AI 中文总结
本研究将Scherding等人的数据驱动降阶框架扩展至带局部催化表面不连续性的高超声速反应流，改进降维方法后，在保证精度的前提下将模拟成本降低50%，可精确捕捉不连续催化特性的影响。
AI 中文摘要
高超声速流动涉及极端的热化学非平衡，其中强烈的能量耗散会导致化学反应、辐射与能量交换紧密耦合。在该工况下，表面化学，尤其是催化壁面反应，会显著影响边界层组分及表面热传递。对这类流动的精确模拟可能需要反复计算详细的热化学库，这是高保真反应流模拟中的主要计算瓶颈。为缓解该成本，我们采用了Scherding等人（2023）提出的数据驱动降阶框架，该框架结合非线性降维、社区聚类与局部代理模型，可高效近似高维热化学映射。本研究首次将该框架扩展至带有局部催化表面不连续性的高超声速反应流，此类流动会引发壁面化学与热传递的急剧变化。为应对热化学状态空间复杂度的提升，我们对降维方法进行了改进，加入Sammon型应力惩罚项，以缓解潜流形的拓扑折叠，提升聚类与代理阶段的稳健性。所得模型可精确捕捉不连续催化特性的影响，包括壁面组分质量分数、扩散通量及表面热传递的急剧梯度，同时在不降低精度的前提下，将整体模拟成本降低50%。
英文摘要
Hypersonic flows involve extreme thermochemical non-equilibrium, where strong energy dissipation leads to tightly coupled chemical reactions, radiation, and energy exchange. In this regime, surface chemistry, particularly catalytic wall reactions, can significantly affect boundary-layer composition and surface heat transfer. Accurate simulations of such flows may require repeated evaluations of detailed thermochemical libraries, which represent a major computational bottleneck in high-fidelity reactive-flow simulations. To mitigate this cost, we employ the data-driven reduced-order framework introduced by Scherding et al. (2023), which combines nonlinear dimensionality reduction, community clustering, and local surrogate models to efficiently approximate high-dimensional thermochemical mappings. In this work, this framework is extended for the first time to hypersonic reactive flows with localized catalytic surface discontinuities, introducing sharp variations in wall chemistry and heat transfer. To address the increased complexity of the thermochemical state space, the dimensionality reduction method is enhanced with a Sammon-type stress penalty that mitigates topological folding of the latent manifold and improves the robustness of the clustering and surrogate stages. The resulting model accurately captures the effects of discontinuous catalytic properties, including sharp gradients in wall species mass fractions, diffusion fluxes, and surface heat transfer, while reducing the overall simulation cost by 50% without compromising accuracy.
