Estimating density, velocity, and pressure fields in supersonic flows using physics-informed BOS
Estimating density, velocity, and pressure fields in supersonic flows using physics-informed BOS
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DOI：
10.1007/s00348-022-03554-y
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发表时间：
2022-08
影响因子：
2.4
通讯作者：
J. Molnar;L. Venkatakrishnan;B. Schmidt;T. Sipkens;S. J. Grauer
中科院分区：
文献类型：
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作者：
J. Molnar;L. Venkatakrishnan;B. Schmidt;T. Sipkens;S. J. Grauer
我们报道了一种新的背景定向纹影（BOS）工作流程，称为“物理信息纹影”，从一对参考图像和扭曲图像中提取密度、速度和压力场。我们的方法使用物理信息神经网络（PINN）来产生同时满足测量数据和控制方程的流场。对于本工作中感兴趣的高速、近似无粘流，我们指定了基于欧拉方程和无转动方程的物理损失。BOS是一种定量流体可视化技术，通常用于表征可压缩流动。位于测量体后面的背景图案图像，用计算机视觉和断层扫描算法进行处理，以确定密度场。至关重要的是，BOS具有一系列病态逆问题，这些问题需要补充信息（即除了图像之外）才能准确地重建流。当前的BOS方法依赖于图像插值或惩罚项来促进全局或分段平滑解决方案。然而，这些算法总是与流动物理不兼容，导致密度场的误差。基于物理信息的BOS使用包含BOS测量模型和控制方程的PINN直接重建所有流场。这一过程提高了密度估计的准确性，还获得了以前无法获得的速度和压力数据。我们通过重建与分析和数值幻象以及单对实验测量相对应的合成数据来证明我们的方法。我们的物理信息重建比传统的BOS估计要准确得多。此外，据我们所知，这项工作代表了首次使用pin从任何类型的实验数据中重建超音速流动。
We report a new workflow for background-oriented schlieren (BOS), termed “physics-informed BOS,” to extract density, velocity, and pressure fields from a pair of reference and distorted images. Our method uses a physics-informed neural network (PINN) to produce flow fields that simultaneously satisfy the measurement data and governing equations. For the high-speed, approximately inviscid flows of interest in this work, we specify a physics loss based on the Euler and irrotationality equations. BOS is a quantitative fluid visualization technique that is commonly used to characterize compressible flow. Images of a background pattern, positioned behind the measurement volume, are processed with computer vision and tomography algorithms to determine the density field. Crucially, BOS features a series of ill-posed inverse problems that require supplemental information (i.e., in addition to the images) to accurately reconstruct the flow. Current methods for BOS rely upon interpolation of the images or a penalty term to promote a globally- or piecewise-smooth solution. However, these algorithms are invariably incompatible with the flow physics, leading to errors in the density field. Physics-informed BOS directly reconstructs all the flow fields using a PINN that includes the BOS measurement model and governing equations. This procedure improves the accuracy of density estimates and also yields velocity and pressure data, which were not previously available. We demonstrate our approach by reconstructing synthetic data that corresponds to analytical and numerical phantoms as well as a single pair of experimental measurements. Our physics-informed reconstructions are significantly more accurate than conventional BOS estimates. Furthermore, to the best of our knowledge, this work represents the first use of a PINN to reconstruct a supersonic flow from experimental data of any kind.