Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Thermal analysis and microstructure of ZA8 alloy solidifying against chills(Springer India sanjiv.goswami@springer.co.in, 2012) Ramesh, G.; Prabhu, K.N.Thermal analysis during solidification of ZA8 alloy against copper, hot die steel and stainless steel chills instrumented with thermocouples was carried out in the present work. The investigation showed that the chill material and coating had a significant effect on the cooling curve of the casting. When casting was solidified against chills, the liquidus and eutectic start temperature of the casting remained nearly the same whereas eutectoid transformation occurred at a higher temperature. Cooling rate curve of the casting solidified against coated chill indicated that formation of solid shell and subsequent re-melting in the case of high thermal conductivity coated chill whereas in lower thermal conductivity coated chill, the re-melting of solid shell was absent. It was found that chilling during solidification causes the morphology of dendrites transform to nearly rounded shape with refinement of lamellar eutectic.Item Development of clay based nanofluids for quenching(2012) Ramesh, G.; Prabhu, K.N.In the present work the effect of addition of nanoclay particles having concentrations of 0.001, 0.01 and 0.1 vol% on cooling performance of water during immersion quenching was investigated. Cooling curve analyses were carried out by using standard ISO/DIS 9950 quench probe. Wetting behavior of nanoquenchant was studied using dynamic contact angle analyzer. The spreading behavior of droplets of quench media on INCONEL 600 substrate indicates improved wetting behavior of nanofluids. The peak cooling rate and cooling rate at 700°C for water decreased by addition of nanoparticles. Further, quenching in nanofluid shows longer vapour blanket stage as compared to water. The estimated flux transients and Grossmann H factor clearly show that decreased cooling performance of water by addition of nanoparticles. Copyright © 2012 ASM International® All rights reserved.Item Effect of quench probe material and section size on cooling severity(2012) Ramesh, G.; Prabhu, K.N.In the present work simulation of heat transfer during quenching was carried out using finite difference heat transfer based SolidCast software. Simulation experiments were aimed to assess the effect of boundary heat transfer coefficient, quench probe material and its size on the cooling curve of the quench probe at geometric centre. Simulation results show that all these parameters had a significant effect on the simulated cooling curve of the probe. For a material, there is a critical diameter above which increase in cooling rate at the centre of the probe is negligible and this critical diameter depends on the thermal conductivity of the material used for quenching. A quenching system with a D/h ratio value of greater 0.000075m3K/W has no significant effect on the cooling rate at the centre of the probe. A simple quantitative model which correlates average cooling rate, probe material, section size and cooling severity of quench media was proposed. The results of the model is independent of characteristics of quench probe used in assessment of cooling severity and could be used effectively for selection of quenchants during heat treatment. Copyright © 2012 ASM International® All rights reserved.Item Determination of multiple heat flux transients during quenching of inconel 600 probe(ASM International joanne.miller@asminternational.org, 2013) Ramesh, G.; Prabhu, K.N.The time temperature data at axial and radial locations were measured during immersion quenching oflnconel 600 probes in a mineral oil quench medium. The temperature data and thermo-physical properties were used as input to an inverse heat conduction model for estimating spatially dependent heat flux transients. The estimated temperature data agreed very well with measured temperature data for increasing number of unknown surface heat flux components. The peak heat flux value decreased to a minimum and then increased to a high value in the axial direction. The inverse analysis indicated non uniform nature of wetting front and boiling of mineral oil on the quench probe surface resulting in large temperature gradients within the quench probe. The present work clearly indicates spatial dependence of boundary heat flux transients even for a simple cylindrical probe and the need for their estimation during quenching.Item A Survey on Vehicle Collision Avoidance Systems: Innovations, Challenges, and Future Prospects(Institute of Electrical and Electronics Engineers Inc., 2025) Ramesh, G.; Kiran Raj, K.M.; Abhishek; Devadiga, M.T.; Manohara, M.; Boloor, S.; Sowjanya, N.Vehicle Collision Avoidance Systems (VCAS) enhance road safety by enabling vehicles to autonomously detect and respond to potential hazards using technologies such as radar, LiDAR, cameras, V2X communication, and machine learning algorithms. Key features like Adaptive Cruise Control, Autonomous Emergency Braking, and Lane Departure Warning help prevent accidents and improve driver assistance. Despite challenges like sensor limitations in adverse conditions, communication delays, and cybersecurity risks, advancements in sensor accuracy, decision-making algorithms, and edge computing continue to drive innovation. This paper highlights the importance of technological improvements, regulatory frameworks, and system interoperability in advancing VCAS adoption and achieving safer, autonomous transportation. © 2025 IEEE.Item Inorganic Chemical Reaction Predictor Using Random Forest and Support Vector Machine(Institute of Electrical and Electronics Engineers Inc., 2025) Ramesh, G.; Sahil, M.; Palan, S.A.; Bhandary, D.; Shetty, S.S.; Poojary, K.K.; Sowjanya, N.The Chemical Reaction Predictor project shall use machine learning approaches to make predictions on chemical reaction effects. When a large enough group of known reactions is available, each identified set of reactants and products can be used to construct a model into which can be fed any set of reactants. It includes data acquisition and data pre-processing, feature selection of reactant properties and reaction conditions, and construction of several predictive models. The first and main goal is to dogmatically apply machine learning models such as Random Forests and Support Vector Machines to attain an accuracy of 60% or higher. Furthermore, we measure the accuracy, and other measures such as precision, recall, and F1 score to determine the efficiency of these models. Finally, while the optimal model is found and implemented, it is brought within a simple graphical user interface that enables the users to input reactants and obtain predicted products. © 2025 IEEE.