Lift coefficient prediction at high angle of attack using recurrent neural network
In this paper, identification of dynamic stall effect of rotor blade is considered. Recurrent Neural Networks have the ability to identify the nonlinear dynamical systems from training data. This paper describes the use of recurrent neural networks for predicting the coefficient of lift ( C Z ) at h...
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| Veröffentlicht in: | Aerospace science and technology 2003-12, Vol.7 (8), p.595-602 |
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| creator | Suresh, S. Omkar, S.N. Mani, V. Guru Prakash, T.N. |
| description | In this paper, identification of dynamic stall effect of rotor blade is considered. Recurrent Neural Networks have the ability to identify the nonlinear dynamical systems from training data. This paper describes the use of recurrent neural networks for predicting the coefficient of lift (
C
Z
) at high angle of attack. In our approach, the coefficient of lift (
C
Z
) obtained from the experimental results (wind tunnel data) at different mean angle of attack
θ
mean is used to train the recurrent neural network. Then the recurrent neural network prediction is compared with experimental ONERA OA212 airfoil data. The time and space complexity required to predict
C
Z
in the proposed method is less and it is easy to incorporate in any commercially available rotor code. |
| doi_str_mv | 10.1016/S1270-9638(03)00053-1 |
| format | Article |
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C
Z
) at high angle of attack. In our approach, the coefficient of lift (
C
Z
) obtained from the experimental results (wind tunnel data) at different mean angle of attack
θ
mean is used to train the recurrent neural network. Then the recurrent neural network prediction is compared with experimental ONERA OA212 airfoil data. The time and space complexity required to predict
C
Z
in the proposed method is less and it is easy to incorporate in any commercially available rotor code.</description><identifier>ISSN: 1270-9638</identifier><identifier>EISSN: 1626-3219</identifier><identifier>DOI: 10.1016/S1270-9638(03)00053-1</identifier><language>eng</language><publisher>Elsevier SAS</publisher><subject>Dynamic stall ; Memory neuron network ; Recurrent multilayer perceptron network ; Unsteady rotor blade analysis</subject><ispartof>Aerospace science and technology, 2003-12, Vol.7 (8), p.595-602</ispartof><rights>2003 Éditions scientifiques et médicales Elsevier SAS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-caaac672bcfe1aebf0a3c8c287e29418d95e760196e6aeaff1be132c8a54546a3</citedby><cites>FETCH-LOGICAL-c338t-caaac672bcfe1aebf0a3c8c287e29418d95e760196e6aeaff1be132c8a54546a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/S1270-9638(03)00053-1$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3549,27923,27924,45994</link.rule.ids></links><search><creatorcontrib>Suresh, S.</creatorcontrib><creatorcontrib>Omkar, S.N.</creatorcontrib><creatorcontrib>Mani, V.</creatorcontrib><creatorcontrib>Guru Prakash, T.N.</creatorcontrib><title>Lift coefficient prediction at high angle of attack using recurrent neural network</title><title>Aerospace science and technology</title><description>In this paper, identification of dynamic stall effect of rotor blade is considered. Recurrent Neural Networks have the ability to identify the nonlinear dynamical systems from training data. This paper describes the use of recurrent neural networks for predicting the coefficient of lift (
C
Z
) at high angle of attack. In our approach, the coefficient of lift (
C
Z
) obtained from the experimental results (wind tunnel data) at different mean angle of attack
θ
mean is used to train the recurrent neural network. Then the recurrent neural network prediction is compared with experimental ONERA OA212 airfoil data. The time and space complexity required to predict
C
Z
in the proposed method is less and it is easy to incorporate in any commercially available rotor code.</description><subject>Dynamic stall</subject><subject>Memory neuron network</subject><subject>Recurrent multilayer perceptron network</subject><subject>Unsteady rotor blade analysis</subject><issn>1270-9638</issn><issn>1626-3219</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><recordid>eNqFkFtLAzEQhYMoWKs_QciT6MNqLt3s7pNI8QYFwctzmKaTNna7qUlW8d-btvrs0wzDOYc5HyGnnF1yxtXVCxcVKxol63MmLxhjpSz4HhlwJVQhBW_28_4nOSRHMb5nkWhGYkCeJ84majxa64zDLtF1wJkzyfmOQqILN19Q6OYtUm_zIYFZ0j66bk4Dmj6EjaXDPkCbR_ryYXlMDiy0EU9-55C83d2-jh-KydP94_hmUhgp61QYADCqElNjkQNOLQNpaiPqCvNrvJ41JVaK8UahAgRr-RS5FKaGclSOFMghOdvlroP_6DEmvXLRYNtCh76PWlS1kBVTWVjuhCb4GANavQ5uBeFbc6Y3BPWWoN7g0UzqLUHNs-9658Pc4tNh0HGDyGQ-uXvSM-_-SfgBrx152Q</recordid><startdate>20031201</startdate><enddate>20031201</enddate><creator>Suresh, S.</creator><creator>Omkar, S.N.</creator><creator>Mani, V.</creator><creator>Guru Prakash, T.N.</creator><general>Elsevier SAS</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20031201</creationdate><title>Lift coefficient prediction at high angle of attack using recurrent neural network</title><author>Suresh, S. ; Omkar, S.N. ; Mani, V. ; Guru Prakash, T.N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-caaac672bcfe1aebf0a3c8c287e29418d95e760196e6aeaff1be132c8a54546a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Dynamic stall</topic><topic>Memory neuron network</topic><topic>Recurrent multilayer perceptron network</topic><topic>Unsteady rotor blade analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Suresh, S.</creatorcontrib><creatorcontrib>Omkar, S.N.</creatorcontrib><creatorcontrib>Mani, V.</creatorcontrib><creatorcontrib>Guru Prakash, T.N.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Aerospace science and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Suresh, S.</au><au>Omkar, S.N.</au><au>Mani, V.</au><au>Guru Prakash, T.N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lift coefficient prediction at high angle of attack using recurrent neural network</atitle><jtitle>Aerospace science and technology</jtitle><date>2003-12-01</date><risdate>2003</risdate><volume>7</volume><issue>8</issue><spage>595</spage><epage>602</epage><pages>595-602</pages><issn>1270-9638</issn><eissn>1626-3219</eissn><abstract>In this paper, identification of dynamic stall effect of rotor blade is considered. Recurrent Neural Networks have the ability to identify the nonlinear dynamical systems from training data. This paper describes the use of recurrent neural networks for predicting the coefficient of lift (
C
Z
) at high angle of attack. In our approach, the coefficient of lift (
C
Z
) obtained from the experimental results (wind tunnel data) at different mean angle of attack
θ
mean is used to train the recurrent neural network. Then the recurrent neural network prediction is compared with experimental ONERA OA212 airfoil data. The time and space complexity required to predict
C
Z
in the proposed method is less and it is easy to incorporate in any commercially available rotor code.</abstract><pub>Elsevier SAS</pub><doi>10.1016/S1270-9638(03)00053-1</doi><tpages>8</tpages></addata></record> |
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| identifier | ISSN: 1270-9638 |
| ispartof | Aerospace science and technology, 2003-12, Vol.7 (8), p.595-602 |
| issn | 1270-9638 1626-3219 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_27823706 |
| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Dynamic stall Memory neuron network Recurrent multilayer perceptron network Unsteady rotor blade analysis |
| title | Lift coefficient prediction at high angle of attack using recurrent neural network |
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