Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

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Computation of flow in natural channels is the key to the solution of numerous engineering problems particularly at the time of flood. Therefore, different approaches have been incorporated for accurate estimation of flow discharge in compound meandering channels. The present work focuses on developing an empirical model for estimating the flow of meandering compound channels using gene-expression programming (GEP) by considering four non-dimensional parameters viz Relative Depth, Sinuosity, Coherence Parameter, and Discharge Ratio (Qvdm/Qbf). The model developed for the meandering compound channels is best suited for sinuosity in the range of 1.04–2. To measure adequacy of the model, the results of the developed model are compared with various discharge estimation methods such as the Single Channel Method (SCM), Divided Channel Method (DCM), Meander Belt Method (MBM), Modified Divided Channel Method (MDCM), Interacting Divided Channel Method (IDCM), and Coherence Method (COHM). Results indicate that the proposed GEP model predicted the ratio of total discharge in compound channel (Qt) to the bank-full discharge (Qbf) in main channel satisfactorily with root mean square error of 0.15 and 0.19 for training and testing dataset, respectively. The developed model is also used to validate with higher sinuous channels having sinuosity exceeding 2.0. Finally, the model was successfully validated with natural flood conditions of River Baitarani, India (sinuosity of 1.334) giving the minimum errors in terms of mean absolute square error of 0.885 and 0.882 for the two overbank flow depths of 7.5 and 8.63 m, respectively. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Department of Thermal and Energy Engineering

School of Mechanical Engineering

Vellore Campus

World University root node

Sustainable Water Resources Management

Gene-expression programming for calculating discharge in meandering compound channels

Wind surface mean pressure coefficient (C̄p) is an essential parameter for the assessment of wind induced forces that is a must input to all structural designs. An extensive experimentation is carried out to obtain pressure coefficient data over the surfaces of C-shaped building models of varying aspect ratio, corner curvature and angle of incidence in a sub-sonic wind tunnel. The studies also include models without corner curvature. In this study, a technique known as Gene-Expression Programming (GEP) is used to develop a model equation using experimental values of pressure coefficient data collected at the grid points of the frontal surface under varying conditions. And this developed model is used to predict surface mean pressure coefficients (Cp). The predicted values of C̄p using the developed model are compared with the corresponding C̄p values obtained by Swami and Chandra (S&amp;C) equation and Muehleisen and Patrizi (M&amp;P) equations. The prediction made by the developed GEP model is also validated with the actual building data of Tokyo Polytechnic University (TPU). The results signify the ability of the model to predict the C̄p values for practical purposes. The error analysis of the results show that the predicted values of C̄p using developed GEP correlation are more close to the experimental values than those obtained by using other two methods. © 2020, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature.

Building Simulation

Gene-expression programming for the assessment of surface mean pressure coefficient on building surfaces

Accurate prediction of shear force distribution along the boundary in open channels is a key to the solution of numerous hydraulic problems. The problem becomes more complicated for meandering compound channels. A model is developed for predicting the percentage of shear force at the floodplain (%Sfp) of two-stage meandering channels using gene-expression programming (GEP) by considering five dimensionless parameters viz. the width ratio, relative depth, sinuosity, bed slope, and meander belt width ratio as the inputs in the model. Basing on the %Sfp, the apparent shear force along the division lines of separation in compound channels is selected for discharge calculation using the conventional channel division methods. An Enhanced Channel Division Method (ECDM) is introduced to calculate discharge by assuming interface line at main channel and floodplain junction. A modified variable-inclined (MVI) interface is suggested having zero apparent shear determined from flow contribution in the main channel and floodplain. The MVI interface is further used to calculate discharge in the meandering compound channels. Performance of the GEP model is tested against other analytical methods of calculating %Sfp. Error between the observed and calculated discharges using the MVI interface is found to be the minimum when compared to other interface methods. The enhance channel division method is successfully applied for validating the two available overbank discharge values for the river Baitarani at Anandapur (drainage area of 8570 sq. km), giving the minimum errors of 0.31% and 1.02% for flow depths of 7.5 m and 8.63 m, respectively. © 2020, Springer Nature B.V.

