National Institute of Technology Calicut

About: National Institute of Technology Calicut is a education organization based out in Kozhikode, Kerala, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 3627 authors who have published 4638 publications receiving 50830 citations. The organization is also known as: Calicut Regional Engineering College & NIT Calicut.

Mechanical and Wear Properties of Al–Al2O3 Metal Matrix Composites Fabricated by the Combined Effect of Stir and Squeeze Casting Method

Abstract: The effects of nano particles on double shear strength and tribological properties of A356 alloy reinforced with Al2O3 nano particles of size 30 nm were investigated. The percentage inclusions of Al2O3 were varied from 0.5 to 1.5 wt%. The particles were added with stirring at 400 rpm and squeeze casting at 750 °C and pressure of 600 MPa in a squeeze casting machine. Comparison of the performance of as cast samples of A356/Al2O3 nano composite was conducted. The tribological properties of the samples were also investigated by pin-on-disk tests at 10, 30 and 50 N load, sliding speed 0.534 m/s and sliding distance 1100 m in dry condition. SEM images of microstructure analysis of the composite, Al2O3 (0.5 and 1 %) particles were well dispersed in the A356 alloy matrix. Partial agglomeration was observed in metal matrix composite with higher (1.5 %) Al2O3 particle contents. The nano dispersed composites containing 0.5 and 1 wt% of Al2O3 nano particles exhibited the highest double shear strength, lesser wear loss and coefficient of friction.

Analysis of static and dynamic performance characteristics of THD journal bearing operating under lubricants containing nanoparticles

Abstract: In this paper, the static and dynamic performance characteristics of journal bearing in terms of load capacity, attitude angle, end leakage, frictional force, threshold speed and damped frequency are presented when the bearing operating under lubricants, which contain nanoparticles and viscosity of these lubricants varies with temperature. The nanoparticles used for the present work are copper oxide (CuO), cerium oxide (CeO2) and aluminum oxide (Al2O3). Viscosity models for the lubricants are developed with the available experimental data. The modified Reynolds and energy equations are used to obtain pressure and temperature distribution across the lubricant film and these equations are solved by using the finiteelement method and a direct iteration scheme. The static and dynamic performance characteristics of journal bearing are computed for various values of eccentricity ratios for isoviscous and thermoviscous lubricants. The computed results show that in isoviscous case, addition of nanoparticles does not change performance characteristics considerably but in thermoviscous case, changes are significant.

Prediction of Liquid–Liquid Flow Patterns in a Y-Junction Circular Microchannel Using Advanced Neural Network Techniques

Abstract: The flow pattern map for a liquid–liquid system in a 600 μm circular microchannel was experimentally investigated for a varying Y-junction confluence angle (10° to 180°). The experimental results showing the distinguishing nature of transition boundaries were established using graphical interpretation. This paper tries to find a better objective flow pattern indicator for vast amounts of experimental data. Studies have been carried out using significant feed-forward back-propagation networks and radial-basis networks such as artificial neural network–pattern recognition (ANN-PR), artificial neural network–function fitting (ANN-FF), cascade-forward network (CFN), probabilistic neural network (PNN), generalized-regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS). From the study, we found that GRNN showed better prediction ability than the other prediction techniques. Discrete- and continuous-time state-space models for the system were also developed using the system identific...

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

Abstract: The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the...