Document Type
Article
Publication Date
6-3-2026
Abstract
Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive three-dimensional (3D) density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a wind tunnel. We call this method WindDensity-MBIR and apply it using simulated data to difficult reconstruction scenarios with sparse data, small projection field of view, and limited angular extent. WindDensity-MBIR can recover high-order features in these scenarios within 10% to 25% error even when the piston, tip, and tilt are removed from the wavefront measurements.
Source Publication
Optical Engineering (ISSN 0091-3286)
Recommended Citation
Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, et al. "WindDensity-MBIR: model-based iterative reconstruction for wind tunnel 3D density estimation," Optical Engineering 65(8), 081820 (3 Jun 2026) https://doi.org/10.1117/1.OE.65.8.081820
Comments
© 2026 The Authors.
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