Christ University

About: Christ University is a education organization based out in Bengaluru, India. It is known for research contribution in the topics: Computer science & Convection. The organization has 2267 authors who have published 2715 publications receiving 14575 citations. The organization is also known as: Christ College & Christ University.

Effect of multiwalled carbon nanotube alignment on the tensile fatigue behavior of nanocomposites

Abstract: The one-dimensional structure of carbon nanotubes makes them highly anisotropic, making them to possess unusual mechanical properties, and hence employed as promising nanofiller for the composite structures. However, various carbon nanotube properties are not completely utilized when they are used as reinforcement in composites due to inadequate and immature processing techniques. In the present work, an attempt has been made to utilize the strong anisotropic nature of multi-walled carbon nanotubes (MWCNTs) for improving the fatigue life of nanocomposites only by considering a very low weight percentage (<0.5 wt%). The anisotropy of MWCNTs was imparted into the nanocomposites by aligning them in the epoxy matrix with DC electric field during composite curing. Nanocomposites were made for three MWCNT loadings (0.1, 0.2, and 0.3 wt%). The tensile fatigue behavior was investigated under stress control by applying cyclic sinusoidal load with the frequency range of 1–3 Hz and stress ratio, R = 0.1. The specime...

Application of Epoxy-Asphalt Composite in Asphalt Paving Industry: A Review with Emphasis on Physicochemical Properties and Pavement Performances

Abstract: One of the failure mechanisms associated with asphalt paving layers, especially on steel deck bridges, is large permanent deformation, which adversely affects its long-term performance in service. Thus, epoxy resin was introduced in asphalt paving industry to tackle permanent deformation of asphalt mixtures due to its thermosetting nature. In this review, epoxy resin as a dominant component of the epoxy-asphalt composite system was first considered, followed by a discussion on its curing methods and curing mechanism. Furthermore, the physicochemical property and mechanical performance of epoxy asphalt and epoxy asphalt mixture were thoroughly examined. Crosslink density of epoxy asphalt dictates its viscosity and thus the allowable construction time. Phase separation and dispersion of asphalt particles in the epoxy matrix was observed for epoxy-asphalt composite, and it showed superior elastic behavior and deformation resistance capability when compared with conventional asphalt materials. Furthermore, epoxy asphalt mixture exhibited significantly higher compressive strength, much better rutting resistance, and superior durability and water resistance properties. However, its low-temperature cracking resistance was slightly compromised.

Review on Emerging Internet of Things Technologies to Fight the COVID-19

Abstract: The Internet of Things (IoT) has been gaining attention in various disciplines ranging from agriculture, health, industries and home automation. When a pandemic first breaks out early detection, isolating the infected, and tracing the contacts are the most important challenges. IoT protocols like Radio-frequency identification (RFID), Wireless Fidelity (WiFi), Global Positioning System (GPS) are gaining popularity for providing solutions to these challenges. IoT based applications in the health sector are benefitting COVID-19 (coronavirus disease of 2019) patients during this pandemic situation. This article explores and reviews the various Internet of Things enabled technologies and applications used in screening, contact tracing, and surveillance. IoT based telemedicine processes are very useful during the pandemic COVID-19. The purpose of this paper is to deliver an overall understanding of the existing and proposed technologies of IoT based solutions to make the situations better during COVID-19.

Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach

Abstract: Stock market prediction is the challenging area for the investors to yield profits in the financial markets. The investors need to understand the financial markets which are more volatile and affected by many external factors. This paper proposes a subtractive clustering based adaptive neuro fuzzy approach for predicting apple stock data prices. The research data used in this study is from 3rd Jan 2005 to 30th Jan 2015. Four technical indicators are proposed in this study. They are Simple moving average for 1 week, simple moving average for 2 weeks, 14 days Disparity and Larry Williams R%. These variables are used as inputs to the neuro fuzzy system to predict the daily apple stock prices. Also, this study compares the proposed work with the ANFIS training method and subtractive clustering method etc. The performance of all these models is analyzed. The measures like training error, testing error, number of rules and number of parameters are calculated and compared for analysis. From the simulation results, the average performance of subtractive clustering based neuro fuzzy approach was found considerably better than the other networks.

Two-Phase Flow of Dusty Casson Fluid with Cattaneo-Christov Heat Flux and Heat Source Past a Cone, Wedge and Plate

Abstract: This article addresses the boundary layer flow and heat transfer in Casson fluid submerged with dust particles over three different geometries (vertical cone, wedge and plate). The aspects of Cattaneo-Christov heat flux and exponential space-based heat source (ESHS) are also accounted. At first, the partial differential equations are transformed into a set of ordinary differential equations via appropriate similarity transformations. Resulting equations are solved via shooting method coupled with the Runge-Kutta-Fehlberg-45 integration scheme. The consequences of dimensionless parameters on velocity and temperature fields of both fluid and dust particles phase are analyzed. The rate of increment/decrement in the skin friction as well as the Nusselt number for various values of physical parameters are also estimated via slope of linear regression line using data points.