Abstract

Flexural vibration of an infinite piezolaminated multilayer hollow cylinder made of piezoelectric layers of 6mm-class and an isotropic CFRP (Carbon fiber-reinforced plastics) layers is studied. The frequency equations are obtained for the traction free outer surface with continuity conditions at the interfaces. The frequency equations solved by using Muller’s method with VC++ programme.Since we are using VC++ programme we will get more accurate frequency values for different wave numbers, so that we can study about the attenuation effect and vibration charcterstics.Numerical results are carried out for the inner hollow, middle and outer piezoelectric layers bonded by CFRP layers and the dispersion curves are compared with that of a similar 3-layer model and a piezoelectric model.

Abstract

In this paper, the use of grey-based Taguchi methods for the optimization of the heat treatment process of ZE41A magnesium alloy. The purpose of optimization is to achieve high performance in material characteristics. A grey relation grade obtained from grey relation analysis is used as the performance characteristic in the Taguchi method. The material characteristics can be improved by selecting the feasible parameters. Taguchi technique has been used to optimize the parameters namely solutionizing temperature, solutionizing time, ageing temperature and ageing time. Experimental results have shown that optimal heat treatment process parameters in mechanical characterization can be determined effectively so as to improve qualities through this new technique.

Abstract

This paper deals with the development of second order mathematical model using Response Surface Methodology (RSM) to predict the surface roughness in terms of machining parameters viz., cutting speed, feed rate and depth of cut. The experimentation has been conducted using full factorial design in the design of experiments (DOE) on CNC turning machine with carbide cutting tool The adequacy of the developed model is verified by using coefficient of determination and analyzed using analysis of variance (ANOVA). The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness on machining of aluminum alloys within the ranges of variables studied. The predicted values are confirmed by using validation experiments.

Abstract

The present work has been carried out to characterize the dynamics of three-phase flow in cylindrical bubble column, run under homogeneous bubble flow and heterogeneous flow conditions using CFD simulation. Investigation has been done to study the effect of process variables such as, inlet gas velocity, solid loading, and particle size on flow pattern in three-phase bubble column. The simulations were performed for air-water-glass beads in a bubble column of 0.6 m height, 0.1 m i.d. and 0.05 m sparger diameter to study the flow pattern. Eulerian-Eulerian three-phase simulations with k-e turbulence for liquid phase were carried out using commercial flow CFD software CFX-5.6, with a focus on characterizing the dynamics properties of gas liquid solid flows. Good agreement was obtained between available experimental data and simulation. The flow pattern profiles obtained for various parametric studies appear to be sensible and logical. The results presented are useful for understanding the dynamics of gas-liquid-solid flows in bubble column and provides a basis for further development of CFD model for three phase systems.

Abstract

In the present day, the important goal in the modern industries is to manufacture high quality and low cost products in just in time. The quality of the products depends on the surface roughness and hence the surface roughness plays an important role in product manufacturing. In this paper, AI based neural network modeling approach is presented for the prediction of surface roughness of Aluminum Alloy products machined on CNC Turning Center. The experiments were conducted based on the principle of Factorial Design of Experiment (DOE) method. Trails were made with different combinations of step size and momentum to select the best learning parameter. The best network structure with least MSE was selected among the several networks. The multiple regression models, which are most widely used as prediction methods, are considered to compare the developed ANN model performance.

Abstract

In the present study numerical calculations are performed for a 4-stroke, single cylinder compression ignition, water-cooled, direct injection engine with a bore of 8.75 cm and a stroke of 11cm. The numerical calculations are performed using a multidimensional code, which solves the governing equations for continuity, mass, momentum, energy and species concentration equations using time marching finite volume method. The spray distribution in the combustion chamber is important to understand the fuel-air mixing and subsequent combustion phenomena. The pre-processor code es-ice is used for dynamic grid generation, computational fluid dynamics code STAR-CD is used for solving the governing equations and post-processing the results. Stochastic Legrangian-Multiphase approach is used in modeling the fuel injection process. A six hole, solid cone type of fuel injector is considered in this analysis.  The geometry of bowl in piston plays very important role in fuel-air mixing. In the present study, a hemispherical bowl (HSB) and deep bowl (DB) is considered for the simulation from  200 bTDC  in compression stroke to 800 aTDC in expansion stroke.  The fuel injection is assumed to start at 50 bTDC (7150 crank angle) and ends at 50 aTDC(7250 crank angle). The engine is assumed to run at 1500 rpm. The spray distribution, number of droplet parcels, droplet temperature, droplet velocity, distribution of fuel droplets after fuel injection at different crank angles is discussed for the two bowl configurations. The effect of piston bowl on the spray penetration and fuel air mixing is presented.

Abstract

Eye blink artifacts and power line noise always disturb the electroencephalograms (EEG) recorded on the scalp and pose serious problems in its signal analysis and interpretation. In this paper, an Independent Component Analysis (ICA) algorithm was applied to extract eye movements and power noise of 50Hz in several sets of EEG data. It is confirmed that ICA method can isolate both superguassian artifacts (eye blinks) and subguassian interference (line noise). It showed that ICA algorithms could well preserve the nonlinear characteristics of EEG after removing the artifacts. Experiment results show that ICA algorithm is a quite powerful technique and suitable for EEG data processing in clinical engineering.