Water Resources Management

Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel

Proper assessment of wind load ensures a durable design of structures under varying wind load conditions. Accurate prediction of surface mean pressure coefficient (C¯p) on any irregular plan shaped buildings is essential to assessing wind loads and structural design. The main objective of this study is to present an equation in the line of Multivariate Adaptive Regression Spline (MARS) approach using experimental data of C¯p. This developed equation can be used satisfactorily for accurate prediction of C¯p values on the surfaces of C-shaped buildings. Extensive experimentation was carried out to determine the coefficient of pressure over the surfaces of C-shaped building models under varying sizes, corner curvatures, and angles of incidence in a sub-sonic wind tunnel. The predicted values of pressure coefficient of different C-shaped buildings using the developed model were compared with the equations developed by Swami and Chandra (S&C) and Muehleisen and Patrizi (M&P). The comparison indicated that the proposed MARS model could predict C¯p values more accurately than S&C and M&P models on frontal and side surfaces. Further, the model was used to validate the results using the actual building, and Tokyo Polytechnic University (TPU) data were employed to show the applicability of the proposed equation. © 2020 Sharif University of Technology. All rights reserved.

Scientia Iranica

Multivariate adaptive regression spline approach to the assessment of surface mean pressure coefficient on surfaces of C-shaped building

Using the experimental data of a wind-induced pressure coefficient, equations for the group method of data handling neural network (GMDH-NN) are developed to predict surface mean pressure coefficients (Cp¯¯¯¯<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><mover accent="true"><mrow><mrow><msub><mrow><mrow><mi>C</mi></mrow></mrow><mrow><mrow><mi>p</mi></mrow></mrow></msub></mrow></mrow><mrow><mo stretchy="true">¯</mo></mrow></mover></mrow></math>) on the frontal surface of different C-shaped building models. Toward this objective, an extensive experiment was carried out to obtain pressure coefficients over the surfaces of the models with varying configurations, corner curvatures, and angles of incidence in a subsonic wind tunnel. The input variables include the curvature ratio (R/D<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><mi>R</mi><mo stretchy="false">/</mo><mi>D</mi></mrow></math>), overall side ratio (D/B<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><mi>D</mi><mo stretchy="false">/</mo><mi>B</mi></mrow></math>), side ratio without curvature (d/b<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><mi>d</mi><mo stretchy="false">/</mo><mi>b</mi></mrow></math>), height ratio (D/H<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><mi>D</mi><mo stretchy="false">/</mo><mi>H</mi></mrow></math>), and angle of incidence (θ<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><mi>θ</mi></mrow></math>) in the radian in the GMDH-NN to develop the model equation. The performance of the GMDH-NN equation is compared with two different methods, namely, the nonlinear regression (NLR) approach through a gene expression programming (GEP) technique and a feed forward neural network (FFNN) through different statistical measures. The results indicate that the proposed GMDH-NN equation satisfactorily predicts the Cp¯¯¯¯<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><mover accent="true"><mrow><mrow><msub><mrow><mrow><mi>C</mi></mrow></mrow><mrow><mrow><mi>p</mi></mrow></mrow></msub></mrow></mrow><mrow><mo stretchy="true">¯</mo></mrow></mover></mrow></math>&nbsp;on the frontal surface with coefficients of determination (R2<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math>) as 0.989 and 0.985 and the scatter index (SI<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mrow><mi>S</mi><mi>I</mi></mrow></math>) as 0.10 and 0.11 for training and testing data, respectively.

