Characterization of the Flow (Breakup) Regimes in a Twin-Fluid Atomizer based on Nozzle Vibrations and Multivariate Analysis

Sikka, Raghav; Halstensen, Maths; Lundberg, Joachim

doi:10.3384/ecp2118522

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, nine experiments were done at specific air-to-liquid mass ratios (ALR) and Weber number (We) based on liquid sheet velocity. The range of dimensionless number corresponds to ALR and the We number is mentioned in (Sikka, Halstensen and Lundberg, 2022) ALR is defined as:…”

Section: Methodsmentioning

confidence: 99%

Spray drop size characterization in an external-mixing bluff-body atomizer based on acoustics and Multivariate Analysis

Sikka¹,

Halstensen²,

Lundberg³

2022

Linköping Electronic Conference Proceedings

View full text Add to dashboard Cite

Air-assist atomizers have been widely used in various applications such as the aerospace industry, internal combustion engines, molten metal, food processing, etc. The mean drop size for these atomizers was obtained through the Shadowgraph imaging technique. This study aims to assess the feasibility of the acoustic chemometrics approach for classifying the atomizer types and predicting the mean drop size, such as Sauter mean diameter (SMD), for a two-phase spray atomizer employed. The droplet size measurements were carried out at three radial locations and one axial location for various air and liquid (water) flow rates. The acoustic signals were recorded through two different sensors: accelerometers and microphones. The main objective of this work is to implement prediction models for the mean drop sizes (SMD) measured at various locations. The model prediction is based on the dimensionless number B, whose unique values correspond to different two-phase flow working conditions. This analysis will further cater to the question that whether the acoustics chemometrics approach, including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS-R), is suitable for extracting valuable information such as predicting mean drop size (SMD) in two-phase flows through recorded acoustic signals.

show abstract

Section: Methodsmentioning

confidence: 99%