2. Thesis and Dissertations

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    Optimization Studies on One-Part Geopolymer Mixes (Pastes, Mortars and Concretes)
    (National Institute of Technology Karnataka, Surathkal, 2024) S, Anil Sagar; Yaragal, Subhash C.; Swaminathan, K.
    The consumption of ordinary Portland cement (OPC) to meet the enormous need for concrete production all over the world, is a global threat for climate change. To reduce massive carbon dioxide emissions associated with the manufacturing of OPC, the geopolymerization process has given rise to the transformation of industrial wastes into strong and durable construction materials such as geopolymer binders. However, these geopolymer binders are based on aluminosilicate by-products and alkali activators. The activators involved in alkali activation process are concentrated aqueous solutions, which are viscous, corrosive and caustic. In addition, complexity in transportation and impracticalities in site such as, difficulty in handling, not user friendly, and hard to use for mass production. This study reports on development of a novel ‘one-part’ or ‘just-add water’ geopolymer binder produced by dry blending the solid aluminosilicate precursors, solid alkali source and then adding free water to the blended dry mix to produce a binder as similar to OPC. One-part geopolymers (OPG) have immense potential in large-scale structures owing to their improved safety and convenience of handling over the conventional geopolymer mixing procedure. This study aims to optimize the mixes by understanding, assessing the influence of binder content, activator dosage and water to geopolymer solids (W/GS) ratio on the fresh and hardened properties of one-part geopolymer mixes (namely pastes, mortars and concretes). Various fly ash and slag-based OPG mixes have been developed and studied. The GGBS substitution was chosen as 25, 50, and 75% by volume of fly ash. The activator dosage was taken as 8, 12, and 16% by mass of total binder content and at varied W/GS ratios of 0.35, 0.40, and 0.45. The test results were utilized to develop models which can predict the desired properties of mixes and optimize the mix proportions of OPG mixes using the response surface method (RSM). The microstructural characterization adopting techniques like Scanning Electron Microscope (SEM), X-Ray Diffraction (XRD), Thermal Gravimetric Analysis (TGA) and Fourier Transform Infrared (FTIR) was carried out to study microstructural changes, mineral phases, thermal mass loss and molecular bonding of OPG mixes. The elevated temperature studies, ecological and cost analysis studies were also performed. x Based on the material characterization observations, the change in GGBS addition, activator dosage, and W/GS ratio were observed to have a considerable impact on both the fresh and hardened properties. The optimum mix proportion of OPG paste obtained was 51.4% GGBS substitution, 12.4% activator content, and 0.32 W/GS ratio with 191 mm flow, 68.6 MPa of compressive strength, 59 and 191 mins of initial and final setting times, respectively. The optimum mix proportion of OPG mortar obtained consists of 49.8% GGBS, 13.6% activator dosage, and 0.37 W/GS ratio. This mix achieved 170.4 mm flow, 57.8 MPa and 5.9 MPa compressive and flexural strengths, respectively and also 1626 microstrain of 180 days drying shrinkage. The optimum mix composition of OPG concrete for achieving a 125 mm slump while maximizing strengths comprises of 75% GGBS, activator dosage of 13.8%, and W/GS ratio of 0.34. This optimized mix achieved compressive, flexural, and split tensile strengths of 73 MPa, 6.2 MPa and 3.9 MPa, respectively. The verification of experimental values of proposed optimized mix are within the absolute deviation of 10% of predicted values, indicating the accuracy of the models and effectiveness of RSM in designing the optimum mix proportions of OPG mixes. Elevated temperature endurance of OPGC mixes increases with both GGBS content and activator dosage. Embodied CO2eq (ECO2eq) and embodied energy (EEeq) increases with increase in activator dosage. The ECO2eq and EEeq of OPG concrete mixes are lower compared to OPC based concrete mixes. Hence the OPG mixes can be considered as more eco-friendly and sustainable materials, as against conventional OPC based mixes.
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    Improved Nature Inspired Algorithms For Optimization Problems In Wireless Sensor Networks
    (National Institute of Technology Karnataka, Surathkal, 2022) Kanchan, Pradeep; D, Pushparaj Shetty
    In a Wireless Sensor Network (WSN), the nodes are placed in random positions and con- nected to each other through networks. The nodes collect data from each other, perform pro- cessing and the results are sent to a Base Station (BS). In simple words, Optimization is selecting the best element, with respect to some criterion, from a given set of alternatives. Most of the research in the field of WSNs have concentrated on optimizing clustering, energy efficiency, network lifetime, coverage, load balancing, fault tolerance, quality of service, etc. Multi Objective Optimization deals with optimizing more than one objective at the same time. This thesis concentrates on developing nature inspired algorithms for energy efficient clus- tering and for improving network lifetime in conjunction with Quantum computing. Also, the aim is to develop an efficient nature inspired algorithm for optimizing target coverage in Ho- mogeneous as well as Heterogeneous WSN using Quantum Computing. For achieving the first 2 objectives (Optimizing Energy Efficiency and Improving Network Lifetime), the nature inspired algorithm, PSO (Particle Swarm Optimization) is used in con- junction with Quantum computing. For the 3rd objective (Optimizing Target Coverage), an- other nature inspired algorithm, MOEAD (Multi Objective Evolutionary Algorithm with De- composition) is used in conjunction with quantum computing.
