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Mukeshkumara P.C.,Anna University | Kumarb J.,Erode Builder Educational Trusts Group of Institutions | Sureshc S.,Indian National Institute of Engineering | Praveen babuc K.,Indian National Institute of Engineering
Journal of Materials and Environmental Science | Year: 2012

In this work, heat transfer coefficients of shell and helically coiled tube heat exchanger using Al2O3 / water nanofluid were studied. This study was done by changing the parallel flow configuration into counter flow configuration under laminar flow regime. The Al2O3 / water nanofluid at 0.4% and 0.8% particle volume concentration were prepared by using two step method. The nanoparticles were characterized by X-Ray diffraction (XRD) and Scanning Electron Microscope (SEM). It is found that the overall heat transfer coefficient of counter flow was 4-8% higher than that of parallel flow at 0.4% nanofluid. The overall heat transfer coefficient was found to be 5-9% higher than that of parallel flow at 0.8% nanofluid. It is studied that there is no considerable effect on heat transfer coefficient on changing flow configuration. This is because of helically coiled tube flow and shell flows are perpendicular direction both in parallel and counter flow configuration. It is also studied the thermal performance of 0.8% nanofluid is higher than 0.4% nanofluid. Source


Mukesh Kumar P.C.,Anna University | Kumar J.,Erode Builder Educational Trusts Group of Institutions | Suresh S.,Indian National Institute of Engineering
Journal of Mechanical Science and Technology | Year: 2013

In this study, the heat transfer and friction factor of a shell and helically coiled tube heat exchanger using Al2O3 / water nanofluid at 0.1%, 0.4% and 0.8% particle volume concentration were tested. The test was conducted under laminar flow condition at 5100 < Rei < 8700. It is found that the overall heat transfer coefficient, inner heat transfer coefficient and experimental inner Nusselt number are 24%, 25% and 28%, respectively, higher than water at 0.8% particle volume concentration of nanofluid. It is observed that the presence of nanoparticles further intensify the formation of secondary flow and proper mixing of fluid when nanofluid passes through the helically coiled tube. Apart from further flow intensification, higher thermal conductivity of nanofluid and random movement of nanoparticles contribute to the enhanced heat transfer coefficient. Also found that the friction factor increases over particle volume concentration and this is due to increased nanofluid viscosity while increasing particle volume concentration. © 2013 The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg. Source


Gurusamy K.,Surya Engineering College | Chakrapani V.,Erode Builder Educational Trusts Group of Institutions
Journal of Computer Science | Year: 2012

Recent advances in data collection, data dissemination and related technologies have inaugurated a new era of research where existing data mining algorithms should be reconsidered from the point of view of securing sensitive data. People have become increasingly unwilling to share their data. This frequently results in individuals either refusing to share their data or providing incorrect data. In turn, such problems in data collection can affect the success of data mining, which relies on sufficient amounts of accurate data in order to produce meaningful results. Based on the analysis of shortcomings of earlier technologies this study proposes a new method for securing numerical and categorical data. In this method the categorical data is converted into Binary form and perturbation based noise is introduced as a security method based on the security level anticipated. Several types of noise addition methods were employed and generalized results were evaluated in terms of misclassification error and privacy level. An average of misclassification error was below 50% for 75-90% security level, which is better than earlier methods which didn't handle categorical data. The results obtained prove that the proposed method outperforms some of the currently existing methods thereby ensuring the possibility of securing sensitive data irrespective of its type being numerical or categorical. © 2012 Science Publications. Source


Murugan N.,Coimbatore Institute of Technology | Ashok Kumar B.,Erode Builder Educational Trusts Group of Institutions
Materials and Design | Year: 2013

Over the last two decades, aluminium matrix composites (AMCs) reinforced with particulate form of ceramics have attracted much attention due to their superior mechanical properties. In recent years, friction stir welding (FSW) is largely employed to successfully join the AMCs reinforced with ceramic particulates. An attempt has been made to develop a regression model to predict the ultimate tensile strength (UTS) of the friction stir (FS) welded AA6061 matrix composite reinforced with aluminium nitride particles (AlNp) by incorporating significant parameters such as tool rotational speed, welding speed, axial force and percentage of AlNp reinforcement in the matrix. A four factor, five level central composite rotatable design has been used to minimize the number of experimental runs. The effects of those factors on UTS of the welded joints have been analyzed using the developed regression model. The developed regression model has been validated by statistical software SYSTAT 12 and statistical tools such as analysis of variance (ANOVA) and student's t test. It was found that the model was accurate. The developed regression model can be effectively used to predict the UTS of FS welded joints at 95% confidence level. It was observed from the investigation that factors considered in this study independently influenced the UTS of the FS welded composite joints. © 2013 Elsevier Ltd. Source


Pugalendhi G.,Kyungpook National University | Vijayakumar A.,Erode Builder Educational Trusts Group of Institutions | Kim K.-J.,Kyungpook National University
International Journal of Data Mining and Bioinformatics | Year: 2016

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. In this paper we propose a rule-based approach called 'Large Coverage Rule' for microarray data classification. The proposed approach is a parameter-free data-driven approach that constructs decision rule based on the expression values of a gene. A simple Rank-Based Scoring algorithm is proposed for selecting informative genes. The performance of the proposed approach is evaluated using ten publicly available gene expression data sets. From the simulation result, it is found that the proposed approach generates compact rules and produces comparatively good classification accuracy than the others. Gene ontology based biological semantics is also carried out to analyse the informative genes. Statistical analysis of test result shows that the generated rules are simple to interpret, highly comprehensible and classifies microarray data accurately. Copyright © 2016 Inderscience Enterprises Ltd. Source

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