Faculty Publications
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Item Multi-criteria optimization of fly ash and iron ore tailing based concretes subjected to elevated temperatures(Associated Cement Companies Ltd., 2019) Yaragal, S.C.; Babu Narayan, K.S.; Kumar, B.; Francis, J.G.Due to the rapid increase in concrete utilization all over the world, there is increased consumption of Ordinary Portland Cement (OPC), natural fine aggregate (NFA), and natural coarse aggregates. Increased use of OPC, is posing a serious threat due to excess CO2 emissions, and its production is highly energy intensive. On the other hand, extraction and processing stone-based fine and coarse aggregates too, is energy intensive, and the virgin resources are fast depleting. Therefore, for sustainable development, efforts are on all over the world to look for alternative materials in place of conventional ones. In this study, it is attempted to partly replace OPC with fly ash (FA) and partly replace NFA by iron ore tailings (IOT) in concretes. The performance of such concretes at ambient and elevated temperatures is also presented. Full factorial design of experiments was adopted with two control factors under three levels of replacement, i.e., FA (0, 15, and 30% by weight of OPC) and IOT (0, 50, and 100% by volume of NFA). Total nine concrete mixes were prepared and tested for their compressive strengths at room temperature, and residual compressive strengths when subjected to various levels of elevated temperatures (200, 400, 600, and 800°C), and cost of these concretes has also been analyzed. Further, three traditional multi–criteria optimization methods, i.e., grey relational analysis (GRA), technique for order of preference by similarity to ideal solution (TOPSIS), and desirability function approach (DFA) were used to optimize concrete mixes. Results showed that TOPSIS based optimization method is more significant when compared to other two methods. Further, FA-based concrete mixes showed improved performance under multi-criteria optimization. © 2019, Associated Cement Companies Ltd. All rights reserved.Item Membrane-based models for service selection in cloud(Elsevier Inc., 2021) Raghavan, S.; Chandrasekaran, K.Cloud service selection is one of the prime areas of research within the ambit of cloud computing, which has gained wide attention in the recent past. The service selection algorithm primarily involves selecting the best service from a set of available services, based on Quality of Service (QoS) attributes. The QoS attributes are the parameters which allow the users to ascertain the actual quality of the service, often quantitatively. Over the years, there have been several methods designed for service selection in the cloud that are primarily sequential, with many being sensitive to changes. Thus, the aim is to propose multiple robust and parallel models for cloud service selection. The proposed models are designed using Membrane Computing paradigm, which is an inherently parallel computing model, combined with the Improved Technique for Order of Preference by Similarity to Ideal Solution (ITOPSIS), a popular Multi-Criteria Decision Making Method. Two methods based on a tactical amalgamation of ITOPSIS and Enzymatic Numerical P System (A membrane computing device variant) structure are proposed here. The proposed parallel models are implemented, tested, and the obtained results are analyzed. The results indicate one model to be robust (less sensitive) and the other to be moderately sensitive. © 2020 Elsevier Inc.Item Various trade-off scenarios in thermo-hydrodynamic performance of metal foams due to variations in their thickness and structural conditions(MDPI, 2021) Trilok, G.; Gnanasekaran, N.; Mobedi, M.The long standing issue of increased heat transfer, always accompanied by increased pressure drop using metal foams, is addressed in the present work. Heat transfer and pressure drop, both of various magnitudes, can be observed in respect to various flow and heat transfer influencing aspects of considered metal foams. In this regard, for the first time, orderly varying pore density (characterized by visible pores per inch, i.e., PPI) and porosity (characterized by ratio of void volume to total volume) along with varied thickness are considered to comprehensively analyze variation in the trade-off scenario between flow resistance minimization and heat transfer augmentation behavior of metal foams with the help of numerical simulations and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) which is a multi-criteria decision-making tool to address the considered multi-objective problem. A numerical domain of vertical channel is modelled with zone of metal foam porous media at the channel center by invoking LTNE and Darcy–Forchheimer models. Metal foams of four thickness ratios are considered (1, 0.75, 0.5 and 0.25), along with varied pore density (5, 10, 15, 20 and 25 PPI), each at various porosity conditions of 0.8, 0.85, 0.9 and 0.95 porosity. Numerically obtained pressure and temperature field data are critically analyzed for various trade-off scenarios exhibited under the abovementioned variable conditions. A type of metal foam based on its morphological (pore density and porosity) and configurational (thickness) aspects, which can participate in a desired trade-off scenario between flow resistance and heat transfer, is illustrated. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Item NORD: NOde Ranking-based efficient virtual network embedding over single Domain substrate networks(Elsevier B.V., 2023) Keerthan Kumar, T.G.; Addya, S.K.; Satpathy, A.; Koolagudi, S.G.Network virtualization (NV) allows the service providers (SPs) to partition the substrate resources in the form of isolated virtual networks (VNs) comprising multiple correlated virtual machines (VMs) and virtual links (VLs), capturing the dependencies. Though NV brought about multiple benefits, such as service isolation, improved quality-of-service (QoS), secure communication, and better utilization of substrate resources, it also introduced numerous research challenges. In this regard, one of the predominant challenges is assigning resources to the virtual components, i.e., VMs and VLs, also termed virtual network embedding (VNE). VNE comprises two closely related sub-problems, (i.) VM embedding and (ii.) VL embedding, and both the problems have been demonstrated to be NP-Hard. In the context of VNE, maximizing the revenue to cost ratio remains the focal point for the SPs as it not only boosts acceptance of VNRs but also effectively utilizes the substrate resources. However, the existing literature on VNE suffers from the following pitfalls: (i.) They only