1. Ph.D Theses
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/11
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Item Calibration of Vehicle and Driver Characteristics for Vissim Model, Ann-Based Sensitivity Analysis, Traffic Management, and Signal Design Using Ga for Mangalore City(National Institute of Technology Karnataka, Surathkal, 2022) Bandi, Marsh M.; George, VargheseThe field of traffic flow modeling has emerged as an important multi-disciplinary area with contributions from traffic-engineers, city-planners, mathematicians, and specialists in the field of computer sciences. Traffic engineers and planners constantly strive to alleviate problems that arise due to bottlenecks in traffic movement. One of the major challenges to traffic management lies in minimizing congestion and facilitating efficient traffic flow. The study of traffic congestion requires a proper understanding of the relationship between vehicle characteristics and driver characteristics to mimic existing traffic flows on urban streets.Simulation approaches permit traffic engineers of developing countries to evolve reliable models to investigate the influence of various factors related to roadways, and driver and vehicle characteristics on traffic flow on urban roads characterized by heterogeneous traffic conditions. These modeling techniques assist in gaining insight into the underlying relationships between the above factors involved. The primary scope of this study is focused on performing investigations using micro- simulation on understanding the traffic characteristics of Mangalore city in India, for heterogeneous traffic composition using VISSIM. A very important traffic circuit of the city connecting Hampankatta Circle, Navbharat Circle, PVS Circle, Bunts Hostel Circle, and Jyothi Circle, was considered for analysis, in addition to the nearby important locations such as Bendoorwell Junction, Balmatta Junction, and St. Theresa’s School Junction. In the initial phase of the study, the links and connectors representing the road network of the city were assigned in the VISSIM modeling environment on a template comprising a 1:5000 high- resolution base-map of the city overlaid with the layout of the roads and junctions using AutoCAD. Data on turning movements of traffic at various junctions was collected for 80 minutes during the peak-hour between 17:00 - 18.30 hours on Tuesday 10th March 2015 as part of this study. In this phase of the study, a number of simulation experiments were performed using the above data for default vehicle and driver characteristics, and the best random seed to be used for simulation was identified as 25, and 42 from among the random seeds from 1-50s tested. In the calibration exercise, the driver and vehicle parameters were fine-tuned in fourteen major stages to minimize errors between the observed data and the simulated results. 75% of the video-graphic data was used for performing testing and calibration, while the remaining 25% of the data was used for validation studies. An ANN-based sensitivity analysis was then performed to identify the relative importance of various vehicle and driver characteristics. A modified Garson’s approach was adopted in this study for the computation of relative sensitivity based on connection weights between the input layer, the three hidden layers, and the output layer for the optimized ANN configuration. Based on the results of the sensitivity analysis, the predictive capability of the simulation model was further enhanced by performing a multi-level extended calibration procedure that provided reliable results as per prescribed standards for traffic simulation. This finalized model was again validated successfully. The fully calibrated VISSIM model was the used inthe later phase of the study, to study the effect of implementing short-term strategies such as widening of existing road-widths, and long-term improvement strategies such as introduction of flyovers at selected critical locations in the city. Additionally, studies were performed using the genetic algorithm (GA) based approach in the design of traffic signal timings for streamlining traffic flows across four important junctions in the city. The objective function in the GA module was formulated based on the HCM average delay model (TRB 2000). The overall approach towards performing calibration studies evolved through the present study is expected to provide the basic framework for calibration and fine-tuning of vehicle and driver characteristics in the development of micro-simulation models. The findings of this study are expected to assist transport planners in developing innovative solutions to urban traffic management, analysis, design, and operation of vehicles on roadways.Item Computational Analysis of Protein Structure and its Subcellular Localization using Amino Acid Sequences(National Institute of Technology Karnataka, Surathkal, 2021) Bankapur, Sanjay S.; Patil, Nagamma.A cell is the basic unit of all organisms. In a cellular life cycle, various complex metabolic activities are being carried out in different cell compartments. Protein plays an important role in many complex metabolic activities. Proteins are generated in the post-transcriptional