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

多孔介质和多孔/流体界面湍流的微观研究：结合 LES、DNS 和神经网络方法

• 批准号: 2042834
• 负责人: Andrey Kuznetsov
• 金额: $ 30.84万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042834&HistoricalAwards=false
• 关键词: microscale; study; turbulent; flow; porous

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.

多孔介质中微尺度湍流输运的动力学（尺度小于孔径）甚至对于简单的多孔基质几何形状也不清楚。这种理解需要将多孔介质中的湍流输运与微尺度流动物理学联系起来。本计画将阐明多孔介质内紊流的流动物理。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产生重大的社会经济影响。 直接应用包括通过在口罩中设计更有效的过滤层来对抗COVID-19。 在能源储存和转换方面也有长期的应用。该项目还将促进学生的教育和培训。研究人员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能力，并有助于培养他们的研究兴趣。最后，研究结果将被纳入研究者的高级对流传热研究生班。该项目的结果对于模拟与工程多孔介质相关的湍流至关重要。将对流场进行相位平均，以获得分解为非平稳平均和波动分量的真实湍流统计。拟议的研究还将结合联合收割机传统的直接数值模拟和大涡模拟与神经网络解释和模拟微尺度湍流的流动物理。将使用神经网络，因为它们上级传统的方法来处理复杂的，不均匀的流场结构。监督分类将用于可视化3D湍流结构，这些湍流结构根据其湍流动能和各向异性进行分类。一个监督自动编码器将被用来开发第一个宏观模型，考虑到不均匀的微尺度流场的贡献。通过实施所提出的方法与严格的参数变化，对微尺度流动物理的观察将导致理解微尺度turbulence.This奖项的主要特征反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

NUMERICAL INVESTIGATION OF TURBULENT CONVECTION HEAT TRANSFER IN POROUS MEDIA COMPOSED OF ROUGH SOLID OBSTACLES

• DOI: 10.1615/ihtc17.370-50
• 发表时间: 2023
• 期刊: Proceeding of International Heat Transfer Conference 17
• 作者: V. Srikanth;Andrey V. Kuznetsov

Symmetry breaking of turbulent flow in porous media composed of periodically arranged solid obstacles

• DOI: 10.1017/jfm.2021.813
• 发表时间: 2018-10
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: V. Srikanth;Ching-Wei Huang;T. Su;A. Kuznetsov

Flow Regimes and Types of Solid Obstacle Surface Roughness in Turbulent Heat Transfer Inside Periodic Porous Media

• DOI: 10.1007/s11242-023-01978-6
• 发表时间: 2022-10
• 期刊: Transport in Porous Media
• 影响因子: 2.7
• 作者: V. Srikanth;Dylan Peverall;A. Kuznetsov

TURBULENT MICROSCALE FLOW FIELD PREDICTION IN POROUS MEDIA USING CONVOLUTIONAL NEURAL NETWORKS

使用卷积神经网络预测多孔介质中的湍流微尺度流场

• DOI: 10.1615/tfec2021.tfl.036698
• 发表时间: 2021
• 期刊: Proceeding of 5-6th Thermal and Fluids Engineering Conference (TFEC
• 作者: Srikanth, Vishal;Huang, Ching-Wei;Harradine, Ryan;Kuznetsov, Andrey V.
• 通讯作者: Kuznetsov, Andrey V.

A macroscopic two-length-scale model for natural convection in porous media driven by a species-concentration gradient

• DOI: 10.1017/jfm.2021.691
• 发表时间: 2021-09
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: S. Gasow;A. Kuznetsov;M. Avila;Yan Jin