Combining Computational Fluid Dynamics Analysis Data with Experimentally Measured Data
This article presents an approach to reducing the time and cost of experimentation in large wind tunnels, such as the 10 × 10 ft. supersonic wind tunnel at NASA Glenn Research Center, by combining computer simulations of test models from Reynolds averaged Navier-Stokes analysis with small sets of wi...
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Veröffentlicht in: | Quality engineering 2011-01, Vol.23 (1), p.46-58 |
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description | This article presents an approach to reducing the time and cost of experimentation in large wind tunnels, such as the 10 × 10 ft. supersonic wind tunnel at NASA Glenn Research Center, by combining computer simulations of test models from Reynolds averaged Navier-Stokes analysis with small sets of wind tunnel data. To demonstrate the viability of the approach, the impact of microramp flow control on the shock wave boundary layer interaction using paired sets of data from both computational fluid dynamics (CFD) analysis and experimental measurements was compared. By combining the CFD results consisting of 15 central composite face-centered (CCF) simulations with a smaller subset of four/five experimental wind tunnel cases, augmented, interlocking combined data sets were generated from which models were developed that allow the prediction of wind tunnel results. No statistically significant differences were found to exist between the predictions from models generated using the augmented interlocking data sets and the models generated using the complete set of 15 wind tunnel cases based on a paired t-test. From an engineering perspective, the same optimal microramp configuration was obtained using models derived from the combined data set as obtained with the complete set of experimental wind tunnel data. |
doi_str_mv | 10.1080/08982112.2010.505221 |
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To demonstrate the viability of the approach, the impact of microramp flow control on the shock wave boundary layer interaction using paired sets of data from both computational fluid dynamics (CFD) analysis and experimental measurements was compared. By combining the CFD results consisting of 15 central composite face-centered (CCF) simulations with a smaller subset of four/five experimental wind tunnel cases, augmented, interlocking combined data sets were generated from which models were developed that allow the prediction of wind tunnel results. No statistically significant differences were found to exist between the predictions from models generated using the augmented interlocking data sets and the models generated using the complete set of 15 wind tunnel cases based on a paired t-test. From an engineering perspective, the same optimal microramp configuration was obtained using models derived from the combined data set as obtained with the complete set of experimental wind tunnel data.</description><identifier>ISSN: 0898-2112</identifier><identifier>EISSN: 1532-4222</identifier><identifier>DOI: 10.1080/08982112.2010.505221</identifier><language>eng</language><publisher>Milwaukee: Taylor & Francis Group</publisher><subject>CFD ; Comparative analysis ; Computational fluid dynamics ; Computer simulation ; D-optimal ; data scaling ; DOE ; Flow control ; Fluid dynamics ; interlocking DOE ; Locking ; Mathematical models ; multiple regression ; Navier-Stokes equations ; Reynolds number ; scale-up ; Studies ; Wind tunnel testing ; Wind tunnels</subject><ispartof>Quality engineering, 2011-01, Vol.23 (1), p.46-58</ispartof><rights>Copyright Taylor & Francis Group, LLC 2011</rights><rights>Copyright Taylor & Francis Ltd. 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To demonstrate the viability of the approach, the impact of microramp flow control on the shock wave boundary layer interaction using paired sets of data from both computational fluid dynamics (CFD) analysis and experimental measurements was compared. By combining the CFD results consisting of 15 central composite face-centered (CCF) simulations with a smaller subset of four/five experimental wind tunnel cases, augmented, interlocking combined data sets were generated from which models were developed that allow the prediction of wind tunnel results. No statistically significant differences were found to exist between the predictions from models generated using the augmented interlocking data sets and the models generated using the complete set of 15 wind tunnel cases based on a paired t-test. From an engineering perspective, the same optimal microramp configuration was obtained using models derived from the combined data set as obtained with the complete set of experimental wind tunnel data.