Cross-spectral analysis of PSP images for estimation of surface pressure spectra corrupted by the shot noise

Ozawa, Yuta; Nonomura, Taku; Mercier, Bertrand; Castelain, Thomas; Bailly, Christophe; Asai, Keisuke

doi:10.1007/s00348-019-2776-4

Cited by 9 publications

(1 citation statement)

References 20 publications

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“…In addition to these, there are many other studies that have taken on the challenge of low-speed unsteady PSP measurements (e.g. Inoue et al, 2021;Sugioka et al, 2019;Noda et al, 2018;Ozawa et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model

Inoba,

Uchida,

Iwasaki

et al. 2022

Preprint

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This study proposes a method for predicting the wind direction against the simple automobile model (Ahmed model) and the surface pressure distributions on it by using data-driven optimized sparse pressure sensors. Positions of sparse pressure sensor pairs on the Ahmed model were selected for estimation of the yaw angle and reconstruction of pressure distributions based on the time-averaged surface pressure distributions database of various yaw angles, whereas the symmetric sensors in the left and right sides of the model were assumed. The surface pressure distributions were obtained by pressure-sensitive paint measurements. Three algorithms for sparse sensor selection based on the greedy algorithm were applied, and the sensor positions were optimized. The sensor positions and estimation accuracy of yaw angle and pressure distributions of three algorithms were compared and evaluated. The results show that a few optimized sensors can accurately predict the yaw angle and the pressure distributions.

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Section: Introductionmentioning

confidence: 99%