Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
6 results
Search Results
Item Optimization of simply-supported symmetrical trough type folded plate roofs using improved move-limit method of sequential linear programming and sequential unconstrained minimization technique is discussed. Improved move-limit method of sequential linear programming has been found to be suitable for optimization of trough type folded plate roofs and using the same, the effect of cost ratio on optimum design variables and the effect of the number of trough units for a given span on optimum design have been studied and discussed. Optimum dimensions have been prepared for various spans normally encountered. © 1995.(Optimum design of trough type folded plate roofs) Lakshmy, T.K.; Bhavikatti, S.S.1995Item Conjunctive use in India's Varada River Basin(American Water Works Association cs-journals@wiley.com, 2009) Ramesh, H.; Mahesha, A.The use of groundwater in conjunction with surface water resources has gained prominence in regions experiencing scarce or uneven distribution of water. In the Varada River Basin in Karnataka, India, for example, an optimization model was developed for the conjunctive use of surface water and groundwater resources because of the increasing demand on agricultural and domestic sectors of this area's water supply. Monsoon rains, which occur only six months a year, predominantly control the basin's agricultural activities. However, the area has an immense need for efficient use of available water resources during the rest of the year. The model, based on linear programming, optimizes the allocation of groundwater and surface water subject to hydraulic and stream flow constraints. The model incorporates policy scenarios that add to the sustainability of the system. The developed conjunctive-use model is simple but effective in computing the optimal use of the Varada basin's water resources.Item Fuzzy logic approach for reactive power coordination in grid connected wind farms to improve steady state voltage stability(Institution of Engineering and Technology journals@theiet.org, 2017) Moger, T.; Dhadbanjan, T.This study presents a fuzzy logic approach for reactive power coordination in grid connected wind farms with different types of wind generator units to improve steady state voltage stability of power systems. The load bus voltage deviation is minimised by changing the reactive power controllers according to their sensitivity using fuzzy set theory. The proposed approach uses only few controllers of high sensitivity to achieve the desired objectives. The 297-bus and 417-bus equivalent grid connected wind systems are considered to present the simulation results. To prove the effectiveness of the proposed approach, a comparative analysis is carried out with the conventional linear programming based reactive power optimisation technique. Results demonstrated that the proposed approach is more effective in improving the system performance as compared with the conventional existing technique. © 2016 The Institution of Engineering and Technology.Item Influence of Process Parameters and its Optimization on Wear Behavior of an Exceptional Aegle Marmelos Polymer/Aluminum Composite(Springer, 2024) Veeranaath, V.; Sahu, R.K.; Priya, I.M.The present paper is focused on the indigenous production of a unique and low-cost Aegle Marmelos natural polymer (AMNP) powder via chemical synthesis and its reinforcement in the aluminum matrix via powder metallurgy. The wear behavior of Aegle Marmelos natural polymer-reinforced (AMNPR) aluminum composites is studied. The effect of control parameters like reinforcement (wt.%) and different sliding parameters on the wear characteristics is discussed. The SEM studies revealed that severe damage due to adhesive wear, delamination, and formation of oxide zones is observed at reinforcement concentrations of 10 wt.% and 15 wt.%. The optical profilometry study also revealed that the roughness of the worn-out samples was maximum at 10 wt.% reinforcement. Further, the process parameters with each characteristic are optimized individually and the optimal parameters are different. To avoid this confounding effect, TOPSIS coupled with CRITIC method is adopted to convert all characteristics into a closeness coefficient (Ci) and optimize at a common parameter level setting. The optimal combination of process parameters for minimum wear characteristics is as follows: reinforcement concentration: 20 wt.%, sliding load: 25 N, sliding speed: 200 rpm, and sliding duration: 4 min. The confirmation test results were validated and showed an improvement of the closeness coefficient by 0.0116. In this study, a statistical multi-regression model is also developed for predicting the closeness coefficient of the developed composites under different parametric conditions. The predicted values obtained from the regression model agreed well with the experimental values, with a mean absolute error of 5.478%. © ASM International 2024.Item A Novel Transformer-Based Approach for Reliability Evaluation of Composite Systems With Renewables and Plug-in Hybrid Electric Vehicles(Institute of Electrical and Electronics Engineers Inc., 2025) Yarramsetty, C.; Moger, T.; Jena, D.; Rao, V.S.This paper proposes a novel hybrid framework that integrates machine learning (ML) techniques with Sequential Monte Carlo Simulation (SMCS) to enhance the reliability assessment of modern power systems incorporating renewable energy resources (RER) and plug-in hybrid electric vehicle (PHEVs) integration. While PHEVs can leverage RER to significantly reduce greenhouse gas emissions, the increased energy demand from large PHEVs fleets poses potential challenges to power system reliability. To address these issues, this research presents an advanced mixed-integer linear programming (MILP) based algorithm for optimizing EV charging. The algorithm prioritizes clean energy utilization through intelligent power allocation strategies while considering cost-revenue trade-offs. A probabilistic model is developed to account for factors such as driving distance, charging times, locations, battery state of charge, and charging needs of PHEVs. The proposed approach is tested on the IEEE RTS-79 test system and evaluates multiple ML architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Transformer models, often combined with boosting algorithms, across three scenarios: base case, uncontrolled charging, and intelligent charging. Results highlight that ML-based approaches, particularly the Transformer model, achieve computational time reductions of up to 49% compared to traditional SMCS methods while maintaining comparable accuracy. The Transformer model identified 1,788 loss-of-load states compared to 1,510 actual instances, requiring only 176 minutes of computation. Among all models, the BiLSTM with Adaptive Boosting (BiLSTM+AB) achieved the lowest overestimation, exceeding actual instances by just 256 states. Performance metrics such as Loss of Load Probability (LOLP) and Expected Demand Not Supplied (EDNS) validate the effectiveness of the proposed ML approaches in balancing accuracy and computational efficiency. © 2013 IEEE.