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<p>Real-time control of electron cyclotron heating (ECH) is critical for optimizing plasma performance and preventing instabilities in advanced fusion devices. We present a comprehensive real-time ECH control system developed on the Large Helical Device (LHD) that enables real-time adjustment of microwave heating in response to evolving plasma conditions. A field-programmable gate array (FPGA) controller actuates the ECH launchers, enabling millisecond-level adjustments of both injection angle and polarization to maximize absorption as plasma density and temperature profiles change. To compute suitable settings rapidly, a generative adversarial network (GAN) model was trained on thousands of past LHD discharges and ray-tracing simulations of ECH to generate control parameters for the deposition position and polarization. In high-density LHD experiments, another machine-learning-based prediction framework enabled the first active avoidance of radiative collapse. The predictor identified an impending collapse about 65 milliseconds in advance, triggering automated ECH power re-targeting and a cutoff of fueling that stabilized the plasma beyond the conventional density limit. These developments demonstrate how real-time ECH control, together with machine-learning-assisted prediction and inference, can sustain stable, high-performance plasmas, highlighting a pathway toward long-duration, steady-state fusion operations.</p>

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Real-Time Control System for Electron Cyclotron Heating Injection on LHD

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Naoki Kenmochi,
  • Tohru Ii Tsujimura,
  • Yoshinori Mizuno,
  • Masaki Nishiura,
  • Kota Okada,
  • Yasuo Yoshimura,
  • Hiroe Igami,
  • Hiromi Takahashi,
  • Ryoma Yanai,
  • Toshiki Takeuchi,
  • Shin Kubo,
  • Takashi Shimozuma,
  • Sakuji Kobayashi,
  • Satoshi Ito,
  • Hidenori Takubo

摘要

Real-time control of electron cyclotron heating (ECH) is critical for optimizing plasma performance and preventing instabilities in advanced fusion devices. We present a comprehensive real-time ECH control system developed on the Large Helical Device (LHD) that enables real-time adjustment of microwave heating in response to evolving plasma conditions. A field-programmable gate array (FPGA) controller actuates the ECH launchers, enabling millisecond-level adjustments of both injection angle and polarization to maximize absorption as plasma density and temperature profiles change. To compute suitable settings rapidly, a generative adversarial network (GAN) model was trained on thousands of past LHD discharges and ray-tracing simulations of ECH to generate control parameters for the deposition position and polarization. In high-density LHD experiments, another machine-learning-based prediction framework enabled the first active avoidance of radiative collapse. The predictor identified an impending collapse about 65 milliseconds in advance, triggering automated ECH power re-targeting and a cutoff of fueling that stabilized the plasma beyond the conventional density limit. These developments demonstrate how real-time ECH control, together with machine-learning-assisted prediction and inference, can sustain stable, high-performance plasmas, highlighting a pathway toward long-duration, steady-state fusion operations.