Optimization of Wells turbine performance using a hybrid artificial neural fuzzy inference system (ANFIS) - Genetic algorithm (GA)

Renewable energy resources are considered as alternative sources of energy for future generations. Wells turbines can be used to convert the reciprocating energy of waves in oceans and seas into rotational energy. Low aero-dynamic efficiency is the main drawback of Wells turbines. The present study showed that the addition of the guide vanes, endplate, or end ring to the base blade significantly enhanced the performance of Wells turbines. Also, we used ANFIS-GA-CFD for optimization of the several design variables (the blade tip thickness, the blade form, and the input-output angels of guide vanes) simultaneously. Therefore, artificial neural fuzzy networks were used to forecast the three benchmark parameters (pressure drop coefficient, torque coefficient, and efficiency) and a genetic algorithm was utilized to approximate the optimized design variables when there are maximum torque, maximum efficiency, and minimum pressure drop. The optimal blade increased the upper limit of efficiency up to 18%, torque coefficient up to 8%, and reduced the pressure drop coefficient up to almost 3.45% compared to the best previous models. Moreover, flow separation was delayed.

Automated summary

The study found that adding guide vanes, endplates, or end rings significantly improved the performance of Wells turbines. Using ANFIS-GA-CFD for optimization of design variables further enhanced efficiency, torque coefficient, and reduced pressure drop coefficient. The optimal blade increased efficiency by up to 18%, torque coefficient by up to 8%, and reduced pressure drop coefficient by almost 3.45% compared to previous models.