WindDensity-MBIR: Model-Based Iterative Reconstruction for Wind Tunnel 3D Density Estimation

Fuente: arXiv
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Autori principali: Weisenburger, Karl J., Buzzard, Gregery T., Bouman, Charles A., Kemnetz, Matthew R.
Natura: Preprint
Pubblicazione: 2026
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author Weisenburger, Karl J.
Buzzard, Gregery T.
Bouman, Charles A.
Kemnetz, Matthew R.
author_facet Weisenburger, Karl J.
Buzzard, Gregery T.
Bouman, Charles A.
Kemnetz, Matthew R.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2602_16621
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle WindDensity-MBIR: Model-Based Iterative Reconstruction for Wind Tunnel 3D Density Estimation
Weisenburger, Karl J.
Buzzard, Gregery T.
Bouman, Charles A.
Kemnetz, Matthew R.
Signal Processing
Fluid Dynamics
Optics
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.
title WindDensity-MBIR: Model-Based Iterative Reconstruction for Wind Tunnel 3D Density Estimation
topic Signal Processing
Fluid Dynamics
Optics
url https://arxiv.org/abs/2602.16621