The SOLPS-ITER edge plasma simulation code has become a primary tool for divertor physics design and target plate heat load prediction 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 paper, 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 apply 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 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 for the edge plasma code SOLPS-ITER to simulate the edge plasma from weeks, months or even half a year to several ms. In this work, a multi-layer perceptron (MLP) model was established with different input parameters to predict the electron temperature, density and parallel heat flux of the inner and outer divertor target plates. It is found that reasonably increasing the upstream plasma parameters as the input to the model can not only enhance the generalization ability of the model and improve the accuracy of model prediction (both reaching more than 90%), but also verify the dependence between the upstream plasma parameters and the divertor target physical quantities. In addition, a more stable ResMLP model is established on the basis of MLP. This work proves the feasibility of using the neural network to predict the heat load of the divertor target plate.
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