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Shock-wave imaging by density recovery from intensity measurements

A method for quantitative estimation of density variation in high-speed flow, which uses light as an interrogating tool, is described. The wavefront distortion of the interrogating beam induced by the compressible flow field is estimated quantitatively, in which the density gradient of the flow fiel...

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Bibliographic Details
Published in:Applied optics (2004) 2018-05, Vol.57 (15), p.4297
Main Authors: Medhi, Biswajit, Hegde, Gopalakrishna M, Reddy, Kalidevapura Jagannath, Roy, Debasish, Vasu, Ram Mohan
Format: Article
Language:English
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Summary:A method for quantitative estimation of density variation in high-speed flow, which uses light as an interrogating tool, is described. The wavefront distortion of the interrogating beam induced by the compressible flow field is estimated quantitatively, in which the density gradient of the flow field is seen as refractive-index gradient by the probing beam. The distorted wavefront is measured quantitatively by using the cross-sectional intensities of the distorted wavefront along the optical axis. Iterative algorithms are developed using both deterministic (Gauss-Newton) and stochastic (ensemble Kalman filter) update strategies to recover unknown parameters such as the phase of the wavefront or the refractive index distribution in the flow directly from the measured intensities. With phase recovered in the first step, a ray tomography algorithm is used to obtain the refractive index and density distributions in the flow from the phase. Experiments are conducted to quantitatively visualize the shock-wave-induced flow field in a shock-tunnel facility. The reconstructed density cross sections, obtained using different reconstruction methods, are presented and compared with those obtained by solving the Navier-Stokes equation using computational fluid dynamic routines. It is observed that the iterative algorithms always outperform those depending on solution of the transport-of-intensity equation. In particular, when using the iterative algorithms, the stochastic search scheme outperforms the Gauss-Newton method.
ISSN:1559-128X
2155-3165
DOI:10.1364/AO.57.004297