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2 results on '"Armin Farvizi"'

1. A Novel Surrogated Approach for Optimizing a Vertical Axis Wind Turbine With Straight Blades

Author

Sepehr Sanaye, Parsa Rezaeian, and Armin Farvizi

Subjects
artificial neural network (ANN), computational fluid dynamics (CFD), genetic algorithm (GA), vertical axis wind turbine (VAWT), Renewable energy sources, TJ807-830
Abstract

ABSTRACT Vertical axis wind turbine (VAWT) has a rotating axis perpendicular to the wind direction. This type of wind turbine that is suitable for urban environments has low wind direction dependency and noise. In this research, a novel surrogated approach for optimizing a VAWT is proposed, used, tested, and verified, which is not reported in literature. The proposed method consisted of 3D computational fluid dynamics (CFD) analysis of wind flow through the wind turbine with FLUENT software by solving the unsteady turbulent equations. However, 3D CFD analysis was time and cost consuming to obtain the output result (power coefficient) from input values (airfoil chord length, pitch angle, and tip speed ratio as turbine design variables). Thus, artificial neural network (ANN) was applied to obtain weight functions to correlate FLUENT software inputs and outputs after learning process. Finally, genetic algorithm was used for maximizing the turbine power coefficient considering three defined design variables. The optimum value of power coefficient was improved to 0.244, and the optimum values of design variables for blade chord length, blade pitch angle, and blade tip speed ratio were 0.218, −0.453, and 1.24, respectively. This novel surrogated method reduced the computational time and cost of VAWT optimizing considerably.

Published
2024
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2. A novel application of optimization and computational fluid dynamics methods for designing combined ejector-compressor refrigeration cycle

Author

Armin Farvizi, Amirhossein Refahi, Mohamad Vahid Rafieinejad, and Sepehr Sanaye

Subjects
Materials science, business.industry, 020209 energy, Mechanical Engineering, Refrigeration, 02 engineering and technology, Building and Construction, Injector, Coefficient of performance, 021001 nanoscience & nanotechnology, Cooling capacity, law.invention, law, 0202 electrical engineering, electronic engineering, information engineering, Exergy efficiency, Vapor-compression refrigeration, 0210 nano-technology, Process engineering, business, Gas compressor, Operating cost
Abstract

A simple ejector refrigeration cycle (ERC) has much lower operating cost in comparison with that for vapor compression refrigeration cycle (VCRC), however it has low coefficient of performance (COP). A combined (hybrid) ejector-compressor refrigeration cycle (CECRC) improves the COP of ERC considerably and still has a low operating cost. A novel two-step method is proposed here to improve the optimum design method of CECRC. The method applies system modeling and multi-objective optimizing of CECRC system (the first step which is not observed in literature) as well as computational fluid dynamics for flow analysis inside ejector (the second step). Results of applying the novel proposed method show that CECRC has C O P t h r = 0.76 in comparison with 0.31 for ejector refrigeration cycle (147% increase) and has C O P m e c h = 11.45 in comparison with 7.73 for compressor refrigeration cycle (48% increase). This COP is even considerably bigger than that reported by other researchers who have studied and designed CECRC and have reported their results for this system in literature. Furthermore for the optimized CECRC, the exergy efficiency ηexergy is about 40% in comparison with 30.6% for ejector refrigeration cycle (increase for 31%). Finally the operational cost (electrical power consumption cost) for providing one refrigeration ton cooling capacity (79.1 $ y e a r − 1 ) is lower than that for compression refrigeration cycle (117.4 $ y e a r − 1 ) which shows 33% reduction.

Published
2019

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