Faculty Publications
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Item Studies on application of vertical axis hydro turbine for sustainable power generation in irrigation channels with different bed slopes(Elsevier Ltd, 2021) Shashikumar, S.; Honnasiddaiah, R.; Hindasageri, V.; Madav, V.The present work is carried out to study the performance of a Savonius rotor for small-scale hydropower generation. It has been observed that some of the irrigation channels available in the rural areas are having enough bed slope to generate kinetic energy, which can be harnessed through a Savonius rotor. An in-house fabricated scale-down model of the Savonius rotor is tested at an inclination of the re-circulating indoor multipurpose tilting flume at 0°, 0.5°, 1.0°, 1.5° and 2.0° to determine performance under controlled conditions. It is observed that at the tip speed ratio of 0.92 and channel inclination of 0.5° compared to 0° inclination, the coefficient of power and coefficient of torque improved to 40% and 10%, respectively. Furthermore, it is found that the torque and power developed by the turbine are maximum at a bed slope of 2.0° owing to the maximum available energy. © 2020 Elsevier LtdItem Experimental and numerical investigation of novel V-shaped rotor for hydropower utilization(Elsevier Ltd, 2021) Shashikumar, S.; Honnasiddaiah, R.; Hindasageri, V.; Madav, V.Hydrokinetic technologies harvest renewable power by harnessing the kinetic energy of water from free-flowing rivers, streams, dam head/tailrace and irrigation channels. Savonius rotor is one of the simple and low-cost vertical axis drag type devices used for the extraction of hydrokinetic power. The main limitation of Savonius hydrokinetic turbine is its low efficiency due to negative torque developed by the returning blade without augmentation techniques. In this paper, an experimental investigation is carried out in a multipurpose tilting water flume using V-shaped rotor blade profiles by maintaining a fixed V-angle of 90°, varying length of V-edges, arc radius and with a constant aspect ratio of 0.7. The simulations were carried out using commercial software, ANSYS Fluent. From the experimental and numerical results, it was found that, the optimum blade profile (V4) has developed a maximum coefficient of power 0.22 and 0.21 respectively, at a tip speed ratio 0.87. It was found that, the maximum coefficient of power of optimal V-shaped blade profile (V4) is 19.3% higher than the semi-circular blade profile. © 2021 Elsevier LtdItem Remote sensing and machine learning based framework for the assessment of spatio-temporal water quality in the Middle Ganga Basin(Springer Science and Business Media Deutschland GmbH, 2022) Krishnaraj, A.; Honnasiddaiah, R.Understanding the dynamics of water quality in any water body is vital for the sustainability of our water resources. Thus, investigating spatio-temporal changes of dominant water quality parameters (WQPs) in any study is indeed critical for proposing the appropriate treatment for the water bodies. Traditionally, concentrations of WQPs have been measured through intensive fieldwork. Additionally, many studies have attempted to retrieve concentrations of WQPs from satellite images using regression-based methods. However, the relationship between WQPs and satellite data is complex to be modeled accurately by using simple regression-based methods. Our study attempts to develop a machine learning model for mapping the concentrations of dominant optical and non-optical WQPs such as electrical conductivity (EC), pH, temperature (Temp), total dissolved solids (TDS), silicon dioxide (SiO2), and dissolved oxygen (DO). In this context, a remote sensing framework based on the extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP) regressor with optimized hyper parameters (HPs) to quantify concentrations of different WQPs from the Landsat-8 satellite imagery is developed. We evaluated six years of satellite data stretching spatially from upstream to downstream Ankinghat to Chopan (20 stations under Central Water Commission (CWC), Middle Ganga Basin) for characterizing the trends of dominant physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the reflectance of the pixels corresponding to the sampling stations, a significant coefficient of determination (R2) in the range of 0.88–0.98 for XGBoost and 0.72–0.97 for MLP were generated, with bands B1–B4 and their ratios more consistent. Indeed, these findings indicate that from a small number of in-situ measurements, we can develop reliable models to estimate the spatio-temporal variations of physico-chemical and biological WQPs. Therefore, models generated from Landsat-8 could facilitate the environmental, economic, and social management of any waterbody. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Multi-spatial-scale land/use land cover influences on seasonally dominant water quality along Middle Ganga Basin(Springer Science and Business Media Deutschland GmbH, 2023) Krishnaraj, A.; Honnasiddaiah, R.Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO2 + NO3, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model’s predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.