Moment preserving constrained resampling with applications to particle-in-cell methods
•A Moment Preserving Constrained Resampling (MPCR) algorithm is developed for particle-in-cell (PIC) simulations of plasmas.•MPCR algorithm is designed to conserve any number of particle and grid moments with a high degree of accuracy.•Applying MPCR to PIC simulations increases the accuracy, reduces...
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| Veröffentlicht in: | Journal of computational physics 2020-05, Vol.409 (C), p.109317, Article 109317 |
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| creator | Faghihi, D. Carey, V. Michoski, C. Hager, R. Janhunen, S. Chang, C.S. Moser, R.D. |
| description | •A Moment Preserving Constrained Resampling (MPCR) algorithm is developed for particle-in-cell (PIC) simulations of plasmas.•MPCR algorithm is designed to conserve any number of particle and grid moments with a high degree of accuracy.•Applying MPCR to PIC simulations increases the accuracy, reduces the compute cost, and prevents the numerical instabilities.•The effectiveness of MPCR is demonstrated with several numerical tests, including gyrokinetic fusion plasma simulations.
The Moment Preserving Constrained Resampling (MPCR) algorithm for particle resampling is introduced and applied to particle-in-cell (PIC) methods to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. The general algorithm partitions the system space into smaller subsets and resamples the distribution within each subset. Further, the algorithm is designed to conserve any number of particle and grid moments with a high degree of accuracy (i.e. machine accuracy). The effectiveness of MPCR is demonstrated with several numerical tests, including a use-case study in gyrokinetic fusion plasma simulations. The computational cost of MPCR is negligible compared to the cost of particle evolution in PIC methods, and the tests demonstrate that periodic particle resampling yields a significant improvement in the accuracy and stability of the results. |
| doi_str_mv | 10.1016/j.jcp.2020.109317 |
| format | Article |
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The Moment Preserving Constrained Resampling (MPCR) algorithm for particle resampling is introduced and applied to particle-in-cell (PIC) methods to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. The general algorithm partitions the system space into smaller subsets and resamples the distribution within each subset. Further, the algorithm is designed to conserve any number of particle and grid moments with a high degree of accuracy (i.e. machine accuracy). The effectiveness of MPCR is demonstrated with several numerical tests, including a use-case study in gyrokinetic fusion plasma simulations. The computational cost of MPCR is negligible compared to the cost of particle evolution in PIC methods, and the tests demonstrate that periodic particle resampling yields a significant improvement in the accuracy and stability of the results.</description><identifier>ISSN: 0021-9991</identifier><identifier>EISSN: 1090-2716</identifier><identifier>DOI: 10.1016/j.jcp.2020.109317</identifier><language>eng</language><publisher>Cambridge: Elsevier Inc</publisher><subject>Accuracy ; Algorithms ; Computational physics ; Computer simulation ; Computing costs ; Constrained optimization ; Distribution function moments ; Particle in cell technique ; Particle resampling ; Particle-in-cell ; Resampling</subject><ispartof>Journal of computational physics, 2020-05, Vol.409 (C), p.109317, Article 109317</ispartof><rights>2020</rights><rights>Copyright Elsevier Science Ltd. May 15, 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c395t-f2fc9d1b20399448c3496c55191d67a58651a9775e7a99aa9750141dcee26bb63</citedby><cites>FETCH-LOGICAL-c395t-f2fc9d1b20399448c3496c55191d67a58651a9775e7a99aa9750141dcee26bb63</cites><orcidid>0000-0002-6356-233X ; 000000026356233X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jcp.2020.109317$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/1600081$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Faghihi, D.</creatorcontrib><creatorcontrib>Carey, V.</creatorcontrib><creatorcontrib>Michoski, C.</creatorcontrib><creatorcontrib>Hager, R.</creatorcontrib><creatorcontrib>Janhunen, S.</creatorcontrib><creatorcontrib>Chang, C.S.</creatorcontrib><creatorcontrib>Moser, R.D.</creatorcontrib><title>Moment preserving constrained resampling with applications to particle-in-cell methods</title><title>Journal of computational physics</title><description>•A Moment Preserving Constrained Resampling (MPCR) algorithm is developed for particle-in-cell (PIC) simulations of plasmas.•MPCR algorithm is designed to conserve any number of particle and grid moments with a high degree of accuracy.•Applying MPCR to PIC simulations increases the accuracy, reduces the compute cost, and prevents the numerical instabilities.•The effectiveness of MPCR is demonstrated with several numerical tests, including gyrokinetic fusion plasma simulations.
