WindDensity-MBIR: Model-Based Iterative Reconstruction for Wind Tunnel 3D Density Estimation
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 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 tip, tilt, and piston are removed from the wavefront measurements.
Cite
@article{arxiv.2602.16621,
title = {WindDensity-MBIR: Model-Based Iterative Reconstruction for Wind Tunnel 3D Density Estimation},
author = {Karl J. Weisenburger and Gregery T. Buzzard and Charles A. Bouman and Matthew R. Kemnetz},
journal= {arXiv preprint arXiv:2602.16621},
year = {2026}
}
Comments
Submitted to the Unconventional Imaging, Sensing, and Adaptive Optics special session of Optical Engineering