Spray characteristics of standard and alternative aviation fuels at high ambient pressure conditions

Shin, Dong Ku; Satija, Aman; Lucht, Robert P.

doi:10.1016/j.expthermflusci.2021.110511

Cited by 29 publications

(3 citation statements)

References 47 publications

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“…The line-of-sight SMD was the averaged SMD over each axial location and weighted by the measured volume flux at each radial location. Therefore, the line-of-sight SMD used in this study was defined as SMD = ∑ r = R r = 1 SM D r × n normalr ∑ n r where n r was the valid volume flux of droplets in the axial direction through the probe volume at every measured point and r was the radial distance of measurement points from the swirler cup axis.…”

Section: Experimental Apparatus and Methodologymentioning

confidence: 99%

Predicting the Sauter Mean Diameter of Swirl Cup Airblast Fuel Injector Based on Backpropagation (BP) Neural Network Model

Fang,

Liu,

Yang

et al. 2023

ACS Omega

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This study was dedicated to introducing a new method for predicting the Sauter mean diameter (SMD) buildup in the swirl cup airblast fuel injector. There have been considerable difficulties with predicting SMD mainly because of complicated flow characteristics in a spray. Therefore, the backpropagation (BP) neural network-based machine learning was applied for the prediction of SMD as a function of geometry, condition parameters, and axial distance such as primary swirl number, secondary swirl number, venturi angle, mass flow rate of fuel, and relative air pressure. SMD was measured by a phase Doppler particle analyzer (PDPA). The results show that the prediction accuracy of the trained BP neural network was excellent with a coefficient of determination (R 2 ) score of 0.9599, root mean square error (RMSE) score of 1.4613, and overall relative error within 20%. Through sensitivity analysis, the relative air pressure drop and primary swirl number were the largest and smallest factors affecting the value of SMD, respectively. Finally, the prediction accuracy of the BP neural network model is far greater than the current prediction correlations. Moreover, for the predicting target in the present study, the BP neural network shows the advantages of a simple structure and short running time compared with PSO-BP and GRNN. All these prove that the BP neural network is a novel and effective way to predict the SMD of droplets generated by a swirl cup airblast fuel injector.

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Section: Experimental Apparatus and Methodologymentioning

confidence: 99%

Predicting the Sauter Mean Diameter of Swirl Cup Airblast Fuel Injector Based on Backpropagation (BP) Neural Network Model

Fang,

Liu,

Yang

et al. 2023

ACS Omega

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“…As part of the NJFCP, a few more studies reported the atomization performance of NG-SPK under a constant pressure environment, however, using different atomizer configurations: (i) air-assist atomizer [19] and (ii) hybrid simplex-air blast atomizer [20][21][22]. Their results highlighted the correlation between the mean droplet diameter and fuel viscosity.…”

Section: Introductionmentioning

confidence: 99%

Influence of Fuel Characteristics on the Alternative Jet Fuel Atomization at Non-Reacting Conditions

Kannaiyan¹,

Sadr

2023

Preprint

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“…These methods are the only ones that allow non-invasive measurements in the dense spray region with high temporal resolution. These methods allow information to be obtained about the processes involved in the atomisation of liquids, whether they are fuels [1,2,3,4] or other substances [5,6]. The disadvantage of 1-D methods is, in turn, the inability to obtain spatial resolution without a time-consuming traversing of the measured volume across the region of interest.…”

Section: Introductionmentioning

confidence: 99%

Multi-Exposure PIV Measurements of Velocity Fields in Sprays

2022

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This paper presents an approach to the use of the PIV method in the diagnosis of sprays generated by an effervescent atomiser. Due to the different density of the liquid phase depending on the distance from the nozzle, problems arise with the correct exposure of images for PIV analysis. The aim of the authors of this paper is to outline the possibility of solving this problem by composing a velocity field from partial measurements. To meet the objectives of the paper, in-house PIV equipment (hardware and software) was used rather than a commercial setup. This allowed for easier handling of the measured data and more sophisticated post-processing than offered by commercial products. It is clear from the results presented that, despite the fundamental differences in the optical properties of the spray particles, it is possible to obtain a velocity field from the discharge zone to the spray region with fine droplets. Moreover, it is possible to combine velocity measurements in the spray cone with measurements in the surrounding environment. Research background: Spray is an environment with an abundance of tracers for PIV analysis (droplets), but their density, size and shape vary dramatically with distance from the nozzle. The use of PIV can therefore be challenging due to the demands of this method for correct image exposure. Purpose of the article: Introduction of the application of the PIV method for environments with variable density and size of tracer particles Methods: PIV, image processing. Findings & Value added: By taking an appropriate approach to acquiring the source PIV images, it is possible to obtain information about the velocities throughout the spray cone as well as in the surrounding environment. The application of the proposed method requires a sufficiently large source data set (images) and sophisticated postprocessing. However, as a result, it is possible to obtain an overall view of the velocity field in the spray cone starting from the area behind the nozzle to the fine droplet region.

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Spray characteristics of standard and alternative aviation fuels at high ambient pressure conditions

Cited by 29 publications

References 47 publications

Predicting the Sauter Mean Diameter of Swirl Cup Airblast Fuel Injector Based on Backpropagation (BP) Neural Network Model

Predicting the Sauter Mean Diameter of Swirl Cup Airblast Fuel Injector Based on Backpropagation (BP) Neural Network Model

Influence of Fuel Characteristics on the Alternative Jet Fuel Atomization at Non-Reacting Conditions

Multi-Exposure PIV Measurements of Velocity Fields in Sprays

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