Meihuizi He, Zongyu Yang, Songfen Liu, Fan Xia and Wulyu Zhong
{"title":"Identifying L-H transition in HL-2A through deep learning","authors":"Meihuizi He, Zongyu Yang, Songfen Liu, Fan Xia and Wulyu Zhong","doi":"10.1088/1361-6587/ad75b7","DOIUrl":null,"url":null,"abstract":"During the operation of tokamak devices, addressing the thermal load issues caused by edge localized modes (ELMs) eruption is crucial. Ideally, mitigation and suppression measures for ELMs should be promptly initiated as soon as the first low-to-high confinement (L-H) transition occurs, which necessitates the real-time monitoring and accurate identification of the L-H transition process. Motivated by this, and by recent deep learning boom, we propose a deep learning-based L-H transition identification algorithm on HL-2A tokamak. In this work, we have constructed a neural network comprising layers of Residual long short-term memory and temporal convolutional network. Unlike previous work based on recognition for ELMs by slice, this method implements recognition on L-H transition process before the first ELMs crash. Therefore the mitigation techniques can be triggered in time to suppress the initial ELMs bursts. In order to further explain the effectiveness of the algorithm, we developed a series of evaluation indicators by shots, and the results show that this algorithm can provide necessary reference for the mitigation and suppression system.","PeriodicalId":20239,"journal":{"name":"Plasma Physics and Controlled Fusion","volume":"21 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plasma Physics and Controlled Fusion","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1361-6587/ad75b7","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, FLUIDS & PLASMAS","Score":null,"Total":0}
引用次数: 0
Abstract
During the operation of tokamak devices, addressing the thermal load issues caused by edge localized modes (ELMs) eruption is crucial. Ideally, mitigation and suppression measures for ELMs should be promptly initiated as soon as the first low-to-high confinement (L-H) transition occurs, which necessitates the real-time monitoring and accurate identification of the L-H transition process. Motivated by this, and by recent deep learning boom, we propose a deep learning-based L-H transition identification algorithm on HL-2A tokamak. In this work, we have constructed a neural network comprising layers of Residual long short-term memory and temporal convolutional network. Unlike previous work based on recognition for ELMs by slice, this method implements recognition on L-H transition process before the first ELMs crash. Therefore the mitigation techniques can be triggered in time to suppress the initial ELMs bursts. In order to further explain the effectiveness of the algorithm, we developed a series of evaluation indicators by shots, and the results show that this algorithm can provide necessary reference for the mitigation and suppression system.
期刊介绍:
Plasma Physics and Controlled Fusion covers all aspects of the physics of hot, highly ionised plasmas. This includes results of current experimental and theoretical research on all aspects of the physics of high-temperature plasmas and of controlled nuclear fusion, including the basic phenomena in highly-ionised gases in the laboratory, in the ionosphere and in space, in magnetic-confinement and inertial-confinement fusion as well as related diagnostic methods.
Papers with a technological emphasis, for example in such topics as plasma control, fusion technology and diagnostics, are welcomed when the plasma physics is an integral part of the paper or when the technology is unique to plasma applications or new to the field of plasma physics. Papers on dusty plasma physics are welcome when there is a clear relevance to fusion.