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Lightweight Concrete Block Masonry Infill: Seismic Response and Sustainability
No full textThe replacement of naturally sourced building materials with more environment-friendly materials has received significant attention in the construction industry in recent years. In that line, Autoclaved Aerated Concrete (AAC), and Cellular Lightweight Concrete (CLC) masonry blocks are gaining popularity over traditional fired clay bricks in infill masonry construction. However, a detailed review of published literature revealed that there are significant knowledge gaps regarding several structural as well as environmental aspects of AAC and CLC block masonry. Infill masonry is no longer considered a non-structural component in seismic-resistant framed buildings, but rather design codes recommend formulations to account for its contribution to the global strength and stiffness of the building. Therefore, appropriate knowledge of the strength properties of AAC and CLC block masonry is necessary for a reasonable evaluation of the seismic behaviour of such buildings. The seismic performance and safety of a structure are directly dependent on the uncertainty associated with the material properties. Ignoring such uncertainties despite the advancement of available computer technology will make structural analysis less satisfactory and less realistic. Although the descriptions of uncertainties in the strength properties of traditional building materials such as structural concrete, steel rebar, clay, and fly-ash brick masonry are available in an ample amount, similar information is not available for AAC and CLC block masonry. The issue of rising greenhouse gas emissions and environmental consequences is well-known. The brick industry in India is the third-largest consumer of coal. Apart from cement and steel reinforcement, fired clay brick is one of the three major contributors to the embodied energy of masonry-infilled reinforced concrete (RC) framed buildings. Embodied energy plays an important role in the choice of building materials and is directly related to the sustainability of the built environment. In view of this, the efficiency of AAC and CLC block masonry to control the embodied energy of the building should be demonstrated to prove its sustainability. In the present study, the uncertainties related to the strength properties that control the resistance capacity of infilled masonry are investigated through laboratory experiments, and the best-fitted probability density functions are recommended. Furthermore, the in-plane seismic performances of typical RC framed buildings infilled with AAC and CLC block
masonry are evaluated in a probabilistic framework considering the recommended probability density functions showing the ineffectiveness of an assumed normal distribution for this purpose. Although lightweight AAC and CLC block masonry slightly increases the seismic risk of the building compared to traditional brick masonry due to its lower strength properties, it can be safely used as an infill material in areas with high seismicity, as it results in the reliability indices within the acceptable limits of the design code. Although AAC and CLC masonry is gaining popularity in RC framed buildings due to their several advantages, the issue of embodied energy in such buildings has not received adequate research attention. This study evaluates the initial embodied energy of selected AAC and CLC block masonry-infilled RC framed buildings and compares it with that of an identical building infilled with traditional clay brick masonry. In addition, it also evaluates the life cycle energy of the selected building infilled with AAC block masonry using the life cycle assessment method. The results of the study show that the use of AAC and CLC block masonry instead of clay brick masonry reduces material flow and the initial embodied energy of the building by approximately 12% and 18%, respectively. The outcomes of this study will help in the selection of AAC and CLC block masonry as a sustainable alternative to traditional clay brick masonry and the development of environmental policies
Nonlinear Numerical Modelling and Analysis of Porous Functionally Graded Curved Structures under Thermomechanical Loading
No full textThe geometrical nonlinear behaviour of porous functionally graded (FG) curved/flat panels under thermomechanical loading is modelled mathematically and presented in this research. Three grading patterns, i.e. power-law (GT-I), sigmoid (GT-II) and exponential (GT-III) types of gradings, are adopted in this research to compute the porous multidirectional graded panel using Voigt’s micromechanical model approach. Additionally, porosity has been introduced in the current model by considering the even (PRT-I) and uneven (PRT-II) distribution patterns through the panel thickness. The practical applications of graded structure in high-performance engineering structures under elevated environmental conditions, the temperature-independent (TID) and temperature-dependent (TD) properties of the individual constituent of FG have been adopted to outline the final