Browsing by Author "Kundapura, Subrahmanya"

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Effect of sliding speed and rise in temperature at the contact interface on coefficient of friction during full sliding of SS304
(National Institute of Technology Karnataka, Surathkal, 2022) P, Palani Kumar; Kundapura, Subrahmanya; Gnanasekaran, N.
Most of the marine, aerospace, mechanical, civil components undergo cyclic loading. When two metal bodies slide or move relative to one another, there will be a mechanical energy loss as a result of friction. In the majority of dry sliding scenarios, it is appropriate to assume that all frictional energy is transferred as heat to the contacting bodies. Frictional heating is what causes the temperature of the sliding bodies to rise, particularly at the contact interface. The temperature on the contact interface may be high enough to have a substantial influence on the outcomes of the sliding mechanism. The following are some of the potential effects of high sliding surface temperatures: surface melting, oxidation, oxidation wear, deterioration of solid, thermo elastic instabilities, and thermo cracking of the sliding components. When two metals are in contact and subjected to friction, heat is generated at the contact interface which plays a vital role in the field of tribology. Pin-on-disk apparatus is used to examine materials sliding. The current research work focuses on the effect of sliding speeds, normal loads, and temperature rise at the contact interface of SS304 alloys subjected to full sliding experiments. Dry sliding experiments were conducted on Rotary Type Pin-on-Disk Tribometer and Finite Element Modelling was carried out using ANSYS Software. Cylindrical pins of radius 3 mm, height 30 mm, and circular disk of diameter 165 mm having flat surface were fabricated to simulate Hertzian contact configuration. Experiments were conducted at three different sliding speeds of 1 m/s, 2 m/s, and 3 m/s under normal load of 1 kg, 1.5 kg and 2 kg and two different wear tracks of diameter (60 mm and 120 mm) respectively. Dry sliding experiments were conducted up to time of 200 s. The rise in temperature were measured using K-type thermocouples and they were located to the pins at 4 mm and 7 mm distance from the contact interface. The temperature and heat flux at the contact surface was predicted using inverse heat transfer method obtained during Pin on Disk experiment. Evolution of pin temperature at the contact interface obtained from Finite Element Analysis results is in good agreement with the experimental result.
Flood Susceptibility Modelling Using Remote Sensing – Machine Learning Approach and Optical Water Quality Analysis of Vembanad Lake System In Kerala, India
(National Institute Of Technology Karnataka Surathkal, 2023) K. S. S., Parthasarathy; Kundapura, Subrahmanya
Wetlands are essential ecosystems that play a significant role in mitigating the impacts of climate change. Wetlands store large amounts of carbon and help to regulate the climate by reducing the amount of carbon dioxide in the atmosphere. They also help to reduce the impacts of extreme weather events, such as floods and hurricanes, by absorbing and retaining water. However, wetlands are also vulnerable to the effects of natural and anthropogenic factors, which can alter their hydrology and lead to the loss of wetland habitats. It is crucial to protect and preserve wetlands to maintain their vital role in mitigating the impacts of climate change. The wetland functions, commodities, and services are lost due to upland land use activities. Hence, accurate and up-to-date information on the upland regions around wetlands is essential. The present research considers the Vembanad Lake System (VLS) in Kerala, India, which is specifically affected by challenging issues to its health and survival. The study area faces threats like encroachment and climate change resulting in floods and alteration in the precipitation patterns. Further, the lake system is endangered by the deteriorating quality of incoming water. Thus, the overall spatio-temporal analysis is critical in protecting and managing water resources in the study region. Anthropogenic activities result in a massive Land Use and Land Cover (LULC) change, and it has become a prominent issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the change in LULC for the short term, i.e., within a decade, is carried out using three Machine Learning (ML) approaches, Random Forest (RF), Classification And Regression Trees (CART), and Support Vector Machine (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poorly at an average accuracy of around 82.5%, CART being the next at 87.5%, and the RF model being good at an average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes, such as barren land and built-up areas. As a result, RF- classified LULC is considered to predict the Spatio-temporal distribution of LULC transition analysis for 2035 and 2050. This analysis was conducted in Idrisi TerrSet software using the Cellular Automata (CA) - Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 iclassified image. The model efficiency obtained was good, with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the study area. Floods have claimed the lives of countless people and caused significant property damage, putting their livelihoods in jeopardy. The study area faced adverse mishappening during the 2018, 2019, and 2021 floods due to the torrential rainfall events. Estimations of flood-inundated areas are prepared from 2018, 2035, and 2050 LULC maps. The extent of flood inundation during the 2018 floods and the possible flood inundation region for the projected LULC in 2035 and 2050 are determined. From the analysis of the 2018 classified image, 14.7 km2 of built-up area was found inundated during the year 2018 floods. The scenario of the 2018 flood event is used to quantify the flood that may occur and inundate the projected