Academic Journal
Static performance prediction of long-pulse negative ion based neutral beam injection experiment
| العنوان: | Static performance prediction of long-pulse negative ion based neutral beam injection experiment |
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| المؤلفون: | Li, Yang, Hu, Chundong, Zhao, Yuanzhe, Gu, Yu, Cui, Qinglong, Xie, Yahong |
| المساهمون: | Comprehensive Research Facility for Fusion Technology Program of China |
| المصدر: | Plasma Physics and Controlled Fusion ; volume 66, issue 6, page 065008 ; ISSN 0741-3335 1361-6587 |
| بيانات النشر: | IOP Publishing |
| سنة النشر: | 2024 |
| الوصف: | The mission of negative ion-based neutral beam injection (NNBI) is to conduct experiments with pulses lasting thousands of seconds. It is crucial to develop a simplified physical calculation model for the long-pulse negative ion source in the current NNBI device. This model will be used to evaluate the advantages and disadvantages of the selected parameters prior to the experiment, and to assist in adjusting and establishing the experimental parameters for the long-pulse ion source experiment. This paper presents the development of a static performance prediction model using a back propagation neural network. The model assesses the yield of negative hydrogen ions and the quantity of electrons in the ion source under specific parameter conditions, utilizing various experimental parameters as input. The experimental data used for this model are derived from historical data generated during the operation of the 2022 NNBI experiment. The test results indicate that under the current optimal hyperparameter condition, the prediction accuracy of H − ion current (I_H − ) is 80.84%, and the prediction accuracy of extraction grid electronic current (I_EG) is 77.57%. This can effectively prevent invalid shots, accurately assess the advantages and disadvantages of the input parameters, and enhance the performance of the long-pulse NNBI device. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| DOI: | 10.1088/1361-6587/ad3e2c |
| DOI: | 10.1088/1361-6587/ad3e2c/pdf |
| الاتاحة: | http://dx.doi.org/10.1088/1361-6587/ad3e2c https://iopscience.iop.org/article/10.1088/1361-6587/ad3e2c https://iopscience.iop.org/article/10.1088/1361-6587/ad3e2c/pdf |
| Rights: | https://iopscience.iop.org/page/copyright ; https://iopscience.iop.org/info/page/text-and-data-mining |
| رقم الانضمام: | edsbas.2EF5BA15 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1088/1361-6587/ad3e2c |
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