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

需要购买两台高速摄像机及其配件。这些构成了可压缩流场的完整三维重建和湍流中声源识别的关键组成部分。数值方法是一种数据同化方法，它可以最大限度地减少直接数值计算合成图像与高速相机真实纹影图像之间的差异。系统地，时间分辨图像的流动从两个不同的角度是用相机拍摄。例如，这些图像可以基于密度梯度或引入的颗粒或烟雾。获得的数据由现有的测量技术补充，例如在麦克风和热像仪的帮助下。在进一步的步骤中，将执行基于Navier-Stokes方程的相同过程的流量计算。为此，必须规定计算的初始条件和边界条件。流动取决于这些条件。特别是对于湍流入口，实际的流型是非常独特的。然而，在计划的研究项目中，初始条件和边界条件没有规定，而是通过优化确定，从而使基于流场和模拟光学设置的高速图像与合成图像之间的差异尽可能小。这个过程也被称为数据同化，并使用类似的方法，最近在物理信息机器学习中获得了动力。在工作组中，只有一个摄像机和一个平面流可以进行相应的前期工作。在这里采购的两台高速摄像机的帮助下，可以调查复杂的三维流动。相机的速度允许研究跨声速流动和气动声学现象。优化的结果是完整的、三维的流动。在数据同化后，所有的流量变量都可供进一步研究。因此，所有通常用于数值计算的分析都可以在实际实验中进行研究。特别地，将执行流的模态分解。这将补充小组内的工作，以确定连贯的湍流结构。到目前为止，这只能在数字的基础上完成。要获得的相机将允许在实验中进行这项工作。

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.