Mechanical

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

School of Mechanical Engineering

Occurrence of combustion phenomenon in porous media has always excited researchers to develop various shape and size of burner so that maximum utilization of energy can be taken achieved. Here in this experiential work, dual layer micro burner was exclusively built to carry out porous media combustion characteristic with different type of discrete material in reaction zone. Presently, only alumina and zirconia are compared in discrete form, while preheat layer was made of porcelain ceramic material (foam type). Reaction zone was restricted to thickness of 20mm while preheat zone at 10mm. A concept of equivalence ratio was aided since it involves premixed combustion of air and butane as fuel mixture. Additionally, burner was made to run under lean to ultra-lean modes and finest temperature were recorded. Both surface and submerged flame was generated effectively. Maximum temperatures recorded during surface and submerged flame condition was better by installing alumina rather than zirconia there by reaching a value of 631C and 470C respectively. Thus maximum thermal efficiency was calculated and found out to be 84%. Finally, emission parameters like NOx and CO where monitored and found out to be well within acceptable limits. © 2018 The Authors, published by EDP Sciences.

Chennai Campus

World University root node

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MATEC Web of Conferences

Comparative study on porous media combustion characteristics using different discrete materials

Porous media combustion is considered to be one of the popular choice due to its tremendous advantages. Such type of combustion liberates not only super stable flame but also maintains emissions parameters below thresholds level. Present study incorporates reaction and preheat layer with discrete and foam type of materials respectively. Burner was made to run in ultra-lean mode. Optimum equivalence ratio was found out to be 0.7 for surface flame, while 0.6 during submerged flame condition. Maximum thermal efficiency was noted to be 81%. Finally, emissions parameters where recorded continuously to measure NOx and CO, which were under global limits. © Published under licence by IOP Publishing Ltd.

IOP Conference Series: Materials Science and Engineering

Investigation of micro burner performance during porous media combustion for surface and submerged flames

This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classification. In phase 1, the required statistical features are extracted from the vibration signals which obtained from the wind turbine through accelerometer. In phase 2, the most dominating or the relevant feature is selected from the extracted features using J48 decision tree algorithm. In phase 3, the selected features are classified using machine learning classifiers namely, K-star (KS), locally weighted learning (LWL), nearest neighbour (NN), k-nearest neighbours (kNN), instance based K-nearest using log and Gaussian weight kernels (IBKLG) and lazy Bayesian rules classifier (LBRC). The results were compared with respect to the classification accuracy and the computational time of the classifier.

International Journal of Engineering & Technology

A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach

Fault occurrence and machine downtime in work area is one of the major concerns in many industries which lead to severe economic losses and causalities. The main causes behind these problems is nothing but in-avoidance of regular checking and periodical inspection of working environment. Here is one of the similar cases, where failures in compressor system lead to several losses in industrial aspect due to its enormous application. So monitoring and diagnosis of faults in compressor systemic proposed in this study to avoid regular breakdown and idle time of machineries in industrial and domestic applications. Out of several faults in compressor, five major and common faults were taken in this study and vibration parameters for each condition is measured using accelerometer sensor. Further signals were extracted and classified through machine learning approach for the efficient diagnosis and detection of faults in compressor system. Copyright © 2018 Inderscience Enterprises Ltd.

Progress in Industrial Ecology, An International Journal

Air compressor fault diagnosis through statistical feature extraction and random forest classifier

In wind turbine, blades are the major component for capturing the wind, however, due to environmental conditions it prompts to get a fault. To overcome this problem, a machine learning based condition monitoring technique is incorporated into the wind turbine to identify the fault classification which occurred in the blade. In this study, a three-bladed horizontal axis wind turbine was chosen and the faults like blade bend, blade cracks, hub-blade loose connection, blade erosion and pitch angle twist were considered as these are the faults mostly affect the turbine blade. In this study, the autoregressive-moving-average (ARMA) features have been extracted from the raw signal and the dominating feature was selected through J48 decision tree algorithm followed by the fault classification using machine learning classifiers. The results were compared with respect to the classification accuracy and their computational time of the classifier. Copyright © 2018 Inderscience Enterprises Ltd.

