Design Optimization of Multipole Galatea Trap Coils by Multiple Population Genetic Algorithm
In order to improve the performance of multipole Galatea traps, this paper proposes an optimized method for the design of coil parameters. Based on an accurate magnetic field model, key parameters describing the multipole Galatea magnetic trap configuration were analyzed to establish an optimization...
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| Veröffentlicht in: | IEEE transactions on plasma science 2016-06, Vol.44 (6), p.1018-1024 |
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| container_title | IEEE transactions on plasma science |
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| creator | Tong, Weiming Tao, Baoquan Jin, Xianji Li, Zhongwei |
| description | In order to improve the performance of multipole Galatea traps, this paper proposes an optimized method for the design of coil parameters. Based on an accurate magnetic field model, key parameters describing the multipole Galatea magnetic trap configuration were analyzed to establish an optimization model using the axial electromagnetic force, weak magnetic field area, and average magnetic mirror ratio as the optimization goals with the coil current as the design variable. Applying the improved multiple population genetic algorithm (MPGA), which has a strong searching ability and a fast convergence speed, enables production of optimization results following selection of the appropriate weight coefficients. Results confirm that optimization design results from MPGA are consistent with the design goals for different weight coefficients. In addition, the performance of multipole Galatea magnetic traps with optimization coil parameters was improved. Finally, the results from finite-element simulation software proved the validity and feasibility of the proposed method. |
| doi_str_mv | 10.1109/TPS.2016.2563978 |
| format | Article |
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Based on an accurate magnetic field model, key parameters describing the multipole Galatea magnetic trap configuration were analyzed to establish an optimization model using the axial electromagnetic force, weak magnetic field area, and average magnetic mirror ratio as the optimization goals with the coil current as the design variable. Applying the improved multiple population genetic algorithm (MPGA), which has a strong searching ability and a fast convergence speed, enables production of optimization results following selection of the appropriate weight coefficients. Results confirm that optimization design results from MPGA are consistent with the design goals for different weight coefficients. In addition, the performance of multipole Galatea magnetic traps with optimization coil parameters was improved. Finally, the results from finite-element simulation software proved the validity and feasibility of the proposed method.</description><identifier>ISSN: 0093-3813</identifier><identifier>EISSN: 1939-9375</identifier><identifier>DOI: 10.1109/TPS.2016.2563978</identifier><identifier>CODEN: ITPSBD</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Average magnetic mirror ratio ; axial electromagnetic force ; Coils ; Convergence ; Design analysis ; Design optimization ; Electromagnetic forces ; Galatea ; Genetic algorithms ; Magnetic analysis ; Magnetic fields ; Magnetic levitation ; Mathematical models ; multiple population genetic algorithm (MPGA) ; Multipoles ; Optimization ; Plasmas ; Simulation ; Software ; Superconducting magnets ; weak magnetic field area</subject><ispartof>IEEE transactions on plasma science, 2016-06, Vol.44 (6), p.1018-1024</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jun 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c324t-b3b296241a7ed0e7329f01cbf809b55daffed9cc5d7a668da4327541aac0008b3</citedby><cites>FETCH-LOGICAL-c324t-b3b296241a7ed0e7329f01cbf809b55daffed9cc5d7a668da4327541aac0008b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7476856$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7476856$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tong, Weiming</creatorcontrib><creatorcontrib>Tao, Baoquan</creatorcontrib><creatorcontrib>Jin, Xianji</creatorcontrib><creatorcontrib>Li, Zhongwei</creatorcontrib><title>Design Optimization of Multipole Galatea Trap Coils by Multiple Population Genetic Algorithm</title><title>IEEE transactions on plasma science</title><addtitle>TPS</addtitle><description>In order to improve the performance of multipole Galatea traps, this paper proposes an optimized method for the design of coil parameters. Based on an accurate magnetic field model, key parameters describing the multipole Galatea magnetic trap configuration were analyzed to establish an optimization model using the axial electromagnetic force, weak magnetic field area, and average magnetic mirror ratio as the optimization goals with the coil current as the design variable. Applying the improved multiple population genetic algorithm (MPGA), which has a strong searching ability and a fast convergence speed, enables production of optimization results following selection of the appropriate weight coefficients. Results confirm that optimization design results from MPGA are consistent with the design goals for different weight coefficients. In addition, the performance of multipole Galatea magnetic traps with optimization coil parameters was improved. Finally, the results from finite-element simulation software proved the validity and feasibility of the proposed method.