Faculty Publications
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Item Variability of streambed hydraulic conductivity in an intermittent stream reach regulated by Vented Dams: A case study(Elsevier B.V., 2018) Naganna, S.R.; Deka, P.C.The hydro-geological properties of streambed together with the hydraulic gradients determine the fluxes of water, energy and solutes between the stream and underlying aquifer system. Dam induced sedimentation affects hyporheic processes and alters substrate pore space geometries in the course of progressive stabilization of the sediment layers. Uncertainty in stream-aquifer interactions arises from the inherent complex-nested flow paths and spatio-temporal variability of streambed hydraulic properties. A detailed field investigation of streambed hydraulic conductivity (Ks) using Guelph Permeameter was carried out in an intermittent stream reach of the Pavanje river basin located in the mountainous, forested tract of western ghats of India. The present study reports the spatial and temporal variability of streambed hydraulic conductivity along the stream reach obstructed by two Vented Dams in sequence. Statistical tests such as Levene's and Welch's t-tests were employed to check for various variability measures. The strength of spatial dependence and the presence of spatial autocorrelation among the streambed Ks samples were tested by using Moran's I statistic. The measures of central tendency and dispersion pointed out reasonable spatial variability in Ks distribution throughout the study reach during two consecutive years 2016 and 2017. The streambed was heterogeneous with regard to hydraulic conductivity distribution with high-Ks zones near the backwater areas of the vented dam and low-Ks zones particularly at the tail water section of vented dams. Dam operational strategies were responsible for seasonal fluctuations in sedimentation and modifications to streambed substrate characteristics (such as porosity, grain size, packing etc.), resulting in heterogeneous streambed Ks profiles. The channel downstream of vented dams contained significantly more cohesive deposits of fine sediment due to the overflow of surplus suspended sediment-laden water at low velocity and pressure head. The statistical test results accept the hypothesis of significant spatial variability of streambed Ks but refuse to accept the temporal variations. The deterministic and geo-statistical approaches of spatial interpolation provided virtuous surface maps of streambed Ks distribution. © 2018 Elsevier B.V.Item Performance enhancement of commercial ethylene oxide reactor by artificial intelligence approach(De Gruyter Open Ltd, 2022) Chowdhury, S.; Lahiri, S.K.; Hens, A.; Katiyar, S.The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, an ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and temperature with other operational parameters. A hybrid multi-objective metaheuristic optimization technique, namely ANN-multi-objective genetic algorithm (MOGA) algorithm was used to develop a Pareto diagram of selectivity versus reactor temperature. The Pareto diagram will help the plant engineers to make a strategy on what operating conditions to be maintained to make a delicate balance between profit and environmental impact. It was also found that by applying this hybrid ANN-MOGA modeling and optimization technique, for a 720 KTA ethylene glycol plant, approximately 32,345 ton/year of carbon-di-oxide emission into the atmosphere can be reduced. Along with the reduction of environmental impact, this hybrid approach enables the plant to reduce raw material cost of nine million USD per annum simultaneously. © 2021 Walter de Gruyter GmbH, Berlin/Boston.Item Reliability, availability and maintainability (RAM) investigation of Load Haul Dumpers (LHDs): a case study(Springer, 2022) Balaraju, B.; Raj, G.R.; Murthy, S.M.Load Haul Dumpers (LHDs) are prominent equipment employed for transportation operations in many of the underground mines. This equipment often suffers from frequent breakdowns due to a variety of technical and managerial practices resulting in increased maintenance costs and loss of production and productivity. Reliability, Availability and Maintainability (RAM) analysis deal with the optimal functioning of equipment, maintenance scheduling, controlling cost, and improvement of availability and performance. Keeping this in view, the current study focused on the estimation of the performance of the equipment using RAM investigation. The required failure and repair data of LHDs were collected from field investigations. Graphical analyses using Trend and serial correlation tests and analytical analysis using Statistic-U test were conducted to validate the Independent and Identical Distribution (IID) nature of the data sets. Based on the above tests, the Renewal Process was adopted to carry out the RAM analysis. The best-fit approximation of datasets was selected by performing the Kolmogorov–Smirnov (K–S) test. In addition to that, the reliability-based Preventive Maintenance time intervals were estimated to improve the percentage of reliability. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.Item Performance analysis of multi-hop FSO convergent with UWOC system for security and tracking in navy applications(Springer, 2022) Bhargava Kumar, B.K.; Naik, R.P.; Krishnan, P.The Navy is ubiquitous in every major geographic area of the world. It is estimated that 60% of global goods are transported by sea. The Navy plays a vital role in offering protection of the sea lanes and the trade transportation, preserves territorial ocean borders and the right to the resources contained in them, and facilitates the response to natural disasters and other disasters. In this paper, we proposed for the first time a multi-hop free-space optical (FSO)—underwater wireless optical communication (UWOC) converging system. It is useful for the secure transport and tracking of goods and missiles through cargo ships for the navy and marine applications. The end-to-end average bit error rate (ABER) and outage probability performance of multi-hop FSO transmission systems converged with UWOC is analysed. The outage and ABER expression of the proposed system was obtained and the results were plotted for different weather conditions, turbulence regimes, pointing error and number of FSO hop scenarios. A case study is done on the extent to which the speed and height of the ship, the wind speed and the links between the ships affect the end-to-end outage performance of the proposed triple hop FSO converging UWOC system. This study is performed in Surathkal, which is located 20 km north of Mangalore. We assumed in this case study that the ships are located near surathkal in the Arabian Sea (GPS coordinates: N 13∘0′38.0988′, E 74∘47′17.4876′), Karnataka, India. Computational complexity of proposed cumulative distribution functions (CDF) have been evaluated with the existing CDF in the literature. In addition to that the expected cost analysis of the proposed communication system provided. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Needle in a Haystack: Detecting Subtle Malicious Edits to Additive Manufacturing G-Code Files(Institute of Electrical and Electronics Engineers Inc., 2022) Beckwith, C.; Naicker, H.S.; Mehta, S.; Udupa, V.R.; Nim, N.T.; Gadre, V.; Pearce, H.; Mac, G.; Gupta, N.Increasing usage of digital manufacturing (DM) in safety-critical domains is increasing attention on the cybersecurity of the manufacturing process, as malicious third parties might aim to introduce defects in digital designs. In general, the DM process involves creating a digital object (as CAD files) before using a slicer program to convert the models into printing instructions (e.g., g-code) suitable for the target printer. As the g-code is an intermediate machine format, malicious edits may be difficult to detect, especially when the golden (original) models are not available to the manufacturer. In this work, we aim to quantify this hypothesis through a red team/blue team case study, whereby the red team aims to introduce subtle defects that would impact the properties (strengths) of the 3-D printed parts, and the blue team aims to detect these modifications in the absence of the golden models. The case study had two sets of models, the first with 180 designs (with two compromised using two methods) and the second with 4320 designs (with 60 compromised using six methods). Using statistical modeling and machine learning (ML), the blue team was able to detect all the compromises in the first set of data, and 50 of the compromises in the second. © 2009-2012 IEEE.Item Neuro-Fuzzy Model for Quantified Rainfall Prediction Using Data Mining and Soft Computing Approaches(Taylor and Francis Ltd., 2023) Vathsala, H.; Koolagudi, S.G.In this paper, we discuss an approach that predicts the quantitative value of rainfall. The proposed algorithm uses a combination of data mining and neuro-fuzzy inference system for prediction. The model is demonstrated on north interior Karnataka (a state in India) rainfall data as a case study. This model is applicable to any geographical area provided apt predictors are included. For north interior Karnataka rainfall prediction predictors are derived from local and global climate conditions. The local condition variables are derived from the mean sea level pressure, temperature, and wind speed in south India. The global variables affecting the north interior Karnataka rainfall include, Darwin sea level pressure, the ENSO indices and southern oscillation. The data mining technique, association rule mining, is used to study the correlation among the predictors; clustering is used for predictor selection as well as membership function creation for fuzzyfication. Neuro-fuzzy inference system is further used for fine tuning the “If-then” rules and crisp value prediction of the rainfall. The prediction accuracy is observed to be good considering Tropical Meteorological Department data. © 2023 IETE.Item Time-series analysis of erosion issues on a human-intervened coast– A case study of the south-west coast of India(Elsevier Ltd, 2023) Parvathy, M.M.; Balu, R.; Dwarakish, G.S.Coastal erosion has long been identified as a cause of concern for the state of Kerala, situated in the Indian subcontinent, affecting the life and livelihood of millions residing in the coastal belt. The increased human interference supplemented by changes in the climatic pattern in recent years has modified the coastal scenario of the state altogether. The present study attempts to evaluate the effect of anthropogenic influences in modifying the coastal scenario to review the efficiency of the coastal management policy adopted by the state over the years. For this purpose, the shorelines extracted from the available multi-temporal satellite images are analysed using DSAS software to calculate the shoreline change rate prior to 2000 (1973-98) and post-2000 (2002-21) using the linear rate of regression method. The study seeks to key out critically eroding areas, subsequently exploring the possible conducive reasons for the changed coastal scenario. The results indicate a reduction of 34.5% in the share of eroding length, with a visible shift in a substantial portion of coastal stretch from the mild erosion category to the stable category. Despite the state's continuous efforts to curb the issue, the long-term shoreline change over the past 49 years (1973–2021) reveals erosion to be dominant in nearly 39.12% of the coastal length, with the share of eroding length in the southern, central and northern regions as 33.8%, 38.67% and 44.04%, respectively. The results point towards the dominance of human interventions accompanied by climate change impacts as the primary reason for