responses. Further, the temperature field variations, namely, the uniform (TD-I), linear (TD-II) and nonlinear (TD-III) types, are introduced to imitate the operational conditions as observed in the real-time structure. The structural model is derived mathematically by employing a HSDT to count the state space deformations. Moreover, due to the large deformation under the loading and environmental conditions, the present mathematical model adopted the Green-Lagrange type of nonlinear strain to count the large deformations. The graded structural system equations are derived suitably using either Hamilton’s principle or variational technique to compute the desired linear/nonlinear eigenvalue/particular solutions. The nonlinear structural responses are computed using the selective numerical integration (Gauss-Quadrature) scheme in conjunction with Picard’s direct iterative technique, Newmark’s constant acceleration steps (time-dependent responses) and isoparametric finite element steps. The graded panel model is discretized using a nine-noded quadrilateral Lagrangian element with nine-nodal degrees of freedom (DOF). A generalized computational algorithm in MATLAB is prepared using the currently developed mathematical model. Initially, the consistency of the numerical solution due to the change in elemental densities has been checked by presenting an adequate number of convergence tests. In addition, to improve the confidence in the prepared computational algorithm of the multidirectional porous graded structure, a few comparisons are undertaken by comparing with those of available solutions (grading in one or more directions). Further, a few linearly graded polymeric composite reinforced with natural fibre has been prepared to conduct the experimentations, including their elastic constants. Lastly, a series of parametric analyses have been conducted to examine the influence of varieties of design-associated parameters (type and magnitude of applied mechanical load, power-exponent, amplitude ratio, temperature and its distribution, geometrical shapes, curvature ratio, end-support conditions, thickness and aspect ratio) on the nonlinear responses (flexural, vibration and time-dependent deflection) of the FG curved/flat panel. Based on the parametric study, a few final recommendations are provided towards the end of this research for references in future research based on graded structural applicability
Classification and Recommendation Techniques for Effective Learning in Flipped Classroom Pedagogy
No full textFlipped learning (FL) is found to be an effective teaching methodology which is accomplished in two stages. In the first stage, students take instructions and learn from pre¬loaded lecture videos (out¬of¬class learning). In the second stage, students carry out various activities such as group discussion, think¬pair¬share, group quiz, etc. in presence of the instructor (in¬class learning). Therefore, students get enough time to brainstorm on the topic learnt from the pre¬loaded lecture. This new learning pedagogy offers quality learning for many students. However, this teaching methodology does not have provision to monitor students while taking lesson from pre-loaded lecture video unlike traditional classroom teaching. This may lead to severe learning incompetence for weak students. On the other side, we know brain’s activity is very vital in deciding cognitive ability. Psycho-cognitive state of weak students can help in understanding their difficulties towards a specific topic and it can play an important role in helping them out. To assess the cognitive state of the people, usage of EEG signal has drawn increased attention in the last few years, particularly in Brain¬Computer Interface design. Capturing EEG signal is a simple, non¬invasive, and commonly used technique for analyzing the functioning of the human brain. Therefore, brain activity during taking lesson can be captured by collecting EEG signals. Researchers have started using multi¬channel EEG headset to capture more fine details. However, students may not be fully attentive with wearing multi¬channel EEG headsets due to heavy weight. Capturing attention for multiple sources involves costly deployment of various equipments. Single channel headset is cost¬effective and easy to wear. This thesis mainly focuses on enhancing the learning ability of the learner by identifying the weak student and recommending the non¬attentive lecture videos in FL pedagogy. Brain signal or electroencephalogram (EEG) of students can be utilized to address these (monitoring in FL and wearing multi¬channel EEG headset) issues by exploiting classification and recommendation techniques using single channel EEG headset in this thesis. In this thesis, an experimental set¬up is developed to monitor the student in flipped learning by capturing the brain wave of individual students passively while they are engaged with lecture video. The siamese neural network is exploited to analyze captured brain waves (EEG signal) in order to classify the students into three categories (weak, good, outstanding) based on their attention level. The experimental result shows that the proposed siamese neural