modification activity of a cell. Initially, the generated proteins are in the linear structure and it is called as protein primary structure. Within the cell, proteins tend to move from one compartment (subcellular location) to other compartments, and based on the environment (in which the primarily structured proteins reside), primary structured proteins transform into secondary and tertiary structures. Tertiary structured proteins interact with nearby structured proteins to form a quaternary structure. A protein performs its biological functions when it attains its respective tertiary structure. Identification of a protein structure and its subcellular locations are challenging and important tasks in the field of medical science. Various health issues are identified and solved via novel drug discoveries and a prior and accurate knowledge of protein structure and its subcellular location helps in developing a respective drug. In order to identify protein structure and its subcellular locations, various biological methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, cell fractionation, fluorescence microscopy, and electron microscopy are used. The main advantage of biological methods is that they are accurate in identifying protein structures and its subcellular locations. The disadvantages of biological methods are that they are time-consuming and very expensive. In this post-genomic era, high-volumes of protein primary structures are decoded by various research communities and are added to protein data banks. Identification of protein structure and its subcellular locations using biological methods are not a feasible option for high-volumes of proteins. Over the decades, various computational methods have been proposed to identify protein structure and its locations; however, the existing computational methods exhibit limited accuracy and hence they are less effective. The main objective of this thesis is to propose effective computational models that contribute to the prediction of protein structure and its subcellular locations. In this regard, four important and specific problems of protein structure and its subcellular location have been solved and they are: (i) multiple sequence alignment, (ii) protein secondary structural class prediction, (iii) protein fold recognition, and (iv) protein subcellular localization prediction. The importance of multiple sequence alignment is that a vital and consistent homologous pattern of proteins can be captured and these patterns will further help in solving protein structure and its subcellular locations. The proposed alignment method includes three main modules: a) an effective scoring system to score the quality of the aligned sequences, b) a progressive-based alignment approach is adopted and modified to align multiple sequences, and c) the aligned sequences are refined using the proposed polynomial-time complexity-based single iterative optimization framework. The proposed method has been assessed on publicly available benchmark datasets and recorded 17.7% improvement over the CLUSTAL X model on the BAliBASE dataset. Identification of protein secondary structural class is one of the important tasks that further help in the prediction of protein tertiary structure. Protein secondary structural class prediction is a supervised problem that falls under the multi-class category. The proposed protein secondary structural class prediction model contains a novel feature modelling strategy that extracts global and local features followed by a novel ensemble of classifiers to predict structural class. The proposed model has been assessed on both publicly available benchmark datasets and derived latest high-volume datasets. The performance of the proposed model recorded an improvement of 5.3% on the 25PDB dataset over one of the best predictors from the literature. A protein fold recognition is a categorization of various folds of a protein that exhibits in tertiary structure. Protein fold recognition is a supervised problem that falls under the multi-class category. The proposed fold recognition model contains a novel and effective feature modelling approach that includes Convolutional and SkipXGram bi-gram techniques to extract global and local features followed by an effective deep learning framework for fold recognition. The proposed model has been assessed on both publicly available benchmark datasets and derived latest high-volume datasets. The performance of the proposed model recorded a relative improvement of 5% on the DD dataset over one of the best predictors from the literature. An effective protein sub-chloroplast localization prediction model is proposed to solve one-level more microscopic problem of subcellular localization. Protein subchloroplast localization is a supervised problem that falls under the multi-class and multi-label category. The proposed protein sub-chloroplast localization prediction model contains a novel feature extraction technique such as SkipXGram bi-gram followed by a deep learning framework for multi-label classification. The proposed model has been assessed on publicly available benchmark datasets and recorded an improvement of (absolute) 30.39% on the Novel dataset