The Moment Preserving Constrained Resampling (MPCR) algorithm for particle resampling is introduced and applied to particle-in-cell (PIC) methods to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. The general algorithm partitions the system space into smaller subsets and resamples the distribution within each subset. Further, the algorithm is designed to conserve any number of particle and grid moments with a high degree of accuracy (i.e. machine accuracy). The effectiveness of MPCR is demonstrated with several numerical tests, including a use-case study in gyrokinetic fusion plasma simulations. The computational cost of MPCR is negligible compared to the cost of particle evolution in PIC methods, and the tests demonstrate that periodic particle resampling yields a significant improvement in the accuracy and stability of the results.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Computational physics</subject><subject>Computer simulation</subject><subject>Computing costs</subject><subject>Constrained optimization</subject><subject>Distribution function moments</subject><subject>Particle in cell technique</subject><subject>Particle resampling</subject><subject>Particle-in-cell</subject><subject>Resampling</subject><issn>0021-9991</issn><issn>1090-2716</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kM1OwzAQhC0EEqXwANwiOKd4ncSJxQlV_ElFXICr5Tob6iiJg-0W8fY4CmdO3l19Y80MIZdAV0CB37SrVo8rRtm0iwzKI7KIA01ZCfyYLChlkAoh4JSced9SSqsirxbk48X2OIRkdOjRHczwmWg7-OCUGbBO4lX1Yzedv03YJWqMi1bBRCYJNhmVC0Z3mJoh1dh1SY9hZ2t_Tk4a1Xm8-HuX5P3h_m39lG5eH5_Xd5tUZ6IIacMaLWrYMpoJkeeVznLBdVGAgJqXqqh4AUqUZYGlEkLFsaCQQ60RGd9uebYkV_O_1gcjvTYB9S4GGFAHCXyKCRG6nqHR2a89-iBbu3dD9CVZnkNVcahYpGCmtLPeO2zk6Eyv3I8EKqeOZStjx3LqWM4dR83trMGY8WDQTRZw0FgbNzmorflH_QuF24PX</recordid><startdate>20200515</startdate><enddate>20200515</enddate><creator>Faghihi, D.</creator><creator>Carey, V.</creator><creator>Michoski, C.</creator><creator>Hager, R.</creator><creator>Janhunen, S.</creator><creator>Chang, C.S.</creator><creator>Moser, R.D.</creator><general>Elsevier Inc</general><general>Elsevier Science Ltd</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>OTOTI</scope><orcidid>https://orcid.org/0000-0002-6356-233X</orcidid><orcidid>https://orcid.org/000000026356233X</orcidid></search><sort><creationdate>20200515</creationdate><title>Moment preserving constrained resampling with applications to particle-in-cell methods</title><author>Faghihi, D. ; 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The Moment Preserving Constrained Resampling (MPCR) algorithm for particle resampling is introduced and applied to particle-in-cell (PIC) methods to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. The general algorithm partitions the system space into smaller subsets and resamples the distribution within each subset. Further, the algorithm is designed to conserve any number of particle and grid moments with a high degree of accuracy (i.e. machine accuracy). The effectiveness of MPCR is demonstrated with several numerical tests, including a use-case study in gyrokinetic fusion plasma simulations. The computational cost of MPCR is negligible compared to the cost of particle evolution in PIC methods, and the tests demonstrate that periodic particle resampling yields a significant improvement in the accuracy and stability of the results.</abstract><cop>Cambridge</cop><pub>Elsevier Inc</pub><doi>10.1016/j.jcp.2020.109317</doi><orcidid>https://orcid.org/0000-0002-6356-233X</orcidid><orcidid>https://orcid.org/000000026356233X</orcidid><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Accuracy Algorithms Computational physics Computer simulation Computing costs Constrained optimization Distribution function moments Particle in cell technique Particle resampling Particle-in-cell Resampling |
| title | Moment preserving constrained resampling with applications to particle-in-cell methods |
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