A microscale study of turbulent flow in the porous medium and at the porous/fluid interface: combining LES, DNS, and Neural Network approaches

即使对于简单的多孔基质几何形状，多孔介质（小于孔径的尺度）中微尺度湍流输运的动力学也不清楚。这种理解需要将多孔介质中的湍流传输与微观尺度的流动物理联系起来。这个项目将阐明多孔介质内部湍流的流动物理。初步结果表明，多孔介质中的微尺度湍流是一种新的物理现象。该项目的科学成果将通过改进多孔介质流动的系统建模，对社会经济产生重大影响。直接应用包括通过设计更有效的口罩过滤层来对抗COVID-19。在能量储存和转换方面也有长期的应用。该项目还将有助于教育和培训学生。研究者计划让本科生和高中生参与计算流体动力学代码和神经网络模型的开发。接触实验室工作和学术研究将使本科生和高中生提高他们的计算技能，并有助于培养他们的研究兴趣。最后，研究成果将被纳入研究者的高级对流传热研究生课程。该项目的结果对于模拟与工程多孔介质相关的湍流具有重要意义。将流场进行相位平均，得到分解为非平稳平均分量和波动分量的真实湍流统计量。将传统的直接数值模拟和大涡模拟与神经网络相结合，对微尺度湍流的流动物理进行解释和建模。神经网络将被使用，因为它们在处理复杂的、不均匀的流场结构方面优于传统方法。监督分类将用于可视化三维湍流结构，并根据其湍流动能和各向异性进行分类。一个有监督的自编码器将用于开发第一个宏观尺度模型，该模型考虑了非均匀微尺度流场的贡献。通过采用严格参数变化的方法，对微尺度流动物理的观测将有助于理解微尺度湍流的主要特征。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The dynamics of microscale turbulence transport in porous media (at the scale smaller than the pore size) is not understood even for simple porous matrix geometries. This understanding requires connecting turbulence transport in porous media to the microscale flow physics. This project will elucidate the flow physics of turbulence inside a porous medium. Preliminary results show that microscale turbulence in porous media constitutes a new physical phenomenon. The scientific outcomes of the project will have significant socio-economic impacts by enabling an improved systemic modeling of porous media flows. Immediate applications include combating COVID-19 through the design of more effective filter layers in masks. There are also long-term applications in energy storage and conversion. The project will also contribute to education and training of students. The investigator plans to engage undergraduate and high school students in the development of computational fluid dynamics code and neural network models. The exposure to lab work and academic research will allow the undergraduate and high school students improve their computational skills and help cultivate their research interests. Finally, the research results will be incorporated into the investigator's graduate class on advanced convection heat transfer.The results from this project are vital for modeling turbulent flow associated with engineering porous media. The flow field will be phase-averaged to obtain the true turbulence statistics decomposed into non-stationary mean and fluctuation components. The proposed research will also combine traditional direct numerical simulation and large-eddy simulation with neural networks to interpret and model the flow physics of microscale turbulence. Neural networks will be used because they are superior to traditional methods for processing the intricate, inhomogeneous structure of the flow field. Supervised classification will be used to visualize 3D turbulent structures which are classified according to their turbulence kinetic energy and anisotropy. A supervised autoencoder will be used to develop the first macroscale model that takes the contribution of the inhomogeneous microscale flow field into consideration. By implementing the proposed methodology with rigorous parameter variation, the observations about the microscale flow physics will lead to understanding the main features of microscale turbulence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.