Faculty Publications
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Item Prediction of Hydrodynamic Coefficients of Stratified Porous Structure Using Artificial Neural Network (ANN)(Springer Science and Business Media Deutschland GmbH, 2023) Gupta, A.; Shilna, K.; Karmakar, D.The breakwater is designed to offer tranquility in the harbor to protect the offshore facilities and also to prevent coastal erosion. The use of soft computing approaches in coastal engineering helps to solve the nonlinear problems and predicts the hydrodynamic performance of the device. In the present study, the artificial neural networks (ANNs) with different topologies are considered to predict the hydrodynamic coefficients for the wave interaction with the stratified porous breakwater. The experimental study is performed to determine the reflection and transmission coefficient for the horizontally stratified porous structure with three layers of different porosity and width of the structure. The hydrodynamic performance is analyzed by considering the feed-forward back-propagation neural network, and the results are compared for different numbers of hidden nodes. Further, the root mean square error (RMSE) and coefficient of correlation (CC) are considered to assess the ability of ANN topologies to predict the transmission coefficient. The numerical results obtained using ANN are noted to fall within the range that represents the network’s ability to predict accurate results. The study performed will provide an insight in the design and analysis of the stratified porous breakwater in the nearshore regions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item A laboratory investigation on perforated hollow piles in two rows was conducted in a two dimensional regular wave flume to study the wave transmission and reflection characteristics. The influence of incident wave steepness, relative clear spacing between the piles and rows of piles on transmission co-efficient and reflection co-efficient have been investigated. The effect of staggering of piles in the rows on both transmission and reflection co-efficients was also studied. The present study has revealed that for perforated pile groups incident wave steepness, relative clear spacing between the piles, relative clear spacing between the rows of piles influence both transmission and reflection co-efficients. Staggering of piles reduces reflection from the perforated piles. Perforated piles have smaller transmission and reflection co-efficient values compared to that of non-perforated piles at lower wave steepness.(Wave transmission and reflection for two rows of perforated hollow piles) Rao, S.; Shirlal, K.G.; Rao, N.B.S.2002Item Physical model studies on wave transmission of a submerged inclined plate breakwater(2009) Rao, S.; Shirlal, K.G.; Varghese, R.V.; Govindaraja, K.R.This paper examines the results of physical model studies conducted in a monochromatic wave flume, to evaluate the wave transmission characteristics of a submerged plate breakwater consisting of a fixed plate of 0.50 m length and 0.003 m thickness. The model was oriented at varying inclinations and submergence. The influence of wave steepness, relative depth, relative submergence and angle of inclination on wave transmission was analysed. It was found that the horizontal plate is effective for short waves with steepness parameter higher than 5×10-3 in relative depth grater than 0.21. The plate oriented at an angle of inclination of 60° is found to be effective for the entire ranges of wave parameters considered for the study and it reduces the wave height by about 40%. © 2009 Elsevier Ltd. All rights reserved.Item Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater(2011) Patil, S.G.; Mandal, S.; Hegde, A.V.; Alavandar, S.The ocean wave system in nature is very complicated and physical model studies on floating breakwaters are expensive and time consuming. Till now, there has not been available a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. This is due to complexity and vagueness associated with many of the governing variables and their effects on the performance of breakwater. In the present paper, Adaptive Neuro-Fuzzy Inference System (ANFIS), an implementation of a representative fuzzy inference system using a back-propagation neural network-like structure, with limited mathematical representation of the system, is developed. An ANFIS is trained on the data set obtained from experimental wave transmission of horizontally interlaced multilayer moored floating pipe breakwater using regular wave flume at Marine Structure Laboratory, National Institute of Technology Karnataka, Surathkal, India. Computer simulations conducted on this data shows the effectiveness of the approach in terms of statistical measures, such as correlation coefficient, root-mean-square error and scatter index. Influence of input parameters is assessed using the principal component analysis. Also results of ANFIS models are compared with that of artificial neural network models. © 2010 Elsevier Ltd. All rights reserved.Item Comprehensive physical model study on wave transmission at plate structure(CAFET INNOVA Technical Society 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2011) Shirlal, K.G.; Rao, S.; Varghese, R.V.Investigators are increasingly concentrating their focus of research on coastal protection which is shifting from the hard measures to eco-friendly solutions with optimal usage of construction materials. A structure located near the water surface is known to be effective in bringing down the wave activity behind it, since; the energy of the waves is concentrated in the region close to the surface. This paper explains the comprehensive experimental study to evaluate the transmission coefficients (Kt) of various configurations of submerged plate structures. Kt for various plate configurations such as single plate, twin plate and combined plate are obtained. It is found that a single plate inclined at 60° to the horizontal is generally effective i. e. Kt ? 