Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance
To develop the high performance and high reliability of turbomachinery just like an aeroengine, distributed collaborative time-varying least squares support vector machine (LSSVM) (called as DC-T-LSSVM) method was proposed for the dynamic probabilistic analysis of high-pressure turbine blade-tip rad...
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| Veröffentlicht in: | Nonlinear dynamics 2016-10, Vol.86 (1), p.205-223 |
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| creator | Fei, Cheng-Wei Choy, Yat-Sze Hu, Dian-Yin Bai, Guang-Chen Tang, Wen-Zhong |
| description | To develop the high performance and high reliability of turbomachinery just like an aeroengine, distributed collaborative time-varying least squares support vector machine (LSSVM) (called as DC-T-LSSVM) method was proposed for the dynamic probabilistic analysis of high-pressure turbine blade-tip radial running clearance (BTRRC). For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective. |
| doi_str_mv | 10.1007/s11071-016-2883-1 |
| format | Article |
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For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective.</description><identifier>ISSN: 0924-090X</identifier><identifier>EISSN: 1573-269X</identifier><identifier>DOI: 10.1007/s11071-016-2883-1</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Automotive Engineering ; Blade tips ; Blades ; Centrifugal force ; Classical Mechanics ; Clearances ; Collaboration ; Computer simulation ; Control ; Deformation ; Dynamic tests ; Dynamical Systems ; Engineering ; High pressure ; Loads (forces) ; Material properties ; Mathematical models ; Mechanical Engineering ; Monte Carlo simulation ; Nonlinear dynamics ; Original Paper ; Probabilistic analysis ; Probabilistic methods ; Probability theory ; Support vector machines ; Thermal analysis ; Thermal expansion ; Turbine blades ; Turbines ; Turbomachinery ; Vibration</subject><ispartof>Nonlinear dynamics, 2016-10, Vol.86 (1), p.205-223</ispartof><rights>Springer Science+Business Media Dordrecht 2016</rights><rights>Copyright Springer Science & Business Media 2016</rights><rights>Nonlinear Dynamics is a copyright of Springer, (2016). 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For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective.</description><subject>Automotive Engineering</subject><subject>Blade tips</subject><subject>Blades</subject><subject>Centrifugal force</subject><subject>Classical Mechanics</subject><subject>Clearances</subject><subject>Collaboration</subject><subject>Computer simulation</subject><subject>Control</subject><subject>Deformation</subject><subject>Dynamic tests</subject><subject>Dynamical Systems</subject><subject>Engineering</subject><subject>High pressure</subject><subject>Loads (forces)</subject><subject>Material properties</subject><subject>Mathematical models</subject><subject>Mechanical Engineering</subject><subject>Monte Carlo simulation</subject><subject>Nonlinear dynamics</subject><subject>Original Paper</subject><subject>Probabilistic analysis</subject><subject>Probabilistic methods</subject><subject>Probability theory</subject><subject>Support vector machines</subject><subject>Thermal analysis</subject><subject>Thermal expansion</subject><subject>Turbine blades</subject><subject>Turbines</subject><subject>Turbomachinery</subject><subject>Vibration</subject><issn>0924-090X</issn><issn>1573-269X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kU9LxDAQxYMouK5-AG8BL16ik7RN06Osf2HBi8LeQpom3SzdtCbtwW9vynoQQU_DDL_3eMND6JLCDQUobyOlUFIClBMmREboEVrQoswI49XmGC2gYjmBCjan6CzGHQBkDMQCqftPr_ZO4yH0tapd5-KYtsZE13qshnRWeot7i7eu3ZIhmBinYPA4hdp5g-tONYaMbsBBNU51OEzeO99i3RkVlNfmHJ1Y1UVz8T2X6P3x4W31TNavTy-ruzXROYORiKJqeJUBE7rWCkReU7BMWGG5KSzPGl02OiEmo4VoRGWhyvJC8cZSW-aaZ0t0ffBNkT8mE0e5d1GbrlPe9FOUVAiA9HMuEnr1C931U_ApnWSMA-O0BPofNXuJMhc8TxQ9UDr0MQZj5RDcXoVPSUHO1chDNTJVI-dq5OzMDpqYWN-a8MP5T9EXt1SQ6w</recordid><startdate>20161001</startdate><enddate>20161001</enddate><creator>Fei, Cheng-Wei</creator><creator>Choy, Yat-Sze</creator><creator>Hu, Dian-Yin</creator><creator>Bai, Guang-Chen</creator><creator>Tang, Wen-Zhong</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-5333-1055</orcidid></search><sort><creationdate>20161001</creationdate><title>Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance</title><author>Fei, Cheng-Wei ; Choy, Yat-Sze ; Hu, Dian-Yin ; Bai, Guang-Chen ; Tang, Wen-Zhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-859d693028cbca084b10f28f8f6e5f63dc7dc9d6e3158d89f09345a6df1f74c63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Automotive Engineering</topic><topic>Blade tips</topic><topic>Blades</topic><topic>Centrifugal force</topic><topic>Classical Mechanics</topic><topic>Clearances</topic><topic>Collaboration</topic><topic>Computer simulation</topic><topic>Control</topic><topic>Deformation</topic><topic>Dynamic tests</topic><topic>Dynamical Systems</topic><topic>Engineering</topic><topic>High pressure</topic><topic>Loads (forces)</topic><topic>Material properties</topic><topic>Mathematical models</topic><topic>Mechanical Engineering</topic><topic>Monte Carlo simulation</topic><topic>Nonlinear dynamics</topic><topic>Original Paper</topic><topic>Probabilistic analysis</topic><topic>Probabilistic methods</topic><topic>Probability theory</topic><topic>Support vector machines</topic><topic>Thermal analysis</topic><topic>Thermal expansion</topic><topic>Turbine blades</topic><topic>Turbines</topic><topic>Turbomachinery</topic><topic>Vibration</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fei, Cheng-Wei</creatorcontrib><creatorcontrib>Choy, Yat-Sze</creatorcontrib><creatorcontrib>Hu, Dian-Yin</creatorcontrib><creatorcontrib>Bai, Guang-Chen</creatorcontrib><creatorcontrib>Tang, Wen-Zhong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</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>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>Nonlinear dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fei, Cheng-Wei</au><au>Choy, Yat-Sze</au><au>Hu, Dian-Yin</au><au>Bai, Guang-Chen</au><au>Tang, Wen-Zhong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance</atitle><jtitle>Nonlinear dynamics</jtitle><stitle>Nonlinear Dyn</stitle><date>2016-10-01</date><risdate>2016</risdate><volume>86</volume><issue>1</issue><spage>205</spage><epage>223</epage><pages>205-223</pages><issn>0924-090X</issn><eissn>1573-269X</eissn><abstract>To develop the high performance and high reliability of turbomachinery just like an aeroengine, distributed collaborative time-varying least squares support vector machine (LSSVM) (called as DC-T-LSSVM) method was proposed for the dynamic probabilistic analysis of high-pressure turbine blade-tip radial running clearance (BTRRC). For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11071-016-2883-1</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0001-5333-1055</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Automotive Engineering Blade tips Blades Centrifugal force Classical Mechanics Clearances Collaboration Computer simulation Control Deformation Dynamic tests Dynamical Systems Engineering High pressure Loads (forces) Material properties Mathematical models Mechanical Engineering Monte Carlo simulation Nonlinear dynamics Original Paper Probabilistic analysis Probabilistic methods Probability theory Support vector machines Thermal analysis Thermal expansion Turbine blades Turbines Turbomachinery Vibration |
| title | Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance |
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