用热力学约束的机器学习模型，加速燃烧模拟并节省超10倍计算成本。
Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics
- 引入熵增约束，确保化学反应路径符合热力学第二定律。
- 在二维甲烷空气火焰模拟中，精度接近详细化学模型，计算速度提升10倍以上。
- 通过残差数据增强，无需重新计算即可适应新工况，适合高保真燃烧仿真研究者。
我们提出一种物理约束的机器学习框架，用于加速湍流反应流的直接数值模拟（DNS）。该模型用代理模型替代详细的化学源项计算，从简化热化学状态预测反应速率。为提升物理一致性，将热力学第二定律作为训练约束，强制熵产生非负，从而限制热化学状态演化至物理解释方向，提高时间积分稳定性。该方法在二维平面贫燃甲烷-空气火焰与湍流场相互作用的DNS中得到验证，模型以高保真度复现详细化学结果，同时实现超过一个数量级的计算成本降低。此外，基于残差的合成数据增强策略，可从原始数据集构建新训练数据，实现无需额外详细化学CFD计算即可准确模拟新进气条件。结果表明，热力学约束的机器学习能为高保真燃烧模拟提供可靠且高效的详细化学替代方案。
原文摘要 · Abstract (English)
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.
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