He, C. X. and Wang, P. and Liu, Y. Z. and Gan, L. (2022) 'Flow Enhancement of Tomographic Particle Image Velocimetry Measurements Using Sequential Data Assimilation.', Physics of Fluids, 34 (3). 035101.
Sequential data assimilation (DA) was performed on three-dimensional flow fields of a circular jet measured by tomography particle image velocimetry (tomo-PIV). The work focused on the in-depth analysis of the flow enhancement and the pressure determination from volumetric flow measurement data. The jet was issued from a circular nozzle with an inner diameter of 𝐷 = 20 mm. A split-screen configuration including two high-speed cameras was used to capture the particle images from four different views for the tomography reconstruction of the voxels in the tomo-PIV measurement. Planar PIV was also performed to obtain the benchmark two-dimensional velocity fields for validation. The adjoint-based sequential DA scheme was used with the measurement uncertainty implanted using a threshold function to recover the flow fields with high fidelity and fewer measurement errors. Pressure was determined by either the direct mode, with implementation directly in the DA solver, or by the separate mode, which included solving the Poisson equation on the DA-recovered flow fields. Sequential DA recovered high signal-to-noise flow fields that had piecewise-smooth temporal variations due to the intermittent constraints of the observations, while only the temporal sequence of the fields at the observational instances was selected as the DA output. Errors were significantly reduced, and DA improved the divergence condition of the threedimensional flow fields. DA also enhanced the dynamical features of the vortical structures, and the pressure determined by both modes successfully captured the downstream convection signatures of the vortex rings.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1063/5.0082460|
|Publisher statement:||The following article has been accepted by Physics of Fluids. After it is published, it will be found at https://aip.scitation.org/journal/phf|
|Date accepted:||08 February 2022|
|Date deposited:||08 February 2022|
|Date of first online publication:||01 March 2022|
|Date first made open access:||08 February 2022|
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