Fuel placement and atomization inside a gas-turbine fuel injector at realistic operating conditions

Shanmugadas, K. P.; Manuprasad, E.S.; Chiranthan, R.N.; Chakravarthy, Satyanarayanan R.

doi:10.1016/j.proci.2020.09.005

Cited by 14 publications

(2 citation statements)

References 8 publications

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“…Wang et al 5 studied the hardware design of the flow field structure and found that the flare influenced the size of the recirculation zone and droplet dispersion. Becker and Hassa 6 and Shanmugadas et al 7 studied the fuel placement and droplet size inside the swirl cup using PDI and PLIF. They highlighted that swirl shear layers should be considered in fuel injector designs for improving atomization and interactions between fuel and the air shear layer.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have proposed empirical and semiempirical SMD correlations. Lefevre et al, 1 Jasuja et al, 14 Urbán et al, 15 Chong et al, 16 Shanmugadas et al, 7 and others have done a lot of meaningful work on the effects of atomizer scale, alternative fuels, impingement angle of air onto a liquid sheet, and classical and prompt atomization on SMD, and proposed many widely used SMD empirical correlations, However, current SMD correlations mainly focused on liquid properties, particularly viscosity. The influence of geometric changes was not validated systematically and the application range of prediction was limited.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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Effect of Secondary Swirl Strength on the External Aerodynamics of a Gas Turbine Fuel Injector

Sahu,

Chugh,

Mittal

et al. 2024

Lecture Notes in Mechanical Engineering

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Prediction model of volume average diameter and analysis of atomization characteristics in electrostatic atomization minimum quantity lubrication

Jia

Liu

et al. 2023

Friction

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Minimum quantity lubrication (MQL) is a relatively efficient and clean alternative to flooding workpiece machining. Electrostatic atomization has the merits of small droplet diameter, high uniformity of droplet size, and strong coating, hence its superiority to pneumatic atomization. However, as the current research hotspot, the influence of jet parameters and electrical parameters on the average diameter of droplets is not clear. First, by observing the shape of the liquid film at the nozzle outlet, the influence law of air pressure and voltage on liquid film thickness (h) and transverse and longitudinal fluctuations are determined. Then, the mathematical model of charged droplet volume average diameter (VAD) is constructed based on three dimensions of the liquid film, namely its thickness, transverse wavelength (λh), and longitudinal wavelength (λz). The model results under different working conditions are obtained by numerical simulation. Comparisons of the model results with the experimental VAD of the droplet confirm the error of the mathematical model to be less than 10%. The droplet diameter distribution span value Rosin-Rammler distribution span (R.S) and percentage concentrations of PM10 (particle size of less than 10 µm)/PM2.5 (particle size of less than 2.5 µm) under different working conditions are further analyzed. The results show that electrostatic atomization not only reduces the diameter distribution span of atomized droplets but also significantly inhibits the formation of PM10 and PM2.5 fine-suspension droplets. When the air pressure is 0.3 MPa, and the voltage is 40 kV, the percentage concentrations of PM10 and PM2.5 can be reduced by 80.72% and 92.05%, respectively, compared with that under the pure pneumatic atomization condition at 0.3 MPa.

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Fuel placement and atomization inside a gas-turbine fuel injector at realistic operating conditions

Cited by 14 publications

References 8 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

Effect of Secondary Swirl Strength on the External Aerodynamics of a Gas Turbine Fuel Injector

Prediction model of volume average diameter and analysis of atomization characteristics in electrostatic atomization minimum quantity lubrication

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