发表机构
University of Edinburgh; RWTH Aachen University; Vrije Universiteit Brussel; Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), VUB-ULB; University of Cambridge; University of Southampton（爱丁堡大学; 亚琛工业大学; 布鲁塞尔自由大学; 布鲁塞尔热流体系统与清洁能源研究所（BRITE），布鲁塞尔自由大学-布鲁塞尔大学; 剑桥大学; 南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
利用CNN分析氢气火焰中进程变量场与化学源项的关系，发现基于水的进程变量可稳健参数化源项，并识别出所需空间尺度，揭示机器学习可发现经典统计隐藏的物理依赖。
AI 中文摘要
卷积神经网络（CNNs）被用作分析工具，以研究贫燃预混氢气火焰中进程变量场与局部化学源项之间的关系。机器学习并非用于建模，而是利用CNN来分析直接数值模拟中空间分辨场所包含的物理信息。CNN特别适合此任务，因为它们利用空间相关性和多尺度结构，从而能够评估空间组织如何影响源项。分析表明，基于水的进程变量$C_{\ m H_2O}$包含了准确参数化化学源项所需的全部信息，而仅凭$C_{\ m H_2}$则不足。加入混合分数$Z$可提高后者的准确性，但当使用$C_{\ m H2O}$的空间场作为输入时，并未给CNN提供显著的额外信息。在层流热扩散不稳定火焰和湍流槽射流氢气火焰中均观察到相同行为，表明$C_{\ m H_2O}$是参数化的稳健单一变量。层流情况下的补充尺度分析显示，需要延伸至少两个层流火焰厚度的空间特征才能准确重建源项，从而识别出携带此信息的进程变量场中的特征尺度。这些结果证明了机器学习如何揭示经典统计分析所隐藏的物理依赖关系。
英文摘要
Convolutional Neural Networks (CNNs) are used as analytical tools to investigate the relationship between the progress variable field and the local chemical source term in lean premixed hydrogen flames. Rather than employing machine learning for modelling, CNNs are leveraged to analyse the physical information contained in spatially resolved fields from direct numerical simulations. CNNs are particularly well suited for this task because they exploit spatial correlations and multi-scale structures, allowing an assessment of how spatial organisation influences the source term. The analysis demonstrates that the progress variable based on water, $C_{\rm H_2O}$, contains all the information required to accurately parametrise the chemical source term whereas $C_{\rm H_2}$ alone does not. The inclusion of the mixture fraction $Z$ improves the accuracy of the latter but provides no significant additional information to the CNN when the spatial field of $C_{\rm H2O}$ is used as input. The same behaviour is observed in both a laminar thermodiffusively unstable flame and a turbulent slot-jet hydrogen flame, indicating that $C_{\rm H_2O}$ is a robust single variable for parametrisation. A complementary scale analysis in the laminar case shows that spatial features extending over at least two laminar flame thicknesses are required to reconstruct the source term accurately, thereby identifying the characteristic scale in the progress variable field that carries this information. These results demonstrate how machine learning can uncover physical dependencies that remain hidden to classical statistical analyses.