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    Workload Optimization In Federated Cloud Environment
    (National Institute of Technology Karnataka, Surathkal, 2022) S R, Shishira; A., Kandasamy
    Cloud computing is an essential paradigm for processing, computing, storing, and com- munication bandwidth. It offers services on an on-demand basis for the user, that is, pay per use. Cloud computing consists of numerous resources, including the provision of networks, databases for storage, servers, virtual machines, and potential application. It is a widely used technique to handle large amounts of data as it provides versatility and functionality for optimization. Customers submit their request for data exchange and to store it in an existing cloud environment. The customer has a huge advantage in paying for the currently required services. In a federated cloud environment, one or more cloud service providers share their servers to handle user requests. It promotes cost savings, service utilization, and performance enhancement. Clients would bene- fit as a Service Quality Agreement exists between the two. The Cloud federation is an evolving technology through which cloud service providers cooperate to provide clients with customized services to enjoy the real benefits of Cloud Computing. The federated service provider achieve better resource usage and Quality of Service by cooperation, thereby enhancing their market prospects. Workloads are the collection of raw inputs provided to the processing arhcitecture. Based on the successful processing of workloads, efficiency can be assessed. Differ- ent workloads have distinct feature sets. The secret to making optimal configuration decisions and improving system performance is by recognizing the characteristics of workloads. Multiple requests are handled quickly under the dynamic cloud environ- ment, which contributes to the resource allocation problem.The cloud will maintain the workflow active through the proper allocation of resources, virtualization software, or repositories. However, the precise load estimation model is important for efficient management of resources. i It is hard to manage a large number of workloads in an enterprise cloud system. Workloads are the sum of data for processing that are provided to the hardware resource. Its behavior and characteristics play an important role in the efficient processing of resource requests. It is also difficult to predict the existence of workloads if they alter excessively. In this thesis, we propose a conceptual framework for efficient prediction and optimization of workloads that can be easily adapted to a system to address this problem to address this problem. Serving the request in considerably less time leads to an issue with resource allocation. In order to auto-scale the resources, it is more comfortable to have previous awareness of the incoming loads. For the better prediction of workloads in the cloud world, a novel architecture is proposed. Predicted workloads could also be configured smoothly for better use without waving off, the SLA negotiated between the provider and customers. Three essentials for the management of cloud resources are considered in the proposed Fitness Function Extraction Model, i.e. CPU, Disk, and Memory storage. This thesis proposes a BeeM-NN architecture by incorporating Workload Neural Network Algorithm and Novel Bee Mutation Optimization Algorithm into a cloud en- vironment for optimized workload prediction. The proposed model initially includes the Fitness Function Extraction Algorithm to retrieve the attribute samples from the Microsoft Azure traces. With the Novel Bee Mutation Optimization Algorithm in the cloud, the expected QoS are optimized. The developed model is tested using the feder- ated cloud service providers workload data traces and is analyzed with the benchmark methods. The result indicated that the proposed model obtained higher accuracy than the existing systems with optimum efficiency in resource and cost usage.
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    Characterization of Magneto-Rheological Fluid and Monotube Damper through Experimental and Computational Analysis
    (National Institute of Technology Karnataka, Surathkal, 2018) T. M, Gurubasavaraju; Kumar, Hemantha; M, Arun
    Magnetorheological fluid belongs to a class of smart materials which exhibit change in their rheological properties, when exposed to an external magnetic field and these properties are completely reversible. By utilizing these special characteristics, the damping force of the MR damper can be controlled and varied in real time applications. The main objective of this research work is to investigate the characteristics of MR fluid and MR damper through experimental as well as computational methods and to evaluate the semi-active suspension with MR dampers performance in terms of ride comfort and road holding of vehicles, when subjected to random road conditions. The rheological characterization of the MR fluid samples under different magnetic fields and fluid gap has been evaluated through experimentation. The measured fluid properties were used for computing the damping force of MR damper. Using single and multi-objective particle swarm optimization techniques, the optimal proportion of iron particles for MR damper application was determined to maximize the shear stress and damping force. The dynamic characterization of MR damper through experimental approach using dynamic test facility at 1.5 Hz and 2 Hz frequencies has been carried out. Also, the influence of material properties of MR damper components on the induced magnetic flux density and geometrical parameters on the damping force was investigated through finite element analysis as well as analytical methods. Multi-objective genetic algorithm and screening optimization techniques were employed to maximize the magnetic flux density and to identify the optimal values of the design variables. Using the analytical method, damping force of the damper was computed for the obtained optimal values of the design variables. It was observed that the damping force of the MR damper whose cylinder is made up of magnetic material was 2.79 times greater than that of MR damper whose cylinder is made up of non-magnetic material. Further, a coupled finite element analysis (FEA) and computational fluid dynamics (CFD) analysis was used for estimating the magnetic flux density and damping force for different input currents. The credibility of the shear mode monotube MR damperanalysis results were validated with experimental results. To overcome certain limitations of shear mode damper, an attempt has been made to realize the mixed mode damper by combining the flow and shear mode operations. The variations in the damping characteristics of flow and mixed mode MR damper under different input were compared with shear mode MR damper. Results showed that combination of two modes of operation could enhance the damping force to a significant level. The damping force of mixed mode MR damper was found to be 3 times greater than that of shear mode MR damper at 2 Hz frequency and 0.4 A current. Based on results obtained from computational analyses, a non-parametric representative model exhibiting the hysteretic behavior of MR damper was developed. The developed nonparametric model was implemented in a quarter car semi-active suspension to determine the dynamic response of the vehicle subjected to random road excitations. Further, this model was implemented in three-wheeler vehicle semi-active suspension system to evaluate its dynamic performance. The outcome showed that the vehicle with non-parametric based MR suspension system provided good vibration isolation for semi-active suspension than passive suspension system in terms of rice comfort and road holding.