Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Energy efficient multi-metric QoS routing using genetic algorithm in MANET

Abstract: Nodes in Mobile Adhoc NETwork (MANET) are suffered by limited bandwidth and frequent changes in the topology due to node mobility. The nodes participating in the network are powered by limited battery resources and the battery depletion can imply network failure. Although establishing correct and efficient routes is an important design issue in MANET, providing energy efficient routes is a more challenging goal because mobile nodes' operation time is the most critical limiting factor. The problem of network exhausting batteries which partitions the entire network can be prevented by considering the energy consumption in MANET. To support real-time communications which require better QoS, metrics like bandwidth, end-to-end delay and energy consumption has to be considered. It is necessary to consider multiple metrics for selecting an efficient path. This multi-metric QoS routing problem can be solved with optimization techniques. An energy efficient genetic algorithm based unicast routing is proposed to provide energy efficient unicast, multipath route by considering multiple QoS parameters such as end-to-end delay, energy consumption, bandwidth and hop count.

Experimental performance of a mobile air conditioning unit with small thermal energy storage for idle stop/start vehicles

Abstract: In this study, an attempt was made to extend the comfort of a passenger car cabin during the compressor off cycle using thermal energy storage (TES) in an HFO-1234yf mobile air conditioning (MAC) unit for idle stop/start vehicles. Fatty acid (OM08), as a phase change material (PCM), with 0.1–0.5 vol% of graphene nanoplatelets (GnPs) was used in this study. It was found that the inclusion of GnPs increases the thermal conductivity and dynamic viscosity of the liquid PCM nanocomposites by ~ 46% and ~ 53%, respectively, with 0.5 vol% of GnPs. During the pull-down cycle, the enhanced thermal conductivity outweighs the increased dynamic viscosity, resulting in a quicker decrease in PCM temperature. The test results revealed that the cabin temperature increases through the addition of TES, with a marginal decrease in the coefficient of performance. The addition of TES with the use of pure PCM increases the compressor power consumption of the MAC system by less than 1%. However, with the inclusion of graphene the power consumption increases with respect to the volume fraction. Without TES, the cabin comfort is extended by 78 s, 60 s, and 43 s for heating loads of 500, 1000, and 1500 W, respectively, and with the inclusion of TES, using pure PCM, the cabin comfort increased by up to 106 s, 87 s, and 63 s, respectively. The inclusion of 0.5 vol% GnPs extends the cabin comfort further by up to 189 s, 147 s, and 105 s for heating loads of 500, 1000, and 1500 W, respectively. Further, the CO2 equivalent emissions of the MAC system with TES using a pure PCM and a PCM nanocomposite are 10.54% and 5.64% lower than that of the system without TES, respectively.

Lie group analysis for the effect of viscosity and thermophoresis particle deposition on free convective heat and mass transfer in the presence of suction/injection

Abstract: An analysis has been carried out to study heat and mass transfer characteristics of an incompressible and Newtonian fluid having temperature-dependent fluid viscosity and thermophoresis particle deposition over a vertical stretching surface with variable stream condition. The Rosseland approximation is used to describe the radiative heat flux in the energy equation. The vertical surface is assumed to be permeable so as to allow for possible wall suction or injection. The governing differential equations are derived and transformed using Lie group analysis. The transformed equations are solved numerically by applying Runge-Kutta Gill scheme with shooting technique. Favorable comparisons with previously published work on various special cases of the problem are obtained. Numerical results for the velocity, temperature and concentration profiles for a prescribed temperature-dependent fluid viscosity and thermophoresis particle deposition parameters are presented graphically to elucidate the influence of the various physical parameters.

Prediction of gas holdup in the three-phase fluidized bed: air/Newtonian and non-Newtonian liquid systems

Abstract: Prediction of gas holdup in the three-phase fluidized bed: air/Newtonian and non-Newtonian liquid systems The application of the three-phase fluidization technology in wastewater treatment and other biochemical processes has been regularly addressed in the past decades. For the design and development of the three-phase fluidized bed reactors, knowledge of the hydrodynamic parameter such as gas holdup is essential and hence in this paper an attempt has been made to study the effect of fundamental and operating variables on gas holdup. On the basis of the experimental results, a unified correlation has been developed to predict gas holdup in the fluidized bed using the Newtonian and the non-Newtonian liquids. The experimental results showed good agreement with those predicted according to the developed correlation.

Detection of abnormalities in retinal images

Abstract: The human eye is the organ which gives us the sense of sight. The eye reflects or emits the light to interpret the shapes, colors, and dimensions of objects in the world. Retina places a major role in the human vision system. The retina gets affected by long-term dietetic mellitus called Diabetic Retinopathy (Microaneurysm). Earlier detection of these abnormalities will prevent the vision loss. This work aims to detect such abnormalities in the retinal image and to classify them based on their severity. To detect the abnormality, two preprocessing and one candidate extraction method are proposed and various stages of abnormalities are classified based on the features like area, mean, standard deviation, entropy etc. Adaptive Neuro Fuzzy Inference System (ANFIS) is an effective tool used for effective screening of retinal abnormalities. ANFIS is used to classify the retinal images as normal, mild, severe depending on their severity.