In this study, we develop a novel framework to extract turbulent combustion closure, including closure for species chemical source terms, from multiscalar and velocity measurements in turbulent flames. The technique is based on a physics-informed neural network (PINN) that combines models for velocity and scalar measurements and a deep operator network (DeepONet) to accommodate spatial measurements and experimental parameters as separate input streams. An additional key innovation is the estimate of the unconditional means of the species’ chemical source terms as additional “observations” to constrain the prediction of these rates. This estimate is based on a convolution of the means of species reaction rates conditioned on principal components of the multiscalar data and the joint probability density functions of these principal components. The PINN-DeepONet method is implemented on the so-called Sydney flames, where training is carried out on 3 flames and validated on 4 flames. The results show that, despite the limited samples of experimental parameters, including the inlet flow and the fuel jet recess length within the air flow, the PINN-DeepONet approach can construct velocity and scalar fields along with important closure terms for turbulent transport and reaction rates.

中文翻译：

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用于从多标量测量中提取湍流燃烧闭合的 PINN-DeepONet 框架


在这项研究中，我们开发了一种新颖的框架，从湍流火焰中的多标量和速度测量中提取湍流燃烧闭合，包括物种化学源项的闭合。该技术基于物理信息神经网络 (PINN)，该网络结合了速度和标量测量模型以及深度算子网络 (DeepONet)，以将空间测量和实验参数容纳为单独的输入流。另一项关键创新是对物种化学源项的无条件平均值的估计，作为额外的“观察”来限制这些速率的预测。该估计基于以多标量数据的主成分和这些主成分的联合概率密度函数为条件的物种反应速率均值的卷积。 PINN-DeepONet 方法在所谓的悉尼火焰上实现，在 3 个火焰上进行训练并在 4 个火焰上进行验证。结果表明，尽管实验参数样本有限，包括入口流量和气流内的燃料喷射凹槽长度，PINN-DeepONet 方法可以构建速度场和标量场以及湍流输运和反应速率的重要闭合项。