LULC 2035 and 2050 scenarios. It is found that the flood will affect about 19.87 km2 and 23.32 km2 of the built-up region, majorly for the 2035 and 2050 projected scenarios, respectively. The goal of this research is to construct effective decision tree-based ML models such as Adaptive Boosting (AdaBoost), RF, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost) for integrating data, processing and generating flood susceptibility maps. Eighteen conditioning parameters, including seven categorical and eleven numerical data, are used for flood modelling using ML. These seven categorical data are converted into 50 numerical data, resulting in a total input data of 61. The Recursive Feature Elimination (RFE) is utilized as the feature selection technique, and 22 layers are chosen to feed into the ML models to generate the flood susceptibility maps. The efficiencies of the models are evaluated using Receiver Operating Characteristic – Area Under Curve (ROC-AUC), F1 score, Accuracy, and Kappa. According to the results obtained, all four ML models demonstrated fairly good performance. However, XGBoost fared well in terms of the model's metrics. The ROC-AUC values of XGBoost, GBM, and AdaBoost for the testing dataset are 0.90, whereas 0.89 for RF. The accuracy varied significantly among the four models, with XGBoost scoring 0.92, followed by GBM (0.88), RF (0.87), and AdaBoost (0.87). The resulting flood susceptibility map can be utilized for early mitigation actions during future floods and for land use planners and emergency managers, assisting in reducing flood risk in regions prone to this hazard. iiWater quality is one of the essential parameters of environmental monitoring; even a slight variation in its characteristics may significantly influence the ecosystem. The water quality of Vembanad Lake is affected by anthropogenic effects such as industrial effluents and tourism. The optical parameters representing water quality, such as diffuse attenuation (Kd), turbidity, Suspended Particulate Matter (SPM), and Chlorophyll-a (Chl-a), are considered in this study to evaluate the water quality of the Vembanad Lake. As this lake is regarded as of ecological importance by the Ramsar Convention and has faced severe concerns over recent years, there was a substantial change in the water quality during the lockdowns of the COVID-19 pandemic. This research aimed to examine the change in water quality using optical data from Sentinel-2 satellites in the ACOLITE processing software from 2016 to 2021. The analyses showed a 2.5% decrease in the values of Kd, whereas SPM and turbidity show a reduction of about 4.3% from the year 2016 to 2021. The flood and the COVID lockdown had an impact on the improvement in the quality of water from 2018 to 2021. The findings indicated that the reduction in industrial activities and tourism had a more significant effect on the improvement in the water quality of the lake. There was no substantial change in the Chl-a until 2020, whereas an average decrease of 12% in Chl-a values was observed throughout 2021. This decrease can be attributed to the reduction in the lake's Hydrological Residence Time (HRT). The outcome of this research depicts augmentation of the change in the LULC pattern and its prediction, future flood-inundation regions, flood susceptibility mapping, and the lake's water quality. The findings of this research work will be a valuable reference to help the government and Non-Government Organisations (NGOs) during strategic planning.
Static Structural Studies on Reinforced Tubular T-Joints of Offshore Jacket Structures
(National Institute Of Technology Karnataka Surathkal, 2023) N., Murugan; Kaliveeran, Vadivuchezhian; Kundapura, Subrahmanya
The oil discovery was one of the most important discoveries in the twentieth century. At the turn of the twentieth century, exploration started based on land-based resources and as the demands increased, the exploration was into sea-based resources. It started in shallow water and later in deep water regions. The jacket platform is the most commonly employed one in the shallow water depths. Stiffening is a reinforcing mechanism that essentially improves the strength as well as enhances the lifetime of the structures. Owing to greater mechanical properties and economic advantages, tubular structures are preferably used in offshore jacket structures. The present study investigates the stiffening effect on the behavior of static strength and stiffness of the tubular T joint of an offshore jacket platform structure subjected to axial compressive load. The tubular T model selected for this study, as per the API (American Petroleum Institute) standard, has the following dimensions: Chord length is 400 mm, diameter is 100 mm, thickness is 4 mm, and brace length is 200 mm, diameter is 50 mm, and thickness is 3 mm. The structural steel material with a Young’s modulus of 205 GPa and a Poisson ratio of 0.3 was considered for the analysis. The axial compressive load of 20 kN to 300 kN with an interval of 20 kN is applied on top of the brace section and the support conditions at the chord end are simply supported. Three-dimensional static structural analysis using the ANSYS software package was carried out to evaluate the effect of stiffeners placed over the chord section of the tubular T joint. An experimental program has been carried out in the laboratory, and the results are presented. A comparative study is conducted between the experimental and numerical for the validation of results. The stiffened configurations considered are: Can around the joint with 6 rings and 4 strips, Can around the joint with 6 rings and 6 strips, Can around the joint with 6 rings and 8 strips. For the given axial compressive load of 20kN, the joint stiffness of the unstiffened tubular T joint is 67.929N/mm. The stiffened configuration increases the joint's local stiffness by more than 100 percent, and a maximum of 140 percent is observed.