over the best predictor from the literature.Item Investigation on Wire Electro Discharge Machining Characteristics of TiNiCu Shape Memory Alloys(National Institute of Technology Karnataka, Surathkal, 2020) Roy, Abhinaba.; S, Narendranath.Shape memory alloys are well known across academia and industries due to their unique functional capabilities, such as shape memory effect and superelasticity besides other useful properties. They are also known for their toughness, resistance to corrosion, improved fatigue life and damping capabilities. Shape memory effect is exhibited by these group of alloys due to reverse martensitic phase transformation which transforms de-twinned martensites back to twinned martensites. This phase transformation of shape memory alloys occurs without any change in state of the material, which contextually known as diffusionless transformation. Superelasticity, on the other hand is exhibited by these alloys, when the alloy is handled at an operating temperature higher than its austenitic temperature. Ni rich NiTi shape memory alloy for example can be processed to be superelastic at room temperature. These incredible qualities qualify shape memory alloys as potential materials for smart applications such as sensors and actuators. A vast majority of these alloys exhibit shape memory effect due to thermal load and some of them are also influenced by a magnetic field. Thermally induced shape memory alloys have formed wide applicability due to ease of use and economic factor. Among these alloys, TiNi based shape memory alloys are most widely researched and put into applications compared to Cu-based or Fe-based alloys. Phase transformation temperature of TiNi based shape memory alloys lie within a nominal operating temperature range (60⁰C-100⁰C) which makes them more suitable for sensing and actuating applications. However, with addition of a ternary element, phase transformation temperature of these alloys can be tailored to specific needs. Addition of Cu as ternary element in TiNi binary alloy system was found to reduce its phase transformation temperature and narrow transformation hysteresis. Cu addition also facilitates thermal conductivity making it more sensitive to change in thermal flux. Therefore, TiNiCu ternary shape memory alloys could be used for much sensitive applications. Major challenge these alloys impose is poor machinability with conventional machining techniques. High tool wear, poor machined surface quality and additional post-machining processes compromise finish quality, accuracy of the end product and increase the cost involved. This is where non-conventional machining techniques proved as an added advantage to process these functional alloys and soon became a more popular choice over conventional machining techniques. Non-conventional machining process like laser beammachining (LBM), water jet machining (WJM), electrochemical machining (ECM) and electrodischarge machining (EDM) result to better machining characteristics compared to conventional machining techniques. due to non-contact nature of the tool-workpiece interface. However, thick recast layer, oxidation, burr formation are some of machining defects that non-conventional machining techniques exhibit. Wire electrodischarge machining (WEDM) is a variant of traditional electrodischarge machine (EDM) where machining is carried out using an wire electrode. Sparking between wire electrode and workpiece results in removal of workpiece material through local melting. Advantage of WEDM over EDM is that through CNC any desired profile can be cut imposing minimum damage to workpiece material. Sensors and actuators incorporating shape memory effect are generally micro shaped components which undergoes microscopic shape change. Major aim of this study is to investigate WEDM characteristics of various homologous TiNiCu shape memory alloys and to optimize machining responses so as to produce components without compromising accuracy and quality. Six different TiNiCu shape memory alloys were vacuum melted and characterized in terms of microstructure, phases present, phase transformation temperatures and microhardness. Optical microscope with image analyzer, X-ray diffractrometer, differential scanning calorimeter and microhardness tester were used to perform aforementioned characterization. Further, to determine the quality of machining, the following output responses namely material removal rate (MRR), surface roughness (SR), kerf width (KW), recast layer thickness (RLT), machined surface microhardness (MH) and machined surface morphology were studied and reported. Ti50Ni25Cu25 exhibited least thermal hysteresis (~6⁰C) which indicates its suitability as ideal material for sensor and actuator applications. Due to varying thermal conductivity of vacuum melted homologous TiNiCu shape memory alloys, variation in WEDM responses were observed. Thereafter, prediction of WEDM responses was carried out using Artificial Neural Network (ANN) and optimization of WEDM responses was performed using Genetic Algorithm (GA). After a thorough investigation, WEDM process parameters to machine homologous TiNiCu shape memory alloys were reported and discussed in detail.Item Design of Adaptive