Horizontal axis wind turbines are some of the most widely used clean energy generators in the world. Horizontal axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultaneously minimize the cost of energy. This work presents the optimization of new MEXICO blades for a horizontal axis wind turbine at the wind speed of 10 m/s. The optimization problem is posed to maximize the power coefficient while the design variables are twist angles on the blade radius and rotating axis positions on a chord length of the airfoils. Computational fluid dynamics was used for the aerodynamic simulation. Surrogate-assisted optimization was applied to reduce computational time. A surrogate model called a Kriging model, using a Gaussian correlation function along with various regression models, was applied while a genetic algorithm was used as an optimizer. The results obtained in this study are discussed and compared with those obtained from the original model. It was found that the Kriging model with linear regression gives better results than the Kriging model with second-order polynomial regression. The optimum blade obtained in this study showed better performance than the original blade at a low wind speed of 10 m/s.

This research study was aimed to develop a new concept design of a very low head (VLH) turbine using advanced optimization methodologies. A potentially local site was chosen for the turbine and based on its local conditions, such as the water head level of <2 meters and the flow rate of <5 m 3 /s. The study focused on the optimization of the turbine blade and guide vane profiles, because of their major impacts on the efficiency of the VLH axial flow turbine. The fluid flow simulation was firstly conducted for the axial turbine, followed by applying the regression analysis concept to develop a turbine mathematical model where the leading-and trailing-edge angles of the guide vanes and the turbine blades were related to the efficiency, total head and flow rate. The genetic algorithms (GA) with multi-objective function was also used to locate the optimal blade angles. Thereafter, the refined design was re-simulated. Following this procedure the turbine efficiency was improved from 82.59% to 83.96% at a flow rate of 4.2 m 3 /s and total head of 2 meters.

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This paper demonstrates design of a Very Low Head axial flow turbine using surrogate-based optimization. The design variables were blade angles between guide vanes and runner blades, whereas the objective function was turbine efficiency. A Latin Hypercube Sampling method was initially used to design the experiment with thirty sampling points, and a Large Eddy Simulation was modeled to analyze the flow for all sampling points. A correlation between design variables and the turbine efficiency was then evaluated using the surrogate models while the optimal design variables were identified. Also, several optimizers were used to tackle the proposed problem and their performances were investigated. The optimal design of blade angles 18   being 10 o , 20 o , 30 o , 40 o , 25 o , 45 o , 55 o and 65 o respectively, increased the turbine efficiency up to 89.87 %. The approach of using surrogate modeling was proved to be very effective and simple for optimizing a design of blade angles of stator-rotor and it can be applied for designing any other new blades.

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