schliessen

Filtern

 

Bibliotheken

Cover Image

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
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
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


@attributes
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
RANK2.0907586
LOCALfalse
PrimoNMBib
record
control
sourceidtranstech_cross
recordidTN_cdi_crossref_primary_10_4028_www_scientific_net_AMR_485_548
sourceformatXML
sourcesystemPC
sourcerecordid10_4028_www_scientific_net_AMR_485_548
originalsourceidFETCH-LOGICAL-c258t-180319919583397a8772c110e9c33e9c948dfc8ff2342dead14d0b8ba39a89380
addsrcrecordideNqNkE1PAyEQhjdGE2v1P3DyxgrsR-FirNqqyTY2tvZKKLAtzcrWZZvVk3_dqTXx6gUmA_PknSeKLimJU8L4Vdd1cdDO-taVTsfetlfDyUuc8izOUn4U9WieM8wFz46hJozhPOfkNDoLYUNInlKW9aKvSW1s5fwKKW_Q6GNrG_cGSFWhBZQAVq2rPapLNKsrZ3Dh3nfOoHlX4-laBYvGVd2hovYrfO9Cq7y2aOq2eya8NU7_jN83agWfQkC3MGMQtIoZni0m59FJqapgL37vfvQ6Hs3vHnHx_PB0NyywZhlvMeUkoUJQkfEkEQPFBwOmKSVW6CSBQ6TclJqXJUtSZqwyNDVkyZcqEYqLhJN-dH3g6gZSNLaUW1hUNZ-SErnXKUGn_NMpQacEnRJ0StAJgJsDoG2UD63Va7mpd42H0P9FfAPmbIeq
sourcetypeAggregation Database
isCDItrue
recordtypeconference_proceeding
display
typeconference_proceeding
titleModeling and Experimental Verification of Solid-Liquid Two-Phase Flow Long-Distance Pipeline Friction Drag Loss Based on LS-SVM
creatorYuan, Xu Yi ; Wu, Jian De ; Wang, Xiao Dong ; Fan, Yu Gang
creatorcontribYuan, Xu Yi ; Wu, Jian De ; Wang, Xiao Dong ; Fan, Yu Gang
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.
identifier
0ISSN: 1022-6680
1ISSN: 1662-8985
2EISSN: 1662-8985
3DOI: 10.4028/www.scientific.net/AMR.485.548
languageeng
publisherTrans Tech Publications Ltd
ispartofAdvanced materials research, 2012, Vol.485, p.548-553
rights2012 Trans Tech Publications Ltd
lds50peer_reviewed
citesFETCH-LOGICAL-c258t-180319919583397a8772c110e9c33e9c948dfc8ff2342dead14d0b8ba39a89380
links
openurl$$Topenurl_article
thumbnail$$Uhttps://www.scientific.net/Image/TitleCover/1675?width=600
search
creatorcontrib
0Yuan, Xu Yi
1Wu, Jian De
2Wang, Xiao Dong
3Fan, Yu Gang
title
0Modeling and Experimental Verification of Solid-Liquid Two-Phase Flow Long-Distance Pipeline Friction Drag Loss Based on LS-SVM
1Advanced materials research
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.
issn
01022-6680
11662-8985
21662-8985
fulltextfalse
rsrctypeconference_proceeding
creationdate2012
recordtypeconference_proceeding
recordideNqNkE1PAyEQhjdGE2v1P3DyxgrsR-FirNqqyTY2tvZKKLAtzcrWZZvVk3_dqTXx6gUmA_PknSeKLimJU8L4Vdd1cdDO-taVTsfetlfDyUuc8izOUn4U9WieM8wFz46hJozhPOfkNDoLYUNInlKW9aKvSW1s5fwKKW_Q6GNrG_cGSFWhBZQAVq2rPapLNKsrZ3Dh3nfOoHlX4-laBYvGVd2hovYrfO9Cq7y2aOq2eya8NU7_jN83agWfQkC3MGMQtIoZni0m59FJqapgL37vfvQ6Hs3vHnHx_PB0NyywZhlvMeUkoUJQkfEkEQPFBwOmKSVW6CSBQ6TclJqXJUtSZqwyNDVkyZcqEYqLhJN-dH3g6gZSNLaUW1hUNZ-SErnXKUGn_NMpQacEnRJ0StAJgJsDoG2UD63Va7mpd42H0P9FfAPmbIeq
startdate20120227
enddate20120227
creator
0Yuan, Xu Yi
1Wu, Jian De
2Wang, Xiao Dong
3Fan, Yu Gang
generalTrans Tech Publications Ltd
scope
0AAYXX
1CITATION
sort
creationdate20120227
titleModeling and Experimental Verification of Solid-Liquid Two-Phase Flow Long-Distance Pipeline Friction Drag Loss Based on LS-SVM
authorYuan, Xu Yi ; Wu, Jian De ; Wang, Xiao Dong ; Fan, Yu Gang
facets
frbrtype5
frbrgroupidcdi_FETCH-LOGICAL-c258t-180319919583397a8772c110e9c33e9c948dfc8ff2342dead14d0b8ba39a89380
rsrctypeconference_proceedings
prefilterconference_proceedings
languageeng
creationdate2012
toplevelpeer_reviewed
creatorcontrib
0Yuan, Xu Yi
1Wu, Jian De
2Wang, Xiao Dong
3Fan, Yu Gang
collectionCrossRef
delivery
delcategoryRemote Search Resource
fulltextno_fulltext
addata
au
0Yuan, Xu Yi
1Wu, Jian De
2Wang, Xiao Dong
3Fan, Yu Gang
formatbook
genreproceeding
ristypeCONF
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
risdate2012
volume485
spage548
epage553
pages548-553
issn
01022-6680
11662-8985
eissn1662-8985
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