transforming the coast, necessitating the need to modify the state's current coastal management policy. The research emphasises the need for a comprehensive coastal management plan for the state to take heed of the changing climatic scenario. © 2023 Elsevier LtdItem Development of a regional-scale erosion vulnerability assessment approach along a human-intervened coast–a case study from the southern part of Kerala, India(Taylor and Francis Ltd., 2025) Parvathy, M.M.; Balu, R.; Dwarakish, G.S.The coastal district of Thiruvananthapuram, situated in the southern part of the state of Kerala, is endowed with sandy beaches, majestic cliffs, scenic backwaters and rocky promontories, aside from hosting a sizeable coastal population, well-known tourist attractions, unique biodiversity, and numerous developmental activities. However, erosion is one hazard that gravely impacts these zones, disturbing the coastal environment and affecting the lives of thousands residing in these regions. The present study attempts to identify the critical vulnerable areas to erosion, considering the combined influence of hazard parameters, i.e. drivers of erosion and risk parameters, i.e. the assets at stake. A multi-criteria decision-making approach, integrated with expert ranking, is adopted in the present study to identify and classify the vulnerable stretches, demanding urgent intervention to prevent further erosion and safeguard vital resources. The results indicate that approximately 10% of the coast falls under low vulnerability, 77% under medium vulnerability, 12 % under high vulnerability, and 1% under very high vulnerability. The coastal reaches of Shangumugham and Anchuthengu are identified as highly vulnerable and in urgent need of intervention. The spatial distribution of vulnerability necessitates a focused and site-specific management plan to tackle the present and possible erosion issues and protect critical resources. © 2025 Indian Society for Hydraulics.Item Empirical and machine learning-based approaches to identify rainfall thresholds for landslide prediction: a case study of Kerala, India(Springer Nature, 2025) Menon, V.; Kolathayar, S.Kerala, a state in India, experiences one of the highest incidences of rainfall-induced landslides. Historical data has been collected and analyzed to devise thresholds for the early detection of landslides. Two empirical approaches based on the relationships between rainfall intensity and duration, as well as cumulative rainfall and duration, have been utilized to identify early warning thresholds for landslides. Five machine learning-based approaches were employed to determine these thresholds. Among the classifiers tested, the K-Nearest Neighbour (KNN) classifier with K=5 demonstrated the highest prediction accuracy compared to other methods in the study.; For the safe and resilient development of cities, disaster risk reduction plays a crucial role, aligning with sustainable development goal 11 of the United Nations. Supporting this objective, the present study developed a machine learning (ML) classifier-based threshold model to determine rainfall thresholds for predicting impending landslides in Kerala, India, using historical data. Using a dataset of 64 rainfall-induced landslide events recorded since the year 2000, rainfall data were collected up to 15 days prior to each landslide to support empirical analysis of intensity-duration and event rainfall-duration thresholds. In cases where exact rainfall durations were unavailable, classification machine learning (ML) models, including K-nearest neighbours (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and logistic regression, were used to determine threshold reliability. Among these, the KNN model with 5 Neighbours achieved the highest performance, with an ROC-AUC of 0.9 and an accuracy of 82%. This model, saved as a pickle file, serves as a core filter in the development of a landslide early warning system. This paper presents the model development and performance comparisons, contributing to a practical, community-centred solution for landslide disaster resilience in Kerala. © The Author(s) 2025.; © The Author(s) 2025.Item Ultrafast molecular dynamics approach to quantify structural and transport properties of ion exchange polymer: a case study on perfluorinated sulfonic acid polymer(Royal Society of Chemistry, 2025) Varshney, S.K.; Koorata, P.K.A computationally efficient molecular dynamics (MD) simulation approach for evaluating the transport and structural properties of ion exchange polymers (IEPs) is proposed. Prediction of transport and structural properties of IEPs using MD simulation is beneficial in understanding structure-property relations and to design advanced tailor-made variants of such polymers. The IEP is a complex network of polymer chains with ionic end groups. Hence, computational robustness plays a key role, especially in large simulation cells, in avoiding iterative and often time-consuming process to arrive at definitive solutions in terms of physical properties. A novel and robust approach is presented in general and evaluated for perfluorosulfonic acid (PFSA) polymer structure as a case study. While prior researches have analysed transport and structural properties of such polymers using MD simulation in detail, there is a lack of information on the model standard and equilibration protocol. To this end, the present article compares the proposed algorithm to conventional approaches for structure equilibration and demonstrate that the variation in diffusion coefficients (water and hydronium ions) reduces as the number of chains increases, with significantly reduced errors observed in 14 and 16 chains models, even at elevated hydration. The proposed method to achieve equilibration is ?200% more efficient than conventional annealing and ?600% more efficient than the lean method. © 2025 The Royal Society of Chemistry.