Kongu Engineering College

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

Experimental and CFD investigations of carbon/SS316 mechanical face seals under different lubricating conditions

Abstract: Purpose – The purpose of this paper is to test mechanical face seals made of carbon/SS316 with different coolants for evaluating its tribological performance. The reliability of a mechanical seal mainly depends on the seal materials and the type of coolant used for the lubrication. Design/methodology/approach – Compressed air, vacuum and nitrogen are the main coolants utilized for the experimental work, and the obtained results are compared with the dry running case for a specified period. The experimental results are also validated with the computational fluid dynamics (CFD) simulation results. Findings – The results shows that the sealing pressure, sliding speed and materials used would be the predominant factors for the seal design. Over compressed air, vacuum and nitrogen cooling techniques were found to be more efficient. Originality/value – The experimental results are also validated with the CFD simulation results. This paper also emphasizes the usage of vacuum as a cooling medium in industries, wh...

Design and performance evaluation of SynRM and ferrite assisted-SynRM for EV application using FEA

Abstract: Due to rapid electrification in transportation, the development of green cars are dramatically increased. The permanent magnet synchronous motor (PMSM) with rare earth PMs are widely used in electric vehicle applications because of its excellent performance. However, the PMSM motor needs larger volume of rare earth PMs and it increases the cost of the machine. Therefore, the electric motors with less PMs are required. This paper deeply investigates the performance of both the motor models for electric vehicle (EV) applications, in terms of efficiency, torque capability, power factor, torque-speed characteristics, torque ripple and demagnetization. With less permanent magnet volume, the reluctance torque should be increased to meet the losses in permanent magnet torque. This is achieved by increasing the saliency ratio. Both the motor models are designed with same number of poles, stator slots, stator outer diameter, stack length and rotor geometry. The control trajectories like MTPA and flux weakening of both SynRM and PMASynRM type motors are compared by using FEA.

TCACWCA: transmission and collusion aware clustering with enhanced weight clustering algorithm for mobile ad hoc networks

Abstract: In case of MANET the routing acts a superior role in the mobile ad-hoc network environment so as to guarantee the consistent communication amongst the source and the destination nodes On the other hand routing in the MANET environment will have a number of challenges because of dynamic behavior of mobile nodes exist in the environment Mobility behavior of mobile nodes will bring about path link breakages that needs novel route for the data transmission to be kept up to date or set up frequently This behavior causes network failure It results in more computational overhead by forwarding hello message to come across the novel nodes gone into the environment With the purpose of overwhelming these issues, the novel approach is presented, that is to say transmission and collusion aware clustering with enhanced weight clustering algorithm (TCAC–WCA) so as to decrease the risk of the forwarding hello packets at every time clustering In this study, the improved weight clustering algorithm is utilized to form the cluster dependent upon the weight on the nodeThe Transmission Range Clustering Algorithm (TRCA)is utilized to eradicate the needless node that there are not presented inside the transmission range and likewise test out the link stability of the node By means of computing the trust level, the collusion is noticed in the wireless link The presented TCAC–WCA is tested dependent upon two metric called expected transmission count (ETX) or path encounter rate (PER) for every identified state with the aim of attaining the ideal routing performance for diverse network conditions (that is to say Static or mobile) Similarly the route node dictionary is built dependent upon the ETX and PER metric The complete assessment of the presented research technique is accomplished in NS2 simulation environment through which it is shown that the presented research technique results in giving improved outcome compared to the previous research techniques

Influence of Tunneling in Cohesionless Soil for Different Tunnel Geometry and Volume Loss under Greenfield Condition

Abstract: This paper presents the numerical analysis of settlement to profile the vulnerable zone or influence zone due to tunneling activities in cohesionless deposits for free field or Greenfield conditions. The analysis considers the factors like saturated density (γsat), unsaturated density (γunsat), angle of shearing resistance (φ), deformation modulus (ES), volume loss (VL), and the support pressure of the shield head at the tunnel face. The obtained results using a finite element program (FEM) PLAXIS 3D are compared with measured and predicted surface settlement using field measuring instruments, and analytical and empirical solution show a reasonable agreement and are found to be conservative. From literature, for Greenfield condition the ground settlement equal to 10 mm is taken as the minimum value to map the influencing zone considering the fact that the structure which lies beyond this zone would undergo negligible settlement. Settlement trough and 10 mm settlement contour characteristics are presented for different tunnel sizes placed at the same depth and the same tunnel size placed at different depths, respectively. Various influencing zones are arrived for the sandy grounds of different denseness based on the parametrical studies involving parameters such as tunnel size “D,” tunnel axis depth “z,” and volume loss “VL.”