Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasma
Abstract Maintaining the high-temperature and pressure conditions required for sustained nuclear fusion is challenging due to the turbulent transport that naturally occurs in the plasma. Developing reliable models for turbulent transport is essential for progress in fusion research and development....
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78394-3 |
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| author | Shinya Maeyama Mitsuru Honda Emi Narita Shinichiro Toda |
| author_facet | Shinya Maeyama Mitsuru Honda Emi Narita Shinichiro Toda |
| author_sort | Shinya Maeyama |
| collection | DOAJ |
| description | Abstract Maintaining the high-temperature and pressure conditions required for sustained nuclear fusion is challenging due to the turbulent transport that naturally occurs in the plasma. Developing reliable models for turbulent transport is essential for progress in fusion research and development. This study proposes multi-fidelity modeling for the improved accuracy of regression models for turbulent transport in magnetic fusion plasma. Multi-fidelity modeling combines low-fidelity data, which have low accuracy but many data points, with high-fidelity data, which are highly accurate but have few data points or small parameter ranges, to enhance the overall predictive accuracy of a model. We used a multi-fidelity information fusion technique, Nonlinear AutoRegressive Gaussian Process regression (NARGP), to solve the regression problems associated with turbulent transport in plasma. We applied NARGP to (i) merge the low-resolution and high-resolution simulation results, (ii) apply regression of turbulence diffusivity to the experimental dataset using linear analyses, and (iii) adapt the quasi-linear transport model to nonlinear simulation results of a particular discharge. We demonstrated that NARGP improved the prediction accuracy of the plasma turbulent transport model. NARGP offers a robust and versatile method for integrating multi-fidelity data, and its broad applicability may contribute to optimizing fusion reactor design and operation. |
| format | Article |
| id | doaj-art-246f3c811e5c4f4ea0737a992fe34276 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-246f3c811e5c4f4ea0737a992fe342762024-12-15T12:09:04ZengNature PortfolioScientific Reports2045-23222024-12-0114111410.1038/s41598-024-78394-3Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasmaShinya Maeyama0Mitsuru Honda1Emi Narita2Shinichiro Toda3National Institute for Fusion ScienceGraduate School of Engineering, Kyoto UniversityGraduate School of Engineering, Kyoto UniversityNational Institute for Fusion ScienceAbstract Maintaining the high-temperature and pressure conditions required for sustained nuclear fusion is challenging due to the turbulent transport that naturally occurs in the plasma. Developing reliable models for turbulent transport is essential for progress in fusion research and development. This study proposes multi-fidelity modeling for the improved accuracy of regression models for turbulent transport in magnetic fusion plasma. Multi-fidelity modeling combines low-fidelity data, which have low accuracy but many data points, with high-fidelity data, which are highly accurate but have few data points or small parameter ranges, to enhance the overall predictive accuracy of a model. We used a multi-fidelity information fusion technique, Nonlinear AutoRegressive Gaussian Process regression (NARGP), to solve the regression problems associated with turbulent transport in plasma. We applied NARGP to (i) merge the low-resolution and high-resolution simulation results, (ii) apply regression of turbulence diffusivity to the experimental dataset using linear analyses, and (iii) adapt the quasi-linear transport model to nonlinear simulation results of a particular discharge. We demonstrated that NARGP improved the prediction accuracy of the plasma turbulent transport model. NARGP offers a robust and versatile method for integrating multi-fidelity data, and its broad applicability may contribute to optimizing fusion reactor design and operation.https://doi.org/10.1038/s41598-024-78394-3Multi-fidelity methodGaussian process regressionTurbulent transport modelingMagnetic fusion |
| spellingShingle | Shinya Maeyama Mitsuru Honda Emi Narita Shinichiro Toda Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasma Scientific Reports Multi-fidelity method Gaussian process regression Turbulent transport modeling Magnetic fusion |
| title | Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasma |
| title_full | Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasma |
| title_fullStr | Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasma |
| title_full_unstemmed | Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasma |
| title_short | Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasma |
| title_sort | multi fidelity information fusion for turbulent transport modeling in magnetic fusion plasma |
| topic | Multi-fidelity method Gaussian process regression Turbulent transport modeling Magnetic fusion |
| url | https://doi.org/10.1038/s41598-024-78394-3 |
| work_keys_str_mv | AT shinyamaeyama multifidelityinformationfusionforturbulenttransportmodelinginmagneticfusionplasma AT mitsuruhonda multifidelityinformationfusionforturbulenttransportmodelinginmagneticfusionplasma AT eminarita multifidelityinformationfusionforturbulenttransportmodelinginmagneticfusionplasma AT shinichirotoda multifidelityinformationfusionforturbulenttransportmodelinginmagneticfusionplasma |