Optimizing the placement of the wind turbines in a wind farm to achieve optimal performance is an active area of research, with numerous research studies being published every year. Typically, the area available for the wind farm is divided into cells (a cell may/may not contain a wind turbine) and an optimization algorithm is used. In this study, the effect of the cell size on the optimal layout is being investigated by reducing it from five rotor diameter (previous studies) to 1/40 rotor diameter (present study). A code is developed for optimizing the placement of wind turbines in large wind farms. The objective is to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. The velocity deficits in the wake of the wind turbines are estimated using a simple wake model. The code is verified using the results from the previous studies. Results are obtained for three different wind regimes: (1) Constant wind speed and fixed wind direction, (2) constant wind speed and variable wind direction, and (3) variable wind speed and variable wind direction. Cost per unit power is reduced by 11.7% for Case 1, 11.8% for Case 2, and 15.9% for Case 3 for results obtained in this study. The advantages/benefits of a refined grid spacing of 1/40 rotor diameter (1 m) are evident and are discussed. To get an understanding of the sensitivity of the power produced to the wake model, optimized layout is obtained for the Case 1 using a different wake model.

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Simulations of a model wind turbine at various tip-speed-ratios were carried out using Tenasi, a node-centered, finite volume unstructured flow solver. The simulations included the tunnel walls, tower, nacelle, hub and the blades. The effect of temporal convergence on the predicted thrust and power coefficients is evaluated and guidelines for best practices are established. The results presented here are for tip-speed-ratios of 3, 6 and 10, with 6 being the design point. All simulations were carried out at a freestream velocity of 10 m s À1 with an incoming boundary layer present and the wind turbine RPM was varied to achieve the desired tip-speed-ratio. The performance of three turbulence models is evaluated. The models include a one-equation model (Spalart-Allmaras), a two-equation model (Menter SST) and the DES version of the Menter SST. Turbine performance as well as wake data at various locations is compared to experiment. All the turbulence models performed well in terms of predicting power and thrust coefficients. The DES model was significantly better than the other two turbulence models for predicting the mean and fluctuating components of the velocity in the wake.

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