Green's Function Integral method for Pressure Reconstruction from Measured Pressure Gradient and the Interpretation of Omnidirectional Integration
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866911448636063744 |
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| author | Wang, Qi Liu, Xiaofeng |
| author_facet | Wang, Qi Liu, Xiaofeng |
| contents | Accurately and efficiently measuring the pressure field is of paramount importance in many fluid mechanics applications. The pressure gradient field of a fluid flow can be determined from the balance of the momentum equation based on the particle image velocimetry (PIV) measurement of the flow kinematics, which renders the experimental evaluation of the material acceleration and the viscous stress terms possible. We present a novel method of reconstructing the pressure fields from error-embedded pressure gradient measurement data. This method utilized Green's function of the Laplacian operator as the convolution kernel that relates pressure to pressure gradient. A compatibility condition on the boundary offers equations to solve for the boundary pressure. This Green's function integral (GFI) method has a deep mathematical connection with the state-of-the-art omnidirectional integration (ODI). As mathematically equivalent to ODI in the limit of an infinite number of line integral paths, GFI spares the necessity of line integration along zigzag integral paths, rendering generalized implementation schemes for both two and three-dimensional problems with arbitrary geometry while bringing in improved computational efficiency. In the current work, we apply GFI to pressure reconstruction of simple canonical and isotropic turbulence flows embedded with error in two-dimensional and three-dimensional domains, respectively. Uncertainty quantification is performed by eigenanalysis of the GFI operator in a square domain. The accuracy and computational efficiency of GFI are evaluated and compared with ODI. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2304_05726 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Green's Function Integral method for Pressure Reconstruction from Measured Pressure Gradient and the Interpretation of Omnidirectional Integration Wang, Qi Liu, Xiaofeng Fluid Dynamics Accurately and efficiently measuring the pressure field is of paramount importance in many fluid mechanics applications. The pressure gradient field of a fluid flow can be determined from the balance of the momentum equation based on the particle image velocimetry (PIV) measurement of the flow kinematics, which renders the experimental evaluation of the material acceleration and the viscous stress terms possible. We present a novel method of reconstructing the pressure fields from error-embedded pressure gradient measurement data. This method utilized Green's function of the Laplacian operator as the convolution kernel that relates pressure to pressure gradient. A compatibility condition on the boundary offers equations to solve for the boundary pressure. This Green's function integral (GFI) method has a deep mathematical connection with the state-of-the-art omnidirectional integration (ODI). As mathematically equivalent to ODI in the limit of an infinite number of line integral paths, GFI spares the necessity of line integration along zigzag integral paths, rendering generalized implementation schemes for both two and three-dimensional problems with arbitrary geometry while bringing in improved computational efficiency. In the current work, we apply GFI to pressure reconstruction of simple canonical and isotropic turbulence flows embedded with error in two-dimensional and three-dimensional domains, respectively. Uncertainty quantification is performed by eigenanalysis of the GFI operator in a square domain. The accuracy and computational efficiency of GFI are evaluated and compared with ODI. |
| title | Green's Function Integral method for Pressure Reconstruction from Measured Pressure Gradient and the Interpretation of Omnidirectional Integration |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2304.05726 |