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arXiv 2301.13282physics.flu-dyn
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基于高斯过程回归的压力梯度重建

Pressure Reconstruction from the Measured Pressure Gradient Using Gaussian Process Regression

Zejian You, Qi Wang, Xiaofeng Liu

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摘要：

利用粒子图像测速技术（PIV），从含有噪声的运动学数据中重建压力场，已经建立了许多数值算法，如压力泊松求解器和全方向积分（ODI）方法。本研究采用高斯过程回归（GPR），一个具有内在去噪机制的概率框架，以解决传统的压力泊松求解器的缺点，并与ODI的性能进行比较。为了评估算法的准确性，GPR和ODI进行了详细的测试，在一个规范的设置强制均匀各向同性湍流从约翰霍普金斯湍流数据库。结果表明，对于各向同性湍流流动，探地雷达具有与优化超参数的ODI相同的精度。然而，探地雷达具有平坦化脉冲信号的趋势。因此，在没有进一步修改的情况下，不适合用脉冲真信号检测流动结构。该框架的误差传播的物理和频谱空间进行了分析和讨论。

英文摘要：

Many numerical algorithms have been established to reconstruct pressure fields from measured kinematic data with noise by Particle Image Velocimetry (PIV), such as the Pressure Poisson solver and the Omni-Directional Integration (ODI) method. This study adopts Gaussian Process Regression (GPR), a probabilistic framework with an intrinsic de-noising mechanism to tackle drawbacks of traditional Pressure Poisson solver and compares the performance with ODI. To evaluate the accuracy of the algorithm, GPR and ODI are tested in detail in a canonical setup of forced homogeneous isotropic turbulence from the Johns Hopkins Turbulence Database. According to the result, GPR has the same level of accuracy as ODI with optimized hyper-parameters for the isotropic turbulence flow. However, GPR has the tendency to flatten impulsive signals. Therefore, without further modifications, it is not suitable to detect flow structures with impulsive true signals. The error propagation of the proposed framework is also analyzed and discussed in both physical and spectral spaces.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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