Comparison of Start-Up Runaway Electron Generation Simulations Using the SCENPLINT Code with JET Experimental Observations
Nuclear Fusion(2025)
ITER Organization
Abstract
This paper explores the process of runaway electron (RE) formation during the start-up of a tokamak discharge. This has been done by simulating the process of RE generation and, importantly their losses, self-consistently with the behavior of many other discharge parameters, using the SCENPLINT code, which has been updated to include RE physics. For the first time, an attempt is made to compare such simulations with experimental observations on start-up RE formation at JET. This proved to be a difficult task due to the many parameters that are self-consistently calculated by the code, the possible explosive growth of RE and the fact that the diagnosis of the simulated parameters is not always very accurate. A sensitivity study on the impact of various simulation assumptions was conducted. It was found that because during the JET start-up the ratio of the electric field to the critical electric field, E / E _c is limited, using the secondary generation model proposed by Aleynikov and Breizman ( Phys. Rev. Lett. 114 155001) ensures a better match to the experimental observations. This model predicts a reduced secondary generation when E / E _c < 5 for start-up plasmas. Furthermore, the simulation results depended strongly on the assumed level of RE losses. Assuming the RE confinement time scales with the discharge current provide a better result. It was not, however, possible to determine which RE loss model, one based on drift-orbit losses or another based on RE diffusion due to magnetic turbulence, provided a better match to the experimental observations. These findings will be crucial to improve predictions of start-up RE formation in future devices such as ITER.
MoreTranslated text
Key words
tokamak,runaway electrons,tokamak start-up
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined