Identifying L-H transition in HL-2A through deep learning

IF 2.1 2区 物理与天体物理 Q2 PHYSICS, FLUIDS & PLASMAS Plasma Physics and Controlled Fusion Pub Date : 2024-09-10 DOI:10.1088/1361-6587/ad75b7
Meihuizi He, Zongyu Yang, Songfen Liu, Fan Xia and Wulyu Zhong
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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.
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通过深度学习识别 HL-2A 中的 L-H 转变
在托卡马克装置的运行过程中,解决边缘局部模式(ELMs)爆发引起的热负荷问题至关重要。理想情况下,一旦发生第一次低约束到高约束(L-H)转变,就应立即启动缓解和抑制 ELM 的措施,这就需要实时监测和准确识别 L-H 转变过程。受此启发,并结合最近的深度学习热潮,我们在 HL-2A 托卡马克上提出了一种基于深度学习的 L-H 转换识别算法。在这项工作中,我们构建了一个由残余长短期记忆层和时序卷积网络层组成的神经网络。与以往基于分片识别 ELM 的工作不同,该方法在第一个 ELM 崩溃之前对 L-H 过渡过程进行识别。因此,缓解技术可以及时触发,以抑制最初的 ELMs 爆发。为了进一步说明该算法的有效性,我们通过拍摄制定了一系列评价指标,结果表明该算法可以为缓解和抑制系统提供必要的参考。
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来源期刊
Plasma Physics and Controlled Fusion
Plasma Physics and Controlled Fusion 物理-物理:核物理
CiteScore
4.50
自引率
13.60%
发文量
224
审稿时长
4.5 months
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.
期刊最新文献
Reduced order modeling for real-time monitoring of structural displacements due to electromagnetic forces in large scale tokamaks He II line intensity measurements in the JET tokamak Sheath constraints on turbulent magnetised plasmas Vertical instability forecasting and controllability assessment of multi-device tokamak plasmas in DECAF with data-driven optimization Identifying L-H transition in HL-2A through deep learning
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