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Cited by 208

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Predictive Model for Ice Formation on Superhydrophobic Surfaces

Open access
Bahadur, Vaibhav, Mishchenko, Lidiya, Hatton, Benjamin, Taylor, J. Ashley, Aizenberg, Joanna, Krupenkin, Tom·2011·Langmuir

Abstract

The prevention and control of ice accumulation has important applications in aviation, building construction, and energy conversion devices. One area of active research concerns the use of superhydrophobic surfaces for preventing ice formation. The present work develops a physics-based modeling framework to predict ice formation on cooled superhydrophobic surfaces resulting from the impact of supercooled water droplets. This modeling approach analyzes the multiple phenomena influencing ice formation on superhydrophobic surfaces through the development of submodels describing droplet impact dynamics, heat transfer, and heterogeneous ice nucleation. These models are then integrated together to achieve a comprehensive understanding of ice formation upon impact of liquid droplets at freezing conditions. The accuracy of this model is validated by its successful prediction of the experimental findings that demonstrate that superhydrophobic surfaces can fully prevent the freezing of impacting water droplets down to surface temperatures of as low as -20 to -25 °C. The model can be used to study the influence of surface morphology, surface chemistry, and fluid and thermal properties on dynamic ice formation and identify parameters critical to achieving icephobic surfaces. The framework of the present work is the first detailed modeling tool developed for the design and analysis of surfaces for various ice prevention/reduction strategies.

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Metadata

Authors
Bahadur, V., Mishchenko, L., Hatton, B., Taylor, J. A., Aizenberg, J., & Krupenkin, T.
Venue
Langmuir
Published
2011-11-11
Volume
27
Issue
23
Pages
14143-14150
ISSN
  • 0743-7463 (ISSN-L)
  • 1520-5827
OpenAlex
W2012020990
Cited by
208
References
0
FWCI
21.19
Type
journal article
Language
en
Open access
green

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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