Tidal current turbine blade optimisation with improved blade element momentum theory and a non-dominated sorting genetic algorithm

Tidal current energy has the advantage of predictability over most other renewable resources. However, due to harsh operating environment and complicated site conditions, developments in this domain have been gradual. Paramount these points is device design optimisation hydrodynamic performance. Recent correction models BEM theory further improved accuracy prediction model. Using an blade element momentum model that capable accurately capturing downwash angle combining it with a well-developed reliable non-dominated sorting genetic algorithm model, effective efficient tidal turbine tool developed presented paper. This novel work incorporated NACA generator reproducing any profile, such allows solver analyse each every profile used spanwise element. As result, very at producing blades optimised not only for local twist chord length, but also suitable profiles be particular The use efficiently explore wide range solutions, outputting number specified condition. performance validated against experimentally blade. coefficient determination (R2) values power thrust are 0.99828 0.99488 respectively when comparing experimental measurements found literature. Furthermore proves predicting high degree accuracy. Further includes implementing computational fluid dynamics validation evaluation.