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Identifying the Driving Processes of Diesel Spray Injection through Mixture Fraction and Velocity Field Measurements at ECN Spray A
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Diesel spray mixture formation is investigated at target conditions using multiple diagnostics and laboratories. High-speed Particle Image Velocimetry (PIV) is used to measure the velocity field inside and outside the jet simultaneously with a new frame straddling synchronization scheme. The PIV measurements are carried out in the Engine Combustion Network Spray A target conditions, enabling direct comparisons with mixture fraction measurements previously performed in the same conditions, and forming a unique database at diesel conditions. A 1D spray model, based upon mass and momentum exchange between axial control volumes and near-Gaussian velocity and mixture fraction profiles is evaluated against the data. The 1D spray model quantitatively predicts the main spray characteristics (average mixture fraction and velocity fields) within the measurement uncertainty for a wide range of parametric variations, verifying that a Diesel spray becomes momentum controlled and has a Gaussian profile. A required input to the model is the jet angle, which is obtained experimentally. Although an expected result for a gas jet, this is the first time that combined datasets of velocity and mixture fraction have been obtained in vaporizing sprays at Diesel conditions (900 K, 60 bar). Finally, these results show that a consistent database can be built using advanced diagnostics performed by different institutions when the boundary conditions are well known as prescribed by the ECN Spray A framework.
CitationMalbec, L., Bruneaux, G., Somers, B., Rousselle, C. et al., "Identifying the Driving Processes of Diesel Spray Injection through Mixture Fraction and Velocity Field Measurements at ECN Spray A," SAE Technical Paper 2020-01-0831, 2020, https://doi.org/10.4271/2020-01-0831.
Data Sets - Support Documents
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