Showing papers by "Ranjit Biswas published in 2017"

A micromorphic computational homogenization framework for heterogeneous materials

Abstract: The conventional first-order computational homogenization framework is restricted to problems where the macro characteristic length scale is much larger than the underlying Representative Volume Element (RVE). In the absence of a clear separation of length scales, higher-order enrichment is required to capture the influence of the underlying rapid fluctuations, otherwise neglected in the first-order framework. In this contribution, focusing on matrix-inclusion composites, a novel computational homogenization framework is proposed such that standard continuum models at the micro-scale translate onto the macro-scale to recover a micromorphic continuum. Departing from the conventional FE 2 framework where a macroscopic strain tensor characterizes the average deformation within the RVE, our formulation introduces an additional macro kinematic field to characterize the average strain in the inclusions. The two macro kinematic fields, each characterizing a particular aspect of deformation within the RVE, thus provide critical information on the underlying rapid fluctuations. The net effect of these fluctuations, as well as the interactions between RVEs, are next incorporated naturally into the macroscopic virtual power statement through the Hill-Mandel condition. The excellent predictive capability of the proposed homogenization framework is illustrated through three benchmark examples. It is shown that the homogenized micromorphic model adequately captures the material responses, even in the absence of a clear separation of length scales between macro and micro.

Comparing and Analyzing the Characteristics of Hadoop, Cassandra and Quantcast File Systems for Handling Big Data

Abstract: Objective: With the emergence of the notion of “Internet of Things (IoT)”, colossal amount of information is being generated through the sensors and other computing devices and chips. This paper is an attempt to provide a lucid comparison among three prominent technologies used for handling Big Data, viz. Hadoop Distributed File System, Cassandra file system and Quantcast file system. Apart for these three premier file systems, the paper also explores a newly proposed A train Distributed System for handling Big Data. Methods: An inner perspective of the above stated file systems in details considering various aspects for handling big data has been described. The paper also provides sagacity on the situations wherein these technologies are useful. Findings: Effective tackling of the five V’s (Variety, Volume, Velocity, Veracity and Value) of Big Data has become a challenging task for the researcher around the world. Hadoop is one such technology which is open source and is capable of handling big data in an effective manner. It breaks the big data into fixed sized chunks known as block and these blocks are saved at distinct locations in a distributed manner. The Cassandra file system is an alternative to Hadoop which eliminates the single point failure problem of Hadoop as it follows master-less peer to peer distributed ring architecture instead of client server architecture. The third technology is the quantcast file system which is written in C++ language. It also handles the big data in an effective and efficient manner. Moreover it claims to save upto fifty percent of the disk space by implementing erasure encoding. Application: The concerned organization to use any of these available frameworks for handling big data depending upon their nature of needs.

“Theory of IRE with (α,β,γ) Norm”: An Engineering Model for Higher Education Management (HEM) & Policy Administration in India

Abstract: This chapter introduces a new theory called by “Theory of IRE with (α,β,γ) Norm” which provides an almost complete solution for Higher Education Management (HEM) & Policy Administration in any vast country like India, China, France, Germany, Australia, Brazil, Indonesia, Pakistan, Malaysia, USA, UK, Canada, Gulf countries and others in the world. The “Theory of IRE with (α,β,γ) Norm” is an engineering model for solving HEM problems, basically seven major problems which are about: (i) How To Continuously Monitor The Real Time Progress of Research Work of the Ph.D. Scholars in the Universities/Institutions in any country by a Common Rule of the ‘Ministry of HRD’ (ii) A New Improved Method for Recruitment of Teachers in Universities (iii) A New Method for Promotion Policy of Teachers In Universities (iv) How to select the ‘Most Suitable Candidate’ for the various prestigious awards/honors in a country (v) How to restrict the publications of bad quality research papers in fake/ bad journals? (vi) How to select the true experts for every visiting team of NAAC of UGC? and (vii) How to select the ‘Most Suitable Candidates’ to fill-up the reserved quota. It is claimed that if this new theory be implemented by the ‘Ministry of HRD (MHRD)’ in all its universities/institutions, then a huge amount of quality-assurance can be achieved in pursuance of Excellence in Higher Education Management & Policy Administration in that Country.