Development of a data-driven neural network model for electron thermal transport in NSTX.
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| Title: | Development of a data-driven neural network model for electron thermal transport in NSTX. |
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| Authors: | Chung, H.1 (AUTHOR), Lee, C.Y.2 (AUTHOR), Choi, G.J.3 (AUTHOR), Kaye, S.M.4 (AUTHOR), LeBlanc, B.P.4 (AUTHOR), Berkery, J.W.4 (AUTHOR), Na, Y.-S.1 (AUTHOR) ysna@snu.ac.kr |
| Source: | Nuclear Fusion. Aug2025, Vol. 65 Issue 8, p1-14. 14p. |
| Subjects: | Electron transport, Tokamaks, Plasma physics, Turbulent flow, Parameterization, Artificial neural networks, Plasma turbulence, Machine learning |
| Abstract: | A data-driven electron thermal transport neural network (ETT-NN) model, trained on TRANSP interpretative analysis results of National Spherical Torus Experiment (NSTX), was developed to enable faster and more accurate ETT computation for spherical tokamaks (STs). The model incorporates both convolutional NNs and recurrent NNs, allowing it to simultaneously account for the spatial and temporal non-localities and multi-scale features of turbulent transport, which have been considered only in a limited manner in conventional models. The model was validated through interpretative analysis and predictive simulations using Tokamak Reactor Integrated Automated Suite for Simulation and Computation, demonstrating relatively high accuracy. Additionally, parameter scans were performed on test discharges known to exhibit specific turbulent modes, such as microtearing mode, trapped electron mode, kinetic ballooning mode, and electron temperature gradient mode. The scanning results revealed that the ETT-NN model exhibits the same trends as those observed in conventional gyrokinetic simulations or theories, while also capturing the global nature of turbulent transport, indicating that the data-driven model accurately reflects the underlying physical characteristics. Furthermore, due to the dimensionless nature of the model, we can feasibly expand its applicability by incorporating data from other devices and uncovering the characteristics of ETT in STs in the future. [ABSTRACT FROM AUTHOR] |
| Copyright of Nuclear Fusion is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187906154 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of a data-driven neural network model for electron thermal transport in NSTX. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chung%2C+H%2E%22">Chung, H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+C%2EY%2E%22">Lee, C.Y.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Choi%2C+G%2EJ%2E%22">Choi, G.J.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kaye%2C+S%2EM%2E%22">Kaye, S.M.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22LeBlanc%2C+B%2EP%2E%22">LeBlanc, B.P.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Berkery%2C+J%2EW%2E%22">Berkery, J.W.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Na%2C+Y%2E-S%2E%22">Na, Y.-S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ysna@snu.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nuclear+Fusion%22">Nuclear Fusion</searchLink>. Aug2025, Vol. 65 Issue 8, p1-14. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electron+transport%22">Electron transport</searchLink><br /><searchLink fieldCode="DE" term="%22Tokamaks%22">Tokamaks</searchLink><br /><searchLink fieldCode="DE" term="%22Plasma+physics%22">Plasma physics</searchLink><br /><searchLink fieldCode="DE" term="%22Turbulent+flow%22">Turbulent flow</searchLink><br /><searchLink fieldCode="DE" term="%22Parameterization%22">Parameterization</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Plasma+turbulence%22">Plasma turbulence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A data-driven electron thermal transport neural network (ETT-NN) model, trained on TRANSP interpretative analysis results of National Spherical Torus Experiment (NSTX), was developed to enable faster and more accurate ETT computation for spherical tokamaks (STs). The model incorporates both convolutional NNs and recurrent NNs, allowing it to simultaneously account for the spatial and temporal non-localities and multi-scale features of turbulent transport, which have been considered only in a limited manner in conventional models. The model was validated through interpretative analysis and predictive simulations using Tokamak Reactor Integrated Automated Suite for Simulation and Computation, demonstrating relatively high accuracy. Additionally, parameter scans were performed on test discharges known to exhibit specific turbulent modes, such as microtearing mode, trapped electron mode, kinetic ballooning mode, and electron temperature gradient mode. The scanning results revealed that the ETT-NN model exhibits the same trends as those observed in conventional gyrokinetic simulations or theories, while also capturing the global nature of turbulent transport, indicating that the data-driven model accurately reflects the underlying physical characteristics. Furthermore, due to the dimensionless nature of the model, we can feasibly expand its applicability by incorporating data from other devices and uncovering the characteristics of ETT in STs in the future. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nuclear Fusion is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1088/1741-4326/adec01 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Electron transport Type: general – SubjectFull: Tokamaks Type: general – SubjectFull: Plasma physics Type: general – SubjectFull: Turbulent flow Type: general – SubjectFull: Parameterization Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Plasma turbulence Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Development of a data-driven neural network model for electron thermal transport in NSTX. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chung, H. – PersonEntity: Name: NameFull: Lee, C.Y. – PersonEntity: Name: NameFull: Choi, G.J. – PersonEntity: Name: NameFull: Kaye, S.M. – PersonEntity: Name: NameFull: LeBlanc, B.P. – PersonEntity: Name: NameFull: Berkery, J.W. – PersonEntity: Name: NameFull: Na, Y.-S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00295515 Numbering: – Type: volume Value: 65 – Type: issue Value: 8 Titles: – TitleFull: Nuclear Fusion Type: main |
| ResultId | 1 |
