Amin Kolahdooz

Bio: Amin Kolahdooz is an academic researcher from De Montfort University. The author has contributed to research in topics: Casting & Topology optimization. The author has an hindex of 7, co-authored 30 publications receiving 219 citations. Previous affiliations of Amin Kolahdooz include University of Mazandaran & Islamic Azad University.

Investigation of Rotating MHD Viscous Flow and Heat Transfer between Stretching and Porous Surfaces Using Analytical Method

Abstract: Hydromagnetic flow between two horizontal plates in a rotating system, where the lower plate is a stretching sheet and the upper is a porous solid plate, is analyzed. Heat transfer in an electrically conducting fluid bonded by two parallel plates is studied in the presence of viscous dissipation. The equations of conservation of mass and momentum and energy are reduced to a nonlinear ordinary differential equations system. Homotopy perturbation method is used to get complete analytic solution for velocity and temperature profiles. Results show an acceptable agreement between this method results and numerical solution. Also the effects of different parameters are discussed through graphs.

Deep Learning Application Pros And Cons Over Algorithm

Abstract: Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.

Optimization of geometrical parameters in a specific composite lattice structure using neural networks and ABC algorithm

Abstract: Due to their light weights and high load carrying capacities, composite structures are widely used in various industrial applications especially in aerospace industry. Strength to weight ratio is known to be as one of the most critical design parameters in these structures. In this paper, geometrical parameters of composite lattice structures are optimized to obtain the desired strength to weight ratio using finite element method, neural networks and ABC algorithm. At first, the finite element model is validated by experimental results and neural network is employed as the fitness function. The ABC algorithm is also applied to achieve the optimized strength to weight ratio. The results obtained from PSO algorithm on the basis of neural network have shown reasonable agreement with those of the finite element simulation. Increasing the thickness of the outer shell causes the structural strength-to-weight ratio to rise by 50 percent. The next effective parameter is reduction of rib angle which provides an increase of 30 percent in strength-to-weight ratio. Although Stiffeners (ribs) have a major role in load carrying, increasing the rib thickness causes the structural weight to rise. Thus compared with the two previous parameters, they do not have a significant effect on the strength of structures.

Effects of important parameters in the production of Al-A356 alloy by semi-solid forming process

Abstract: There are many routes to produce feedstock with globular microstructure. Cooling slope methods has been attracted as the result of its simplicity and also produces of the globular shape billets quicker than other methods. In this method molten metal is poured on a tilted slope that is cooled by water circulating underneath. Due to shear stress exerted to the slurry, it is solidified with globular microstructure. By simulation of semi-solid casting with cooling slope the effects of different pouring conditions on the microstructure of A356 aluminum alloy are investigated. The simulations are carried out by a CFD code called FLOW3D. The average diameter and shape factor of primary α-Al particles from experiments and the time duration of slurry presence on the slope, solid fraction of the slurry, strain rate and turbulence from the simulations were investigated. Comparing the results of simulations with experimental results showed that for having the best microstructure with higher sphericity and lowest particle size, the residence time of slurry on the cooling slope must be enough, while the shear stress and turbulence must be as high as possible. Also, combination of the process parameters including pouring temperature, tilt angle and slope length should lead to adequate value of t f , while solid fraction of slurry at the exit of slope is about 30–35%.

Effect of space dependent magnetic field on free convection of Fe3O4–water nanofluid

Abstract: Effect of spatially variable magnetic field on ferrofluid flow and heat transfer is investigated. The enclosure is filled with Fe3O4–water nanofluid. Control volume based finite element method (CVFEM) is applied to solve the governing equations. The combined effects of ferrohydrodynamic and magnetohydrodynamic have been taken into account. The influences of Magnetic number, Hartmann number, Rayleigh number and nanoparticle volume fraction on the flow and heat transfer characteristics have been examined. Results show that enhancement in heat transfer decrease with increase of Rayleigh number while for two other active parameters different behavior is observed. Also it can be concluded that Nusselt number is an increasing function of Magnetic number, Rayleigh number and nanoparticle volume fraction while it is a decreasing function of Hartmann number.

Analytical investigation of Jeffery-Hamel flow with high magnetic field and nanoparticle by Adomian decomposition method

Abstract: In this study, the effects of magnetic field and nanoparticle on the Jeffery-Hamel flow are studied using a powerful analytical method called the Adomian decomposition method (ADM). The traditional Navier-Stokes equation of fluid mechanics and Maxwell’s electromagnetism governing equations are reduced to nonlinear ordinary differential equations to model the problem. The obtained results are well agreed with that of the Runge-Kutta method. The present plots confirm that the method has high accuracy for different α, Ha, and Re numbers. The flow field inside the divergent channel is studied for various values of Hartmann number and angle of channel. The effect of nanoparticle volume fraction in the absence of magnetic field is investigated.