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Statistical Moments Transport Model for the prediction of slug flow properties
•A new model is proposed for the evolution of slug length distributions.•Model is based on the direct transport of statistical moments.•Validation with six different experimental data sets.•Agreement with experimental data is generally better than existing models.•Computational cost is up to two ord...
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Published in: | International journal of multiphase flow 2019-11, Vol.120, p.103086, Article 103086 |
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container_title | International journal of multiphase flow |
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creator | Fagundes Netto, J.R. Gonçalves, G.F.N. Silva Freire, A.P. |
description | •A new model is proposed for the evolution of slug length distributions.•Model is based on the direct transport of statistical moments.•Validation with six different experimental data sets.•Agreement with experimental data is generally better than existing models.•Computational cost is up to two orders of magnitude lower than alternatives.
The present work introduces the Statistical Moments Transport (SMT) Model for a description of the mean and standard deviation values of bubble and liquid slug lengths in horizontal, inclined and vertical flows. The model considers gas depressurization and the interaction (coalescence) between long bubbles. Results are compared to three other theoretical approaches – unit cell, slug tracking and slug capturing models – and six different experimental data sets. The gain in computing time as compared to the slug tracking model even in relatively short pipes is of two orders of magnitude. |
doi_str_mv | 10.1016/j.ijmultiphaseflow.2019.103086 |
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The present work introduces the Statistical Moments Transport (SMT) Model for a description of the mean and standard deviation values of bubble and liquid slug lengths in horizontal, inclined and vertical flows. The model considers gas depressurization and the interaction (coalescence) between long bubbles. Results are compared to three other theoretical approaches – unit cell, slug tracking and slug capturing models – and six different experimental data sets. The gain in computing time as compared to the slug tracking model even in relatively short pipes is of two orders of magnitude.</description><identifier>ISSN: 0301-9322</identifier><identifier>EISSN: 1879-3533</identifier><identifier>DOI: 10.1016/j.ijmultiphaseflow.2019.103086</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Bubble distributions ; Slug flow ; Statistical moments</subject><ispartof>International journal of multiphase flow, 2019-11, Vol.120, p.103086, Article 103086</ispartof><rights>2019 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c373t-338270af964b207744347c8e88582b9c5278775297d955f6a47f5fae8c27900c3</citedby><cites>FETCH-LOGICAL-c373t-338270af964b207744347c8e88582b9c5278775297d955f6a47f5fae8c27900c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Fagundes Netto, J.R.</creatorcontrib><creatorcontrib>Gonçalves, G.F.N.</creatorcontrib><creatorcontrib>Silva Freire, A.P.</creatorcontrib><title>Statistical Moments Transport Model for the prediction of slug flow properties</title><title>International journal of multiphase flow</title><description>•A new model is proposed for the evolution of slug length distributions.•Model is based on the direct transport of statistical moments.•Validation with six different experimental data sets.•Agreement with experimental data is generally better than existing models.•Computational cost is up to two orders of magnitude lower than alternatives.
The present work introduces the Statistical Moments Transport (SMT) Model for a description of the mean and standard deviation values of bubble and liquid slug lengths in horizontal, inclined and vertical flows. The model considers gas depressurization and the interaction (coalescence) between long bubbles. Results are compared to three other theoretical approaches – unit cell, slug tracking and slug capturing models – and six different experimental data sets. The gain in computing time as compared to the slug tracking model even in relatively short pipes is of two orders of magnitude.</description><subject>Bubble distributions</subject><subject>Slug flow</subject><subject>Statistical moments</subject><issn>0301-9322</issn><issn>1879-3533</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqNkLFOwzAQhi0EEqXwDp7YUhw7ju0FCVVAkQoMlNlynTN1lMaR7YJ4e1KFiYnppP-k77_7ELouyaIkZX3TLny7P3TZDzuTwHXha0FJqcYlI7I-QbNSClUwztgpmo1ZWShG6Tm6SKklhHBRsRl6ecsm-5S9NR1-Dnvoc8KbaPo0hJjHpIEOuxBx3gEeIjTeZh96HBxO3eEDH2vHPAwQs4d0ic6c6RJc_c45en-43yxXxfr18Wl5ty4sEywXjEkqiHGqrraUCFFVrBJWgpRc0q2ynAopBKdKNIpzV5tKOO4MSEuFIsSyObqduDaGlCI4PUS_N_Fbl0Qf7ehW_7Wjj3b0ZGcErCYAjFd-eog6WQ-9HR-MYLNugv8v6ged_XkS</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Fagundes Netto, J.R.</creator><creator>Gonçalves, G.F.N.</creator><creator>Silva Freire, A.P.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20191101</creationdate><title>Statistical Moments Transport Model for the prediction of slug flow properties</title><author>Fagundes Netto, J.R. ; Gonçalves, G.F.N. ; Silva Freire, A.P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c373t-338270af964b207744347c8e88582b9c5278775297d955f6a47f5fae8c27900c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Bubble distributions</topic><topic>Slug flow</topic><topic>Statistical moments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fagundes Netto, J.R.</creatorcontrib><creatorcontrib>Gonçalves, G.F.N.</creatorcontrib><creatorcontrib>Silva Freire, A.P.</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of multiphase flow</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fagundes Netto, J.R.</au><au>Gonçalves, G.F.N.</au><au>Silva Freire, A.P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Statistical Moments Transport Model for the prediction of slug flow properties</atitle><jtitle>International journal of multiphase flow</jtitle><date>2019-11-01</date><risdate>2019</risdate><volume>120</volume><spage>103086</spage><pages>103086-</pages><artnum>103086</artnum><issn>0301-9322</issn><eissn>1879-3533</eissn><abstract>•A new model is proposed for the evolution of slug length distributions.•Model is based on the direct transport of statistical moments.•Validation with six different experimental data sets.•Agreement with experimental data is generally better than existing models.•Computational cost is up to two orders of magnitude lower than alternatives.
The present work introduces the Statistical Moments Transport (SMT) Model for a description of the mean and standard deviation values of bubble and liquid slug lengths in horizontal, inclined and vertical flows. The model considers gas depressurization and the interaction (coalescence) between long bubbles. Results are compared to three other theoretical approaches – unit cell, slug tracking and slug capturing models – and six different experimental data sets. The gain in computing time as compared to the slug tracking model even in relatively short pipes is of two orders of magnitude.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.ijmultiphaseflow.2019.103086</doi></addata></record> |
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subjects | Bubble distributions Slug flow Statistical moments |
title | Statistical Moments Transport Model for the prediction of slug flow properties |
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