Evaluating convective heat transfer coefficients using neural networks
Liquid crystal thermography combined with transient conduction analysis is often used to deduce local values of convective heat transfer coefficients. Neural networks based on the backpropagation algorithm have been successfully applied to predict heat transfer coefficients from a given set of exper...
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| Veröffentlicht in: | International journal of heat and mass transfer 1996, Vol.39 (11), p.2329-2332 |
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| container_end_page | 2332 |
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| container_issue | 11 |
| container_start_page | 2329 |
| container_title | International journal of heat and mass transfer |
| container_volume | 39 |
| creator | Jambunathan, K. Hartle, S.L. Ashforth-Frost, S. Fontama, V.N. |
| description | Liquid crystal thermography combined with transient conduction analysis is often used to deduce local values of convective heat transfer coefficients. Neural networks based on the backpropagation algorithm have been successfully applied to predict heat transfer coefficients from a given set of experimentally obtained conditions. Performance characteristics studied on numerous network configurations relevant to this application indicate that a 3-6-3-1 arrangement yields the least errors with convergence improving directly with both the global learning rates and those of individual layers. |
| doi_str_mv | 10.1016/0017-9310(95)00332-0 |
| format | Article |
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Neural networks based on the backpropagation algorithm have been successfully applied to predict heat transfer coefficients from a given set of experimentally obtained conditions. Performance characteristics studied on numerous network configurations relevant to this application indicate that a 3-6-3-1 arrangement yields the least errors with convergence improving directly with both the global learning rates and those of individual layers.</description><identifier>ISSN: 0017-9310</identifier><identifier>EISSN: 1879-2189</identifier><identifier>DOI: 10.1016/0017-9310(95)00332-0</identifier><identifier>CODEN: IJHMAK</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Algorithms ; Applied sciences ; Backpropagation ; Boundary conditions ; Energy ; Energy. Thermal use of fuels ; Errors ; Exact sciences and technology ; Heat conduction ; Heat transfer ; Image processing ; Liquid crystals ; Mathematical models ; Neural networks ; Theoretical studies. 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Neural networks based on the backpropagation algorithm have been successfully applied to predict heat transfer coefficients from a given set of experimentally obtained conditions. Performance characteristics studied on numerous network configurations relevant to this application indicate that a 3-6-3-1 arrangement yields the least errors with convergence improving directly with both the global learning rates and those of individual layers.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Backpropagation</subject><subject>Boundary conditions</subject><subject>Energy</subject><subject>Energy. Thermal use of fuels</subject><subject>Errors</subject><subject>Exact sciences and technology</subject><subject>Heat conduction</subject><subject>Heat transfer</subject><subject>Image processing</subject><subject>Liquid crystals</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Theoretical studies. Data and constants. Metering</subject><subject>Thermography (temperature measurement)</subject><issn>0017-9310</issn><issn>1879-2189</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1996</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKv_wEMP4sdhdbL52M1FkNKqUPCi5xCzE41ud2uyW_Hfm7XFY0_DMM_7DjyEnFK4pkDlDQAtMsUoXCpxBcBYnsEeGdGyUFlOS7VPRv_IITmK8WNYgcsRmc_Wpu5N55u3iW2bNdrOr3HyjqabdME00WFIB3TOW49NFyd9HNgG-2DqNLrvNnzGY3LgTB3xZDvH5GU-e54-ZIun-8fp3SKzIi-6rLS8Kg3nheQVxQoQmQPlFFJnBVNGALMObSFVzgSnFpG_VhZy5pxTnHE2Jheb3lVov3qMnV76aLGuTYNtH3XBhSwBJEvk-U4ylzynOVUJ5BvQhjbGgE6vgl-a8KMp6EGvHmTpwZ1WQv_p1ZBiZ9t-E62pXXJlffzPMpBSMJGw2w2GycraY9Bx8Gix8iG51lXrd__5BcSsjpo</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Jambunathan, K.</creator><creator>Hartle, S.L.</creator><creator>Ashforth-Frost, S.</creator><creator>Fontama, V.N.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7TC</scope></search><sort><creationdate>1996</creationdate><title>Evaluating convective heat transfer coefficients using neural networks</title><author>Jambunathan, K. ; Hartle, S.L. ; Ashforth-Frost, S. ; Fontama, V.N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c527t-8c4d8a44764d1ed0ee3f09f9e1fc539a503cfec76923541cee4bdc023fff94343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Backpropagation</topic><topic>Boundary conditions</topic><topic>Energy</topic><topic>Energy. Thermal use of fuels</topic><topic>Errors</topic><topic>Exact sciences and technology</topic><topic>Heat conduction</topic><topic>Heat transfer</topic><topic>Image processing</topic><topic>Liquid crystals</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Theoretical studies. Data and constants. 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| ispartof | International journal of heat and mass transfer, 1996, Vol.39 (11), p.2329-2332 |
| issn | 0017-9310 1879-2189 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_745680063 |
| source | Elsevier ScienceDirect Journals |
| subjects | Algorithms Applied sciences Backpropagation Boundary conditions Energy Energy. Thermal use of fuels Errors Exact sciences and technology Heat conduction Heat transfer Image processing Liquid crystals Mathematical models Neural networks Theoretical studies. Data and constants. Metering Thermography (temperature measurement) |
| title | Evaluating convective heat transfer coefficients using neural networks |
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