Modeling of precipitation in reverse micellar systems

A model of the precipitation process in reverse micelles has been developed to calculate the size of fine particles obtained therein. While the method shares several features of particle nucleation and growth common to precipitation in large systems, complexities arise in describing the processes of nucleation, due to the extremely small size of a micelle and of particle growth caused by fusion among the micelles. Occupancy of micelles by solubilized molecules is governed by Poisson statistics, implying most of them are empty and cannot nucleate of its own. The model therefore specifies the minimum number of solubilized molecules required to form a nucleus which is used to calculate the homogeneous nucleation rate. Simultaneously, interaction between micelles is assumed to occur by Brownian collision and instantaneous fusion. Analysis of time scales of various events shows growth of particles to be very fast compared to other phenomena occurring. This implies that nonempty micelles either are supersaturated or contain a single precipitated particle and allows application of deterministic population balance equations to describe the evolution of the system with time. The model successfully predicts the experimental measurements of Kandori ct al.(3) on the size of precipitated CaCO3 particles, obtained by carbonation of reverse micelles containing aqueous Ca(OH)(2) solution.

Study of embedded lead-tin nanoparticles in aluminum matrix

Nanoembedded lead-tin alloys in aluminum matrix were synthesized by rapid solidification processing. These melt-spun aluminum alloys were then investigated using XRD, EDX and TEM. The XRD study reveals that the melt-spun samples contain elemental aluminum, lead and tin. The TEM analysis shows that embedded particles in aluminium matrix have a distinct two-phase contrast of lead and tin. The lead and tin in these nanoalloys exhibit an orientation relationship with the matrix aluminum and with each other. DSC studies were conducted to reveal the melting and solidification characteristics of these embedded nanoalloys. DSC thermograms exhibit features of multiple solidification exotherms on thermal cycling, which can be attributed to sequential melting and solidification of lead and tin in the respective alloys.

Synthesis and Microstructure of Laser Surface Alloyed Al–Sn–Si Layer on Commercial Aluminum Substrate

A new type of bearing alloy containing ultrafine sized tin and silicon dispersions in aluminum was designed using laser surface alloying and laser remelting techniques. The microstructures of these non-equilibrium processed alloys were studied in detail using scanning and transmission electron microscopy. The microstructures revealed three distinct morphologies of tin particles namely elongated particles co-existing with silicon, globular particles, and very fine particles. Our detailed analyses using cellular growth theories showed that the formation of these globular tin particles was due to the pinching off of the tin rich liquid in the inter-cellular space by the growth of aluminum secondary dendrite arms. Evidence of fine recrystallized aluminum grains at the top layer due to constrained solidification was shown. Thermal analyses suggested that melting of the spherical shaped tin particles was controlled by the binary aluminum-tin eutectic reaction, whereas non-spherical tin particles melted via the tin-silicon eutectic reaction.

The ω Phase Formation during Laser Cladding and Remelting of Quasicrystal Forming AlCuFe on Pure Aluminum

We report the formation ω phase in the remelted layers during laser cladding and remelting of quasicrystal forming Al65Cu23.3Fe11.7 alloy on pure aluminum. The ω phase is absent in the clad layers. In the remelted layer, the phase nucleates at the periphery of the primary icosahedral phase particles. A large number of ω phase particles forms enveloping the icosahedral phase growing into aluminum rich melt, which solidify as α-Al solid solution. On the other side it develops an interface with aluminum. A detailed transmission electron microscopic analysis shows that ω phase exhibits orientation relationship with icosahedral phase. The composition analysis performed using energy dispersive x-ray analyzer suggests that this phase has composition higher aluminum than the icosahedral phase. The analysis of the available phase diagram information indicates that the present results represent large departure from equilibrium conditions. A possible scenario of the evolution of the ω phase has been suggested.

Determination of solid fraction evolution during solidification of aluminum alloy in presence of linear electromagnetic stirring

In many industrial casting processes, knowledge of the solid fraction evolution during the solidification process is a key factor in determining the process parameters such as cooling rate, stirring intensity and in estimating the total solidification time. In the present work, a new method of estimating solid fraction is presented, which is based on calorimetric principles. In this method, the cooling curve data at each point in the melt, along with the thermal boundary conditions, are used to perform energy balance in the mould, from which solid fraction generation during any time interval can be estimated. This method is applied to the case of a rheocasting process, in which Al-Si alloy (A356 alloy) is solidified by stirring in a cylindrical mould placed in the annulus of a linear electromagnetic stirrer. The metal in the mould is simultaneously cooled and stirred to produce a cylindrical billet with non-dendritic globular microstructure. Temperature is measured at key locations in the mould to assess the various heat exchange processes prevalent in the mould and to monitor the solidification rate. The results obtained by energy balance method are compared with those by the conventional procedure of calculating solid fraction using the Schiel equation.

Regionalization of precipitation in data sparse areas using large scale atmospheric variables - A fuzzy clustering approach

Delineation of homogeneous precipitation regions (regionalization) is necessary for investigating frequency and spatial distribution of meteorological droughts. The conventional methods of regionalization use statistics of precipitation as attributes to establish homogeneous regions. Therefore they cannot be used to form regions in ungauged areas, and they may not be useful to form meaningful regions in areas having sparse rain gauge density. Further, validation of the regions for homogeneity in precipitation is not possible, since the use of the precipitation statistics to form regions and subsequently to test the regional homogeneity is not appropriate. To alleviate this problem, an approach based on fuzzy cluster analysis is presented. It allows delineation of homogeneous precipitation regions in data sparse areas using large scale atmospheric variables (LSAV), which influence precipitation in the study area, as attributes. The LSAV, location parameters (latitude, longitude and altitude) and seasonality of precipitation are suggested as features for regionalization. The approach allows independent validation of the identified regions for homogeneity using statistics computed from the observed precipitation. Further it has the ability to form regions even in ungauged areas, owing to the use of attributes that can be reliably estimated even when no at-site precipitation data are available. The approach was applied to delineate homogeneous annual rainfall regions in India, and its effectiveness is illustrated by comparing the results with those obtained using rainfall statistics, regionalization based on hard cluster analysis, and meteorological sub-divisions in India. (C) 2011 Elsevier B.V. All rights reserved.

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

On the mechanism of plastic flow at 86°k in quenched and aged aluminum

The plastic flow of quenched aluminium at 86°K was investigated by ‘differential-stress’ creep tests in order to evaluate the rate-controlling mechanism in as-quenched and fully aged states. The experimental values of activation volume (4·3 × 10−21 cm3 for as-quenched and 5·5×l0−21cm3 for fully aged) and the total energy for thermal activation process (0·4 ev for both) are in accordance with the jog hardening and loop hardening mechanisms in quenched and fully aged states respectively.