High Mach number inlets face complex challenges such as shock wave/boundary layer interference, adversely impacting the aerodynamic characteristics and stable operating boundaries. Traditional computational fluid dynamics (CFD) simulations for performance and flow field analysis are slow, and data-driven deep learning technologies struggle with predicting flow fields in complex aerodynamic phenomena like shock wave/boundary layer interactions, boundary layer separation, and Mach disks. This study introduces a rapid, robust method for reconstructing hypersonic viscous inlet flow fields using inlet geometry design parameters. This method is called shock wave and boundary layer dominated two-dimensional multiphysical field reconstruction model based on multi-scale sensory field fusion residual physical information neural network (PIMSRF_ResNet), ensuring high-precision training data. Using the optimal Pareto solution set from multi-objective optimization with a small sample intelligence method, 150 sets of multi-physics fields under Ma10 conditions with varying geometric design parameters are calculated. Compared to traditional pure data-driven models, PIMSRF_ResNet's structural similarity in the test set reaches 0.9773, with a peak signal-to-noise ratio close to 30 dB and a correlation coefficient exceeding 98%. These experimental results demonstrated that the proposed method is a promising tool for real-time prediction of complex internal flow fields dominated by high Mach number shock waves and boundary layers.

中文翻译：

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基于小样本物理信息神经网络的激波和边界层主导的高超声速入口流场重建

高马赫数入口面临复杂的挑战，例如冲击波/边界层干扰，对空气动力学特性和稳定的运行边界产生不利影响。用于性能和流场分析的传统计算流体动力学 (CFD) 模拟速度缓慢，数据驱动的深度学习技术难以预测复杂空气动力学现象（如冲击波/边界层相互作用、边界层分离和马赫盘）中的流场。本研究介绍了一种使用入口几何设计参数重建高超声速粘性入口流场的快速、稳健的方法。该方法称为基于多尺度感觉场融合残差物理信息神经网络的冲击波和边界层主导的二维多物理场重建模型（PIMSRF_ResNet），保证了训练数据的高精度。利用小样本智能方法多目标优化得到的最优Pareto解集，计算了Ma10条件下不同几何设计参数的150组多物理场。与传统的纯数据驱动模型相比，PIMSRF_ResNet在测试集中的结构相似度达到0.9773，峰值信噪比接近30 dB，相关系数超过98%。这些实验结果表明，该方法是实时预测以高马赫数冲击波和边界层为主的复杂内部流场的有前途的工具。