Spray characteristics of diesel, biodiesel, polyoxymethylene dimethyl ethers blends and prediction of spray tip penetration using artificial neural network

Biodiesel and polyoxymethylene dimethyl ethers (PODE) are both alternative fuels with great potential. The spray characteristics of D100 (neat diesel), DP20 (80 vol. % diesel and 20 vol. % PODE), B100 (neat biodiesel), and BP20 (80 vol. % biodiesel and 20 vol. % PODE) under different injection press...

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Bibliographic Details
Published in:Physics of fluids (1994) 2022-01, Vol.34 (1)
Main Authors: Liu, Yu, Tian, Junjian, Song, Zhihui, Li, Fengyu, Zhou, Wenliang, Lin, Qizhao
Format: Article
Language:English
Subjects:
Alternative fuels
Artificial neural networks
Biodiesel fuels
Combustion chambers
Diesel engines
Ethers
Fluid dynamics
Mixtures
Neural networks
Penetration
Performance prediction
Physics
Spray characteristics
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Summary:Biodiesel and polyoxymethylene dimethyl ethers (PODE) are both alternative fuels with great potential. The spray characteristics of D100 (neat diesel), DP20 (80 vol. % diesel and 20 vol. % PODE), B100 (neat biodiesel), and BP20 (80 vol. % biodiesel and 20 vol. % PODE) under different injection pressures were investigated in a constant volume combustion chamber. The results show that the spray tip penetration of biodiesel blends is greater than that of diesel blends, and the penetration of DP20 is larger than that of D100, while the difference between BP20 and B100 is not significant. The spray cone angle, spray projected area, and liquid core area of biodiesel blends are smaller than that of diesel blends. Due to the low viscosity of PODE, the addition of PODE can increase the spray cone angle, spray projected area, and liquid core area of both diesel and biodiesel. The effect of PODE on biodiesel makes its spray characteristics close to that of pure diesel, which provides convenience for the application of biodiesel in conventional diesel engines. At the same time, computer technology was used to develop the best artificial neural network (ANN) model to predict the spray tip penetration of different fuels. The root mean square error of the model is as low as 0.343 938 mm, and the coefficient of determination is 0.999 737 5, indicating that the model has excellent predictive performance. Finally, to increase the application range of the ANN model, the mathematical expression of the spray tip penetration corresponding to the model was given.
ISSN:1070-6631
1089-7666
DOI:10.1063/5.0077405