journal article·2020-11-01Open access

Particle acceleration and pressure gradient in a solitary wave traveling over a horizontal bed

Chang Lin, Ming-Jer Kao, James Yang, Rajkumar Venkatesh Raikar, Juan-Ming Yuan, Shih-Chun Hsieh

AIP Advances· Volume 10· Issue 11

Abstract

This study presents experimental data on the hydrodynamic features of a nonlinear solitary wave of height H0 = 2.9 cm propagating over a horizontal bed in otherwise still water depth h0 = 8.0 cm. High-speed particle image velocimetry is used to quantify the local and convective particle accelerations and pressure gradients under the solitary wave. It is found that the magnitudes of the horizontal and vertical particle local accelerations are invariably larger than their convective counterparts, highlighting the important contribution of local acceleration to the pressure gradient in a solitary wave. The dimensionless pressure gradient in the horizontal direction is favorable, zero, and adverse for time before, at, and after the wave crest passes the section of interest. The dimensionless adverse pressure gradient exhibits a maximum for the dimensionless time T[=t(g/h0)1/2, where g is the gravitational acceleration and t is the time with respect to the crest passing the section] = 1.39, at which the dimensionless local acceleration has a negative maximum. Subsequently, flow reversal takes place above the bed surface. The relationship is elucidated between flow reversal at the bed surface and the evolution of the adverse pressure gradient in the near-bed zone, where uniform horizontal/free stream velocity exists.
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Research context

OpenAlex topics: Fluid Dynamics and Turbulent Flows · Coastal and Marine Dynamics · Aeolian processes and effects

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Civil Engineering, National Chung Hsing University 1 , Taichung 40227, Taiwan

Department of Civil and Architectural Engineering, Royal Institute of Technology 2 , Stockholm SE-10044, Sweden

Department of Civil Engineering, KLE Dr. M.S. Sheshgiri College of Engineering and Technology 3 , Udyambag, Belagavi, Karnataka 590008, India

Department of Data Science and Big Data Analytics, Providence University 4 , Taichung 43301, Taiwan

Axesea Engineering Technology Co., Ltd. 5 , Taichung 407034, Taiwan

Funding

Swedish Hydropower Center · Ministry of Science and Technology, Taiwan · Royal Institute of Technology

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