Simulation and Optimization of Venturi Injector by Machine Learning Algorithms
Publication: Journal of Irrigation and Drainage Engineering
Volume 146, Issue 8
Abstract
This paper discusses the problem of low injection rates from Venturi injectors. The optimal combination of key structural parameters for Venturi injectors was investigated using a simulation software platform based on machine learning algorithms. The research considered different nozzle diameters under an inlet pressure of 0.3 MPa and outlet pressure of 0.1 MPa. For the various nozzle diameters, the optimal ranges of the contraction angle (20°–30°), diffusion angle (8°–10°), throat length (40–50 mm), and ratio of throat diameter to nozzle diameter (1.5–1.66) were found, and the parameter combinations that maximized the injection rate were determined. A regression model was used to predict the maximum injection rate with different nozzle diameters. For a nozzle diameter of 4 mm, the maximum injection rate increased by about 200% compared with the original model. In addition, a regression model for the prediction of the injection rate based on injector structural parameters was construction using data from physical injector models and verified by a three-dimensional printer. The model may be used to quickly and effectively design or predict the injection rate for different structural parameters of the Venturi injector.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
We are grateful for research grants from the National Scientific and Technological Project of China (2016YFC0400105) and the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences (CAAS) (2018–2020). This study was also supported by the Special Fund of State Key Laboratory of Simulation and Regulation of a Water Cycle in a River Basin, China Institute of Water Resources and Hydropower Research (IWHR) (SKL2018TS05). In addition, we thank Stuart Jenkinson, Ph.D., from Liwen Bianji, Edanz Group China, for editing the English text of a draft of this manuscript.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 146 • Issue 8 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Mar 26, 2019
Accepted: Mar 4, 2020
Published online: May 29, 2020
Published in print: Aug 1, 2020
Discussion open until: Oct 29, 2020
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