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- W2041379803 abstract "The mechanism model needs to assume a lot of prerequisites, but it is lack of these conditions in the solid-liquid two-phase flow in long-distance pipeline, therefore there will be a deviation between the friction drag loss of mechanism model and the actual value. This paper adopted the least square support vector machine (LS-SVM) to fix the value of the mechanism model, and increase the prediction accuracy. Meanwhile, for improving real-time online LS-SVM performance, introducing the local LS-SVM. The experiment result shows, LS-SVM and local LS-SVM greatly improved the forecast accuracy, compared with the mechanism model correction." @default.
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- W2041379803 date "2012-02-01" @default.
- W2041379803 modified "2023-09-26" @default.
- W2041379803 title "Modeling and Experimental Verification of Solid-Liquid Two-Phase Flow Long-Distance Pipeline Friction Drag Loss Based on LS-SVM" @default.
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- W2041379803 doi "https://doi.org/10.4028/www.scientific.net/amr.485.548" @default.
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