Journal Articles
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Item Effect of climate change on Netravathi riverflow(2010) Shetkar, R.V.; Mahesha, A.The adequacy of freshwater resources for future is difficult to assess due to complex and rapidly changing environmental and social parameters. There is uncertainty with respect to the prediction of climate change and its effect on planning and management of water resources. Higher temperature and reduced precipitation would lead to larger deficiencies in the supply and demand for water. This might cause deterioration in the quality of freshwater adding strain on the already fragile balance between supply and demand. Although the effect of climate change on water resources is uncertain and site specific, the perception is that it will result into increased extreme events and hence increased risk of flooding and droughts. This paper aims at assessing the trends of temperature, precipitation and river flow for the Netravathi river, a tropical river of south India. The river water utilization at present is less than 1% of the average annual flow. The river flow is neither controlled nor altered due to manmade structures hence may be considered as natural flow. From the analysis, it is important to note that the temperature is rising and there is declining trend in precipitation and stream flow during the study period of 30 years (1971 to 2001). Also, the low flow frequency analysis shows an upward trend. Similar analyses carried out for the number of days of flow peaks above a threshold value indicate that the high flow frequency trend is declining and the magnitude of these high flow events is also decreasing. The outcome of the present study indicates a definitive, decreasing trend in the river flow due to climate change and a forecasting mechanism may be essential in the future for the sustainable development of the available water resources. © 2010 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Fuzzy logic modeling for groundwater level forecasting of west coast region in India(2011) Dandagala, D.; Deka, P.C.Forecasting the groundwater table in unconfined aquifer is essential for efficient planning of conjunctive use in a basin. In this study, fuzzy logic (FL) models have been developed for groundwater level forecasting in west coast humid region of Karnataka state, India. The FL modeling was carried out to forecast the groundwater table by one week lead time at three different sites over the study area. Mamdani fuzzy inference system was adopted in the present study and finally centroid of area defuzzification method has been applied to obtain crisp output. The results concluded that the FL model performed quite satisfactorily as assessed by various performance indices such as Root mean square error, Coefficient of correlation, and Mean absolute error. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Managing seismic risk in ancient structures: Coupled variables under numerical and experimental approaches(2013) Pineda, P.; Venkat Reddy, D.Analysis and evaluation of seismic reliability of masonry cultural heritage buildings is a difficult task, owing to a great number of uncertainties. In thesefew lines, many questions are posed, trying to learn from the wisdom of ancient times and remembering the advantages of using multidisciplinary tools when the seismic safety is the main concern. © 2013 CAFET-INNOVA TECHNICAL SOCIETY.Item Efficient assessment of structural reliability in presence of random and fuzzy uncertainties(American Society of Mechanical Engineers (ASME), 2014) Balu, A.S.; Rao, B.N.This paper presents an efficient uncertainty analysis for estimating the possibility distribution of structural reliability in presence of mixed uncertain variables. The proposed method involves high dimensional model representation for the limit state function approximation, transformation technique to obtain the contribution of the fuzzy variables to the convolution integral and fast Fourier transform for solving the convolution integral. In this methodology, efforts are required in evaluating conditional responses at a selected input determined by sample points, as compared to full scale simulation methods, thus the computational efficiency is accomplished. The proposed method is applicable for structural reliability estimation involving any number of fuzzy and random variables with any kind of distribution. Copyright © 2014 by ASME.Item Evaluating uncertainty of the soil and water assessment tool (SWAT) model in the upper cauvery basin, Karnataka, India(CAFET INNOVA Technical Society 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2015) Kumar Raju, B.C.; Nandagiri, L.Quantification of uncertainties associated with hydrological models are essential for accurate assessment of water balance components and optimal planning and management of water and land resources at basin-scale. The present study was taken up to evaluate the uncertainties associated with the Soil and Water Assessment Tool (SWAT) model using for two different techniques: i) Generalized Likelihood Uncertainty Estimation (GLUE) and ii) Sequential Uncertainty Fitting (SUFI-2) techniques. The study was carried out in the Upper Cauvery River basin (36,682 km2) located in the humid to sub-humid region of Karnataka State, India. The calibration of the model was carried out using the Nash – Sutcliffe (NS) coefficient as the objective function for both GLUE and SUFI-2 techniques. The P-factor was 67% and 71% of observed streamflow data bracketed by the 95% prediction uncertainty (95PPU) for GLUE and SUFI-2 respectively during calibration period and corresponding values of 54% and 61% during validation period. Overall results indicate the applicability of SWAT model with moderate levels of uncertainty in large basins located in the humid tropics. The calibrated SWAT model can be used for assessment of water balance components and land use management scenarios in the Upper Cauvery Basin. © 2015 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Sensitivity of Pushover Curve to Material and Geometric Modelling-An Analytical Investigation(Elsevier Ltd, 2015) Panandikar, N.; Babu Narayan, K.S.Nonlinear static analysis or pushover analysis developed over the last two decades and became the preferred procedure for design and seismic performance evaluation, as this procedure is relatively simple and considers post-elastic behaviour. It provides information on seismic demands imposed by the design ground motion on the structural system and its components. Generation of pushover curve from analysis for reinforced concrete structure involves tremendous amount of computational efforts as the input data for analysis itself is quite exhaustive. The analysis results are very sensitive to the techniques employed in geometric and material modelling. This paper envisages presenting the sensitivity of pushover analysis results to geometric and material modelling parameters by comparing the analysis results with