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Modeling and Experimental Verification of Solid-Liquid Two-Phase Flow Long-Distance Pipeline Friction Drag Loss Based on LS-SVM

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... Full description

Journal Title: Advanced materials research 2012, Vol.485, p.548-553
Main Author: Yuan, Xu Yi
Other Authors: Wu, Jian De , Wang, Xiao Dong , Fan, Yu Gang
Format: Konferenzbeitrag Konferenzbeitrag
Language: English
Publisher: Trans Tech Publications Ltd
ID: ISSN: 1022-6680
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recordid: cdi_crossref_primary_10_4028_www_scientific_net_AMR_485_548
title: Modeling and Experimental Verification of Solid-Liquid Two-Phase Flow Long-Distance Pipeline Friction Drag Loss Based on LS-SVM
format: Conference Proceeding
creator:
  • Yuan, Xu Yi
  • Wu, Jian De
  • Wang, Xiao Dong
  • Fan, Yu Gang
ispartof: Advanced materials research, 2012, Vol.485, p.548-553
description: 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.
language: eng
source:
identifier: ISSN: 1022-6680
fulltext: no_fulltext
issn:
  • 1022-6680
  • 1662-8985
  • 1662-8985
url: Link


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descriptionThe 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.
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descriptionThe 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.
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atitleModeling and Experimental Verification of Solid-Liquid Two-Phase Flow Long-Distance Pipeline Friction Drag Loss Based on LS-SVM
btitleAdvanced materials research
date2012-02-27
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volume485
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notesSelected, peer reviewed papers from the 2012 2nd International Conference on Information Science, Automation and Material System, (ISAM 2012), April 21-22, 2012, Changsha, China
abstractThe 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.
pubTrans Tech Publications Ltd
doi10.4028/www.scientific.net/AMR.485.548