High-Speed-Camera-System for 3D-Measurements of Flow Fields

用于流场 3D 测量的高速摄像系统

• 批准号: 525772482
• 金额: --
• 依托单位: Universität Bayreuth
• 依托单位国家: 德国
• 项目类别: Major Research Instrumentation
• 财政年份: 2023
• 资助国家: 德国
• 起止时间: 2022-12-31 至 无数据
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/525772482?language=en
• 关键词: Speed; Camera; System; 3D; Measurements

Two high-speed cameras and accessories are to be purchased. These form the key component for the complete three-dimensional reconstruction of compressible flow fields and for the identification of sound sources in turbulent flows. The numerical method for this is a data assimilation, which minimizes the difference between synthetic images from a direct numerical calculation and the real Schlieren images from the high-speed cameras. Methodically, time-resolved images of a flow from two different perspectives are to be taken with the cameras. These images can be based, for example, on density gradients or on introduced particles or smoke. The data obtained are complemented by existing measurement technology, for example with the help of microphones and a thermal camera. In a further step, flow calculations of the same process based on Navier-Stokes equations will be performed. For this purpose, initial and boundary conditions for the calculation have to be specified. The flow depends on these conditions. Especially for a turbulent inlet, the actual flow pattern is very unique. In the planned research projects, however, initial and boundary conditions are not specified, but are determined by optimization in such a way that the difference between the high-speed images and synthetic images based on the flow field and the modeled optical setup is as small as possible. This process is also called data assimilation and uses similar methods which recently acquire momentum in Physics-informed Machine Learning. Corresponding preliminary work with only one camera and a plane flow is available in the working group. With the help of the two high-speed cameras proposed for procurement here, complex, three-dimensional flows can be investigated. The speed of the cameras allows the investigation of transsonic flows and aeroacoustic phenomena. The result of the optimization is the complete, three-dimensional flow. All flow variables are available for further investigation after data assimilation. All analyses, which are usually reserved for numerical computation, can thus be investigated on the real experiment. In particular, a modal decomposition of the flow is to be performed. This will complement work within the group to determine coherent turbulent structures. So far, this can only be done on a numerical basis. The cameras to be acquired will allow this work to be carried out on the experiment instead.

将购买两个高速摄像头和配件。这些构成了可压缩流场的完整三维重建以及在湍流中识别声源的关键组件。为此，数值方法是数据同化，它可以最大程度地减少直接数值计算中的合成图像与高速摄像机的真实Schlieren图像之间的差异。有条理地，从两个不同角度来看，将使用相机从两个不同的角度拍摄流动的时间分辨图像。这些图像可以例如，例如密度梯度或引入的颗粒或烟雾。获得的数据与现有的测量技术相辅相成，例如借助麦克风和热摄像头。在另一个步骤中，将执行基于Navier-Stokes方程的相同过程的流量计算。为此，必须指定计算的初始条件和边界条件。流量取决于这些条件。特别是对于湍流入口，实际流动模式非常独特。但是，在计划的研究项目中，未指定初始条件和边界条件，而是通过优化确定的，以使基于流场和建模光学设置的高速图像和合成图像之间的差异尽可能小。该过程也称为数据同化，并使用类似的方法，这些方法最近在物理知识的机器学习中获得了动力。工作组只有一个相机的相应初步工作，只有一个摄像头和平面流。借助在这里提出的两个高速摄像机，可以研究复杂的三维流动。摄像机的速度允许研究跨音量流和空气现象。优化的结果是完整的三维流。所有流量变量均可在数据同化后进一步研究。因此，所有通常用于数值计算的分析都可以在实际实验上进行研究。特别是，将执行流动的模态分解。这将补充小组内的工作，以确定相干的湍流结构。到目前为止，这只能在数值基础上完成。要购买的摄像机将允许在实验上进行这项工作。