Lattice Boltzmann parallel simulation of microflow dynamics over structured surfaces. (May 2017)

• Record Type: Journal Article
• Authors: Zhou, Wenning; Yan, Yuying; Liu, Xunliang; Liu, Baiqian
• Is Part Of: Advances in engineering software. Volume 107(2017)
• Page Start: 51
• Page End: 58
• Publication Date: 2017-05
• Subjects: Surface structures -- Wettability -- Drag reduction -- Lattice Boltzmann method -- Parallel computing -- OpenMP; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2017.02.001
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A Parallel lattice Boltzmann method was developed to investigate microflow dynamics over structured surfaces. A maximum speedup of 2.95 was obtained for 4 threads on a multi-core CPU platform. The effects of different structured surfaces on wettabilities and drag reduction were presented. Hierarchical morphology on the surfaces has the most profound effects on microflow dynamics. Abstract: In the present work, a parallel lattice Boltzmann multiphase model was developed to investigate the effects of surface structures on wettabilities and flow dynamics in a microchannel. The theory of wetting transition was firstly discussed. Then three types including triangular, rectangle and hierarchical shaped microstructures were constructed on the surface to examine the effects on wettabilities and drag reduction. It was found that flow behaviour is strongly affected by the surface morphology of the channel. The results indicated that hierarchical structures on the surface could improve the hydrophobicity significantly. For rectangular structures, they can improve the hydrophobicity with the increase of height and distance ratio h/d of the structures, and the improvement will reach its optimal hydrophobicity when the value h/d is over a certain value of 0.6. Moreover, to accelerate computational speed, the Open Multi-Processing (OpenMP) was employed for the parallelization of the model. A maximum speedup of 2.95 times was obtained for 4 threads on a multi-core CPU platform.