A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study

The present world persists with a twin crisis of energy consumption and the environmental degradation. Finding a compromise between them provides a breakthrough in the research in energy containments of the engine attachments. Oil pump has role of providing the transmission of oil to other engine parts and acts as the coolant for the moving parts. Conventional oil pump with pressure relief valve is its loss lot of energy in oil re-circulation due to the discharge effect. On contrary, the variable displacement oil pump has an effect on reduction of oil pressure using eccentric ring without having any compromise with the energy consumption. This paper proposes model and experimental methodology of a variable displacement Gerotor oil pump for lubricating the internal combustion engine. This particular unit is performed extremely in terms of rotational speed, delivery pressure and displacement variation. The results from the experimental campaign have been compared with simulations using ANSYS CFX Immerest solid method. © 2018 SAE International. All Rights Reserved.

VIT Unitversity

Department of Design and Automation

Vellore Campus

SAE Technical Paper Series

Modelling and Analysis of Variable Displacement Oil Pump for Automobile Applications

Combined cycle power plants, especially the combination of gas turbine and steam turbine power plants as the topping and bottoming cycles respectively, have been used for power generation for a long period of time. The resultant efficiency is greater than that of the individual cycles. This paper deals with the design and energy and exergy analysis of a 250 MWe combined cycle power plant. Exergy destruction was found to be maximum in the combustor, followed by gas turbine and steam turbine, and stack. Hence any effort to improve/optimize the combined cycle power plant must focus on the combustor and the turbines as they involve high irreversibilities. The energy and exergy efficiencies have been found to be 40.7% and 39.1% respectively. Also, part load performance at 200 MWe and 150 MWe has been conducted. © IAEME Publication.

International Journal of Mechanical Engineering and Technology

Thermodynamic study of a 250 mwe combined cycle power plant at full load and part load conditions

In the industrial environment, the flow of materials is a complex and very expensive process. For reducing the expenses, it requires an effort to find favorable and flexible systems. Fork lift trucks are widely used in industries for transfer actions. The complexity of manually operated fork lift trucks shall be reduced by low cost automation. This paper deals with sensor interfaced automatic fork lifter for the effective materials flow. This fork-lift truck will identify the different pallets automatically based upon their surface color by the color sensor interfaced with it. The digital output of the color sensor varies with respect to the wavelength of the color. This output decides the selection or rejection of the pallets for loading as well as unloading anywhere. Line following technique is used for transportation between sections to avoid the complexity of conventional memory mapping technique. © 2017 IEEE.

IEEE Sensors Journal

Sensor Integration-Based Approach for Automatic Fork Lift Trucks

Gas Turbines in Aerospace applications operate at extreme conditions of temperatures and stresses. The Blade/Vane components of these turbines have sculpted, twisted and hollow aerofoil geometry made using Nickel based super alloys through the investment casting process. Hollow wax patterns produced with use of ceramic cores are the first input to the investment casting process. The quantification and prediction of dimensional deviations due to shrinkage & warpage, which are dependent on injection parameters, shape of part and gating system adopted, is mandatory to arrive at acceptable tooling and castings. The insight of flow pattern in the die cavity imparts knowledge of possible weld lines, air-trap, jetting etc. The gating system may be designed, based on simulation results, to minimise such defect. The effects of injection molding parameters such as holding time, packing pressure and melt temperature on shrinkage and warpage of green part using moldex3D software are studied in this paper. © 2018 Elsevier Ltd. All rights reserved.

Materials Today: Proceedings

Analysis of Shrinkage &amp; Warpage in Ceramic Injection Molding of HPT Vane Leading Edge Core of a Gas Turbine Casting

In the present study we investigate the buoyancy induced turbulent flow characteristics in a ceiling ventilated horizontal channel with an internal heat source. Numerical analysis is performed in the channel for two different Grashof number Gr=7.88 × 1011 and 1.18 × 1012 and the volumetric blockage ratio is varied from 0.012 to 0.055. It is found that by increasing the Grashof number to Gr=1.18 × 1012, the net mass flow rate through the central, upstream and downstream vents are increased by 23%, 16% and 15% respectively. It is also identified that by increasing the volumetric blockage ratio, the average temperature for Gr=7.88 × 1011 and 1.18 × 1012 are increased by 26.4% and 38.6%; while the average longitudinal velocity are decreased by 56% and 44% respectively. © IAEME Publication.