发表机构
Indian Institute of Technology Gandhinagar（印度理工学院甘地纳加尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对湍流燃烧预测模拟的挑战，综述了将先验知识融入建模流程的物理引导机器学习（PGML）在湍流-化学相互作用建模中的应用，以解决纯数据驱动模型的缺陷。
AI 中文摘要
湍流燃烧的预测模拟仍具挑战性，因为刚性化学动力学、湍流输运、分子混合与热释放会在未解析尺度上发生非线性相互作用。在滤波或平均格式中，反应速率是未封闭的，而详细化学反应的积分是计算成本的主要来源。混合限制模型、火焰面与流形方法、条件矩封闭、输运概率/滤波密度函数（PDF/FDF）格式等传统封闭模型虽融入了大量物理认知，但依赖结构假设，在其校准范围外可能失效。机器学习因具备近似复杂非线性函数的能力，可作为互补方法，在湍流燃烧中，该能力可用于放宽限制性封闭假设、从数据中学习未解析的非线性映射，并加速详细化学计算等昂贵操作。然而，纯数据驱动模型可能违反守恒定律与热化学一致性，在训练域外的外推性能差，还会嵌入其中的计算流体动力学（CFD）求解器失稳。物理引导机器学习（PGML）通过在建模流程的各个环节融入先验知识来解决这些失效模式，这些环节包括训练数据与输入特征、模型架构、损失函数、混合封闭结构以及感知求解器的验证协议。本文在此框架内综述了用于湍流-化学相互作用（TCI）建模的PGML。
英文摘要
Predictive simulation of turbulent combustion remains challenging because stiff chemical kinetics, turbulent transport, molecular mixing, and heat release interact nonlinearly across unresolved scales. In the filtered or averaged formulations, reaction rate is unclosed, and the integration of detailed chemistry constitutes the dominant computational cost. Conventional closures such as mixing-limited models, flamelet and manifold methods, conditional moment closure, and transported probability/filtered density function (PDF/FDF) formulations, encode substantial physical insight, but rely on structural assumptions that may lose validity outside their calibrated regimes. Machine learning offers a complementary approach because of its ability to approximate complex nonlinear functions. In turbulent combustion, this capability can be used to relax restrictive closure assumptions, learn unresolved nonlinear mappings from data, and accelerate expensive computations such as detailed chemistry computation. However, purely data-driven models may violate conservation laws and thermochemical consistency. They may also extrapolate poorly outside the training domain and destabilize the CFD solvers in which they are embedded. Physics-guided machine learning (PGML) addresses these failure modes by incorporating prior knowledge throughout the modeling pipeline: in the training data and input features, the model architecture, the loss function, the hybrid closure structure, and the solver-aware validation protocol. This article reviews PGML for turbulence-chemistry interaction (TCI) modeling within this framework.
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