Identifying L-H transition in HL-2A through deep learning
arxiv(2024)
摘要
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 (LSTM) and Temporal Convolutional Network (TCN). 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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