Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929307969912832 |
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| author | Rodriguez-Fernandez, P. Howard, N. T. Saltzman, A. Kantamneni, S. Candy, J. Holland, C. Balandat, M. Ament, S. White, A. E. |
| author_facet | Rodriguez-Fernandez, P. Howard, N. T. Saltzman, A. Kantamneni, S. Candy, J. Holland, C. Balandat, M. Ament, S. White, A. E. |
| contents | This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO. This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_12610 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers Rodriguez-Fernandez, P. Howard, N. T. Saltzman, A. Kantamneni, S. Candy, J. Holland, C. Balandat, M. Ament, S. White, A. E. Plasma Physics Machine Learning Computational Physics This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO. This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies. |
| title | Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers |
| topic | Plasma Physics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2312.12610 |