Experimentally Validated Simulations of 50 {\mu}m X-ray PIV Tracer Particles

TL;DR

This study compares Beer-Lambert ray-tracing and Monte Carlo N-Particle simulations for predicting X-ray imaging performance, validating with experimental data to aid the design of XPIV systems and tracer particles.

Contribution

It demonstrates that Beer-Lambert simulations are effective and computationally efficient for XPIV system design, with MCNP providing only marginally better accuracy.

Findings

AGS particles are visible with SNR > 1 in 100 ms images.

Beer-Lambert approach predicts contrast effectively and is less costly.

MCNP offers slightly more accurate predictions but at much higher computational expense.

Abstract

We evaluate Beer-Lambert (BL) ray-tracing and Monte Carlo N-Particle (MCNP) photon tracking simulations for prediction and comparison of X-ray imaging system performance. These simulation tools can aid the methodical design of laboratory-scale X-ray particle image velocimetry (XPIV) experiments and tracer particles by predicting image quality. Particle image signal-to-noise ratio (SNR) is used as the metric of system performance. Simulated and experiment data of hollow, silver-coated, glass sphere tracer particles (AGSF-33) are compared. As predicted by the simulations, the AGSF-33 particles are visible with a SNR greater than unity in 100~ms exposure time images, demonstrating their potential as X-ray PIV or particle tracking velocimetry (XPTV) tracers. The BL approach predicts the image contrast, is computationally inexpensive, and enables the exploration of a vast parameter space for system design. MCNP simulations, on the other hand, predict experiment images slightly more accurately, but are more than an order of magnitude more computationally expensive than BL simulations. For most practical XPIV system design applications, the higher computational expense of MCNP is likely not justified by the modest accuracy improvement compared to BL.