Predict the last closed-flux surface evolution without physical simulation

Author:

Wan ChenguangORCID,Bai ShuhangORCID,Yu ZhiORCID,Yuan QipingORCID,Huang YaoORCID,Liu XiaojuanORCID,Hu YeminORCID,Li Jiangang

Abstract

Abstract One of the main challenges in developing effective control strategies for the magnetic control system in tokamaks has been the difficulty in obtaining the last closed-flux surface (LCFS) evolution results from control commands. We have developed a data-driven model that combines a predictive model and a surrogate model for physics simulation programs. This model is capable of predicting the LCFS without relying on physical simulation codes. Addressing the data characteristics of LCFS, we have proposed a specialized discretization approach to achieve dimensionality reduction. Furthermore, we have excluding the control references, the model can be seamlessly integrated into the control system, providing real-time LCFS prediction. Following comprehensive testing and multifaceted evaluation, our model has demonstrated highly satisfactory results of 95% or above, meeting practical requirements.

Funder

CASHIPS Director’s Fund

Postdoctoral Research Foundation of China

National Postdoctoral Program for Innovative Talents

Comprehensive Research Facility for Fusion Technology Program of China

National Natural Science Foundation of China

National Key R&D project

National MCF Energy R&D Program

Publisher

IOP Publishing

Subject

Condensed Matter Physics,Nuclear and High Energy Physics

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3