A Pareto optimal multi-objective optimization for a horizontal axis wind turbine blade airfoil sections utilizing exergy analysis and neural networks

In this study a multi-objective genetic algorithm is utilized to obtain a Pareto optimal set of solutions for geometrical characteristics of airfoil sections for 10-meter blades of a horizontal axis wind turbine. The performance of the airfoil sections during the process of energy conversion is evaluated deploying a 2D incompressible unsteady CFD solver and the second law analysis. Artificial neural networks are trained employing CFD obtained data sets to represent objective functions in an algorithm which implements exergetic performance and integrity characteristics as optimization objectives. The results show that utilizing the second law approach along with Pareto optimality concept leads to a set of precise solutions which represent minimum energy waste, maximum efficiency, and topmost stability. Furthermore, enhanced rotor performance coefficients are observed through a BEM study which compares conventional designs with the second law obtained configurations. Exergy analysis is believed to be an efficient tool in the optimal design of wind turbine blades with the capability of determining the amount of lost opportunities to do useful work.