network based model outperforms other classification models. Feature extraction technique can be used to improve the accuracy of the classification techniques. Two feature extraction techniques are proposed in this thesis. A popular feature extraction technique called Local Binary Pattern (LBP) is adapted to extract unique features from collected electroencephalogram (EEG) signals. The unsupervised deep neural network technique called variational autoencoder (VAE) is also exploited to extract useful features from captured EEG signals. Finally, standard classification techniques are used to classify the attention level of students. Cognitive state of an individual student is analyzed using brain waves signals while taking instructions in the absence of an instructor. A recommendation technique termed as Lecture Video Recommendation in Flipped Learning (LRFL) is proposed for effective learning in flipped classroom. In this technique, the brain waves (Electroencephalogram (EEG)) signal is analyzed using unsupervised learning (clusters) techniques to group similar behaviors exhibited by student over video duration. Based on this analysis, proposed recommendation technique detects non¬attentive video and suggests for retaking the lesson. Results demonstrate the effectiveness of our recommender technique. Data acquired at our laboratory for research on flipped learning has been utilized in all experiments
Probing the Star-formation Activities of Galaxies Residing in Void and Filaments using AstroSat
No full textThe distribution of galaxies around the cosmic web is in-homogeneous. On megaparsec scales, galaxies cluster together to form groups and galaxy clusters which are located at the intersection of long filaments. A vast region between the galaxy clusters, known as the void, contains relatively fewer galaxies. The clusters, groups, voids, and filaments constitute the large-scale structures (LSSs) of the Universe. The LSSs may leave a characteristic imprint on the photometric and chemical properties of the constituent galaxies. Apart from the environment, the stellar mass of galaxies also strongly influences galaxy evolution. This work aims to understand the intertwined effect of environment and stellar mass on galaxies residing in different LSSs. We primarily work with proposed Ultraviolet Imaging Telescope (UVIT)/AstroSat deep imaging observations. The imaging surveys cover small portions of a few LSSs, i.e., the voids and filaments situated 300 Mpc from us. We present a total of six void galaxies and 18 filament galaxies having FUV observation based on the deep UV imaging survey. Of these, nine galaxies are newly detected in our observation. We calculate star formation rates and colors, model spectral energy distribution (SED) using multi-band fluxes, and dissect the structural properties of the detected galaxies. Our analysis reveals a dominant fraction of bluer galaxies over the red ones in the void region probed while we detected equal number of star-forming and non star-forming galaxies in filaments. We find that the large-scale environment weakly impacts the ongoing star formation in galaxies. We studied the star formation properties of the most massive galaxy, I Zw 81 detected in our void galaxy sample. The 2D structural decomposition of the galaxy revealed the presence of multiple components such as a nuclear point source, a bar, a ring, and an inner exponential disk followed by an outer low surface brightness disk. The star formation inside the lenticular galaxy is concentrated within the central few kpc region enclosing the bar. The galaxy hosts a low-luminosity AGN which does not impact the central star formation. We report that the minor merger interaction enhances star formation in the galaxy and perhaps aids bar formation. The results are peculiar from the standpoint of a massive barred lenticular galaxy. Our study on the filament galaxies confirms the influence of the environment on their evolution. The number density of high-mass and passive galaxies increases near the filament axis. We do not detect FUV emission in galaxies within 0.4 Mpc from the filament axis. The mass-assembly mode of filament galaxies shows stronger dependence on their environment than stellar mass. We uncover extended-UV emission in a few filament galaxies, which hints towards the possibility of cold-mode gas accretion in the LSS
Multiphysics-Multiscale Model Implementation for Thermoacoustic Response Prediction of Plant Fibre-Reinforced Hybrid Polymer Composite-An Experimental Verification
