Session: Rising Stars of Mechanical Engineering Celebration & Showcase

Paper Number: 150175

150175 - Longevity of Superhydrophobic Surface Due to Gas Diffusion and Turbulent Flows 

Superhydrophobic surface (SHS) traps a layer of micro/nano-scale gas pockets within surface texture when immersed in liquid. Due to the presence of gas, the SHS has a wide range of applications from drag reduction, heat and mass transfer enhancement, anti-icing, anti-corrosion, and anti-biofouling. Unfortunately, the gas on SHS could be unstable and replaced by water, due to various factors such as gas dissolution and turbulent flows.

In the first part, we performed experimental and numerical studies to understand the gas diffusion process and the resulting SHS longevity. In the numerical work, we model the SHS longevity by first solving the spatial-temporal evolution of the gas concentration in the liquid and then calculating the mass flux based on Fick’s first law. In the experimental work, we fabricated SHS with regular patterns (holes and posts) and measured the SHS longevity by a non-intrusive optical technique based on light reflection. We found that the gas diffusion over SHS can be approximately as a one-dimensional diffusion problem. As gas being dissolved into the liquid, the profiles of gas concentration at different times are self-similar, and the mass flux reduces with time (t) at a rate of 1/t^0.5. We examined the impacts of texture parameters (including pitch, gas fraction, texture height, and advancing contact angle) and undersaturation level of liquid on the SHS longevity. We found a simple analytical model for SHS longevity, which agreed well with experimental data and worked for SHS with various texture geometries and texture sizes. Moreover, we found that the SHS longevity increased when using a gas permeable material.

In the second part, we experimentally studied the stability of the gas trapped on SHS in turbulent flows. We considered SHS consisting of transverse grooves of various dimensions. The experiments were performed in a turbulent channel flow facility, where the mean flow speed varied from 0.5 to 5 m/s and the Reynolds number based on mean flow speed and channel height Re_m varied from 2000 to 20000. The status of gas layer on SHS was imaged by a reflected-light microscopy. We found that as increasing Reynolds number, the SHS experienced a sudden wetting transition from Cassie-Baxter state to Wenzel state. A metastable state where the liquid partially filled the grooves was not observed. Moreover, we found that the wetting transition was delayed or occurred at a higher Reynolds number as increasing texture height and reducing groove width. The trend between texture size and the critical Reynolds number for wetting transition was well captured by existing theoretical models based on the force-balance at the gas-liquid interface. Moreover, we showed that grooves with T-shape maintained a stable plastron in turbulent flows at a higher Reynolds number.  

Overall, our results provide guidance for the advanced manufacture and implementation of SHS in real-world engineering systems.

Presenting Author: Hangjian Ling University of Massachusetts Dartmouth

Presenting Author Biography: Hangjian Ling is an Assistant Professor in Mechanical Engineering at University of Massachusetts Dartmouth. He received his Ph.D. from Johns Hopkins University in 2017 and was a Postdoctoral Scholar at Stanford University from 2017 to 2019. He is broadly interested in fluid dynamic problems with connections to material, biological, and environmental sciences. He develops and implements novel optical and laser technologies to characterize the fluid and particle motions across multiple length scales. His current research includes: (i) flow interaction with superhydrophobic surface for developing next generation of multi-functional materials; (ii) turbulent flow interactions with rough, soft and porous surfaces; (iii) collective animal behavior, and (iv) measuring 3D motions of living and non-living objects including bacteria, cells and bubbles. His work has been supported by NSF and ONR. He received NSF CAREER award in 2023.

Authors:

Hangjian Ling University of Massachusetts Dartmouth