Robust Controllers for Renewable Energy Sources Integrated Smart Grid System(National Institute of Technology Karnataka, Surathkal, 2020) G, Hemachandra.; Sharma, K Manjunatha.Energy supply and consumption from conventional fossil fuel is seen as a factor to global warming and deterioration of the environment. It is essential to use clean, non-polluting and alternative energy sources. Wind energy conversion technologies have proved attractive and competitive in terms of conventional fossil energy technologies with increased demand for electricity. It may reduce the negative impacts of traditional energy sources on the environment and reducing dependency on fossil fuels. Because of its high efficiency, the wind energy system can be an alternative source of energy for the future. The most frequently used variable-speed wind turbine is to enhance energy capture at distinct wind speeds. Self-excitation, elevated efficiency, power density, a wide variety of velocity, certainty and full isolation of the PMSG from the power grid have rendered it preferable for various wind systems. In addition to the wind power system, photovoltaic (PV) system developments are heightened the need for injecting the PV power in to the grid. PV array is composed of series and parallel PV cell combinations to maintain the required current and voltage levels operate in centralized grid connected inverter. However, substantial power losses have been reported due to the imbalanced generation between PV panels, which is mainly due to partial shading. Fuel cell (FC) act as continuous power source to mitigate the intermittent nature of PV and wind system. FC’s are clean and high efficient independent power generating source with zero emissions. Investigation of the performance of robust and non-linear controllers under varying wind speed scenarios is explored as a preliminary study. It is discovered that automated robust controller design is essential for the renewable power systems applications. Proposed research work intends to address the maximum power tracking issue for the autonomous wind power system and grid connected PMSG based wind energy conversion system, sub-module level PV system, and fuel cell. Genetic algorithm is used to design a new robust Quantitative Feedback Theory (QFT) controller based on automatic loop shaping methodology. The outcome of research work iiiis to extract the maximum power from hybrid renewable energy sources with automated robust QFT control strategy.Item Experimental Investigations on Diamond Burnishing of 17-4 Ph Stainless Steel under Sustainable Cooling Environments(National Institute of Technology Karnataka, Surathkal, 2019) B, Sachin.; S, Narendranath.; Chakradhar, D.The materials which initiate more tool wear, heat, cutting force, and poor surface finish during machining are termed to be difficult-to-cut materials. The precipitation hardenable (PH) stainless steel is one of the interesting family of steels which can attain hardness up to 49 HRC. In these family of stainless steels, 17-4 PH stainless steel has attracted engineers across the world because of its superior corrosion resistance and high strength, which is not possible to find in any of the steel grades. Owing to low thermal conductivity, high strength and admirable wear resistance properties, it has been classified under difficult to cut materials. It is a special type of PH martensitic stainless steel which consists of martensite along with a small quantity of austenite. Compressor blades of steam turbines are subjected to high temperature, vibration, and stress inducement. These issues can cause damage to the engine. Hence, the first set of compressor blades can be manufactured with PH stainless steel to avoid the problems arising due to foreign object damage. Machining of such kind of steels results in poor surface quality and also the production cost is more. Burnishing is one of the preferred secondary finishing operations which is usually performed after machining to achieve the mirror finish of the surface. To achieve the superior surface characteristics of the difficult to cut material, it is preferred to cool the burnishing zone with an appropriate lubricant. Millions of workers throughout the world get affected by working under different kinds of cutting fluids or coolants. Aerosol particles or mist are some of the hazardous elements which will be generated during the application of different types of cutting fluids during machining and which affects the operator’s health. Cryogenic machining has emerged as an alternative cutting fluid in the last two decades. Liquid nitrogen (LN2) will be sprayed at the interface of the tool and workpiece. It is environmental friendly coolant when compared to other conventional coolants. During the burnishing process, because of the pressure created in the burnishing zone, the temperature at that region increases. By the application of LN2, the temperature can be reduced, which results in improved surface integrity of the material.A high-quality finishing of the mechanical parts is necessary to attain the improved fatigue resistance and a low friction ratio. Hence the finishing processes are turned out to be a major drive for industrial