No full textIn this research a multiphysics-multiscale material model in the framework of higher-order polynomial displacement model has been developed to predict the vibroacoustic responses of fruit-extracted natural fibre (spongy Luffa cylindrica) and nanotube-reinforced polymeric hybrid composite with and without temperature effect. To achieve the objective a coupled FEM-BEM algorithm is derived for the evaluation of the thermoacoustic responses via the proposed higher-order mathematical model. Moreover, the proposed algorithm is designed to take care of the structural modelling and its surrounding fluid with the help of an isoparametric finite element and boundary element in association with the necessary coupling effects. Also, the material model is so generic that it can predict the responses on a macro mechanical scale, including each scale effect of different fibre dimensions (Luffa and nanotube). The general baffled system governing differential equation is derived with the help of Hamilton’s principle (eigenvalue) in combination with Helmholtz’s wave equation. The sound data (radiation efficiency and sound pressure level) are obtained by solving the system equation for different ranges of frequencies. Additionally, the derived numerical model accuracy has been verified for a few alkalies-treated natural fibrereinforced composite components fabricated with and without the inclusion of the multiwall-carbon nanotube (MWCNT) plate components for experimental testing. Also, a few experiments are carried out for the natural frequencies and the sound parameters, including their experimental elastic properties. Before the thermoacoustic analysis, the thermal buckling load parameters has been evaluated and the critical temperature of the hybrid composite is Tcr = 47.9°C. After the adequate numbers of comparisons with and without temperature effect (numerical/experimental), the model is engaged for different influential parametric analyses. In conclusion, the effects of one or more prominent design input data affecting the thermoacoustic responses have been explored using the multidimensional material model. Further, a few critical applicability, including the model proficiencies, is engraved suitably according to the present solution and its future orientations
Development of Machine Learning-Based Advanced Driver Assistance System for Smart Vehicular Applications
No full textAdvanced Driver Assistance System (ADAS) is an intelligent way of accident prevention in road traffic. ADAS is used for abnormal driving activity prediction and to guide the driver for its rectification. Most road accidents are caused due to careless driving techniques adopted by human drivers. Advanced computer vision tools and powerful Graphical Processing Units (GPUs) are capable of developing end-to-end tracking systems for complex traffic scenarios. The primary objective is to predict abnormal driving activities to prevent road traffic accidents. Monitoring and detecting driving activities are the critical roles performed by computer vision-based systems. Human beings can do this work quickly because of their superior visual and cognitive abilities. However, continuously watching a driver’s video feed for long periods is a tedious task for humans, which is sometimes impossible, not interesting, and may result in errors. Consequently, this makes automated machine vision-based techniques an ideal choice. The State-of-the-Art (SOTA) literature has several studies that can be used to create systems that automatically detect and monitor driver distraction. The application of ADAS involves monitoring the driver’s activity based on driver pose analysis, driver’s behavior analysis, basic emotion recognition to increase driver comfort, monitoring driver health conditions for risk assessment, etc. The main focus of recent ADAS research has been the relationship between the increased risk of Cardiovascular Disease (CVD) and traffic accidents. Advances in the Internet of Things (IoT) and automotive technology offer the possibility of integrating healthcare into vehicles for driver safety and health. This thesis aims to investigate and analyze some new algorithms with Machine Learning (ML) and Deep Learning (DL) based approaches to predict the driver’s activity in an in-vehicle scenario. This thesis considers both heart rate and in-vehicle camera data for the development of ADAS system. A two-stage framework is proposed for the preliminary screening of commercial drivers prior to actual driving evaluation using ML techniques. First, it aims to address the healthcare of cardiac drivers in resource-constrained scenarios, such as bus terminals with paramedic staff. Model performance is estimated using the publicly available cardiovascular disease benchmark databases and shows superiority over the SOTA techniques. The system then generates a warning for no driving in the event of predicted heart disease and stores the expected abnormal parameters in a Comma-Separated Value (CSV) file for screening by experienced cardiologists working at nearby super specialty hospitals. Email-based data communication has been established to transfer the CSV file to the hospital, reducing the burden on drivers, who regularly visit the hospital. Analyzing driving behavior is also vital to ensure safety. The proposed work on driver behavior analysis uses DL approaches such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to identify driving activity and categorize driving behavior as normal, aggressive, and drowsy. The UAH-DriveSet dataset, a publicly accessible smartphone dataset is used to model this work as a time-series classification task. Further, the identification of driving abnormalities is supported by Facial Expression Recognition (FER), eye-closure analysis, head position