Monalisa Mallick is a Doctoral Research Fellow of the Department of Civil Engineering, National Institute of Technology Rourkela, India. She received a post-graduate degree in Water Resources Engineering from the same institute in 2014. Her doctoral research topic is experimental and numerical investigation of wind induced pressure on C-shaped building models. Her areas of research interests include building engineering, aerodynamics, computational fluid dynamics artificial intelligence technique, wind engineering, and water resources engineering.
EDUCATION
PhD NIT Rourkela
RESEARCH INTERESTS
Aerodynamics, Wind Engineering, Computational Fluid Dynamics, Building Engineering, Hydrology.
Anticipating Surface Mean Pressure Coefficient on Inner Surface of C-Shaped Irregular Buildings Using Artificial Intelligence Methodology M. Mallick, A. Mohanta, A. Kumar, K. C. Patra Journal of Aerospace Engineering, 2023 Analysis of the effect of pressure on the unsymmetrical plan-shaped buildings due to impact of wind forces is more complicated than the symmetrical plan-shaped buildings. Toward this, the present paper is focuses on the study of the wind effects on the inner face of C-shaped unconventional buildings by considering the surface mean pressure coefficient (Cp¯) as the major influencing parameter. Experimental investigation was carried out to obtain the pressure coefficient (Cp) over the surfaces of the C-shaped building models by considering some important configurations like the side ratio, frontal ratio, height ratio, and angle of incidence in a subsonic wind tunnel. In the present study, model equations to predict the Cp¯ are developed by applying various artificial intelligence (AI) approaches such as the model tree (MT) and group method of data handling (GMDH) with neural network (GMDH-NN) as well as combinatorial (GMDH-C) algorithm to the experimental results. In the AI approach, the side width ratio (D/d), frontal ratio (B/d), depth ratio (H/d), relative width ratio (B/b), angle of incidence (θ), and face angle (ϕ) are considered as input to the algorithm to develop the model equation for predicting Cp¯. The performances of the model equations are tested through various statistical error analyses. The importance of input parameters is also analyzed through the sensitivity analysis technique. The results clearly indicate that the proposed GMDH-NN model is the best alternative approach to predict the surface mean pressure coefficient on C-shaped buildings with coefficient of determination (R2) of 0.96 and 0.92 during the training and testing phases respectively. To verify the model equation more accurately, the predicted results are also tested through uncertainty analysis which gave satisfactory results for GMDH-NN as compared to MT and GMDH-C, approaches.
Multivariate adaptive regression spline approach to the assessment of surface mean pressure coefficient on surfaces of C-shaped building Monalisa Mallick, Abinash Mohanta, Awadhesh Kumar Scientia Iranica, 2020 Proper assessment of wind load enables durable design of structures under varying wind load conditions. The accurate prediction of pressure coefficient on any irregular plan shaped buildings is essential for the assessment of wind loads and the 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 surface mean pressure coefficient. This developed equation can be used satisfactorily for the prediction of pressure coefficient values accurately on the surfaces of C-shaped buildings. An extensive experimentation was carried out to obtain coefficient of pressure over the surfaces of C-shaped models under varying sizes, corner curvature and angle of incidence in a sub-sonic wind tunnel. The predicted values of pressure coefficient of different C-shaped buildings using developed model are compared with equations developed by Swami and Chandra’s (S&C) and Muehleisen and Patrizi’s (M&P). The comparison indicates that the proposed MARS model predicts pressure coefficient values more accurately than those by S&C and M&P models on frontal as well as side surfaces. Further, the model is used to validate with the actual building, Tokyo Polytechnic University (TPU) data to show the applicability of the proposed equation.
Prediction of Wind-Induced Mean Pressure Coefficients Using GMDH Neural Network Monalisa Mallick, Abinash Mohanta, Awadhesh Kumar, Kanhu Charan Patra Journal of Aerospace Engineering, 2020 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...
Analysis of curvature effect on C-shaped buildings Monalisa Mallick, Awadhesh Kumar, Kanhu charan Patra ACM International Conference Proceeding Series, 2019 The distribution of wind-induced pressure coefficient on the surfaces of the C-shaped building with the varying angles of incidence and with and without round corner has been studied. For this, experiments have been carried out on a typical C-shaped building plan in a sub-sonic open circuit wind tunnel. Two different configurations of C-shaped models i.e., with outer curved and without outer curved C-shaped models were tested. The experimental findings were showed over an extended range of angles of incidence (0° to 180°) at an interval of 30°. Using Digital Sensor Array (DSA), the pressure coefficient data were recorded at the pressure tapping provided in a grid pattern throughout the surfaces. This procedure was repeated with all the surfaces undertaken, angle of incidence and building plan configuration. The surfaces data of pressure coefficient enabled the determination of mean pressure coefficient at the selected tapping locations. The surface pressure was found to vary significantly with the location on a particular surface and surfaces as well as with the angle of incidence. Pressure coefficient was influenced by building configuration, the extent of curved corners, wind angle of incidence, wind flow behavior and surroundings on buildings. It has been observed that the curvature is effective in reducing pressure coefficient corresponding to no curvature. The experimental results thus obtained were supported by Numerical analysis. To achieve this, numerical investigation was carried out by using ANSYS FLUENT software. The analysis was carried out using Computational Fluid Dynamic (CFD) with k-e viscosity model and the results obtained were compared with the corresponding experimental data. Experimental and numerical study is carried out for comparison purposes and results have good agreement.
Modelling of Wind Pressure Coefficients on C-Shaped Building Models Monalisa Mallick, Abinash Mohanta, Awadhesh Kumar, Vivek Raj Modelling and Simulation in Engineering, 2018 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.