0.6 to a large extent. However, the twin and combined plate structures improved the performance. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater(Elsevier Ltd, 2012) Patil, S.G.; Mandal, S.; Hegde, A.V.Planning and design of coastal protection works like floating pipe breakwater require information about the performance characteristics of the structure in reducing the wave energy. Several researchers have carried out analytical and numerical studies on floating breakwaters in the past but failed to give a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. Computational intelligence techniques, such as, Artificial Neural Networks (ANN), fuzzy logic, genetic programming and Support Vector Machine (SVM) are successfully used to solve complex problems. In the present paper, a hybrid Genetic Algorithm Tuned Support Vector Machine Regression (GA-SVMR) model is developed to predict wave transmission of horizontally interlaced multilayer moored floating pipe breakwater (HIMMFPB). Furthermore, optimal SVM and kernel parameters of GA-SVMR models are determined by genetic algorithm. The GA-SVMR model is trained on the data set obtained from experimental wave transmission of HIMMFPB using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. The results are compared with ANN and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in terms of correlation coefficient, root mean square error and scatter index. Performance of GA-SVMR is found to be reliably superior. b-spline kernel function performs better than other kernel functions for the given set of data. © 2011 Elsevier Ltd. All rights reserved.Item Quarter circular breakwater: Prediction of transmission using multiple regression and artificial neural network(Marine Technology Society Inc. mtsdir@erols.com, 2014) Goyal, R.; Singh, K.; Hegde, A.V.The physical model study of coastal structures is a nonlinear process influenced by innumerable parameters. As a result of a lack of definite systems, intricacies, and high costs involved in the physical models, we need a simple mathematical tool to predict wave transmission through quarter circular breakwater (QBW). QBW is a state-of-theart breakwater essentially based on the exploitation of the concepts of semicircular breakwater. This paper discusses the use of soft computing tools such as MATLAB based multiple regression (MR) and artificial neural network (ANN) to predict the wave transmission coefficient of QBW. To assess the accuracy of the proposed model and its ability to forecast, correlation coefficient and mean squared error are availed. On comparing the results obtained from MR and ANN, it is concluded that ANN gives more accurate results and can be used as a powerful tool for the modeling of hydrodynamic breakwater transmission through QBW. It serves as a viable alternative to the conventional physical model to simulate the hydrodynamic transmission performance of QBW.Item Sound radiation and transmission loss characteristics of a honeycomb sandwich panel with composite facings: Effect of inherent material damping(Academic Press, 2016) Arunkumar, M.P.; Jagadeesh, M.; Jeyaraj, J.; Gangadharan, K.V.; Mailan Chinnapandi, M.C.L.This paper presents the results of numerical studies carried out on vibro-acoustic and sound transmission loss behaviour of aluminium honeycomb core sandwich panel with fibre reinforced plastic (FRP) facings. Layered structural shell element with equivalent orthotropic elastic properties of core and orthotropic properties of FRP facing layer is used to predict the free and forced vibration characteristics. Followed by this, acoustic response and transmission loss characteristics are obtained using Rayleigh integral. Vibration and acoustic characteristics of FRP sandwich panels are compared with aluminium sandwich panels. The result reveals that FRP panel has better vibro-acoustic and transmission loss characteristics due to high stiffness and inherent material damping associated with them. Resonant amplitudes of the response are fully controlled by modal damping factors calculated based on modal strain energy. It is also demonstrated that FRP panel can be used to replace the aluminium panel without losing acoustic comfort with nearly 40 percent weight reduction. © 2016 Elsevier LtdItem Sound transmission loss characteristics of sandwich aircraft panels: Influence of nature of core(SAGE Publications Ltd info@sagepub.co.uk, 2017) Arunkumar, M.P.; Jeyaraj, J.; Gangadharan, K.V.; Mailan Chinnapandi, M.C.Sandwich panel which has a design involving acoustic comfort is always denser and larger in size than the design involving mechanical strength. The respective short come can be solved by exploring the impact of core geometry on sound transmission characteristics of sandwich panels. In this aspect, the present work focuses on the study of influence of core geometry on sound transmission characteristics of sandwich panels which are commonly used as aircraft structures. Numerical investigation has been carried out based on a 2D model with equivalent elastic properties. The present study has found that, for a honeycomb core sandwich panel in due consideration to space constraint, better sound transmission characteristics can be achieved with lower core height. It is observed that, for a honeycomb core sandwich panel, one can select cell size as the parameter to reduce the weight with out affecting the sound transmission loss. Triangular core sandwich panel can be used for low frequency application due to its increased transmission loss. In foam core sandwich panel, it is noticed that the effect of face sheet material on sound transmission loss is significant and this can be controlled by varying the density of foam. © 2016, © The Author(s) 2016.
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