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The turbulent bubble break-up cascade. Part 1. Theoretical developments

TLDR
The authors analytically quantify locality by extending the population balance equation in conservative form to derive the bubble-mass transfer rate from large to small sizes, and show that scalings relevant to turbulent bubbly flows, including those postulated by Garrett et al. (2000) and observed in breaking-wave experiments and simulations, are consistent with a strongly local transfer rate.
Abstract
Breaking waves entrain gas beneath the surface. The wave-breaking process energizes turbulent fluctuations that break bubbles in quick succession to generate a wide range of bubble sizes. Understanding this generation mechanism paves the way towards the development of predictive models for large-scale maritime and climate simulations. Garrett et al. (2000) suggested that super-Hinze-scale turbulent breakup transfers entrained gas from large to small bubble sizes in the manner of a cascade. We provide a theoretical basis for this bubble-mass cascade by appealing to how energy is transferred from large to small scales in the energy cascade central to single-phase turbulence theories. A bubble break-up cascade requires that break-up events predominantly transfer bubble mass from a certain bubble size to a slightly smaller size on average. This property is called locality. In this paper, we analytically quantify locality by extending the population balance equation in conservative form to derive the bubble-mass transfer rate from large to small sizes. Using our proposed measures of locality, we show that scalings relevant to turbulent bubbly flows, including those postulated by Garrett et al. (2000) and observed in breaking-wave experiments and simulations, are consistent with a strongly local transfer rate, where the influence of non-local contributions decays in a power-law fashion. These theoretical predictions are confirmed using numerical simulations in Part 2, revealing key physical aspects of the bubble break-up cascade phenomenology. Locality supports the universality of turbulent small-bubble break-up, which simplifies the development of subgrid-scale models to predict oceanic small-bubble statistics of practical importance.

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Citations
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Journal ArticleDOI

Turbulence theories and statistical closure approaches

TL;DR: A review of the history of modern turbulence theory can be found in this paper, where Kolmogorov's analysis of the turbulent cascade and inertial range is considered as a landmark work.
Journal ArticleDOI

The turbulent bubble break-up cascade. Part 2. Numerical simulations of breaking waves

TL;DR: In this article, the authors present an analytical toolkit for population balance analysis in two-phase flows, including the expected -10/3 power-law exponent for the super-Hinze-scale size distribution, which suggests the emergence of different physical mechanisms during different phases of the breaking wave evolution.

LES Of Atomizing Spray with Stochastic Modeling of Secondary Breakup

TL;DR: In this paper, a stochastic subgrid model for large-eddy simulation of atomizing spray is developed, and the size and number density of newly produced droplets are governed by the evolution of this PDF in the space of droplet-radius.
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Turbulent Flows With Drops and Bubbles: What Numerical Simulations Can Tell Us—Freeman Scholar Lecture

TL;DR: In this article, a review of the numerical methods commonly employed and their advantages and limitations is presented, and the authors focus on the issues arising from the limited range of scales that can be possibly solved, and define a series of guidelines and best practices that are important in the analysis of the simulations and the development of new numerical methods.
Journal ArticleDOI

Strong free-surface turbulence in breaking bores: a physical study on the free-surface dynamics and air–water interfacial features

TL;DR: In this paper, a detailed analysis of the free-surface dynamics of a breaking roller's free surface was performed using both top and side views of the roller and optical flow data.
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