Evaluation of a deep-water wave breaking criterion

Abstract

An experimental and numerical study is presented to investigate the breaking criterion of Song and Banner [J. Phys. Oceanogr. 32, 2541 (2002)] who suggested a threshold range of (1.4±0.1)×10−3 for a predictive wave breaking parameter, measuring the rate of change in the local energy maximum and the local wave number, to differentiate between wave trains that lead to breaking and those that do not. To examine the breaking criterion experimentally, four separate wave groups of progressive surface gravity waves with slowly decreasing frequency are generated mechanically in a two-dimensional wave tank. Surface elevations as a function of time are measured using capacitance wave probes; surface elevations as a function of space prior to and during breaking are obtained by recording subregions with an imaging system and combining the measurements from repeated experiments. In addition, nonlinear numerical solutions for the surface elevation profiles for the four wave groups are obtained by solving a set of nonlinear evolution equations using a pseudospectral method and are compared to experiments and linear predictions. It is found that the breaking criterion of Song and Banner is sensitive to the choice of the local wave number, but that a particular local wave number based on the local wave geometry distinguishes wave groups leading to breaking from wave groups that do not break. It is shown that the lead time between the parameter exceeding the threshold and incipient wave breaking increases as wave breaking intensifies. The total energy loss is related strongly to this parameter immediately prior to breaking.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 01, 2008
Source ID
10.1063/1.2939396

Entities

People

  • Marc Perlin
  • Wooyoung Choi
  • Zhigang Tian

Organizations

  • New Jersey Institute of Technology
  • Office of Naval Research
  • University of Michigan

Tags

Fields of Study

  • Physics

Readers

  • Marine Hydrodynamics
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Regression Analysis.

Technology Areas

  • Space