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.