Abdullah Al-Faruk

Bio: Abdullah Al-Faruk is an academic researcher from University of Southern Queensland. The author has contributed to research in topics: Turbine & Savonius wind turbine. The author has an hindex of 5, co-authored 11 publications receiving 67 citations. Previous affiliations of Abdullah Al-Faruk include Khulna University of Engineering & Technology.

Geometrical optimization of a swirling Savonius wind turbine using an open jet wind tunnel

Abstract: It has been suggested that waste heats or naturally available heat sources can be utilized to produce swirling flow by a design similar to that of split channels which is currently used to initiate fire whirls in laboratories. The new design combines the conventional Savonius wind turbine and split channel mechanisms. Previous computational and preliminary experimental works indicate a performance improvement in the new design (named as swirling Savonius turbine) compared to the conventional Savonius design. In this study, wind tunnel experiments have been carried out to optimize the swirling Savonius turbine geometry in terms of maximum power coefficient by considering several design parameters. The results indicate that the blade overlap ratio, hot air inlet diameter and the condition of the top end plate have significant influence on power and torque coefficients, while a larger aspect ratio and closed top end plate have some favourable effects on the performance. The optimum configuration has been tested in four different wind velocities to determine its influence on the performance, and power coefficients were found to be higher in high wind velocities. The performance comparison of optimum configuration with conventional Savonius rotor showed an increase of 24.12% in the coefficient of power.

Prediction of Weld Bead Geometry and Penetration in Electric Arc Welding using Artificial Neural Networks

Abstract:  Abstract— Weldment characteristics like penetration, bead geometry and depth of heat affected zone are extremely important characteristics for structural integrity. Electric arc welding process is used throughout the world for its simplicity and versality. Electrode diameter, current, voltage, arc travel speed, electrode feed rate, arc length and arc spread are influential factors in deciding the weldment characteristics. In this present works the effects of these process parameters on weldment characteristics in case of electric arc welding process was studied. Bead on plate experiments was conducted using a manual feed based metal arc welding machine. Weldment characteristics like depth of penetration, depth of heat affected zone and bead geometry were examined. An artificial neural network based modeling of the experiments was successfully done to predict the patterns of results obtained from the experiments. characteristics. With the increase in electrode diameter the area of arc increases, thus the energy density decreases. The depth of penetration decreases with the increasing electrode diameter. The contour of the weldment geometry gets effected by the electrode feed rate and the arc travel rate (table speed). With the increase in arc travel length the bead width decreases with consequent possibility of occurrence of defects like undercutting. With higher arc travel rate, the depth of penetration decreases to reach a steady level. It is very important to consider all the welding parameters while studying the weldment characteristics. With different welding conditions the resulting characteristics of weldment differs significantly. It is generally difficult and time consuming to model the process numerically. Artificial neural networks (ANN) based approaches can be utilized to model and predict the patterns obtained from the experiments. In this work ANN with supervised learning has been utilized successfully for predicting bead geometry, depth of penetration and depth of heat affected zone for different welding conditions. The result obtained from the experiment and ANN closely matched thus proving the suitability of us ing ANN for predicting the weldment characteristics. Artificial neural network modeling has been chosen for this work due to its capability to solve difficult and complex problems and recently welding related many researchers have been using ANN model to understand and predict their

Numerical investigation of performance of a new type of savonius turbine

Abstract: Wind energy is non-polluting and is freely available in many areas and is gaining popularity among researchers. Among wind turbines, Horizontal Axis Wind Turbines (HAWT) are more common compared to Vertical Axis Wind Turbines (VAWT) mainly because of their higher efficiency. However, Savonius turbine of VAWT type has many advantages over others such as simplicity in construction, lower cost of production, constancy of the output power regardless of the wind direction, and good starting torque at low wind speed. It is conceived that the efficiency of the Savonius turbines can be increased using industrial wasted heat primarily due to their structural similarity with the split chambers which are often used to induce fire-whirls in laboratories. This work deals with a basic design of such combined configuration and compares its efficiency with a conventional Savonius turbine. The computational results show that the new design has a higher steady state angular velocity as well as higher coefficient of power than the conventional Savonius turbine.

Effects of flow parameters on the performance of vertical axis swirling type Savonius wind turbine

Abstract: Wind tunnel experiments were carried out on a geometrically optimal swirling Savonius turbine by varying flow parameters to determine their effects on power and torque coefficients. The optimum geometrical configuration used in this experiment was adopted from an earlier study that features 0.20 blade overlap ratio, 195˚ blade arc angle, and 1.06 rotor aspect ratio of a 320 mm diameter rotor with closed top end plate. The parameters considered in this experimental are the hot air temperature, hot air mass flow rate, hot air inlet diameter of swirl chamber, and the free-stream wind velocity. The results indicate that higher hot air temperature and hot air mass flow rate promotes performance of the turbine while power coefficient reached maximum at a certain hot air inlet diameter. Tests on the optimum geometry at four wind velocities revealed that power coefficients are higher in higher wind velocities.

A Novel Parametric Modeling Method and Optimal Design for Savonius Wind Turbines

Abstract: Under the inspiration of polar coordinates, a novel parametric modeling and optimization method for Savonius wind turbines was proposed to obtain the highest power output, in which a quadratic polynomial curve was bent to describe a blade. Only two design parameters are needed for the shape-complicated blade. Therefore, this novel method reduces sampling scale. A series of transient simulations was run to get the optimal performance coefficient (power coefficient C p) for different modified turbines based on computational fluid dynamics (CFD) method. Then, a global response surface model and a more precise local response surface model were created according to Kriging Method. These models defined the relationship between optimization objective Cp and design parameters. Particle swarm optimization (PSO) algorithm was applied to find the optimal design based on these response surface models. Finally, the optimal Savonius blade shaped like a “hook” was obtained. Cm (torque coefficient), Cp and flow structure were compared for the optimal design and the classical design. The results demonstrate that the optimal Savonius turbine has excellent comprehensive performance. The power coefficient Cp is significantly increased from 0.247 to 0.262 (6% higher). The weight of the optimal blade is reduced by 17.9%.