Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid
A numerical study of laminar mixed convection in a two-sided lid-driven cavity filled with a water–Al 2O 3 nanofluid is presented. The top and bottom walls of the cavity are kept at different temperatures and can slide in the same or opposite direction. The vertical walls are thermally insulated. An...
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| Veröffentlicht in: | International journal of thermal sciences 2012-02, Vol.52, p.102-111 |
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| creator | Aminossadati, S.M. Kargar, A. Ghasemi, B. |
| description | A numerical study of laminar mixed convection in a two-sided lid-driven cavity filled with a water–Al
2O
3 nanofluid is presented. The top and bottom walls of the cavity are kept at different temperatures and can slide in the same or opposite direction. The vertical walls are thermally insulated. An Adaptive Network-based Fuzzy Inference System (ANFIS) approach is developed, trained and validated using the results of a Computational Fluid Dynamics (CFD) analysis. The results show that ANFIS can successfully be used to predict the fluid velocity and temperature as well as the heat transfer rate of the cavity, with reduced computation time and without compromising the accuracy.
► Nanofluid laminar mixed convection in a two-sided lid-driven cavity is studied. ► ANFIS and CFD are used to examine the thermal behaviour of cavity. ► Computation time is reduced by using ANFIS without compromising accuracy. ► Higher heat transfer rates at higher values of
ϕ and lower values of Ri. ► Heat transfer enhancement is a function of aspect ratio and lid-driven direction. |
| doi_str_mv | 10.1016/j.ijthermalsci.2011.09.004 |
| format | Article |
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2O
3 nanofluid is presented. The top and bottom walls of the cavity are kept at different temperatures and can slide in the same or opposite direction. The vertical walls are thermally insulated. An Adaptive Network-based Fuzzy Inference System (ANFIS) approach is developed, trained and validated using the results of a Computational Fluid Dynamics (CFD) analysis. The results show that ANFIS can successfully be used to predict the fluid velocity and temperature as well as the heat transfer rate of the cavity, with reduced computation time and without compromising the accuracy.
► Nanofluid laminar mixed convection in a two-sided lid-driven cavity is studied. ► ANFIS and CFD are used to examine the thermal behaviour of cavity. ► Computation time is reduced by using ANFIS without compromising accuracy. ► Higher heat transfer rates at higher values of
ϕ and lower values of Ri. ► Heat transfer enhancement is a function of aspect ratio and lid-driven direction.</description><identifier>ISSN: 1290-0729</identifier><identifier>EISSN: 1778-4166</identifier><identifier>DOI: 10.1016/j.ijthermalsci.2011.09.004</identifier><language>eng</language><publisher>Kidlington: Elsevier Masson SAS</publisher><subject>Adaptive systems ; ANFIS ; Applied sciences ; Chemistry ; Colloidal state and disperse state ; Computational fluid dynamics ; Condensed matter: structure, mechanical and thermal properties ; Energy ; Energy. Thermal use of fuels ; Exact sciences and technology ; Fluid dynamics ; Fundamental areas of phenomenology (including applications) ; Fuzzy ; General and physical chemistry ; Heat transfer ; Holes ; Inference ; Laminar flows ; Laminar flows in cavities ; Lid-driven cavity ; Mixed convection ; Nanofluids ; Nanomaterials ; Nanostructure ; Physical and chemical studies. Granulometry. Electrokinetic phenomena ; Physics ; Richardson number ; Theoretical studies. Data and constants. Metering ; Thermal properties of condensed matter ; Thermal properties of small particles, nanocrystals, nanotubes ; Walls</subject><ispartof>International journal of thermal sciences, 2012-02, Vol.52, p.102-111</ispartof><rights>2011 Elsevier Masson SAS</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-cefe7d5f5bf6e4b1601ea54f933bf69afea72dc76600c3a281c711d1895ec0a43</citedby><cites>FETCH-LOGICAL-c386t-cefe7d5f5bf6e4b1601ea54f933bf69afea72dc76600c3a281c711d1895ec0a43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1290072911002730$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25400257$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Aminossadati, S.M.</creatorcontrib><creatorcontrib>Kargar, A.</creatorcontrib><creatorcontrib>Ghasemi, B.</creatorcontrib><title>Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid</title><title>International journal of thermal sciences</title><description>A numerical study of laminar mixed convection in a two-sided lid-driven cavity filled with a water–Al
2O
3 nanofluid is presented. The top and bottom walls of the cavity are kept at different temperatures and can slide in the same or opposite direction. The vertical walls are thermally insulated. An Adaptive Network-based Fuzzy Inference System (ANFIS) approach is developed, trained and validated using the results of a Computational Fluid Dynamics (CFD) analysis. The results show that ANFIS can successfully be used to predict the fluid velocity and temperature as well as the heat transfer rate of the cavity, with reduced computation time and without compromising the accuracy.
