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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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图 1 训练和预测的基本流程
Figure 1. Schematic of the basic workflow for training and prediction.
图 2 机器学习网络结构示意图
Figure 2. Schematic diagram of the machine learning network structure.
图 3 不同数量的上游参数作为模型输入的损失值图像
Figure 3. Loss values with different numbers of upstream parameters as model inputs.
图 4 不同壁粒子再循环对应的偏滤器外靶板电子密度和电子温度曲线图
Figure 4. Electron density and temperature profiles at the outer divertor target plate for different wall particle recycling conditions.
图 5 不同数量的上游参数作为模型输入的测试集相关系数
Figure 5. Test correlation coefficients versus upstream parameter count.
图 6 外靶板的电子密度、温度和平行热流预测值、真实值
Figure 6. Predicted and true values of electron density, electron temperature, and parallel heat flux on the outer target.
图 7 残差网络的原理图
Figure 7. Schematic diagram of residual network principles.
图 8 ResMLP模型的测试集相关系数
Figure 8. Test set correlation coefficients of the ResMLP model.
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