consider system resources or (ii.) limited topological attributes. However, both attributes are quintessential in accurately capturing the VNRs and the substrate network dependencies, thereby augmenting the revenue to cost ratio. This paper proposes an efficient VNE strategy called, NOde Ranking-based efficient virtual network embedding over single Domain substrate networks (NORD), to maximize the revenue to cost ratio. To address the problem of VM embedding, NORD utilizes a hybrid entropy and the technique for order of preference by similarity to ideal solution (TOPSIS) based ranking strategy for VMs and servers considering both system and topological attributes that effectively capture the dependencies. Once the ranking is generated, A greedy VM embedding followed by shortest path VL embedding completes the assignment. Simulation results confirm that NORD attains a 40% and 61% increment in average acceptance and revenue-to-cost ratios compared to the baselines. © 2023 Elsevier B.V.Item MOGA and TOPSIS-based multi-objective optimization of wire EDM process parameters for Ni50.3-Ti29.7-Hf20 alloy(Elsevier Ltd, 2023) Balaji, B.; Narendranath, N.Conventional machining techniques face challenges in processing Ni-Ti-Hf alloys, which exhibit superior properties and are increasingly considered promising materials for high-temperature shape memory actuator applications. Thus, this article focuses on investigating the effect of Wire Electric Discharge Machining (WEDM) input parameters, namely discharge time (TON), pause time (TOFF), gap voltage (SV), and wire travel speed (WF), on the surface quality and shape memory properties of these alloys. These parameters were optimized to obtain a better removal rate (MRR) and surface finish quality (Ra) by employing a hybrid approach of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Genetic Algorithm (MOGA). TON emerged as the most influencing parameter for both MRR and Ra, and the sample machined using optimal parameter setting, which had a MRR of 5.287 mm3/min and Ra of 2.335 µm, showed better surface quality with fewer surface defects and irregularities, lower recast layer thickness of 10.057 µm, and better shape memory properties with less than 15 % deviation in their latent heat of transformation values and a less than 5ºC change in their austenite and martensite transformation temperature values, which indicates MOGA was successful in finding a trade-off between the two responses. © 2023 Elsevier LtdItem Experimental investigation and optimization of performance, emission, and vibro-acoustic parameters of SI engine fueled with n-propanol and gasoline blends using ANN-GA coupled with NSGA3-modified TOPSIS hybrid approach(Elsevier Ltd, 2024) Kirankumar, K.R.; Kumar, G.N.; Kamath, N.; Gangadharan, K.V.In the present study, performance, emission, and vibro-acoustic studies were conducted on a spark ignition (SI) engine fueled with gasoline and an n-propanol blend at variable compression ratio (CR), speed, and propanol blend fraction (PBF). Experimental data were used to model an artificial neural network (ANN) trained with a genetic algorithm (GA). ANN predictive responses were employed to establish regression relationships between brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), oxides of nitrogen (NOx), carbon monoxide (CO), hydrocarbon (HC), resultant vibration acceleration (RVA), and sound pressure level (SPL) with operating parameters using response surface methodology (RSM). These models served as objective functions in the non-dominated sorting genetic algorithm-3 (NSGA3), a multi-objective optimization (MOO) technique, to optimize responses and obtain non-dominated solutions. These solutions were filtered using a modified technique for order preference by similarity to the ideal solution (TOPSIS) to obtain a compromised optimal solution. ANN-GA model outcomes showed high accuracy, with coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.979 to 0.993 and 0.0381 to 0.0643, respectively. NSGA3 coupled with modified TOPSIS identified optimal operating conditions at 1271.77 RPM, a CR of 11.96, and a PBF of 33.26 %. © 2024 Elsevier LtdItem SEDViN: Secure embedding for dynamic virtual network requests using a multi-attribute matching game(Academic Press Inc., 2025) Kumar, T.G.K.; Kumar, R.; Achal, A.M.; Satpathy, A.; Addya, S.K.Network virtualization (NV) has gained significant attention as it allows service providers (SP) to share substrate network (SN) resources. It is achieved by partitioning them into isolated virtual network requests (VNRs) comprising interrelated virtual machines (VMs) and virtual links (VLs). Although NV provides various advantages, such as service separation, enhanced quality-of-service, reliability, and improved SN utilization, it also presents multiple scientific challenges. In this context, one pivotal challenge encountered by the researchers is secure virtual network embedding (SVNE). The SVNE encompasses assigning SN resources to components of VNR, i.e., VMs and VLs, adhering to the security demands, which is a computationally intractable problem, as it is proven to be NP-Hard. In this context, maximizing the acceptance and revenue-to-cost ratios remains of utmost priority for SPs as it not only increases the revenue but also effectively utilizes the large pool of SN resources. Though VNE is a well-researched problem, the existing literature has the following flaws: (i.) security features of VMs and VLs are ignored, (ii.) limited consideration of topological attributes, and (iii.) restricted to static VNRs. However, SPs need to develop an embedding framework that overcomes the abovementioned pitfalls. Therefore, this work proposes a framework Secure Embedding for Dynamic Virtual Network requests using a multi-attribute matching game (SEDViN). In SedViN, the deferred acceptance algorithm (DAA) based matching game is used for effective embedding. SEDViN operates primarily in two steps to obtain a secure embedding of dynamic VNRs. Firstly, it generates a unified ranking for VMs and servers using a combination of entropy and a technique for order of preference by similarity to the ideal solution (TOPSIS), considering network, security, and system attributes. Taking these as inputs, in the second step, VNR embedding is conducted using the deferred acceptance approach based on a one-to-many matching strategy for VM embedding and VL embedding using the shortest path algorithm. The performance of SEDViN is evaluated through simulations and compared against different baseline approaches. The simulation outcomes exhibit that SEDViN surpasses the baselines with a gain of 56% in the acceptance and 44% in the revenue-to-cost ratios. © 2025 Elsevier Inc.