Department of Instrumentation

School of Electrical Engineering

Journal of Aerospace Engineering

Prediction of Wind-Induced Mean Pressure Coefficients Using GMDH Neural Network

Accurate prediction of shear stress distribution along the boundary in an open channel is the key to the solution of numerous critical engineering problems. This paper investigated the distribution of boundary shear force at the cross section of meandering compound channels. The research focused on developing a model for predicting shear force using a machine learning (ML) method, the multivariate adaptive regression spline (MARS). A nonparametric regression methodology was adopted in MARS for developing a model of shear force percentage in the floodplain of two-stage meandering channels. The width ratio, relative depth, sinuosity, bed slope, and meander belt width ratio of the channel were input variables to the model. The influence of each parameter on predicting the percentage of shear force in the floodplain was also analyzed by adopting a sensitivity analysis. Performance of the MARS model was evaluated by three different machine learning techniques - the group method of data handling (GMDH), support vector machine (SVM) and k-nearest neighbor (KNN) - through different statistical measures. The results indicated that the proposed MARS model predicted the shear force percentage in the floodplain satisfactorily, with a coefficient of determination (R2) of 0.94 and 0.93 and a scatter index (SI) of 0.053 and 0.044 for the training and testing phases, respectively. Moreover, the model was successfully applied for validating the two available overbank discharge values for the Baitarani River at Anandapur (drainage area of 8,570 km2), giving the minimum errors of the evaluated methods in terms of mean absolute scaled error (MASE) of 0.014 and 0.066 for flow depths of 7.5 and 8.63 m, respectively. © 2019 American Society of Civil Engineers.

Journal of Irrigation and Drainage Engineering

MARS for Prediction of Shear Force and Discharge in Two-Stage Meandering Channel

Computational fluid dynamics (CFD) is often used to predict flow structures in developing areas of a flow field for the determination of velocity field, pressure, shear stresses, effect of turbulence and others. A two phase three-dimensional CFD model along with the large eddy simulation (LES) model is used to solve the turbulence equation. This study aims to validate CFD simulations of free surface flow or open channel flow by using volume of fluid method by comparing the data observed in hydraulics laboratory of the National Institute of Technology, Rourkela. The finite volume method with a dynamic sub grid scale was carried out for a constant aspect ratio and convergence condition. The results show that the secondary flow and centrifugal force influence flow pattern and show good agreement with experimental data. Within this paper over-bank flows have been numerically simulated using LES in order to predict accurate open channel flow behavior. The LES results are shown to accurately predict the flow features, specifically the distribution of secondary circulations both for in-bank channels as well as over-bank channels at varying depth and width ratios in symmetrically converging flood plain compound sections. © 2018, The Institution of Engineers (India).

Journal of The Institution of Engineers (India): Series A

LES Modeling with Experimental Validation of a Compound Channel having Converging Floodplain

Estimating Manning's roughness coefficient (n) is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning's n in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio (α), relative depth (β), sinuosity (s), Channel bed slope (So), and meander belt width ratio (ω) are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning's n satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning's coefficient and subsequently discharge through Manning's equation in the compound meandering channels are found to be quite adequate. © 2019 by the authors.

Fulltext

Hydrology

Anticipate Manning's coefficient in meandering compound channels

Designs of buildings are changing with emerging demands of several aesthetical features and efficient design based on geometry. Development of new building materials and construction techniques have enabled us to build new buildings which are tall and unsymmetrical, but unfortunately such structures are more susceptible to wind loads. Thus it becomes necessary to estimate wind loads with higher degree of confidence. Although ample information regarding wind load on symmetrical and regular structure is available in various international codes, they lack the study of effect of wind forces on unsymmetrical structures. This paper presents experimental and numerical studies of the wind effect on commonly used C-shaped buildings with varying aspect ratio and its optimization caused by the alteration of angle of incidence. Furthermore, results obtained by numerical analysis have been validated with the experimental one. For this study, numerical analysis has been carried out using ANSYS Fluent with k-ϵ model of turbulence. Computational fluid dynamics (CFD) techniques is used to evaluate the surface pressure on various faces of the model for angle of attack of 0° to 180° at an interval of 30° in a subsonic open circuit wind tunnel. The results found by CFD technique are well compared with the experimental results which suggest the feasibility of using this technique of predicting wind pressures on building efficiently and accurately. © 2018 Monalisa Mallick et al.