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    • Neural Network Model of Neutral Beam Injection in the EAST Tokamak to Enable Fast Transport Simulations

    Z. Wang,S. Morosohk,T. Rafiq,E. Schuster,M. D. Boyer,W. Choi

    FUSION ENGINEERING AND DESIGN（2023）

    Lehigh Univ

    Cited 2|Views32
    Abstract
    The neutral beam injection (NBI) system in EAST produces energetic neutral particles, which collide with electrons and ions in tokamak plasmas and heat the plasmas through Coulomb collisions. Moreover, it drives a non-inductive source of current, due to the charge-exchange collision between neutral particles and ions, and injects toroidal torque, which generates a toroidal rotation of the plasma. The effect caused by the NBI system, such as plasma heating, current drive, total neutron rate, momentum transfer, and shine-through, are modeled by a comprehensive module called NUBEAM. However, NUBEAM is computationally intensive since it relies on Monte Carlo methods. In this work, a neural network model has been developed as a surrogate model for NUBEAM in EAST. The database for neural-network model training, validation and testing is generated by running TRANSP for experimental discharges from recent EAST campaigns (after the latest NBI upgrade) while using the NUBEAM module. Simulation results illustrate that the trained neural network has the capability of replicating the predictions made by NUBEAM while demanding a significantly shorter execution time. These results indicate that surrogate models like the one proposed in this work could enable fast transport simulations for EAST after integrating them into a control-oriented predictive code such as COTSIM.
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    NUBEAM,Neutral beam injection,Neural network model,DNN,EAST
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    【要点】：本文提出了一种基于神经网络的中性束注入（NBI）模型，用于快速模拟东方超导 Tokamak（EAST）中的粒子传输过程，以替代计算成本较高的传统 Monte Carlo 方法。
    

    【方法】：作者通过运行包含 NUBEAM 模块的 TRANSP 代码，模拟 EAST 近期的实验放电数据，生成了用于训练、验证和测试神经网络模型的数据库。
    

    【实验】：使用生成的数据库，训练了一种神经网络模型，该模型能够复现 NUBEAM 的预测结果，同时大幅缩短执行时间。实验结果表明，所提出的替代模型可以集成到控制导向的预测代码中，如 COTSIM，用于快速传输模拟。