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

TL;DR

WindDensity-MBIR introduces a Bayesian iterative reconstruction method for non-invasive 3D density measurement in wind tunnel turbulence, effectively handling sparse and limited-view data scenarios.

Contribution

It formulates wind tunnel 3D density estimation as a Bayesian sparse-view tomography problem and develops a novel iterative reconstruction algorithm.

Findings

Successfully reconstructs high-order features with 10-25% error in challenging scenarios.

Handles sparse data, small FOV, and limited angular extent effectively.

Operates without relying on spline basis assumptions.

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.