoretical models to a field monitoring or laboratory experimental data. Double-reservoir diffusion (Transient Through-Diffusion) experiments are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These design parameters are estimated by manual parameter adjusting techniques (also called eye-fitting) like Pollute. In this work an automated inverse model is developed to estimate the mass transport parameters from transient through-diffusion experimental data. The proposed inverse model uses particle swarm optimization (PSO) algorithm which is based on the social behaviour of animals for finding their food sources. Finite difference numerical solution of the transient through-diffusion mathematical model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation.The working principle of the new solver is demonstrated by estimating mass transport parameters from the published transient through-diffusion experimental data. The estimated values are compared with the values obtained by existing procedure. The present technique is robust and efficient. The mass transport parameters are obtained with a very good precision in less time

Seismic Microzonation: Principles,Practices and Experiments

This paper presents an overview of the seismic microzonation and the grade/level based study along with methods used for estimating hazard. The principles of seismic microzonation along with some current practices are discussed. Summary of seismic microzonation experiments carried out in India is presented. A detailed work of seismic microzonation of Bangalore has been presented as a case study. In this case study, a seismotectonic map for microzonation area has been developed covering 350 km radius around Bangalore, India using seismicity and seismotectonic parameters of the region. For seismic microzonation Bangalore Mahanagar Palike (BMP) area of 220 km2 has been selected as the study area. Seismic hazard analysis has been carried out using deterministic as well as probabilistic approaches. Synthetic ground motion at 653 locations, recurrence relation and peak ground acceleration maps at rock level have been generated. A detailed site characterization has been carried out using borehole with standard penetration test (SPT) ―N‖ values and geophysical data. The base map and 3-dimensional sub surface borehole model has been generated for study area using geographical information system (GIS). Multichannel analysis of surface wave (MASW)method has been used to generate one-dimensional shear wave velocity profile at 58 locations and two- dimensional profile at 20 locations. These shear wave velocities are used to estimate equivalent shear wave velocity in the study area at every 5m intervals up to a depth of 30m. Because of wider variation in the rock depth, equivalent shear for the soil overburden thickness alone has been estimated and mapped using ArcGIS 9.2. Based on equivalent shear wave velocity of soil overburden thickness, the study area is classified as ―site class D‖. Site response study has been carried out using geotechnical properties and synthetic ground motions with program SHAKE2000.The soil in the study area is classified as soil with moderate amplification potential. Site response results obtained using standard penetration test (SPT) ―N‖ values and shear wave velocity are compared, it is found that the results based on shear wave velocity is lower than the results based on SPT ―N‖ values. Further, predominant frequency of soil column has been estimated based on ambient noise survey measurements using instruments of L4-3D short period sensors equipped with Reftek 24 bit digital acquisition systems. Predominant frequency obtained from site response study is compared with ambient noise survey. In general, predominant frequencies in the study area vary from 3Hz to 12Hz. Due to flat terrain in the study area, the induced effect of land slide possibility is considered to be remote. However, induced effect of liquefaction hazard has been estimated and mapped. Finally, by integrating the above hazard parameters two hazard index maps have been developed using Analytic Hierarchy Process (AHP) on GIS platform. One map is based on deterministic hazard analysis and other map is based on probabilistic hazard analysis. Finally, a general guideline is proposed by bringing out the advantages and disadvantages of different approaches.

A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels

Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

Multi-Mode Phonon-Controlled Field Emission from Carbon Nanotubes: Modeling and Experiments

The main idea proposed in this paper is that in a vertically aligned array of short carbon nanotubes (CNTs) grown on a metal substrate, we consider a frequency dependent electric field, so that the mode-specific propagation of phonons, in correspondence with the strained band structure and the dispersion curves, take place. We perform theoretical calculations to validate this idea with a view of optimizing the field emission behavior of the CNT array. This is the first approach of its kind, and is in contrast to the the conventional approach where a DC bias voltage is applied in order to observe field emission. A first set of experimental results presented in this paper gives a clear indication that phonon-assisted control of field emission current in CNT based thin film diode is possible.

Fluctuation quench and hydrophobic collapse of polymers and biopolymers in water-DMSO binary mixture at low DMSO concentration

Binary mixtures have strong influence on activities of polymers and biopolymers even at low cosolvent concentration. Among the several aqueous binary mixtures studied, water-DMSO especially stands out for its unusual behavior at certain specific concentrations of DMSO. In the present work, we study the effect of water-DMSO binary mixture on polymers and biopolymers by taking a simple linear hydrocarbon chain of intermediate length (n = 30) and the protein lysozyme, respectively. We find that at a mole fraction of 0.05 of DMSO (x(DMSO) = 0.05) in aqueous solution, the hydrocarbon chain adopts the collapsed conformation as the most stable and rigid state. In this case of 0.05 mole fraction of DMSO in bulk, the DMSO concentration in the first hydration layer around the polymer is found to be as large as 17%. Formation of such hydrophobic environment around the polymer is the reason for the collapsed state gaining so much stability. Interestingly, similar quench of conformational fluctuation is also observed for the protein investigated. It is observed that in the case of alkane polymer chains, long wavelength fluctuation gets easily quenched, the polymer being purely hydrophobic. However, in case of the protein, quench of fluctuation is prominent only at the hydrophobic surface, and quench of long wavelength fluctuation becomes insignificant for the full protein. As protein contains both hydrophobic and hydrophilic moieties, the extent of quench of conformational fluctuation with respect to that in pure water is almost half for the biopolymer complex (16.83%) than the same for pure hydrophobic polymer chain (32.43%).

Clustering Based Large Margin Classification: A Scalable Approach using SOCP Formulation

This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture distributions, where each component of the mixture has a spherical covariance, the second order statistics of the components can be estimated efficiently using clustering algorithms like BIRCH. For each cluster, the second order moments are used to derive a second order cone constraint via a Chebyshev-Cantelli inequality. This constraint ensures that any data point in the cluster is classified correctly with a high probability. This leads to a large margin SOCP formulation whose size depends on the number of clusters rather than the number of training data points. Hence, the proposed formulation scales well for large datasets when compared to the state-of-the-art classifiers, Support Vector Machines (SVMs). Experiments on real world and synthetic datasets show that the proposed algorithm outperforms SVM solvers in terms of training time and achieves similar accuracies.