发表机构
Department of Mechanical and Industrial Engineering（机械与工业工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出物理约束机器学习框架加速湍流反应流DNS，用替代模型预测反应速率，引入热力学第二定律约束提高稳定性，在二维火焰DNS验证，计算成本降低超量级，基于残差的数据增强策略可参数探索，为燃烧模拟提供可靠高效替代模型。
AI 中文摘要
我们提出了一个物理约束机器学习框架，用于加速湍流反应流的直接数值模拟（DNS）。该模型用一个从简化热化学状态预测反应速率的替代模型取代了详细化学源项的直接评估。为提高物理一致性，将热力学第二定律作为训练约束，通过强制非负熵产生，限制热化学状态的演化至物理上可行的方向，并提高时间积分过程中的稳定性。该方法在二维平面贫预混甲烷 - 空气火焰与湍流场相互作用的DNS上得到验证。模型能高保真地再现详细化学结果，同时计算成本降低超一个数量级。此外，基于残差的合成数据增强策略通过从原始数据集构建新训练数据实现参数探索，无需额外详细化学CFD运行就能在新入口条件下进行精确模拟。这些结果表明，热力学约束机器学习可为高保真燃烧模拟中的详细化学提供可靠且计算高效的替代模型。
英文摘要
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.
