PIV/BOS Synthetic Image Generation in Variable Density Environments for Error Analysis and Experiment Design

TL;DR

This paper introduces a ray tracing-based image generation method for realistic PIV and BOS experiment simulation, aiding in error analysis, experiment design, and development of image analysis tools.

Contribution

The paper presents a novel, realistic image simulation framework for PIV and BOS experiments incorporating optical effects and density gradients, with open-source implementation.

Findings

Simulated light ray displacements match BOS theory results.

Realistic images replicate optical aberrations and perspective effects.

Framework supports error analysis and experiment design.

Abstract

We present an image generation methodology based on ray tracing that can be used to render realistic images of Particle Image Velocimetry (PIV) and Background Oriented Schlieren (BOS) experiments in the presence of density/refractive index gradients. This methodology enables the simulation of aero-thermodynamics experiments for experiment design, error, and uncertainty analysis. Images are generated by emanating light rays from the particles or dot pattern, and propagating them through the density gradient field and the optical elements, up to the camera sensor. The rendered images are realistic, and can replicate the features of a given experimental setup, like optical aberrations and perspective effects, which can be deliberately introduced for error analysis. We demonstrate this methodology by simulating a BOS experiment with a known density field obtained from direct numerical simulations (DNS) of homogeneous buoyancy driven turbulence, and comparing the light ray displacements from ray tracing to results from BOS theory. The light ray displacements show good agreement with the reference data. This methodology provides a framework for further development of simulation tools for use in experiment design and development of image analysis tools for PIV and BOS applications. An implementation of the proposed methodology in a Python-CUDA program is made available as an open source software for researchers.