AI 中文总结
研究针对大涡模拟湍流模型在预混火焰模拟中的不足，开发基于深度神经网络的增强涡粘性封闭项，经联合校准与评估，该模型能降低后验误差，在不同达姆科勒数下保持稳定准确，为湍流燃烧建模提供适用框架。
AI 中文摘要
大涡模拟（LES）湍流模型常常无法捕捉预混火焰中化学热释放的影响以及由此产生的湍流调制，这凸显了对一个在广泛物理状态下都保持准确的框架的需求。我们基于深度神经网络开发了一种增强涡粘性封闭项，通过基于伴随的优化和可微编程与LES解联合校准，确保与控制偏微分方程（PDEs）一致。研究了几种目标函数和训练方法，并评估了每个模型在广泛的达姆科勒数范围内进行插值和外推的能力。相对于Smagorinsky模型基线，最佳神经网络模型将LES原始变量中的后验误差降低了25 - 50%，将解析的雷诺应力和标量通量中的后验误差降低了60%以上。关键的是，该模型在达姆科勒数范围内具有通用性，即使在样本外条件下也能保持稳定性和准确性。这些结果表明，PDE一致的深度学习封闭项可以在湍流预混火焰的LES中恢复平均场和解析的湍流统计量，因此可以为湍流燃烧建模提供一个广泛适用的框架。
英文摘要
Large-eddy simulation (LES) turbulence models often fail to capture the effects of chemical heat release and the resulting modulation of turbulence in premixed flames, underscoring the need for a framework that remains accurate across a broad range of physical regimes. We develop an augmented eddy-viscosity closure, based on deep neural networks calibrated jointly with the LES solution using adjoint-based optimization and differentiable programming, ensuring consistency with the governing partial differential equations (PDEs). Several objective functions and training methods are examined, and each model is assessed for its capability to interpolate and extrapolate across a wide range of Damköhler numbers. Relative to the Smagorinsky-model baseline, the best neural network model improves a posteriori errors in the LES primitive variables by 25-50% and in the resolved Reynolds stress and scalar flux by more than 60%. Crucially, the model generalizes across Damköhler number regimes, maintaining stability and accuracy even for out-of-sample conditions. These results demonstrate that PDE-consistent deep learning closures can recover both mean fields and resolved turbulence statistics in LES of turbulent premixed flames and can therefore provide a broadly applicable framework for turbulent combustion modeling.
