Wettability Prediction for 3D-Printed Surfaces Using Reverse Engineering and Computational Fluid Dynamics Simulations

Additive manufacturing (3D printing) is a promising approach to creating packings for laboratory-scale distillation columns. Current research focuses on experiments and simulations to tailor packing geometry regarding performance (e.g., pressure drop, fluid distribution). These performance benchmarks are, in large part, dependent on the wettability of the manufactured surface. Research shows that the 3D-printing process settings affect wetting significantly. This effect must be quantified to accurately assess the effectiveness of printed packing geometry. Due to the interdependence of wetting, surface roughness, and involved substances, the required experimental effort is not feasible. Sessile drop experiments show that analytical models underpredict the resulting wettability. In this study, a novel method to address this issue is introduced. The rough surface of a printed sample is reverse-engineered, and CFD simulations are performed to predict the static contact angle. The results show agreement between the computational model and experimental investigations.

Automated summary

Additive manufacturing (3D printing) is a promising approach for creating packings for laboratory-scale distillation columns. Current research focuses on tailoring packing geometry through experiments and simulations to improve performance. The wetting behavior of the printed surface is found to be significantly affected by the 3D-printing process settings, and it needs to be accurately quantified for evaluating the effectiveness of printed packing geometry.