An anisotropic double Gaussian (DG) model for analytical wake modeling to predict the streamwise wake velocity behind an isolated non-yawed horizontal-axis wind turbine is proposed. The proposed model is based upon the conservation of mass and momentum inside a streamtube control volume. The wake growth rate parameters to distinguish the wake expansion rate between lateral and vertical directions were tuned based on numerical and measurement data of utility-scale turbines. It was found that the proposed model can give feasible predictions within the full-wake region under different inflow conditions. In addition, the other analytical models based on top-hat shape and single Gaussian approaches were evaluated for comparison. The root-mean-square error statistical analysis was used to evaluate the performance of each examined model under different flow conditions. In general, the proposed model outperformed the other examined models in all wake region categories, particularly within the near-wake region and the onset of the far-wake region, which are beyond the scope of the conventional approach for analytical wake modeling. This advantage gives the potential for the proposed model to provide a better prediction for the wake flow estimation within tightly packed wind farms.

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This study proposes the use of the genetic algorithm (GA) method in hydraulic turbine optimization for renewable energy applications. The algorithm is used to optimize the performance of a two-dimensional hydrofoil cascade for an axial-flow hydraulic turbine. The potential flow around the cascade is analyzed using the surface vorticity panel method, with a modified coupling coefficient to deal with the turbine cascade. Each section of the guide vane and runner blade hydrofoil cascade is optimized to satisfy the shock-free criterion, which is the fluid dynamic ideal to achieve minimum profile losses. Comparison is also made between the direct and random switching methods for the GA crossover operator. The optimization results show that the random switching method outperforms the performance of the direct switching method in terms of the resulting solutions, as well as in terms of the computational time required to reach convergence. As an alternative to experimental trials, the performance of both turbine designs are predicted and analyzed using the three-dimensional computational fluid dynamics (CFD) approach under several operating conditions. The simulation results show that the optimized design, which is obtained by applying the shock-free criterion using the GA, successfully improves the performance of the initial turbine design.

The double-Gaussian (DG) approach for analytical wake modeling leads to a better understanding of the wake transition mechanism within a full-wake region behind a non-yawed horizontal-axis wind turbine (HAWT). To date, a key parameter of the wake expansion in the DG model still has yet to be defined explicitly instead of tuning, thus limiting its usability for practical applications. The present work aims to overcome this limitation by proposing a simple linear wake expansion function for the DG model constructed from the existing parameters based on the conservation of mass and momentum. Considering the physical and statistical approaches, the proposed function is

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