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International Journal of Heat and Mass Transfer
Article . 2022 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
SSRN Electronic Journal
Article . 2022 . Peer-reviewed
Data sources: Crossref
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Computationally-Derived Submodel for Thermally-Induced Secondary Atomization

Authors: Paolo Guida; Alberto Ceschin; Francisco E. Hernández Pérez; Hong G. Im; William L. Roberts;

Computationally-Derived Submodel for Thermally-Induced Secondary Atomization

Abstract

Thermally-induced secondary atomization (TISA) enables enhanced atomization, better mixing and faster evaporation in multi-component sprays. Despite its importance in a number of applications, TISA is not yet well understood. In this work we study numerically the effects of key physical parameters on TISA dynamics, with particular emphasis on breakup. To this end, we simulated a series of cases for suspended droplets in microgravity conditions, varying the number of bubbles near the liquid-gas interface. We performed a total of 800 simulations with different fluid properties investigating a wide hyperspace. In particular, we varied viscosity, surface tension, number and size of bubbles, as well as the droplet size, identifying two main parameters necessary for modelling purposes: the breakup time, τb, and maximum normalized surface area, S f . Here we defined the breakup time as the time between the beginning of the simulation and the maximum surface area observed. We also calculated the Pearson’s coefficient to estimate the influence of each variable on the parameters of interest, understanding that the size of the largest bubble controlled S f while the Ohnesorge number strongly influenced τb. We further employed the dataset to formulate simple mathematical correlations for S f and τb by performing a multi-variable regression. Moreover, we looked into the dynamics of the secondary droplets generated by the process, demonstrating that the velocity and size of the ejected droplets are linked to the size of the bubbles that generate them. ; The work was sponsored by the Clean Combustion Research Center at King Abdullah University of Science and Technology (KAUST) under Competitive Research Grant (CRG) 4079-01-01. Computational resources were provided by the KAUST Supercomputing Laboratory (KSL).

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Saudi Arabia
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
Green