用熵约束让机器学习模拟燃烧既准又快，计算省10倍多，还能用旧数据预测新工况。
该研究提出一个物理约束的机器学习框架，利用非负熵生成约束训练替代模型，替换湍流反应流DNS中的详细化学源项。在二维平面预混甲烷-空气火焰DNS中验证，模型高保真复现详细化学结果，计算成本降低超过一个数量级。残差合成数据增强策略允许从原始数据集构造新训练数据，在新入口条件下无需额外详细化学CFD即可准确模拟。
Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics
We present a physics-constrained machine learning framework for accelerating the direct numerical simulation (DNS) of turbulent reacting flows. The model replaces the direct evaluation of detailed chemical source terms with a surrogate that predicts reaction rates from a reduced thermochemical state. To improve physical consistency, the second law of thermodynamics is incorporated as a training constraint by enforcing non-negative entropy generation, which restricts the evolution of the thermochemical state to physically admissible directions and improves stability during time integration. The approach is demonstrated on DNS of a two-dimensional planar lean premixed methane-air flame interacting with a turbulent flow field. The model reproduces detailed-chemistry results with high fidelity while achieving more than an order-of-magnitude reduction in computational cost. Furthermore, a residual-based synthetic data augmentation strategy enables parametric exploration by constructing new training data from the original dataset, allowing accurate simulation at new inlet conditions without additional detailed-chemistry CFD runs. These results demonstrate that thermodynamically constrained machine learning can provide reliable and computationally efficient surrogates for detailed chemistry in high-fidelity combustion simulations.