Abstract A chemistry reduction approach based on machine learning is proposed and applied to direct numerical simulation (DNS) of a turbulent non-premixed syngas oxy-flame interacting with a cooled wall. The training and the subsequent application of artificial neural networks (ANNs) rely on the processing of ‘thermochemical vectors’ composed of species mass fractions and temperature (ANN input), to predict the corresponding chemical sources (ANN output). The training of the ANN is performed aside from any flow simulation, using a turbulent non-adiabatic non-premixed micro-mixing based canonical problem with a reference detailed chemistry. Heat-loss effects are thus included in the ANN training. The performance of the ANN chemistry is then tested a-posteriori in a two-dimensional DNS against the detailed mechanism and a reduced mechanism specifically developed for the operating conditions considered. Then, three-dimensional DNS are performed either with the ANN or the reduced chemistry for additional a-posteriori tests. The ANN reduced chemistry achieves good agreement with the Arrhenius-based detailed and reduced mechanisms, while being in terms of CPU cost 25 times faster than the detailed mechanism and 3 times faster than the reduced mechanism when coupled with DNS. The major potential of the method lies both in its data driven character and in the handling of the stiff chemical sources. The former allows for easy implementation in the context of automated generation of case-specific reduced chemistry. The latter avoids the Arrhenius rates calculation and also the direct integration of stiff chemistry, both leading to a significant CPU time reduction.

中文翻译：

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使用从非预混微混合模型训练的机器学习进行化学还原：具有侧壁效应的合成气湍流氧火焰在 DNS 中的应用

摘要 提出了一种基于机器学习的化学还原方法，并将其应用于与冷却壁相互作用的湍流非预混合成气氧火焰的直接数值模拟 (DNS)。人工神经网络 (ANN) 的训练和后续应用依赖于对由物种质量分数和温度（ANN 输入）组成的“热化学矢量”的处理，以预测相应的化学来源（ANN 输出）。除了任何流动模拟之外，还使用基于湍流非绝热非预混微混合的典型问题和参考详细化学来执行 ANN 的训练。因此，热量损失效应包含在 ANN 训练中。然后在二维 DNS 中根据详细机制和为所考虑的操作条件专门开发的简化机制对 ANN 化学的性能进行后验测试。然后，使用 ANN 或减少的化学物质进行三维 DNS，以进行额外的后验测试。ANN 简化化学与基于 Arrhenius 的详细和简化机制实现了良好的一致性，同时在 CPU 成本方面比详细机制快 25 倍，与 DNS 结合时比简化机制快 3 倍。该方法的主要潜力在于它的数据驱动特性和刚性化学源的处理。前者允许在自动生成特定于案例的还原化学的背景下轻松实施。