An Efficient Surrogate Model of Secondary Electron Formation and Evolution
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| Format: | Preprint |
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2024
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| _version_ | 1866917871160918016 |
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| author | McDevitt, Christopher J. Arnaud, Jonathan Tang, Xian-Zhu |
| author_facet | McDevitt, Christopher J. Arnaud, Jonathan Tang, Xian-Zhu |
| contents | This work extends the adjoint-deep learning framework for runaway electron (RE) evolution developed in Ref. [C. McDevitt et al., A physics-constrained deep learning treatment of runaway electron dynamics, Submitted to Physics of Plasmas (2024)] to account for large-angle collisions. By incorporating large-angle collisions the framework allows the avalanche of REs to be captured, an essential component to RE dynamics. This extension is accomplished by using a Rosenbluth-Putvinski approximation to estimate the distribution of secondary electrons generated by large-angle collisions. By evolving both the primary and multiple generations of secondary electrons, the present formulation is able to capture both the detailed temporal evolution of a RE population beginning from an arbitrary initial momentum space distribution, along with providing approximations to the saturated growth and decay rates of the RE population. Predictions of the adjoint-deep learning framework are verified against a traditional RE solver, with good agreement present across a broad range of parameters. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_13044 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Efficient Surrogate Model of Secondary Electron Formation and Evolution McDevitt, Christopher J. Arnaud, Jonathan Tang, Xian-Zhu Plasma Physics This work extends the adjoint-deep learning framework for runaway electron (RE) evolution developed in Ref. [C. McDevitt et al., A physics-constrained deep learning treatment of runaway electron dynamics, Submitted to Physics of Plasmas (2024)] to account for large-angle collisions. By incorporating large-angle collisions the framework allows the avalanche of REs to be captured, an essential component to RE dynamics. This extension is accomplished by using a Rosenbluth-Putvinski approximation to estimate the distribution of secondary electrons generated by large-angle collisions. By evolving both the primary and multiple generations of secondary electrons, the present formulation is able to capture both the detailed temporal evolution of a RE population beginning from an arbitrary initial momentum space distribution, along with providing approximations to the saturated growth and decay rates of the RE population. Predictions of the adjoint-deep learning framework are verified against a traditional RE solver, with good agreement present across a broad range of parameters. |
| title | An Efficient Surrogate Model of Secondary Electron Formation and Evolution |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2412.13044 |