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Iranian Journal of Mechanical Engineering Transactions of the ISME، جلد ۲۱، شماره ۲، صفحات ۰-۰
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عنوان انگلیسی Wind farm layout optimization using a Reynolds‐averaged Navier–Stokes model and a genetic algorithm
چکیده انگلیسی مقاله The placement of wind turbines in a wind farm can considerably affect the total output power. Using computational fluid dynamics and genetic algorithm, the optimal arrangement of turbines in a given wind farm was determined. A three-dimensional Reynolds-averaged Navier-Stokes simulation was conducted on a 660 kW three-bladed horizontal axis turbine. The airflow was assumed to be steady state and a pressure-based approach was adopted to solve the governing equations. Subsequently, an engineering expression for the wake evolution was developed and validated. By employing the characteristics of the wake propagation, the appropriate distances between the adjacent turbines were calculated. To find the optimal placement of the turbines, a purpose-built genetic algorithm was employed to minimize the objective function defined by the ratio of the wind farm cost to the output power. The results show that the final configuration is in line with the outcomes of the previous study. The sensitivity analysis of the genetic algorithm with respect to its parameters including the population size and the mutation rate was also performed to guarantee that the final layout is an optimal one.
کلیدواژه‌های انگلیسی مقاله Wind farm layout,Optimization,RANS,Genetic Algorithms
نویسندگان مقاله Abolfazl Pourrajabian |
Department of Energy, Materials and Energy Research Center (MERC), 14155-4777, Tehran, Iran

Reza Ebrahimi |
K.N Toosi University

Morteza Rahmanpour |
Faculty of Mechanical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

Saeed Rahgozar |
Department of Energy, Materials and Energy Research Center (MERC), 14155-4777, Tehran, Iran

Maziar Dehghan |
Department of Energy, Materials and Energy Research Center (MERC), 14155-4777, Tehran, Iran

نشانی اینترنتی https://jmee.isme.ir/article_45001_202e35e8c0f32e0b227224e2548d0575.pdf
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