Prediction of Fishbone Linear Instability in Tokamaks with Machine Learning Methods
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916135536951296 |
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| author | Liu, Z. Y. Qiu, H. R. Fu, G. Y. Xiao, Y. Chen, Y. C. Wang, Z. J. Wei, Y. X. |
| author_facet | Liu, Z. Y. Qiu, H. R. Fu, G. Y. Xiao, Y. Chen, Y. C. Wang, Z. J. Wei, Y. X. |
| contents | A machine learning based surrogate model for fishbone linear instability in tokamaks is constructed. Hybrid simulations with the kinetic-magnetohydrodynamic (MHD) code M3D-K is used to generate the database of fishbone linear instability, through scanning the four key parameters which are thought to determine the fishbone physics. The four key parameters include (1) central total beta of both thermal plasma and fast ions, (2) the fast ion pressure fraction, (3) central value of safety factor $q$ and (4) the radius of $q=1$ surface. Four machine learning methods including linear regression, support vector machines (SVM) with linear kernel, SVM with nonlinear kernel and multi-layer perceptron are used to predict the fishbone instability, growth rate and real frequency, mode structure respectively. Among the four methods, SVM with nonlinear kernel performs very well to predict the linear instability with accuracy $\approx$95%, growth rate and real frequency with $R^2\approx$98%, mode structure with $R^2\approx$98%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction of Fishbone Linear Instability in Tokamaks with Machine Learning Methods Liu, Z. Y. Qiu, H. R. Fu, G. Y. Xiao, Y. Chen, Y. C. Wang, Z. J. Wei, Y. X. Plasma Physics A machine learning based surrogate model for fishbone linear instability in tokamaks is constructed. Hybrid simulations with the kinetic-magnetohydrodynamic (MHD) code M3D-K is used to generate the database of fishbone linear instability, through scanning the four key parameters which are thought to determine the fishbone physics. The four key parameters include (1) central total beta of both thermal plasma and fast ions, (2) the fast ion pressure fraction, (3) central value of safety factor $q$ and (4) the radius of $q=1$ surface. Four machine learning methods including linear regression, support vector machines (SVM) with linear kernel, SVM with nonlinear kernel and multi-layer perceptron are used to predict the fishbone instability, growth rate and real frequency, mode structure respectively. Among the four methods, SVM with nonlinear kernel performs very well to predict the linear instability with accuracy $\approx$95%, growth rate and real frequency with $R^2\approx$98%, mode structure with $R^2\approx$98%. |
| title | Prediction of Fishbone Linear Instability in Tokamaks with Machine Learning Methods |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2402.15051 |