► Nanofluid laminar mixed convection in a two-sided lid-driven cavity is studied. ► ANFIS and CFD are used to examine the thermal behaviour of cavity. ► Computation time is reduced by using ANFIS without compromising accuracy. ► Higher heat transfer rates at higher values of
ϕ and lower values of Ri. ► Heat transfer enhancement is a function of aspect ratio and lid-driven direction.</description><subject>Adaptive systems</subject><subject>ANFIS</subject><subject>Applied sciences</subject><subject>Chemistry</subject><subject>Colloidal state and disperse state</subject><subject>Computational fluid dynamics</subject><subject>Condensed matter: structure, mechanical and thermal properties</subject><subject>Energy</subject><subject>Energy. Thermal use of fuels</subject><subject>Exact sciences and technology</subject><subject>Fluid dynamics</subject><subject>Fundamental areas of phenomenology (including applications)</subject><subject>Fuzzy</subject><subject>General and physical chemistry</subject><subject>Heat transfer</subject><subject>Holes</subject><subject>Inference</subject><subject>Laminar flows</subject><subject>Laminar flows in cavities</subject><subject>Lid-driven cavity</subject><subject>Mixed convection</subject><subject>Nanofluids</subject><subject>Nanomaterials</subject><subject>Nanostructure</subject><subject>Physical and chemical studies. Granulometry. Electrokinetic phenomena</subject><subject>Physics</subject><subject>Richardson number</subject><subject>Theoretical studies. Data and constants. Metering</subject><subject>Thermal properties of condensed matter</subject><subject>Thermal properties of small particles, nanocrystals, nanotubes</subject><subject>Walls</subject><issn>1290-0729</issn><issn>1778-4166</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNqNkE1v1DAQhiNEJUrhP1hIiFPCOF-OuVWlBaRKXOBsee2xOotjL3Z2S3rtH69XWyGOnGx5Hr8z81TVOw4NBz5-3Da0Xe4wzdpnQ00LnDcgG4D-RXXOhZjqno_jy3JvJdQgWvmqep3zFgCEBHlePV5avVvogCzgch_Tr3qjM1rm9g8PK6PgMGEwyPKaF5yZDtqvmTKLjs30p4AmhgOahWIoNNOshNSZbKl4srVNJTowow-0rMyR96VwT8tdIYMO0fk92TfVmSvz49vn86L6eXP94-prffv9y7ery9vadNO41AYdCju4YeNG7Dd8BI566J3suvIitUMtWmvEOAKYTrcTN4Jzyyc5oAHddxfVh1PuLsXfe8yLmikb9F4HjPus5NhNU9sJXshPJ9KkmHNCp3aJZp1WxUEdxaut-le8OopXIFURXz6_f26js9HeJR0M5b8J7dADtIMo3OcTh2XnA2FSJeko21IqRpWN9D_tngB7gKP8</recordid><startdate>20120201</startdate><enddate>20120201</enddate><creator>Aminossadati, S.M.</creator><creator>Kargar, A.</creator><creator>Ghasemi, B.</creator><general>Elsevier Masson SAS</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20120201</creationdate><title>Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid</title><author>Aminossadati, S.M. ; Kargar, A. ; Ghasemi, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c386t-cefe7d5f5bf6e4b1601ea54f933bf69afea72dc76600c3a281c711d1895ec0a43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Adaptive systems</topic><topic>ANFIS</topic><topic>Applied sciences</topic><topic>Chemistry</topic><topic>Colloidal state and disperse state</topic><topic>Computational fluid dynamics</topic><topic>Condensed matter: structure, mechanical and thermal properties</topic><topic>Energy</topic><topic>Energy. Thermal use of fuels</topic><topic>Exact sciences and technology</topic><topic>Fluid dynamics</topic><topic>Fundamental areas of phenomenology (including applications)</topic><topic>Fuzzy</topic><topic>General and physical chemistry</topic><topic>Heat transfer</topic><topic>Holes</topic><topic>Inference</topic><topic>Laminar