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

Mechanical

The depletion of conventional fuel reserves, ambient conditions along with increased global warming and emission standards have emerged the research interests in utilisation of advanced combustion concepts on existing engines. This also made concern over the capability of using alternate energy sources along with conventional fuels. This research work indicates the results of analysis conducted to investigate the performance, exhaust emission and combustion characteristics of a VCR diesel engine fuelled with non-edible biodiesel at a rated speed of 1500 rpm with 300 bar injection pressure at three compression ratios. The test fuel was derived from Prosopis Juliflora seed oil methyl ester blends15% (B15) and 25% (B25) by volume. Biodiesel was produced by the transesterification process using methanol along with KOH as catalyst. The combustion characteristics investigated were rise in cylinder pressures, net heat release rate, cumulative heat release rate and mass fraction of fuel burned. The lower heat release rates, increased cylinder pressures were observed for both the blends compared to diesel. Increased brake thermal efficiency observed at higher compression ratio for B25 blend. It has also been observed that the emissions were decrease in trend with increase in compression ratios. © 2020 Institut za Arhitekturu i Urbanizam Srbije.

School of Mechanical Engineering

Chennai Campus

World University root node

International Journal of Renewable Energy Research

Experimental analysis of VCR engine operated with prosopis juliflora biodiesel blends

The vital predicament that mankind will face in near future is the deficiency of energy. Heavy dependency of fossil fuels for driving the systems make the problem too complex by adding the Green House Gas (GHG) emission also. Across the sphere, consumption pattern confirms the major role of building sector, mostly for space conditioning application. Passive cooling by Latent Heat Thermal Energy Storage is well-established method. In this work commercially available Phase Change Material (PCM) is used for latent heat storage. The PCM is inserted in the brick cavity. The performance of two types of PCM insert is compared, ie, macro encapsulation at the middle of the brick and microencapsulation near the outer wall of the brick. The results show that, the centrally encapsulated brick performs better in heating cycle, but during night time the heat entry into the living space is more. The micro encapsulation at offset to the wall side justifies for the night use. © Published under licence by IOP Publishing Ltd.

Fulltext

IOP Conference Series: Earth and Environmental Science

Building bricks with PCM inserts for Passive cooling applications

The evolving automotive industry with the rapid growth of using fuel resources causes a shortfall of fuel availability. The engine maintenance has great importance and it is essential to develop a fault detection system and condition monitoring is done to reduce the damage-causing circumstances to improve engine safety. The engine tested at the rated speed of 1800 rpm by varying the loads. Captured vibration data is analyzed by using feature extraction and classification algorithms to obtain the best performance and minimum vibrations of blends. The best classification accuracy blend results are selected and used to develop a neural network model and to predict the vibration signatures. The neural network model is developed with a feedforward backpropagation algorithm and using Levenberg–Marquardt as training function. The developed ANN model is trained and vibration signatures are predicted for the selected blend B25 with the classification accuracy of 97%. © Springer Nature Singapore Pte Ltd. 2020.

Advances in Intelligent Systems and Computing

Intelligent condition monitoring of a CI engine using machine learning and artificial neural networks

In an automobile, the brake is an essential part responsible for the control of the vehicle. Any failure in the brake system impacts the vehicle's motion. It will generate frequent catastrophic effects on the safety of the vehicle cum passenger. Thus the brake system plays a vital role in an automobile and hence condition monitoring of the brake system is necessitated. Vibration-based condition monitoring techniques are gaining momentum. This study is one such attempt to categorize the faults that occur in a hydraulic brake system through vibration analysis. In this research, the performance of an artificial intelligence technique called Artificial Immune Recognition System for brake fault diagnosis has been reported. A hydraulic brake system test setup was fabricated. The vibration signals under good and faulty conditions of a brake system were acquired using a piezoelectric transducer. The statistical parameters were extracted from the vibration signals. The best features were identified using an attribute evaluator. The selected features were then classified using various versions of the Artificial Immune Recognition System (AIRS). The classification accuracy of such artificial intelligence techniques has been reported and discussed. © 2020 IEEE.

Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020

Vibration-Based Fault Detection in a Hydraulic Brake System using Artificial Immune Recognition System with Statistical Features

Electric mobility is an emerging mode of transportation, causing rapid growth of battery pack usage in electric vehicles. These battery packs were limited working life to be used in electric Vehicles. In addition, the disposal is becoming as a major threat to an environment. So, there is a need to increase the working life of battery pack used in electric vehicles to reduce the burden on the environment. In this regard, this paper is focused on studying the life cycle estimation of the battery pack. Battery pack degradation was observed through its self-discharge pattern and also due to the urge to increase the rate of charging. This research study was focused on understanding the degradation rate due to self-discharge and also due to fast charging patterns in battery packs. Estimation of self-discharge is depending on voltage, time and state of charge of the battery pack. Simulation analysis will carry out to evaluate state of charge analysis of the electric vehicle battery pack. By working on the optimization of self-discharge pattern, working life of the electric vehicle battery pack will increase. By growing energy storage capacity electric vehicle battery pack, fast charging can only be achieved using a high-power charging system. This leads to power dissipation inside electric vehicle battery pack modules. This results heat generation inside the battery cell that cause a critical effect on battery pack safety, performance, lifetime. This degradation rate was used as the key factor in estimating the working life of the battery pack for electric vehicle application. In this work, open-circuit voltage approach was used for developing the predictive model. In opencircuit voltage approach electrical vehicle battery equivalent model is established by mat lab software focusing on improving the suitability on various scenarios. © 2020SAE International. All Rights Reserved.

SAE Technical Papers

Predicting Life-Cycle Estimation of Electric Vehicle Battery Pack through Degradation by Self Discharge and Fast Charging

The state of cutting tool determines the quality of surface produced on the machined parts. A faulty tool produces poor surface, inaccurate geometry and non-economic production. Thus, it is necessary to monitor tool condition for a machining process to have superior quality and economic production. In the present study, fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique. Cutting force and vibration signals were acquired to monitor tool condition during machining. A set of four tooling conditions namely healthy, worn flank, broken insert and extended tool overhang have been considered for the study. The machine learning technique was applied to both vibration and cutting force signals. Discrete wavelet features of the signals have been extracted using discrete wavelet transformation (DWT). This transformation represents a large dataset into approximation coefficients which contain the most useful information of the dataset. Significant features, among features extracted, were selected using J48 decision tree technique. Classification of tool conditions was carried out using Naïve Bayes algorithm. A 10 fold cross validation was incorporated to test the validity of classifier. A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique. © 2020 Tech Science Press. All rights reserved.

SDHM Structural Durability and Health Monitoring

Comparative study on tool fault diagnosis methods using vibration signals and cutting force signals by machine learning technique

Surgical treatment for spinal disorders, such as cervical disc herniation and spondylosis, includes the removal of the intervertebral disc and replacement of biological or artificial materials. In the former case, bone graft is used to fill the space, and this conventional procedure is termed anterior cervical discectomy and fusion (ACDF). The latter surgery is termed as artificial disc replacement ADR) or cervical disc arthroplasty (CDA). Surgeries are most commonly performed at one or two levels. The present study was designed to determine the external (range of motion, ROM) and internal (anterior and posterior load sharing) responses of the spines with one-level and two-level surgeries in both models (ACDF and CDA) using a previously validated finite element model (FEM) of the subaxial cervical spinal column. The FEM simulated the vertebra (cancellous core and cortical shell of the body, posterior elements – laminae, pedicles and spinous processes), discs (anulus fibers, ground substance, and nucleus pulposus), anterior and posterior ligaments of the disc and facet joints, and interspinous and supraspinous ligaments. Appropriate material properties were assigned to the spinal components. The United States Food Drug Administration-approved Mobi-C was used for the CDA option. The FEM was exercised under pure flexion and extension moment loading of 2 Nm in the intact state. The overall ROM of the column was obtained. The hybrid loading protocol applied moments that matched the ROM in the intact spine for both one-level (C5–C6) and two-level (C5–C7) ACDF and CDA surgeries. ROM at the level(s) of surgery, termed the index level was obtained. These data along with anterior column load (ACL) and posterior column load (PCL) sharing were obtained for all surgical options at superior and inferior segments (termed adjacent segment outputs). Results for both one-level and two-level surgeries showed that ACDFs decreases ROM at the index level, while CDAs increase motions compared to the intact normal spine. The ROM, ACL, and PCL increased at both adjacent levels for the ACDF while CDA showed a decrease. Although two-level surgeries resulted in increased these biomechanical variables, greater changes to adjacent segment biomechanics in ACDF may accelerate adjacent segment disease. Decreased ROM and lower load sharing in CDAs may limit adjacent segment effects such as accelerated degeneration. Their increased posterior load sharing, however, may need additional attention for patients with suspected facet joint disease. © 2020

