Mathematical Modelling of Particle-Laden Turbulent Flows

含颗粒湍流的数学建模

• 批准号: RGPIN-2020-04778
• 负责人: Lightstone, Marilyn
• 金额: $ 3.35万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760222
• 关键词: Mathematical; Modelling; Particle; Laden; Turbulent

This research program is concerned with the development of mathematical models to predict the motion of particulates in gaseous turbulent flows. Particle-laden turbulent flows occur in a broad range of engineering problems including pollution dispersion in atmospheric flows, spray combustion, ventilation, and medicinal aerosol drug delivery systems. Computational fluid dynamics (CFD) provides the engineering designer or analyst with information on the fluid flow and particle velocities and concentrations within the domain. This allows for development of enhanced engineering designs. There is therefore a need for robust mathematical models of particulate motion that are able to capture the complexities of the fluid-turbulence/particulate interaction, while also maintaining reasonable computational effort. Fluid turbulence influences a vast array of particle-flow characteristics including the dispersion and drift of particles, the rate of deposition on solid surfaces, and the rate of mixing of fuel droplets or solid particles with air for combusting flows. Spatial variations in turbulence intensity can result in particles accumulating in regions of low turbulent kinetic energy. Even within homogeneous turbulent flows, local clustering of the particles can occur due to the influence of underlying turbulent structure; particles with a time constant similar to the smallest turbulent time scale can be ejected from vortical turbulent structures and tend to concentrate locally in regions of high strain and low vorticity.  Moreover, the presence of the particles can influence the underlying fluid velocity field.    Two-way coupling between the gas and particulate phase is complex. For example, depending on the particle time constant and size relative to the important turbulent scales, particles can enhance turbulence production through vortex shedding or can act to modulate the turbulent kinetic energy as a result of a transfer of momentum from the fluid to the particles. The objective of this research program is to develop particle-laden flow models that accurately capture the complex physics of particle-fluid turbulence interactions. This includes the ability to predict the impacts of unresolved turbulence structure, turbulence inhomogeneity and anisotropy, and two-way coupling.  This research will build on the previous work that we have done on model development and assessment for large eddy simulation and Reynolds-averaged Navier-Stokes turbulence modelling frameworks for CFD simulation of particle-laden turbulent flows.  Direct numerical simulation is used as a benchmarking tool.  This research will lead to improved models for incorporation into commercial or open-source CFD codes used in industry.  In addition, the rigorous training that the highly qualified personnel will receive will provide industry with CFD analysts with a strong theoretical foundation which is critical for successful CFD simulation and analysis.

这个研究项目是关于数学模型的发展，以预测气体湍流中颗粒的运动。颗粒负载湍流存在于广泛的工程问题中，包括大气流动中的污染分散、喷雾燃烧、通风和药用气溶胶给药系统。计算流体动力学（CFD）为工程设计人员或分析人员提供了有关区域内流体流动、颗粒速度和浓度的信息。这允许开发增强的工程设计。因此，需要有一个强大的粒子运动数学模型，能够捕捉流体-湍流/粒子相互作用的复杂性，同时保持合理的计算工作量。流体湍流影响着大量的颗粒流动特性，包括颗粒的分散和漂移，固体表面的沉积速率，以及燃烧流中燃料液滴或固体颗粒与空气的混合速率。湍流强度的空间变化会导致粒子聚集在低湍流动能区域。即使在均匀湍流中，由于底层湍流结构的影响，颗粒也会发生局部聚集；具有与最小湍流时间尺度相似的时间常数的颗粒可以从涡旋湍流结构中喷射出来，并倾向于局部集中在高应变和低涡度区域。此外，颗粒的存在会影响下伏流体速度场。气相和颗粒相之间的双向耦合是复杂的。例如，取决于粒子的时间常数和相对于重要湍流尺度的大小，粒子可以通过涡流脱落增强湍流的产生，或者可以作为动量从流体转移到粒子的结果来调节湍流动能。本研究计划的目标是开发颗粒负载流模型，以准确捕捉颗粒-流体湍流相互作用的复杂物理。这包括预测未解决的湍流结构、湍流不均匀性和各向异性以及双向耦合的影响的能力。这项研究将建立在之前的工作基础上，我们已经完成了大涡模拟的模型开发和评估，以及用于颗粒负载湍流CFD模拟的reynolds -平均Navier-Stokes湍流建模框架。直接数值模拟被用作基准测试工具。这项研究将导致模型的改进，以纳入工业中使用的商业或开源CFD代码。此外，高素质人才将接受严格的培训，为行业CFD分析师提供强大的理论基础，这对于成功的CFD模拟和分析至关重要。