flows</topic><topic>Laminar flows in cavities</topic><topic>Lid-driven cavity</topic><topic>Mixed convection</topic><topic>Nanofluids</topic><topic>Nanomaterials</topic><topic>Nanostructure</topic><topic>Physical and chemical studies. Granulometry. Electrokinetic phenomena</topic><topic>Physics</topic><topic>Richardson number</topic><topic>Theoretical studies. Data and constants. Metering</topic><topic>Thermal properties of condensed matter</topic><topic>Thermal properties of small particles, nanocrystals, nanotubes</topic><topic>Walls</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aminossadati, S.M.</creatorcontrib><creatorcontrib>Kargar, A.</creatorcontrib><creatorcontrib>Ghasemi, B.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>International journal of thermal sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aminossadati, S.M.</au><au>Kargar, A.</au><au>Ghasemi, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid</atitle><jtitle>International journal of thermal sciences</jtitle><date>2012-02-01</date><risdate>2012</risdate><volume>52</volume><spage>102</spage><epage>111</epage><pages>102-111</pages><issn>1290-0729</issn><eissn>1778-4166</eissn><abstract>A numerical study of laminar mixed convection in a two-sided lid-driven cavity filled with a water–Al
2O
3 nanofluid is presented. The top and bottom walls of the cavity are kept at different temperatures and can slide in the same or opposite direction. The vertical walls are thermally insulated. An Adaptive Network-based Fuzzy Inference System (ANFIS) approach is developed, trained and validated using the results of a Computational Fluid Dynamics (CFD) analysis. The results show that ANFIS can successfully be used to predict the fluid velocity and temperature as well as the heat transfer rate of the cavity, with reduced computation time and without compromising the accuracy.
► Nanofluid laminar mixed convection in a two-sided lid-driven cavity is studied. ► ANFIS and CFD are used to examine the thermal behaviour of cavity. ► Computation time is reduced by using ANFIS without compromising accuracy. ► Higher heat transfer rates at higher values of
ϕ and lower values of Ri. ► Heat transfer enhancement is a function of aspect ratio and lid-driven direction.</abstract><cop>Kidlington</cop><pub>Elsevier Masson SAS</pub><doi>10.1016/j.ijthermalsci.2011.09.004</doi><tpages>10</tpages></addata></record> |
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| ispartof | International journal of thermal sciences, 2012-02, Vol.52, p.102-111 |
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| subjects | Adaptive systems ANFIS Applied sciences Chemistry Colloidal state and disperse state Computational fluid dynamics Condensed matter: structure, mechanical and thermal properties Energy Energy. Thermal use of fuels Exact sciences and technology Fluid dynamics Fundamental areas of phenomenology (including applications) Fuzzy General and physical chemistry Heat transfer Holes Inference Laminar flows Laminar flows in cavities Lid-driven cavity Mixed convection Nanofluids Nanomaterials Nanostructure Physical and chemical studies. Granulometry. Electrokinetic phenomena Physics Richardson number Theoretical studies. Data and constants. Metering Thermal properties of condensed matter Thermal properties of small particles, nanocrystals, nanotubes Walls |
| title | Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid |
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