that of experimental investigations. Attempt has been made to understand the sensitivity of parameters like variation in material properties, inaccuracies in placement of reinforcement, effect of confinement of concrete and modelling techniques for elements and plastic hinges. SAP-2000 has been utilised in the current investigation and results have been highlighted suggesting strategies to enhance pushover analysis capabilities. © 2015 Elsevier B.V.Item Control Strategy to Maximize Power Extraction in Wind Turbine(Taylor and Francis Inc. 325 Chestnut St, Suite 800 Philadelphia PA 19106, 2016) RAJENDRAN, R.; Jena, D.This article deals with nonlinear control of variable speed wind turbine (VSWT), where the dynamics of the wind turbine (WT) is obtained from a single mass model. The main objective of this work is to maximize the energy capture form the wind with reduced oscillation on the drive train. The generator torque is considered as the control input to the WT. In general the conventional control techniques such as Aerodynamic Torque Feed-Forward (ATF) and Indirect Speed Control (ISC) are unable to track the dynamic aspect of the WT. To overcome the above drawbacks the nonlinear controllers such Sliding Mode Controller (SMC) and SMC with integral action (ISMC) with the estimation of effective wind speed are proposed. The Modified Newton Raphson (MNR) is used to estimate the effective wind speed from aero dynamic torque and rotor speed. The proposed controller is tested with different wind profiles with the presence of disturbances and model uncertainty. From the results the proposed controller was found to be suitable in maintaining a trade-off between the maximum energy capture and reduced transient on the drive train. Finally both the controllers are validated by using FAST (Fatigue, Aerodynamics, Structures, and Turbulence) WT simulator. © Association of Energy Engineers (AEE).Item An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling(Elsevier Ltd, 2018) Prusty, B.R.; Jena, D.In this paper, the risk assessment of a PV integrated power system is accomplished by computing the over-limit probabilities and the severities of events such as under-voltage, over-voltage, over-load, and thermal over-load. These aspects are computed by performing temperature-augmented probabilistic load flow (TPLF) using Monte Carlo simulation. For TPLF, the historical data for PV generation, ambient temperature, and load power, each collected at twelve specific time instants of a day for the past five years are pre-processed by using three linear regression models for accurate uncertainty modeling. For PV generation data, the developed model is capable of filtering out the annual predictable periodic variation (owing to positioning of the Sun) and decreasing production trend due to ageing effect whereas, for ambient temperature and load power, the corresponding models accurately remove the annual cyclic variations in the data and their growth. The simulations pertaining to the aforesaid risk assessment are performed on a PV integrated New England 39-bus test system. The system over-limit risk indices are calculated for different PV penetrations and input correlations. In addition, the changes in the values of TPLF model parameters on the statistics of the result variables are analyzed. The risk indices so obtained help in executing necessary steps to reduce system risks for reliable operation. © 2017 Elsevier LtdItem A Markov Chain Monte Carlo-Metropolis Hastings Approach for the Simultaneous Estimation of Heat Generation and Heat Transfer Coefficient from a Teflon Cylinder(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Kumar, H.; Kumar, S.; Gnanasekaran, N.; Balaji, C.This paper reports the use of Markov Chain Monte Carlo (MCMC) and Metropolis Hastings (MH) approach, to solve an inverse heat transfer problem. Three-dimensional, steady state, conjugate heat transfer from a Teflon cylinder of dimensions 100 mm diameter and 100 mm length with uniform volumetric internal heat generation is considered. The goal is to estimate volumetric heat generation and heat transfer coefficient, given the temperature data at certain fixed location on the surface of the cylinder. The internal volumetric heat generation is specified as input and the temperature and heat transfer coefficient values are obtained by a numerical solution to the governing equation. The temperature values also depend on heat transfer coefficient which is obtained by solving Navier–Stokes equation to obtain flow information. In order to reduce the computational cost, a neural network is trained from the computational fluid dynamics simulations. This is posed as an inverse problem wherein volumetric heat generation and heat transfer coefficient are unknown but the temperature data is known by conducting experiments. The novelty of the paper is the simultaneous determination of volumetric heat generation and heat transfer coefficient for the experimentally measured steady-state temperatures from a Teflon cylinder using MCMC-MH as an inverse model in a Bayesian framework and finally, the estimates are reported in terms of mean, maximum a posteriori, and the standard deviation which is the uncertainty associated with the estimated parameters. © 2018 Taylor & Francis Group, LLC.Item A Bayesian inference approach: estimation of heat flux from fin for perturbed temperature data(Springer India, 2018) Kumar, H.; Gnanasekaran, N.This paper reports the estimation of the unknown boundary heat flux from a fin using the Bayesian inference method. The setup consists of a rectangular mild steel fin of dimensions 250×150×6 mm3 and an aluminium base plate of dimensions 250×150×8 mm3. The fin is subjected to constant heat flux at the base and the fin setup is modelled using ANSYS14.5. The problem considered is a conjugate heat transfer from the fin, and the Navier–Stokes equation is solved to obtain the flow parameters. Grid independence study is carried out to fix the number of grids for the study considered. To reduce the computational cost, computational fluid dynamics (CFD) is replaced with artificial neural network (ANN) as the forward model. The Markov Chain Monte Carlo (MCMC) powered by Metropolis–Hastings sampling algorithm along with the Bayesian framework is used to explore the estimation space. The sensitivity analysis of the estimated temperature with respect to the unknown parameter is discussed to know the dependency of the temperature with the parameter. This paper signifies the effect of a prior model on the execution of the inverse algorithm at different noise levels. The unknown heat flux is estimated for the surrogated temperature and the estimates are reported as mean, Maximum a Posteriori (MAP) and standard deviation. The effect of a-priori information on the estimated parameter is also addressed. The standard deviation in the estimation process is referred to as the uncertainty associated with the estimated parameters. © 2018, Indian Academy of Sciences.