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Experimentally validated x-ray image simulations of 50 μm x-ray PIV tracer particles

Jason T. Parker, Simo A. Mäkiharju

2022Measurement Science and Technology16 citationsDOI

Abstract

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.

Topics & Concepts

Tracking (education)TRACERMonte Carlo methodParticle tracking velocimetryParticle image velocimetryParticle (ecology)PhysicsImage qualityRay tracing (physics)VelocimetryOpticsComputational physicsImage (mathematics)Computer scienceTurbulenceComputer visionMathematicsNuclear physicsMechanicsStatisticsPsychologyGeologyOceanographyPedagogyAdvanced X-ray and CT ImagingNuclear reactor physics and engineeringNuclear Physics and Applications
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