</description><subject>Average magnetic mirror ratio</subject><subject>axial electromagnetic force</subject><subject>Coils</subject><subject>Convergence</subject><subject>Design analysis</subject><subject>Design optimization</subject><subject>Electromagnetic forces</subject><subject>Galatea</subject><subject>Genetic algorithms</subject><subject>Magnetic analysis</subject><subject>Magnetic fields</subject><subject>Magnetic levitation</subject><subject>Mathematical models</subject><subject>multiple population genetic algorithm (MPGA)</subject><subject>Multipoles</subject><subject>Optimization</subject><subject>Plasmas</subject><subject>Simulation</subject><subject>Software</subject><subject>Superconducting magnets</subject><subject>weak magnetic field area</subject><issn>0093-3813</issn><issn>1939-9375</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkM9LwzAYhoMoOKd3wUvBi5fO_Gia5jimTkHZwHkTQtqmMyNtapIe5l9vRocHT9_hfd6XjweAawRnCEF-v1m_zzBE-QzTnHBWnIAJ4oSnnDB6CiYQcpKSApFzcOH9DkKUUYgn4PNBeb3tklUfdKt_ZNC2S2yTvA0m6N4alSylkUHJZONknyysNj4p98c8xmvbD2asLVWngq6Sudlap8NXewnOGmm8ujreKfh4etwsntPX1fJlMX9NK4KzkJakxDzHGZJM1VAxgnkDUVU2BeQlpbVsGlXzqqI1k3le1DIjmNGIywpCWJRkCu7G3d7Z70H5IFrtK2WM7JQdvEAFphnPMCURvf2H7uzguvidQCxqyynjBwqOVOWs9041one6lW4vEBQH3SLqFgfd4qg7Vm7GilZK_eEsY3kRiV_dVXvf</recordid><startdate>201606</startdate><enddate>201606</enddate><creator>Tong, Weiming</creator><creator>Tao, Baoquan</creator><creator>Jin, Xianji</creator><creator>Li, Zhongwei</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>201606</creationdate><title>Design Optimization of Multipole Galatea Trap Coils by Multiple Population Genetic Algorithm</title><author>Tong, Weiming ; Tao, Baoquan ; Jin, Xianji ; Li, Zhongwei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c324t-b3b296241a7ed0e7329f01cbf809b55daffed9cc5d7a668da4327541aac0008b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Average magnetic mirror ratio</topic><topic>axial electromagnetic force</topic><topic>Coils</topic><topic>Convergence</topic><topic>Design analysis</topic><topic>Design optimization</topic><topic>Electromagnetic forces</topic><topic>Galatea</topic><topic>Genetic algorithms</topic><topic>Magnetic analysis</topic><topic>Magnetic fields</topic><topic>Magnetic levitation</topic><topic>Mathematical models</topic><topic>multiple population genetic algorithm (MPGA)</topic><topic>Multipoles</topic><topic>Optimization</topic><topic>Plasmas</topic><topic>Simulation</topic><topic>Software</topic><topic>Superconducting magnets</topic><topic>weak magnetic field area</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tong, Weiming</creatorcontrib><creatorcontrib>Tao, Baoquan</creatorcontrib><creatorcontrib>Jin, Xianji</creatorcontrib><creatorcontrib>Li, Zhongwei</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on plasma science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tong, Weiming</au><au>Tao, Baoquan</au><au>Jin, Xianji</au><au>Li, Zhongwei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Design Optimization of Multipole Galatea Trap Coils by Multiple Population Genetic Algorithm</atitle><jtitle>IEEE transactions on plasma science</jtitle><stitle>TPS</stitle><date>2016-06</date><risdate>2016</risdate><volume>44</volume><issue>6</issue><spage>1018</spage><epage>1024</epage><pages>1018-1024</pages><issn>0093-3813</issn><eissn>1939-9375</eissn><coden>ITPSBD</coden><abstract>In order to improve the performance of multipole Galatea traps, this paper proposes an optimized method for the design of coil parameters. Based on an accurate magnetic field model, key parameters describing the multipole Galatea magnetic trap configuration were analyzed to establish an optimization model using the axial electromagnetic force, weak magnetic field area, and average magnetic mirror ratio as the optimization goals with the coil current as the design variable. Applying the improved multiple population genetic algorithm (MPGA), which has a strong searching ability and a fast convergence speed, enables production of optimization results following selection of the appropriate weight coefficients. Results confirm that optimization design results from MPGA are consistent with the design goals for different weight coefficients. In addition, the performance of multipole Galatea magnetic traps with optimization coil parameters was improved. Finally, the results from finite-element simulation software proved the validity and feasibility of the proposed method.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPS.2016.2563978</doi><tpages>7</tpages></addata></record> |
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| subjects | Average magnetic mirror ratio axial electromagnetic force Coils Convergence Design analysis Design optimization Electromagnetic forces Galatea Genetic algorithms Magnetic analysis Magnetic fields Magnetic levitation Mathematical models multiple population genetic algorithm (MPGA) Multipoles Optimization Plasmas Simulation Software Superconducting magnets weak magnetic field area |
| title | Design Optimization of Multipole Galatea Trap Coils by Multiple Population Genetic Algorithm |
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