Abinash Mohanta

Bio: Abinash Mohanta is an academic researcher from VIT University. The author has contributed to research in topics: Wind tunnel & Volume of fluid method. The author has an hindex of 5, co-authored 14 publications receiving 55 citations. Previous affiliations of Abinash Mohanta include National Institute of Technology, Rourkela.

Anticipate Manning’s Coefficient in Meandering Compound Channels

Abstract: 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 ( S o ) , 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.

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

Abstract: 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 dist...

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

Abstract: Wind surface mean pressure coefficient (Cp) 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 Cp using the developed model are compared with the corresponding Cp values obtained by Swami and Chandra (S&C) equation and Muehleisen and Patrizi (M&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 Cp values for practical purposes. The error analysis of the results show that the predicted values of Cp using developed GEP correlation are more close to the experimental values than those obtained by using other two methods.

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

Abstract: 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 c...

Modelling of Wind Pressure Coefficients on C-Shaped Building Models

Abstract: 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-e 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.

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm : A Novel Predictive Model for Hydrological Application

Abstract: The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

Short-Term Water Demand Forecasting Model Combining Variational Mode Decomposition and Extreme Learning Machine

Abstract: Accurate water demand forecasting is essential to operate urban water supply facilities efficiently and ensure water demands for urban residents. This study proposes an extreme learning machine (ELM) coupled with variational mode decomposition (VMD) for short-term water demand forecasting in six cities (Anseong-si, Hwaseong-si, Pyeongtaek-si, Osan-si, Suwon-si, and Yongin-si), South Korea. The performance of VMD-ELM model is investigated based on performance indices and graphical analysis and compared with that of artificial neural network (ANN), ELM, and VMD-ANN models. VMD is employed for multi-scale time series decomposition and ANN and ELM models are used for sub-time series forecasting. As a result, ELM model outperforms ANN model. VMD-ANN and VMD-ELM models outperform ANN and ELM models, and the VMD-ELM model produces the best performance among all the models. The results obtained from this study reveal that the coupling of VMD and ELM can be an effective forecasting tool for short-term water demands with strong nonlinearity and non-stationarity and contribute to operating urban water supply facilities efficiently.

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

Abstract: Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water’s temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior (R2 = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.

Effects of internal angle between limbs of “Y” plan shaped tall building under wind load

Abstract: Y plan shaped building is usually a triaxially symmetrical building where three separate limbs are bridged in a central core portion. Due to some engineering or architectural requirements, the symmetricity among the wings is not maintained properly, and the limb angle modification is unavoidable. This paper demonstrates a comprehensive study on mean pressure, force and moment coefficients of different types of “Y” plan shaped tall buildings by varying the internal angles between limbs by 30° for various Wind incidence angles (WIAs) from 0° to 180° at an interval of 30°. To maintain the same plan area, the limb sizes are slightly changed accordingly. Numerical analysis has been carried out to generate a similar type of flow condition as per terrain category II of IS:875 (Part 3) −2015. ANSYS CFX is used for the simulation. For validation of the present computational setup, a graphical comparison is made on the Commonwealth Advisory Aeronautical Research Council (CAARC) building model. Some previous wind tunnel results on “Y” shaped building are also compared with our numerical data. The distribution of pressure over the surfaces, mean pressure coefficients and force coefficients are evaluated for each “Y” type building model, and the results are represented graphically to understand the extent of nonconformities due to such angular modifications in the plan. Finally, Fourier expressions of WIA are proposed for obtaining force and moment coefficients for different building models. The accuracies of the fitted models are measured by R2 value.