Statistical Characterization of Flow-Blurring Outflow Regimes Based on Shadowgraph Imaging

Flow-blurring injectors exhibit multiple outflow regimes whose transitions depend on the combined effects of operating conditions, fluid properties, and injector geometry. In this study, flow-blurring atomization is investigated experimentally using high-speed Shadowgraph Imaging Technique (SIT) to systematically characterize regime behavior across a broad parameter space. A total of 486 operating conditions are examined using three working fluids with distinct thermophysical properties and three injector geometries with gap-toorifice ratios H/D ∈ {0.15, 0.2, 0.25}. Four distinct outflow regimes, namely Dripping, Kink, Transient Spray, and Developed Spray, are consistently identified, forming an ordered progression in atomization intensity and flow unsteadiness. Statistical analysis of the operating dimensionless parameters shows that regime transitions cannot be uniquely described using individual operating variables, as substantial overlap persists, particularly for intermediate regimes. Class-conditioned univariate and bivariate probability density functions indicate that regime occurrence is governed by coupled variations in multiple parameters and is strongly influenced by injector geometry. Dimensionality and redundancy assessments further support the feasibility of representing regime transitions along a reduced coordinate, while validation across injector geometries highlights the importance of explicitly accounting for geometric effects to achieve transferable regime descriptions. The results provide an experimental basis for regime classification in flow-blurring atomization and offer guidance for injector design and operating-condition selection.

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Research Explorer (The University of Manchester)
Published
2026-04-15
Primary Topic
Fluid Dynamics and Heat Transfer
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article

Statistical Characterization of Flow-Blurring Outflow Regimes Based on Shadowgraph Imaging

Erfan Vaezi, S. Amirreza S. Madani, Amir; id_orcid 0000-0003-4747-277X Keshmiri, Seyed; id_orcid 0009-0008-5515-1304 Mirfasihi
Research Explorer (The University of Manchester)
Fluid Dynamics and Heat Transfer
article

Statistical Characterization of Flow-Blurring Outflow Regimes Based on Shadowgraph Imaging

Erfan Vaezi, S. Amirreza S. Madani, Amir; id_orcid 0000-0003-4747-277X Keshmiri, Seyed; id_orcid 0009-0008-5515-1304 Mirfasihi
article en

Abstract

Flow-blurring injectors exhibit multiple outflow regimes whose transitions depend on the combined effects of operating conditions, fluid properties, and injector geometry. In this study, flow-blurring atomization is investigated experimentally using high-speed Shadowgraph Imaging Technique (SIT) to systematically characterize regime behavior across a broad parameter space. A total of 486 operating conditions are examined using three working fluids with distinct thermophysical properties and three injector geometries with gap-toorifice ratios H/D ∈ {0.15, 0.2, 0.25}. Four distinct outflow regimes, namely Dripping, Kink, Transient Spray, and Developed Spray, are consistently identified, forming an ordered progression in atomization intensity and flow unsteadiness. Statistical analysis of the operating dimensionless parameters shows that regime transitions cannot be uniquely described using individual operating variables, as substantial overlap persists, particularly for intermediate regimes. Class-conditioned univariate and bivariate probability density functions indicate that regime occurrence is governed by coupled variations in multiple parameters and is strongly influenced by injector geometry. Dimensionality and redundancy assessments further support the feasibility of representing regime transitions along a reduced coordinate, while validation across injector geometries highlights the importance of explicitly accounting for geometric effects to achieve transferable regime descriptions. The results provide an experimental basis for regime classification in flow-blurring atomization and offer guidance for injector design and operating-condition selection.

Research Explorer (The University of Manchester)
Aero Thermo Technology (United States) (US)
Openalex Percentile: Top 4%
Fluid Dynamics and Heat Transfer
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