【摘要】 提出了一种流动特征嵌入(embedding flow-feature network, EFFN)代理模型，通过将流场信息融入代理模型中，提高了代理模型的预测精度，同时令代理模型具有流动特征预测能力.EFFN模型对训练数据样本总量的需求与传统用于气动优化的代理模型一致甚至更少.它在样本数量相同的情况下比传统代理模型拥有更高的预测精度，并且它能够准确预测流动特征，同时一定程度上解决了代理模型物理可解释性差的问题.由于EFFN模型相较传统代理模型提供了更可靠的预测值，在气动优化设计中拥有更好的优化结果.对二维叶型总体气动性能优化的结果表明，基于DBN模型的优化叶型总压损失系数相对减少17.3%,而EFFN模型的优化叶型总压损失系数相对减少18.0%,基于EFFN模型优化叶型的损失性能得到更好地改善.更多还原

【Abstract】 A flow feature embedding proxy model(embedding flow feature network, EFFN) was proposed, to improve the prediction accuracy of the proxy model by integrating the flow field information into the proxy model, and enable the proxy model to predict flow features. The requirement for the total number of training data samples in the EFFN is consistent or even less than that of traditional surrogate models used for aerodynamic optimization. It has higher prediction accuracy than traditional surrogate models with the same sample size, and can accurately predict flow characteristics, while to some extent solving the problem of poor physical interpretability of surrogate models. Meanwhile, due to the more reliable values predicted by the EFFN, it has better optimization results in aerodynamic optimization design. The results of optimizing the aerodynamic performances of the 2D blade profiles show that, the total pressure loss coefficient of the optimized blade profile based on the DBN model relatively decreases by 17.3%, while the total pressure loss coefficient of the optimized blade profile based on the EFFN model relatively decreases by 18.0%. The loss performance of the optimized blade profile based on the EFFN model was highly improved.更多还原