estimation, etc. Six basic emotions are used as the basis for facial expression identifications: happiness, surprise, anger, sadness, fear, and disgust. The FER system will continuously monitor the driver through the dashboard camera to identify the driver’s irresponsibility and provide timely assistance for safety. The FER framework is studied using different pre-trained Convolutional Neural Network (CNN) models such as AlexNet, SqueezeNet, and VGG19. The performance of the proposed model has been verified on six publicly accessible benchmark databases namely FER2013, JAFFE, KDEF, CK+, SFEW, and KMU-FED and shows superiority over the SOTA techniques. Literature studies on driver distraction monitoring use in-vehicle DL-based ADAS to reduce the risk of traffic accidents. DL algorithms are difficult to implement in resource-constrained low-cost embedded devices such as Raspberry Pi or mobile phones due to the computational resource requirements for in-vehicle use. Hence, in this work, lightweight models are designed using SqueezeNet 1.1 with last-layer modification to employ it in Raspberry Pi for FER and Distracted Driving Detection (DDD) tasks. Each work is carried out by testing it on different benchmark databases with its performance evaluated by comparing the results obtained by it with those of the benchmark and recent SOTA techniques
Studies on Wear Response of Waste Marble Dust Based Composites and Coatings
No full textThe research reported in the thesis is based on the characterization and wear response analysis of a new class of polymer composites and thermal spray coatings developed using waste marble dust. To attain the purpose of the study, a treasure of property data has been produced by conducting several micro-structural studies, mechanical/physical tests and wear trials on these composites and coatings under controlled laboratory conditions. The experimental findings suggest that marble dust, in spite of being a waste is quite suitable to be used as a functional particulate filler in the fabrication of polymer composites and is eminently coatable on metal substrates. This research shows that the physical and mechanical properties are greatly affected by the incorporation of micro-sized waste marble dust in polyester. Tribo-performance tests on the composites and an exhaustive analysis of the test results suggest that the wear resistance of the hardened neat polyester and glass-polyester composites improves substantially with the incorporation of marble dust in the resin. The filler content is found to be one of the most significant control factors affecting the wear rate of the composites both in the dry-sliding and erosion wear modes. This research also shows that waste marble dust is eminently coatable on metal substrates following the high-velocity oxy-fuel spraying route. The coating quality in terms of the deposition efficiency, thickness, adhesion strength and micro-hardness values is fairly good. These basic coating characteristics can be further improved by the pre-mixing of conventional spray grade powders of nickel-chrome with raw marble dust while preparing the feedstock. This work reveals that the compositional/microstructural, mechanical and tribological properties of the coatings depend greatly on the feedstock composition, substrate properties as well as on the spray parameters. The coatings are found to exhibit excellent wear resistance both in dry-sliding and erosion modes. The thesis thus essentially focuses on the value-added utilization of a waste like marble dust in the making of composites and coatings
A Detailed Investigation on the Phase Transformation Mechanism During Electrodeposition of Cu(In,Ga)Se2 Films
No full textCIGS-based solar cells, which belong to the second generation of solar cells, have achieved a power conversion efficiency of over 23% through the use of vacuum-based fabrication techniques. Despite their impressive performance, these vacuum-based techniques face a significant challenge in terms of their high cost. One potential solution to this challenge is the adoption of scalable non-vacuum-based techniques, such as electrodeposition. Electrodeposition has emerged as a promising alternative, as it offers a more cost-effective approach compared to vacuum-based methods. In fact, CIGS devices fabricated using electrodeposition techniques have already surpassed the efficiency achieved by vacuum-based techniques by a margin of 15%. In these devices, CIGS thin film is utilized as the absorber, playing a crucial role in capturing sunlight and converting it into electricity. There have been no attempts made to comprehend the process by which the CIGS film forms using electrochemical impedance spectroscopy (EIS). While examining the stability of the device or the alloy film alone is important, it is also possible to perform EIS at different stages of deposition during the formation of the CIGS film. This approach can help determine a more feasible pathway for electrochemical deposition. Additionally, studying the stability of various combinations of baths (including unitary, binary, ternary, and quaternary) in a Na2SO4 solution can provide valuable information about the