Journal of the Mechanical Behavior of Biomedical Materials

External and internal responses of cervical disc arthroplasty and anterior cervical discectomy and fusion: A finite element modeling study

This paper describes the thermal analysis of building block used for passive cooling application. For this purpose, a concrete block with cavities, drilled near the outer wall, is filled with micro encapsulated Phase Change Material (PCM). The commercially available PCM, HS 29 is used for this analysis. The micro encapsulated PCM filled in the cavities and during the heating cycle, The PCM absorbs latent heat and melts. This will prevent the heat entry into the living space and hence cool the space. The thermal performance of the brick is analyzed by Ansys-Fluent 14.5. The two-dimensional numerical analysis has been carried out using finite element analysis by seeing heat and mass transfer in the PCM. The effectiveness of PCM in the building bricks has been estimated by relating three different brick types, viz, normal solid bricks with air filled in the cylindrical cavity and bricks with PCM filled in the cylindrical cavity. In this case, the PCM filling holes are offset from center of the brick towards the exposed surface to reduce the re-entry of heat during solidification cycle. From the analysis is found that brick filled with PCM is more effective for reduction in heat flux while using for passive cooling application of buildings. © 2020 Author(s).

AIP Conference Proceedings

Heat transfer analysis of building brick filled with microencapsulated phase change material

In this experimental study, the center point of our work is to evaluate the impact of dimethyl carbonate (DMC) fuel blended with diesel with regard to the engine performance, emissions, and combustion characteristics in dual fuel engine fueled by biogas at various DMC proportions and biogas flow rates. A water-cooled, four stroke, single cylinder Compression Ignition (CI) engine is adjusted to run in dual fuel mode. A simulated biogas is obtained by mixing CH4 and CO2 that are kept in red and black cylinders, respectively. Throughout the experimental process the engine is run at a constant speed of 1800 rpm. Three proportions of DMC are taken which are 10, 20, and 30% by volume and blended with pure diesel. Biogas flow rates taken are 12 lpm and 16 lpm. An experimental study was performed and it is found that a maximum of 3% drop in brake thermal efficiency at high DMC proportions but at low biogas flow rates, the efficiency increased up to 2.7%. NOx emissions were observed to be low on addition of DMC and further decreased at high biogas flow rates. The maximum reduction of 57.5% is observed when compared to diesel-only mode. CO emissions increased upto 28% at high DMC fractions. In combustion characteristics, the Maximum Heat Release Rate (MHRR) decreased at high DMC fractions and high biogas flow rates but decreased, at high loads. The maximum cylinder pressure also decreased at high DMC fractions and at high biogas flow rates. The period of combustion increases when DMC fraction is increased. © 2020 Taylor &amp; Francis Group, LLC.

Energy Sources, Part A: Recovery, Utilization and Environmental Effects

Investigations on Biogas Fueled Dual Fuel DIesel Engine Employing Dimethyl Carbonate as a Fuel Blend

This study focuses on real-Time condition monitoring of the compressed ignition (CI) engine using the various vibration signatures acquired for the different combinations of fuels. The vibration signals acquired through shear accelerometer by maintaining a constant speed of 1800 rpm with varying load conditions from no load (0kg) to full load (20kg) with equal intervals of 5kg. Statistical features are extracted from each particular signal and determination of features is carried out. Classification is performed using a random tree and naive Bayes algorithms which identifies the minimal vibrations and best performance among the selected blends. The best classification accuracy is found for naive Bayes algorithm with 96.4%. The selected best combination blend data is used further for training the developed neural network model with neurons in hidden layers and a trained model was tested to predict the vibration signatures with the average 0.97 correlation coefficient. © 2020 IEEE.