Biofouling and surface contamination on turbine blades can substantially increase aerodynamic drag, which reduces efficiency and accelerates corrosion-driven material degradation. To address these challenges, biomimetic coatings have been heavily experimentally researched as passive mitigation surface treatments. The use of superhydrophobic (SHB) and superhydrophilic (SHL) biomimetic coatings reduces the adhesion of fouling organisms and is considered to provide antifouling, anti-icing, anti-corrosion, and self-cleaning properties.

Although extensive state-of-the-art analyses of these surface textures have yielded volumes of data, this data is disconnected and lacks the coherency needed to identify, design and optimise an effective surface technology. This is due to the lack of informed selection data of texture morphologies and densities, which is due to the lack of related simulation studies. As a result, most experimental studies randomly select and test texture morphologies without understanding how specific structural features influence biological and mechanical surface degradation phenomena.

This paper presents a study on the interfacial influence of various biomimetic surface morphologies and densities on water droplet impacts. Three different morphologies with different interpillar distances were simulated to create either SHB or SHL surfaces on Ansys Fluent. Results indicated that morphologies influence the maximum pressure and maximum spreading diameter of the impacting droplet. Phase 2 of this study will provide preliminary data on morphology-design optimisation. Seven parameters were evaluated using one-factor-at-a-time sensitivity analysis. In this stage, coupled level-set and VOF method was used and the results showed in SHB textured surfaces the impact velocity and droplet diameter strongly influence the outcomes. For SHL surface designs, the ambient temperature had the largest effect on the outcomes and showed more nonlinear relationships. Due to the nonlinear effect, further factorial analyses will use a design of experiments (DOE) approach, such as the Box-Behnken method, to investigate the selective/collective synergistic/antagonistic influences of the selected parameters.