stability behavior of individual species as well as combinations of species. By employing equivalent EIS circuits, it is possible to estimate the dominant mechanism involved when a particular species undergoes transformation. This allows for an understanding of how different species interact when considered individually or combined with one or two other species. Such an analysis can shed light on how the phase transformation mechanism is influenced, for example, by the addition of a species, which may enhance or hinder the overall deposition process. Consequently, utilizing these combinations of baths can offer a comprehensive understanding of all the mechanisms involved in electrochemical deposition. Unfortunately, no in-depth investigation has been conducted thus far to provide a concrete understanding of the electrochemical phenomena underlying the formation of the CIGS film. In this study, an attempt has been made to acquire an in-depth understanding of the electroplating mechanism of Cu (In, Ga) Se2 quaternary thin films on FTO-glass substrates. A systematic approach starting with the study of unitary, binary, ternary, and finally quaternary deposition of the elements have been performed through cyclic voltammetry (CV). The salts used were CuCl2, H2SeO3, InCl3 and GaCl3 for each stage of deposition with varying combinations and proportions. CVs were first done for unitary Cu, Se, In and Ga elements, then binary combinations of Cu-In, Cu-Se, Cu-Ga, In-Se, In-Ga, and Se-Ga system, ternary baths of Cu-In-Se and Cu-In-Ga, In-Se-Ga and Cu-Se-Ga systems. A thorough structural and elemental analysis, carrier densities, type of conductivity was carried out for the as-deposited films. In ternary baths, only Cu-In-Se films did show good photo-electrochemical (PEC) behavior. For this bath, an attempt was made to check whether by increasing In content would affect the PEC behavior. The deposition was done in presence of surfactants and was found that the films were compact and finer with Cu-poor surface (In-rich). Single step deposition of CIGS was found to be impossible, hence Ga was attempted to incorporate in the films through a two-stack approach. From the position and intensity of the cathodic peaks and the phase analysis, plausible mechanism has been proposed for the quaternary alloy. After that an effort has been made to gain a thorough knowledge on the phase transformation mechanism of quaternary thin films through electrochemical impedance spectroscopy (EIS). Here also, a methodical strategy that starts with the investigation of unitary, binary, ternary and finally quaternary deposition. The information obtained from EIS i.e. solution resistance (Rs), double layer capacitance (Cdl), charge transfer resistance (Rct), film resistances (Rfilm), film capacitances (Cfilm) were used to find the mechanism. For unitary baths, Cu has highest Rct and high Cdl under the optimized concentration. Binary baths further explained that Se-Ga and In-Ga can be used to perform one stage of deposition considering the low Rct and moderate Cdl. The ternary bath combinations favored CIS as one stage of deposition in a similar manner (i.e., low Rct and Cdl). While using the quaternary bath, Rct and Cdl were found to have values 483.6 Ω.cm2 and 285.78 μF/cm2. From the impedance pattern, it was deduced that, the deposition takes place in two layers i.e. CIS and CGS ternary films which eventually give the quaternary composition. Further the stability of the films was also evaluated in Na2SO4 solution, the CIGS film is found to be sufficiently stable
Development of μ-ECDM System with Different Process Modes for Machining of Micro Features and Nanoparticles Synthesis
No full textMicromachining of difficult-to-machine materials is of prime focus nowadays. There are wide variety of advanced micromachining processes which are paving the way to achieve desired outcomes such as higher material removal rate (MRR), better surface quality, higher machining depths, reduced heat affected zones, and minimal workpiece damage. One such process is the micro electrochemical discharge machining (μ-ECDM) process which exhibits the capability to achieve the above-mentioned outcomes. But, in this process, proper replenishment of electrolyte at the machining zone is a challenging issue. This can be overcome by some assistance incorporation into the system and proper tuning of the process parameters. In this research work, an in-house developed μ-ECDM system incorporated with assistances like tool rotation and ultrasonication of electrolyte is used to micromachine nonconducting (borosilicate glass and Polymethyl methacrylate (PMMA)), composites (Carbon fiber reinforced polymer (CFRP) and Glass fiber reinforced polymer GFRP), and conducting materials (commercially pure titanium and Aluminum 7075 (Al7075) alloy) to analyze the machining responses like MRR, tool wear rate (TWR), overcut (OC), circularity (Cir), surface roughness (SR), channel width (Wd) and channel depth. To analyze these machining responses various experimental parameters are considered in this study. The parameters include voltage (V), type of electrolyte, concentration (wt%), frequency (kHz), duty factor (%DF), feed rate (μm/s), interelectrode gap (mm), immersion depth (mm), tool electrode material, auxiliary electrode material, tool rotation rate (rpm) and ultrasonication frequency. Among the experimental parameters, some are chosen to be variable process parameters and some to be constant parameters depending on the material that is being machined. The selection of variable and constant parameters is carried out by conducting rigorous pilot experimentation. The variable process parameters are chosen at three levels and experiments were designed based on the L9 orthogonal array or face center cubic-response surface methodology (FCC-RSM). The experiments are conducted according to the respective design layout and their corresponding analysis of variance (ANOVA) is performed. Then a linear or quadratic-based regression model is obtained from the statistical software which gives the relation between the process parameters and machining responses. Further, optimization is performed using a multi-objective JAYA algorithm (MOJAYA) to obtain a set of Pareto optimal solutions and then the multi-attributed decision making (MADM) R-method is used to obtain the best compromise among the available set of Pareto optimal solutions. A MATLAB code for both the MOJAYA algorithm and R-method is developed and validated with existing literature. The optimal solution obtained from MOJAYA coupled with R-method is validated by conducting an experiment at the nearest integer parametric setting and it is found that the error percentage is in good agreement not exceeding ±10. Further, in this work, attention has paid on the debris being treated as unwanted particles are dispersed in the electrolyte during micro-machining of titanium and borosilicate. The debirs of titanium and borosilicate obtained at the optimal process parameters setting is collected and filtered separately. Various diagnostic studies are performed for the characterization of debris (particles) and found that the particles obtained lie in the range of nano to submicron range. The particles obtained are nanoparticles of TiO2 and borosilicate which has numerous applications in various industries
Forbidden Induced Subgraphs of Power Graphs of Finite Groups
No full textFor different algebraic structures like groups, semigroups, rings, vector spaces, etc, we can prescribe various graph structures. The power graph is one such major graph representation which was initially defined for semigroups using the power associative law. The directed power graph of semigroup S is a digraph with vertex set is S and for u; v 2 S, u 6= v, there is an arc (u; v) if v = us for some natural number s. The corresponding underlying graph is referred as the (undirected) power graph of S. Above definition remains similar for power graph of groups. For a finite group G, its power graph is denoted by P(G).
Forbidden subgraph plays significant role in graph theory. A number of important graph classes including threshold graphs, split graphs, chordal graphs, cographs and perfect graphs can be defined in terms of forbidden induced subgraphs. A graph is called H–free if it does not contain H as its induced subgraphs. In this case H is said to be the forbidden subgraph of . Our problem is to characterize groups of finite order G for which P(G) belongs to either of the classes of forbidden subgraps. In this regard, our major conclusion is that every power graph is perfect.
A graph is called cograph if it forbids P4. We completely characterize the nilpotent groups having P4-free power graph. We identify finite groups G and H for which the power graph of G _ H is P4-free. For finite simple groups we show that in most of the cases their power graphs are not cographs: the only ones for which the power graphs are cographs are the cyclic groups Cp, PSL(3; 4), certain PSL(2; q), certain Sz(q) and the alternating groups A5 and A6. However, a complete determination of these groups involve some hard number-theoretic problems.
A simple graph is said to be a chordal graph if it does not possess any chordless cycle of vertices four or more. In this thesis we investigate chordalness of power graph of finite groups. First we characterize direct product of finite groups having chordal power graph. We classify all simple groups of Lie type whose power graph is chordal. Further we conclude: no sporadic simple group has a chordal power graph. We also show that almost all groups of order upto 47 has chordal power graph.
A graph is self-complementary if = . In this thesis we investigate the existence of G such that P(G) is self-complementary. Now if a graph is not self-complementary the natural question arise: how close it is to be a self-complementary graph? A way to measure this is the self-complementary index of the graph. The self-complementary index of is the largest order of a graph 0 such that both 0 and 0 are induced subgraphs of . We provide a upper and lower bounds of this index. Moreover we recognize which proper power graphs are complementary in the sense of different classes of forbidden subgraphs as well in sense of different graph theoretic properties like unicyclicity, 2-connectedness, etc