Unknown

Dataset Information

0

Improved Stable Drag Reduction of Controllable Laser-Patterned Superwetting Surfaces Containing Bioinspired Micro/Nanostructured Arrays.


ABSTRACT: Superwetting surfaces are widely used in many engineering fields for reducing energy and resistance loss. A facile and efficient method using laser etching has been used to fabricate and control superwettable drag reduction surfaces. Inspired by the self-cleaning theory of lotus leaves, we propose controllable patterned bionic superhydrophobic surfaces (BSSs) simulating the uneven micro/nanostructures of lotus leaves. The superhydrophobicity and drag reduction ratios at low velocities are highly improved using a laser ablation method on metal substrates. However, unstable air layers trapped on superhydrophobic surfaces are usually cut away by a high-velocity flow, which greatly reduces the drag reduction performance. The fabricated bionic superhydrophobic/hydrophilic surfaces (BSHSs) with alternated hydrophilic strips can build a large surface energy barrier to bind the three-phase contact line. It maintains the stable drag reduction by capturing the air bubbles attached to the hydrophilic strips at a high velocity. Three-dimensional simulation analysis and equipment to measure the weak friction of a self-assembled solid-liquid interface are used to explain the drag reduction mechanism and measure the drag reduction ratios at different flow speeds. BSSs achieve an improved drag reduction effect (maximum 52.76%) at a low velocity (maximum 1.5568 m/s). BSHSs maintain an improved and steady drag reduction effect at high speed. The drag reduction ratios can be maintained at about 30% at high speed, with a maximum value of 4.448 m/s. This research has broad application prospects in energy saving, liquid directional transportation, and shipping due to their robust superhydrophobic properties and stable drag reduction effect.

SUBMITTER: Rong W 

PROVIDER: S-EPMC8771958 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC4928507 | biostudies-other
| S-EPMC9418429 | biostudies-literature
| S-EPMC5291323 | biostudies-literature
| S-EPMC5253354 | biostudies-literature
| S-EPMC3877160 | biostudies-literature
| S-EPMC4817132 | biostudies-literature
| S-EPMC6213816 | biostudies-other
| S-EPMC7577196 | biostudies-literature
| S-EPMC1458636 | biostudies-literature