Identifying and tracking bubbles and drops in simulations: a toolbox for obtaining sizes, lineages, and breakup and coalescence statistics

11/14/2020
by   Wai Hong Ronald Chan, et al.
0

Knowledge of bubble and drop size distributions in two-phase flows is important for characterizing a wide range of phenomena, including combustor ignition, sonar communication, and cloud formation. The physical mechanisms driving the background flow also drive the time evolution of these distributions. Accurate and robust identification and tracking algorithms for the dispersed phase are necessary to reliably measure this evolution and thereby quantify the underlying mechanisms in interface-resolving flow simulations. The identification of individual bubbles and drops traditionally relies on an algorithm used to identify connected regions. This traditional algorithm can be sensitive to the presence of spurious structures. A cost-effective refinement is proposed to maximize volume accuracy while minimizing the identification of spurious bubbles and drops. An accurate identification scheme is crucial for distinguishing bubble and drop pairs with large size ratios. The identified bubbles and drops need to be tracked in time to obtain breakup and coalescence statistics that characterize the evolution of the size distribution, including breakup and coalescence frequencies, and the probability distributions of parent and child bubble and drop sizes. An algorithm based on mass conservation is proposed to construct bubble and drop lineages using simulation snapshots that are not necessarily from consecutive time-steps. These lineages are then used to detect breakup and coalescence events, and obtain the desired statistics. Accurate identification of large-size-ratio bubble and drop pairs enables accurate detection of breakup and coalescence events over a large size range. Together, these algorithms enable insights into the mechanisms behind bubble and drop formation and evolution in flows of practical importance.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 4

page 5

page 7

page 13

page 20

06/11/2020

Robust model training and generalisation with Studentising flows

Normalising flows are tractable probabilistic models that leverage the p...
09/10/2018

Flow Length and Size Distributions in Campus Internet Traffic

Efficiency of numerous flow-oriented solutions proposed in the literatur...
01/22/2020

To schedule or not to schedule: when no-scheduling can beat the best-known flow scheduling algorithm in datacenter networks

Conventional wisdom for minimizing the average flow completion time (AFC...
09/10/2018

Wasserstein Gradients for the Temporal Evolution of Probability Distributions

Many studies have been conducted on flows of probability measures, often...
05/01/2021

A robust, high-order implicit shock tracking method for simulation of complex, high-speed flows

High-order implicit shock tracking is a new class of numerical methods t...
12/11/2019

LightFDG: An Integrated Approach to Flow Detection and Grooming in Optical Wireless DCNs

LightFDG is an integrated approach to flow detection (FD) and flow groom...
10/27/2021

Stabilising viscous extensional flows using Reinforcement Learning

The four-roll mill, wherein four identical cylinders undergo rotation of...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.