Void Fraction Under Breaking Waves

Abstract

Bubble injection due to breaking waves within the surf zone is inferred by measuring void fraction using a 3 m vertical array of eight conductivity cells in conjunction with video pixel intensity. Void fraction errors associated with the conductivity measurements are examined, including vertical variations in the temperature and conductivity (measured), proximity effects near the surface, and estimates of the surface elevation using pressure sensors. Energy loss is due to conversion of kinetic and potential energy of a wave to buoyant potential energy by the injection of air into the water column, which is then lost as the bubbles raise to the surface and escape to the atmosphere. Void fractions up to 40% were observed in intense breaking events penetrating to depths over 0.5 m confined within the crest trough region. Production of potential energy due to buoyancy of bubbles was nearly instantaneous with the majority of energy dissipating within 0.25 s. Pixel intensity qualitatively correlated with surface elevation and injection events. Crests in cross shore intensity time stack plots are clearly visible and show good correlation with breaking events. However, pixel intensity values did not correlate quantitatively with surface elevation or production of buoyant potential energy.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1999
Accession Number
ADA374390

Entities

People

  • Ronald J. Piret

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Entrainment
  • Buoyancy
  • Conductivity
  • Data Sets
  • Deep Water
  • Elevation
  • Energy
  • Energy Production
  • Frequency
  • Frequency Response
  • Intensity
  • Kinetic Energy
  • Measurement
  • Potential Energy
  • Production
  • Research Facilities
  • Wave Power

Readers

  • Acoustical Oceanography.
  • Combustion and Flow Dynamics.
  • Space/Atmospheric Physics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference