Abstract
The existing research literature lacks comprehensive investigations into assessing the structural performance of marine renewable energy conversion devices, particularly 3D printed turbine blades, which often rely solely on computational modelling without experimental validation methods and/or established mechanical characterization techniques. This leads to significant uncertainty regarding the performance of 3D printed turbine blades manufactured by additive manufacturing technology. This study aims to fill this gap by proposing a procedure for evaluating the structural integrity of commercial small-scale tidal turbine blade (5 KW) manufactured using fused filament fabrication 3D printing with a linear infill pattern. This is achieved by developing a combined experimental, hydrodynamic, and finite element approach with the view to inspect the micro-mechanical properties of representative volume elements of 3D printed microstructures using homogenization technique. The results of mechanical testing and hydrodynamic modelling are used to create a finite element model of the 3D printed blade, allowing for stress and failure analysis. Findings indicate that while integrating 3D printed materials into blade design via 3D printing technology is feasible, the choice of materials is limited to high stiffness composite filaments. Finally, experimental validation of numerical results, particularly full field strain distribution maps obtained by digital image correlation technique for flexural testing and laboratory-scale 3D printed blade, confirms the accuracy of the finite element results. Finite element-based homogenization techniques provide valuable insights into potential failure modes in 3D printed tidal turbine blades. However, the expedited calculation of orthotropic properties through finite element analysis proves to be a faster mechanical characterization method compared to experimental approaches. The proposed methodology in this study facilitates quicker iterative design of 3D printed blades, thereby reducing the need for repeated experiments and ultimately lowering manufacturing costs.