Inverting turbulence: flow patterns and parameters from sparse data

反演湍流：来自稀疏数据的流动模式和参数

• 批准号: EP/X017273/1
• 负责人: George Papadakis
• 金额: $ 25.73万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX017273%2F1
• 关键词: Inverting; turbulence; flow; patterns; parameters

Our ability to compute turbulent flows with scale-resolving simulations, like Large Eddy and Direct Numerical simulations, has grown tremendously in the past decades. In these simulations, problem parameters and boundary conditions are specified and the forward problem is solved. In many real-life settings however, this information maybe uncertain or not available at all. For many of these turbulent flows observational data, such as velocity or scalar measurements, are available at several sensor locations. These sensors can be either static or mobile (for example wearable devices that monitor air quality are now cheap and easily affordable). The available observational data can be assimilated with the governing equations to recover the missing information. This is achieved by formulating and solving an optimisation problem that minimises the difference between the estimated and observed values at the sensor points. The solution to this problem provides the velocity and scalar fields that satisfy the equations and optimally match with the available observations. This is known as the inverse problem and in this sense turbulence is "inverted". Available methods to solve this optimisation problem however either fail or quickly become intractable for turbulent flows (due to the so called "butterfly effect"). We aim to break the impasse by formulating a new approach with affordable computational cost and apply it to an environmental problem, flow and pollutant dispersion around a building. Success in this endeavour can open a new direction of research, and can lead to entirely new technologies, as described in more detail in the case of support.

在过去的几十年里，我们用尺度分辨模拟（如大涡模拟和直接数值模拟）计算湍流的能力得到了极大的发展。在这些模拟中，指定问题参数和边界条件，并解决正问题。然而，在许多现实生活中，这些信息可能不确定或根本不可用。对于许多这些湍流观测数据，如速度或标量测量，可在几个传感器位置。这些传感器可以是静态的，也可以是移动的（例如，监测空气质量的可穿戴设备现在很便宜，而且很容易负担得起）。可用的观测数据可以同化的控制方程，以恢复丢失的信息。这是通过制定和解决优化问题，最大限度地减少在传感器点的估计值和观测值之间的差异。该问题的解决方案提供了满足方程并与可用观测最佳匹配的速度场和标量场。这就是所谓的逆问题，在这个意义上，湍流是“反向的”。然而，解决这个优化问题的可用方法要么失败，要么很快变得难以处理湍流（由于所谓的“蝴蝶效应”）。我们的目标是打破僵局，制定了一个新的方法与负担得起的计算成本，并将其应用到环境问题，流动和污染物扩散的建筑物。这一努力的成功可以开辟一个新的研究方向，并可以导致全新的技术，如在支助方面更详细地描述的那样。

项目成果

Decomposition of power number in a stirred tank and real time reconstruction of 3D large-scale flow structures from sparse pressure measurements

搅拌罐中功率数的分解以及稀疏压力测量的 3D 大规模流动结构的实时重建

• DOI: 10.1016/j.ces.2023.118881
• 发表时间: 2023
• 期刊: Chemical Engineering Science
• 影响因子: 4.7
• 作者: Mikhaylov K

On the role of the laminar/turbulent interface in energy transfer between scales in bypass transition

• DOI: 10.1017/jfm.2023.194
• 发表时间: 2022-07
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: H. Yao;G. Papadakis

Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification

• DOI: 10.1016/j.jcp.2023.112377
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Kyriakos D. Kantarakias;G. Papadakis

Flow Reconstruction Around a Surface-Mounted Prism from Sparse Velocity and/or Scalar Measurements Using a Combination of POD and a Data-Driven Estimator

结合使用 POD 和数据驱动估算器，通过稀疏速度和/或标量测量重建表面安装棱镜周围的流动

• DOI: 10.1007/s10494-023-00417-2
• 发表时间: 2023
• 期刊: Flow, Turbulence and Combustion
• 作者: Lu S