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本文首次针对HL-3装置, 采用机器学习方法预测偏滤器靶板等离子体参数, 为未来快速预测大型聚变堆偏滤器热负荷奠定基础. 将机器学习应用于边缘等离子体物理中, 可以显著缩短大型边缘程序SOLPS-ITER模拟所需的时间, 从几周、几个月甚至半年缩短至毫秒级. 研究发现, 通过增加内外偏滤器区的辐射损失作为模型的输入参数, 能够明显提高预测精度(超过90%), 同时增强训练模型的适用性, 可以同时精确预测内外偏滤器靶板热流, 并验证了该特征参数与偏滤器靶板物理量之间的依赖关系. 该工作不仅为偏滤器物理研究提供了有效的方法, 也为未来跨装置预测偏滤器靶板参数提供了坚实的基础.
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关键词:
- HL-3 /
- 机器学习 /
- 神经网络 /
- 偏滤器靶板热负荷 /
- SOLPS-ITER
The SOLPS-ITER edge plasma simulation code has become a primary tool for designing divertor physics and predicting target plate heat load in fusion research. However, SOLPS-ITER-based divertor design requires not only substantial computational time but also intensive hardware resources, which fundamentally limits its application in advancing divertor optimization, particularly in large-scale fusion reactor divertor design. In this work, the machine learning method is used for the first time to predict the plasma parameters of the divertor target plate for HL-3, which provides a theoretical basis for predicting the heat load of divertor in large fusion reactor in the future. Based on the simulation of the edge plasma code SOLPS-ITER, we first build a database of HL-3 edge plasma parameters, including the upstream inner/outer midplane region and divertor target region. Then, we use the machine learning method and combine with the database to develop an artificial neural network model. Finally, the artificial neural network is used to train a model by using the boundary plasma parameters of the HL-3 device, and the heat load of the divertor target plate is predicted by the given upstream plasma parameters. This work can effectively shorten the time of simulating edge plasma by SOLPS-ITER code from weeks, months or even half a year to several milliseconds. In this work, a multi-layer perceptron (MLP) model is established with different input parameters to predict the electron temperature, density, and parallel heat fluxes of the inner and outer divertor target plates. It is found that reasonably increasing upstream plasma parameters as inputs to the model can not only improve the model’s generalization ability and the accuracy of prediction (both reaching over 90%), but also verify the correlation between upstream plasma parameters and divertor target physical quantities. In addition, a more stable ResMLP model is established on the basis of MLP. This work demonstrates the feasibility of using the neural networks to predict the heat load of the divertor target plate. -
Keywords:
- HL-3 /
- machine learning /
- neural network /
- divertor target heat load /
- SOLPS-ITER
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表 1 模型的详细输入参数
Table 1. Detailed input parameters of the model.
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