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Author

Mehran Masdari

Other affiliations: Sharif University of Technology
Bio: Mehran Masdari is an academic researcher from University of Tehran. The author has contributed to research in topics: Airfoil & Angle of attack. The author has an hindex of 6, co-authored 41 publications receiving 166 citations. Previous affiliations of Mehran Masdari include Sharif University of Technology.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the optimization study of an airfoil type Savonius turbine is presented, focusing on maximizing the power coefficient of the turbine, using a Discrete Vortex Method (DVM) for calculating power coefficient, a Class Shape Transfer (CST) function code for generating coordinates of airfoils and a Salp Swarm Algorithm (SSA) code for optimization.

49 citations

Journal ArticleDOI
15 Jul 2017-Energy
TL;DR: In this paper, a new linearization method has been used for chord and twist distributions by crossing tangent line through different points on them and the results have determined the best point along chord and twisting distribution which has higher total power coefficient in the linearization process.

45 citations

Journal ArticleDOI
TL;DR: In this article, an ant colony optimization algorithm has been used to optimize the geometry of a wind turbine and also investigate the influence of geometrical parameters on the performance of the turbine in 1% and 8% turbulence intensities.

30 citations

Journal ArticleDOI
TL;DR: In this paper, the response of the SDM under single frequency sinusoidal pitching motions is computed and the associated pitching moment coefficient damping is obtained using two methods of classical Fourier coefficients and multilayer perceptron (MLP) artificial neural network.

15 citations

Journal ArticleDOI
TL;DR: In this article, a series of simulations are carried out to assess the viability of an oscillating deformable trailing-edge flap (DTEF) in load and dynamic stall (DS) control on a pitching wind turbine airfoil which experiences deep DS at Re
Abstract: Unsteady operating environment of a horizontal axis wind turbine can induce excessive loads on the blades, originating from rapid variations in angle of attack and consequently dynamic stall (DS) occurrence. Therefore, it is of utmost importance to control the flow around a blade by which fatigue damage is likely to happen. Using two-dimensional incompressible unsteady Reynolds-averaged Navier–Stokes equations in OpenFOAM package, a series of simulations are carried out to assess the viability of an oscillating deformable trailing-edge flap (DTEF) in load and DS control on a pitching wind turbine airfoil which experiences deep DS at Re = 420,000. Results reveal whether or not the airfoil is equipped with an oscillating DTEF, DS vortex forms at high angles of attack. The size, strength and traveling of the DS vortex, however, can be influenced by out-of-phase deflection of the DTEF. More effectively, the change in the airfoil camber line during flap oscillation can remarkably affect the pressure distribution around the airfoil, and hence, significant load alleviation and mean lift enhancement are achievable, all of which help the wind turbine performance and enhance the life span of the components. Moreover, a parametric study on flap size and amplitude of deflection together with a comparison between a discrete flap and a DTEF suggests an out-of-phase oscillation of a large gently curved DTEF, up to 30% of the total chord, with similar amplitude and frequency with respect to the airfoil is the best condition under which fatigue load control as well as enhancement in resultant load for a blade rotation can take place.

11 citations


Cited by
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Journal ArticleDOI
TL;DR: The proposed Dynamic Salp swarm algorithm (DSSA) outperformed the original SSA and the other well-known optimization algorithms over the 23 datasets in terms of classification accuracy, fitness function values, the number of selected features, and convergence speed.
Abstract: Recently, many optimization algorithms have been applied for Feature selection (FS) problems and show a clear outperformance in comparison with traditional FS methods. Therefore, this has motivated our study to apply the new Salp swarm algorithm (SSA) on the FS problem. However, SSA, like other optimizations algorithms, suffer from the problem of population diversity and fall into local optima. To solve these problems, this study presents an enhanced version of SSA which is known as the Dynamic Salp swarm algorithm (DSSA). Two main improvements were included in SSA to solve its problems. The first improvement includes the development of a new equation for salps’ position update. The use of this new equation is controlled by using Singer's chaotic map. The purpose of the first improvement is to enhance SSA solutions' diversity. The second improvement includes the development of a new local search algorithm (LSA) to improve SSA exploitation. The proposed DSSA was combined with the K-nearest neighbor (KNN) classifier in a wrapper mode. 20 benchmark datasets were selected from the UCI repository and 3 Hadith datasets to test and evaluate the effectiveness of the proposed DSSA algorithm. The DSSA results were compared with the original SSA and four well-known optimization algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Lion Optimizer (ALO), and Grasshopper Optimization Algorithm (GOA). From the obtained results, DSSA outperformed the original SSA and the other well-known optimization algorithms over the 23 datasets in terms of classification accuracy, fitness function values, the number of selected features, and convergence speed. Also, DSSA accuracy results were compared with the most recent variants of the SSA algorithm. DSSA showed a significant improvement over the competing algorithms in statistical analysis. These results confirm the capability of the proposed DSSA to simultaneously improve the classification accuracy while selecting the minimal number of the most informative features.

128 citations

Journal ArticleDOI
01 May 2020-Energy
TL;DR: This paper proposes an Opposition-based Learning Modified Salp Swarm Algorithm (OLMSSA) for accurate identification of the two-diode model parameters of the electrical equivalent circuit of the PV cell/module and demonstrates that OLMSSA is highly competitive and even significantly better than the reported results of the majority of recently-developed parameter identification methods.

105 citations

Journal ArticleDOI
TL;DR: An improved Harris Hawks Optimizer (HHO) is proposed that considers the salp swarm algorithm (SSA) as a competitive method to enhance the balance between its exploration and exploitation trends and achieves a more stable performance compared to HHO, SSA, and many other well-known methods.

89 citations

Journal ArticleDOI
TL;DR: In this article, Wang et al. analyzed the intellectual background, current research status and state-of-the-art knowledge structure of WPG-related literature using CiteSpace based scientometric investigation.

63 citations

Journal ArticleDOI
TL;DR: In this paper, an improved methodology is applied on actuator disc in order to take all the operational and geometrical characteristics into account such as airfoil type, angular velocity, twist, and chord distribution.

60 citations