Abstract

While measuring heart sound of the fetal by phonocardiographic devices, the heart beat of the mother and background sound cannot be avoided in the input signal. It is especially crucial when the subject is obese and fetal is in the critical condition with very poor cardiac output. In such cases high susceptibility to ambient noise makes the diagnostic evaluation of the phonocardiographic signal erroneous.

Adaptive noise cancellation is a particular application of adaptive filter. This filter tracks the dynamic nature of a system and allows elimination of the unwanted part of signal. In this paper using adaptive filters a noise reducer is developed for fetal heart sound de-noising. Recursive Least Squares (RLS) algorithm is used in the design of the reducer to obtain the most optimum cancellation of the overall noise. The design is implemented by using Digital Signal Processing (DSP) technology and found effective in most practical application. The developed adaptive noise reducer has been tested with various fetal heart sound signals and results show that the proposed technique is suitable and viable for de-noising purpose.

Abstract

In this paper the effectiveness of code division multiple access (CDMA) and time division multiple access (TDMA) multiplexing using M-ary Frequency Shift Keying (MFSK) is considered. The approach is based on the comparison between these two types by calculating the bit rate, noise immunity and the transmitted power of Digital Communication System (DCS). The use of Wide-band CDMA instead of TDMA in Spread Spectrum Digital Communication Systems (SSDCS) using Spread Spectrum Signals (SSS) increases the bit rate and decreases the noise immunity of the system. This decrease can be compensated by increasing the transmitted power (signal to- noise ratio, SNR).

Abstract

Digital image enhancement techniques provide a multitude of choices for improving the visual quality of diagnostic images. Appropriate choice of such techniques is greatly influenced by the imaging modality, task at hand and viewing conditions. The principal objective of enhancement is to process a given image so that the result is more suitable than the original image for a specific application. This paper focuses on spatial domain techniques for radiographic image enhancement, with particular reference to intensity transformation methods, histogram equalization, neighborhood processing methods. The radiographic or x-ray image as an input image is subjected to the enhancement methods and enhanced image is obtained as output image. Signal detectability has been compared in terms of signal-to-noise-ratio (SNR) and contrast-to-noise-ratio (CNR) to results obtained from the enhancement methods. Moreover, the proposed enhancement methods have been compared in terms of invariant moments, standard deviation and correlation coefficient.

Abstract

Many researchers have concluded that bio-diesel holds a promise as an alternative fuel for diesel engines because of its advantages compared to conventional diesel fuel. The efficiency of a Diesel engine depends upon several factors like the conversion rate of chemical energy of the fuel into heat. Cetane number of diesel fuels is an important parameter because of its direct impact on the performance, emission and combustion-generated noise in diesel engines. Conventional method of determining the cetane number requires CFR engine with primary reference fuels. This process is well established for diesel fuel even though it is a costlier and time-consuming process. In this work, an attempt was made to develop correlations to predict cetane number for selected bio fuels. The bio fuels considered were Pungam Methyl Esters (PGME), Sunflower Methyl Esters (SFME), Rice Bran Methyl Esters (RBME), Palm Oil Methyl esters (POME) and Jetropha Oil Methyl Esters (JOME). Chennai Petroleum Corporation Limited, Chennai, India supplied fuels with known cetane numbers of 25, 51 and 63. These reference fuels were tested in a 4.4 kW naturally aspirated stationary, air-cooled, four-stroke, direct injection engine coupled with an electrical dynamometer. This engine was instrumented with AVL piezo-electric pressure transducer and angle encoder.  The transducer and the angle encoder were interfaced with personal computer to store the pressure versus crank angle data. The AVL Indimeter software was used to analyze the collected data. Initially, experiments were conducted with fuels of known cetane number at various loads. From these results ignition delay was determined and tabulated for the above conditions. The correlations were developed based on ignition delay for the known cetane number of fuels at all loads. The same experimental procedure was followed for all the bio-fuels. The correlations were used to find the cetane number of bio-fuels. It was found that cetane number predicted by the correlations matched with available data for the bio-fuels.

Abstract

The chemical, mechanical, and physical properties of cast metal products are greatly determined by the microstructure formed during solidification. Microstructural parameter Secondary Dendrite Arm Spacing (SDAS) has been observed to have a significant impact upon the yield strength, ultimate tensile strength and elongation of cast products. A comprehensive model which can predict the SDAS values, will allow the aluminum industry to effectively troubleshoot, develop and improve different alloys. In the present investigation, a hybrid Artificial Neural Network (ANN) — Genetic Algorithm (GA) model is developed for predicting the SDAS values in aluminum alloy castings. Adaptation and optimization of network weights using GA is proposed as a mechanism to improve the performance of ANN model. The process parameters considered for predicting SDAS values are: chill volume heat capacity, insulation on riser and pouring temperature. Solidification simulations are carried out using finite difference method for obtaining the data in order to develop the ANN model. The proposed model is validated experimentally.