Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion
Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion
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DOI：
10.1016/j.combustflame.2021.111814
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发表时间：
2022-02
影响因子：
4.4
通讯作者：
Kevin M. Gitushi;Rishikesh Ranade;T. Echekki
中科院分区：
文献类型：
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作者：
Kevin M. Gitushi;Rishikesh Ranade;T. Echekki
湍流燃烧模型通常面临着所谓的火焰状模型和PDF状模型之间的权衡。火焰状模型的特点是选择一组有限的规定的时刻，这是运输代表的组成空间和它的统计的歧管。PDF类方法的目的是直接评估关闭项与非线性化学源项的能量和物种方程。它们在运行中生成数据，可用于加速基于PDF的模型的仿真。通过构建飞行中的火焰状模型来建立用于实施PDF类方法的加速方案的关键成分，可以潜在地节省计算，同时保持解决闭合项的能力。本研究对这些成分进行了研究。它们包括使用主成分分析（PCA）将组合空间降维到低维流形的基于数据的降维。的主成分（PC）作为时刻，其特征的歧管和条件的热化学标量的手段进行评估，在这些PC。第二个要素涉及采用一种新的深度学习框架DeepONet来构建联合PC的PDF，作为类似火焰的方法中常见的假定形状的替代方法。我们还研究了旋转的PC到独立的组件（IC）是否可以提高他们的统计独立性。这些成分的组合进行了研究，使用悉尼湍流非预混火焰与非均匀入口的实验数据的基础上。构造的PDF和条件平均模型的组合能够充分地再现热化学标量的无条件统计，并且在PC之间建立可接受的统计独立性，这进一步简化了联合PC的PDF的建模。
Turbulent combustion modeling often faces a trade-off between the so-called flamelet-like models and PDF-like models. Flamelet-like models, are characterized by a choice of a limited set of prescribed moments, which are transported to represent the manifold of the composition space and its statistics. PDF-like approaches are designed to directly evaluate the closure terms associated with the nonlinear chemical source terms in the energy and species equations. They generate data on the fly, which can be used to accelerate the simulation of PDF-like based models. Establishing key ingredients for implementing acceleration schemes for PDF-like methods by constructing flamelet-like models on the fly can potentially result in computational saving while maintaining the ability to resolve closure terms. These ingredients are investigated in this study. They include a data-based dimensional reduction of the composition space to a low-dimensional manifold using principal component analysis (PCA). The principal components (PCs) serve as moments, which characterize the manifold; and conditional means of the thermo-chemical scalars are evaluated in terms of these PCs. A second ingredient involves adapting a novel deep learning framework, DeepONet, to construct joint PCs’ PDFs as alternative methods to presumed shapes common in flamelet-like approaches. We also investigate whether the rotation of the PCs into independent components (ICs) can improve their statistical independence. The combination of these ingredients is investigated using experimental data based on the Sydney turbulent nonpremixed flames with inhomogeneous inlets. The combination of constructed PDFs and conditional mean models are able to adequately reproduce unconditional statistics of thermo-chemical scalars, and establish acceptable statistical independence between the PCs, which simplify further the modeling of the joint PCs’ PDFs.