Breaking wave field statistics with a multi-layer model

Jiarong Wu, Stéphane Popinet, Luc Deike

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The statistics of breaking wave fields are characterised within a novel multi-layer framework, which generalises the single-layer Saint-Venant system into a multi-layer and non-hydrostatic formulation of the Navier-Stokes equations. We simulate an ensemble of phase-resolved surface wave fields in physical space, where strong nonlinearities, including directional wave breaking and the subsequent highly rotational flow motion, are modelled, without surface overturning. We extract the kinematics of wave breaking by identifying breaking fronts and their speed, for freely evolving wave fields initialised with typical wind wave spectra. The distribution, defined as the length of breaking fronts (per unit area) moving with speed to following Phillips (J. Fluid Mech., vol. 156, 1985, pp. 505-531), is reported for a broad range of conditions. We recover the scaling without wind forcing for sufficiently steep wave fields. A scaling of based solely on the root-mean-square slope and peak wave phase speed is shown to describe the modelled breaking distributions well. The modelled breaking distributions are in good agreement with field measurements and the proposed scaling can be applied successfully to the observational data sets. The present work paves the way for simulations of the turbulent upper ocean directly coupled to a realistic breaking wave dynamics, including Langmuir turbulence, and other sub-mesoscale processes.

Original languageEnglish (US)
Article numberA12
JournalJournal of Fluid Mechanics
StatePublished - Jul 31 2023

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Applied Mathematics


  • air/sea interactions
  • surface gravity waves
  • wave breaking


Dive into the research topics of 'Breaking wave field statistics with a multi-layer model'. Together they form a unique fingerprint.

Cite this