Data-driven modeling methods have become one of the main technologies for predicting aerodynamic heat in hypersonic conditions. However, due to the limitations of wind tunnel experimental conditions, the spatial distribution of aerothermal wind tunnel experimental data is often sparse, and the sample size is relatively small. Furthermore, there is a lack of direct correlation in the aerodynamic heat distribution data among different shapes of vehicles, which poses challenges for constructing high-performance data-driven aerodynamic heat prediction models. To address these issues, this paper proposes a high-precision aerodynamic heat modeling and prediction method based on data augmentation and transfer learning. First, integrating the concept of data fusion, we propose to enhance the sparse aerothermal wind tunnel experimental data by using deep neural networks and introducing low-precision numerical computation results. Next, based on the close physical correlation between boundary layer outer edge information and wall surface aerodynamic heat, we construct the aerodynamic heat prediction model ED-ResNet using a double-series residual neural network. Finally, by fine-tuning the ED-ResNet model for transfer learning, high-precision predictions of aerothermal wind tunnel experimental results for different shaped vehicles are achieved under small sample conditions. Verification using hypersonic double-ellipsoid, blunt cone, and blunt bicone shows that after data augmentation, the prediction error of the aerodynamic heat prediction model is significantly reduced to 1/3 of that when data augmentation is not used. Moreover, through transfer learning, the model effectively leverages existing hypersonic double-ellipsoid aerothermal wind tunnel experimental data to achieve high-precision predictions of aerodynamic heat distribution for blunt cone and blunt double cone under different incoming flow conditions, with normalized root mean square error(NRMSE) maintained below 10 %.

中文翻译：

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基于数据增强和迁移学习的高精度气动热预测方法


数据驱动的建模方法已成为预测高超声速条件下空气动力学热的主要技术之一。然而，由于风洞实验条件的限制，航热风洞实验数据的空间分布往往稀疏，样本量相对较小。此外，不同形状车辆之间的空气动力学热分布数据缺乏直接相关性，这给构建高性能数据驱动的空气动力学热预测模型带来了挑战。针对这些问题，该文提出了一种基于数据增强和迁移学习的高精度气动热建模与预测方法。首先，融合数据融合的概念，提出利用深度神经网络和引入低精度数值计算结果来增强稀疏气热风洞实验数据。接下来，基于边界层外边缘信息与壁面气动热之间的密切物理相关性，利用双级数残差神经网络构建气动热预测模型 ED-ResNet。最后，通过微调 ED-ResNet 模型进行迁移学习，在小样本条件下实现了对不同形状车辆的航热风洞实验结果的高精度预测。使用高超声速双椭球、钝锥和钝双锥验证表明，数据增强后，气动热预测模型的预测误差显著降低到不使用数据增强时的 1/3。 此外，通过迁移学习，该模型有效地利用现有的高超声速双椭球气热风洞实验数据，实现了对不同进流条件下钝锥和钝双锥气动热分布的高精度预测，归一化均方根误差 （NRMSE） 保持在 10% 以下。