</description><subject>CFD</subject><subject>Comparative analysis</subject><subject>Computational fluid dynamics</subject><subject>Computer simulation</subject><subject>D-optimal</subject><subject>data scaling</subject><subject>DOE</subject><subject>Flow control</subject><subject>Fluid dynamics</subject><subject>interlocking DOE</subject><subject>Locking</subject><subject>Mathematical models</subject><subject>multiple regression</subject><subject>Navier-Stokes equations</subject><subject>Reynolds number</subject><subject>scale-up</subject><subject>Studies</subject><subject>Wind tunnel testing</subject><subject>Wind tunnels</subject><issn>0898-2112</issn><issn>1532-4222</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqF0U1r3DAQBmBRGug26T_owfTSk5MZ2VpJpxA2n5AQKKVXMbblVkG2NpJN4n8fbba95JCeRgzPDIxexr4iHCMoOAGlFUfkxxxyS4DgHD-wFYqKlzXn_CNb7Ui5M5_Y55QeAFApXa3Yr00YGje68XeRX9t5osmFkXxx6WfXFefLSINrU3GWe0tyqTiniYonN_0pLp63NrrBjhN5vxR3ltIcbfcqjthBTz7ZL3_rIftxefFzc13e3l_dbM5uy7bSaipRSGpaLmqB2AspobaNoLoB3gDIrq7XSnHdQodcN9YiNR0X1KlOWqmqQ_Z9v3Qbw-Ns02QGl1rrPY02zMlowLUALeG_Uuk1B1mtZZbf3siHMMd8fEaoJVa1xozqPWpjSCna3mzzR1BcDILZJWL-JWJ2iZh9InnsdD_mxj7EgZ5C9J2ZaPEh9pHG1iVTvbvhBaTIkR0</recordid><startdate>20110101</startdate><enddate>20110101</enddate><creator>Anderson, Bernhard H.</creator><creator>Haller, Harold S.</creator><general>Taylor & Francis Group</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>U9A</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope></search><sort><creationdate>20110101</creationdate><title>Combining Computational Fluid Dynamics Analysis Data with Experimentally Measured Data</title><author>Anderson, Bernhard H. ; Haller, Harold S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c398t-157abc254511f57704eb5a4b02b007d4468829c0d129bee1abd25ad8d7e783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>CFD</topic><topic>Comparative analysis</topic><topic>Computational fluid dynamics</topic><topic>Computer simulation</topic><topic>D-optimal</topic><topic>data scaling</topic><topic>DOE</topic><topic>Flow control</topic><topic>Fluid dynamics</topic><topic>interlocking DOE</topic><topic>Locking</topic><topic>Mathematical models</topic><topic>multiple regression</topic><topic>Navier-Stokes equations</topic><topic>Reynolds number</topic><topic>scale-up</topic><topic>Studies</topic><topic>Wind tunnel testing</topic><topic>Wind tunnels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Anderson, Bernhard H.</creatorcontrib><creatorcontrib>Haller, Harold S.</creatorcontrib><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><jtitle>Quality engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Anderson, Bernhard H.</au><au>Haller, Harold S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combining Computational Fluid Dynamics Analysis Data with Experimentally Measured Data</atitle><jtitle>Quality engineering</jtitle><date>2011-01-01</date><risdate>2011</risdate><volume>23</volume><issue>1</issue><spage>46</spage><epage>58</epage><pages>46-58</pages><issn>0898-2112</issn><eissn>1532-4222</eissn><abstract>This article presents an approach to reducing the time and cost of experimentation in large wind tunnels, such as the 10 × 10 ft. supersonic wind tunnel at NASA Glenn Research Center, by combining computer simulations of test models from Reynolds averaged Navier-Stokes analysis with small sets of wind tunnel data. To demonstrate the viability of the approach, the impact of microramp flow control on the shock wave boundary layer interaction using paired sets of data from both computational fluid dynamics (CFD) analysis and experimental measurements was compared. By combining the CFD results consisting of 15 central composite face-centered (CCF) simulations with a smaller subset of four/five experimental wind tunnel cases, augmented, interlocking combined data sets were generated from which models were developed that allow the prediction of wind tunnel results. No statistically significant differences were found to exist between the predictions from models generated using the augmented interlocking data sets and the models generated using the complete set of 15 wind tunnel cases based on a paired t-test. From an engineering perspective, the same optimal microramp configuration was obtained using models derived from the combined data set as obtained with the complete set of experimental wind tunnel data.</abstract><cop>Milwaukee</cop><pub>Taylor & Francis Group</pub><doi>10.1080/08982112.2010.505221</doi><tpages>13</tpages></addata></record> |
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subjects | CFD Comparative analysis Computational fluid dynamics Computer simulation D-optimal data scaling DOE Flow control Fluid dynamics interlocking DOE Locking Mathematical models multiple regression Navier-Stokes equations Reynolds number scale-up Studies Wind tunnel testing Wind tunnels |
title | Combining Computational Fluid Dynamics Analysis Data with Experimentally Measured Data |
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