innovation all over the globe. Some of the secondary finishing processes such as grinding, lapping, honing, and polishing have been widely used to achieve the super finish of the surface. However, to improve the surface quality and geometrical accuracy of the component, burnishing has been introduced. Burnishing is also one of the well-known secondary finishing process used to improve the functional performance of the component. Diamond burnishing is one of the chipless finishing processes where the spherical tip of the tool made up of natural diamond, slides on the surface of the workpiece which causes plastic deformation. Directly after machining, the workpiece can be diamond burnished to acquire improved surface integrity. It is an economical and compatible process which can be applied on ferrous and nonferrous materials to achieve the mirror-like surface finish. It has a higher level of efficiency when compared to grinding, lapping, and polishing processes. The main objective of this research work is to investigate the influence of process parameters on the surface integrity characteristics while diamond burnishing of 17-4 PH stainless steel under varying working environments. To achieve the best feasible surface integrity properties of the material, the present research work has been classified into four phases. In the first phase, one factor at the time approach (OFATA) was used to find out the influence of control factors such as burnishing speed, burnishing feed and burnishing force on performance characteristics such as surface roughness, surface hardness, surface morphology, surface topography, subsurface microhardness and residual stress using a commercially available diamond burnishing tool. The cryogenic cooling, minimum quantity lubrication (MQL), and dry environments were considered for the study. In the second phase, a novel diamond burnishing tool was designed and fabricated to improve the performance characteristics of the material. To analyze its performance under all the three environments, OFATA was used. Further, the study was extended to investigate the influence of two more process parameters such as the number of tool passes and diamond sphere diameter on the performance characteristics in the cryogenic cooling condition. Inthe third phase, the optimization of process parameters was performed by Taguchi’s Grey Relational Analysis (TGRA). In the fourth phase, a mathematical model was developed for surface roughness and surface hardness by Response Surface Methodology (RSM). The developed regression equation was used to perform multi-objective optimization using genetic algorithm (GA). The optimal process parameters were achieved, which will be beneficial in improving the performance of the component.Item Electrical Power Distribution System Management Under Deregulation Regime(National Institute of Technology Karnataka, Surathkal, 2013) Manjunatha Sharman, K.; Panduranga Vittal, K.The distribution system in the electrical power network is the most vital section being nearest to the consumers. The effectiveness of the power delivery to the loads is governed by the design, operation and maintenance of the distribution network. Over the years, the researchers are attempting to achieve improvements in the distribution system performance by adopting newer topologies, strategies for network design and control. In this context, globally the need for distribution system improvement is acknowledged by all countries and since past decade distribution sector reforms are being executed by initiating newer government policies which led to de-regulation regime worldwide. This thesis addresses the issues of DG insertion to distribution system in deregulation regime. The analysis carried out evaluates the feasibility of an Industrial captive power plant to operate as a DG Source, complex issues associated with multiple DG sources insertion to distribution system and impact of DG sources in network reconfiguration. A tool which facilitates decision on power export by an industrial captive power plant to grid has been developed. This tool accounts existing load pattern and generation scenario of the industrial unit. The proposed analytical approach gives with emphasis on choice of improving any specific parameter from either technical or economical perspective. The strategic technique developed proposes a comprehensive index termed as Network Performance Enhancement Index (NPEI). This index is a combination of indices related to loss reduction, voltage profile improvement, voltage regulation, voltage stability. Adapting this index provides enough alternatives to the designer so that he can decide on the most feasible solution. The technique designed for service restoration enumerates the situations of islanding of DGs due to fault in any part of network and guides the operator for supply of local loads in such a situation. This work proposes most feasible schemes for DG insertion to overcome the difficulties in implementation of the conventional fixed solutions schemes. The software tools adopted are SKM Power Tools and MATLAB and all evaluations are done using standard bus structures reported in literature and nearby captive plant data of an industry.