Liquid Metal Flow Studied by Positron Emission Tracking

Dybalska, Agnieszka; Caden, A. J.; Parker, David J.; Wedderburn, John; Griffiths, W.D.

doi:10.1007/s11663-020-01897-7

Cited by 5 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By detecting the tracer successively, a time-dependent trajectory is developed which can be analysed to deter-mine system properties such fully three-dimensional velocity fields, tracer re-circulation times, and diffusivity [1]. PEPT has been used extensively over the last 30 years to study a variety of equipment ranging from coffee roasters, washing machines, and liquid metal castings [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Model of the Large Modular Array for Positron Emission Particle Tracking

et al. 2023

View full text Add to dashboard Cite

Positron emission particle tracking (PEPT) is a non-invasive technique used to study fluid, granular, and multi-phase systems of interest to academia and industry which employs position-sensitive radiation detectors to record gamma rays in coincidence and track the movement of discrete sources. A modular detector array, the Large Modular Array (LaMA), has been constructed at the University of Birmingham's Positron Imaging Centre (PIC) to enable custom detector geometries. In order to estimate the LaMA's performance characteristics prior to experimentation, assist in developing optimised camera geometries, and determine ideal PEPT tracer characteristics, a Monte Carlo model of LaMA is created and subsequently validated with experimental measurements. Validation is achieved through comparisons of the spatial resolution and count-rate response following the National Electrical Manufacturers Association (NEMA) industry standard protocol. Notably, the model's pulse-processing chain is autonomously calibrated to match experimental measurements using a recently developed technique which applies an evolutionary algorithm. Ultimately, the simulated spatial resolution matches the experiment to within 5%. In addition, the total, true, and corrupted count-rates are reproduced to a mean error of 3.41% over the range of source activities tested. This calibrated detector model strengthens the PIC's modelling capabilities. To facilitate future research, this model has been made publicly available through the PIC's GitHub repository.

show abstract

Section: Introductionmentioning

confidence: 99%

Monte Carlo Model of the Large Modular Array for Positron Emission Particle Tracking

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The radioactive decay of these particles emits positrons that are annihilated on or near the particles, resulting in γ-radiation that is picked up by an array of sensors, allowing for the triangulation of each particle position. Because of their high energy, the γ photons penetrate even very dense media, making it possible to visualize, for example, the flow of liquid metals [2] or of colloidal drugs in the bloodstream [3]. The triangulation process naturally recovers the full position of the particles, so the reconstructed trajectories are inherently three dimensional.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic framework for uncertainty quantification in positron emission particle tracking

2023

View full text Add to dashboard Cite

Positron Emission Particle Tracking (PEPT) is an imaging method for the visualization of fluid motion, capable of reconstructing three-dimensional trajectories of small tracer particles suspended in nearly any medium, including fluids that are opaque or contained within opaque vessels. The particles are labeled radioactively, and their positions are reconstructed from the detection of pairs of back-to-back photons emitted by positron annihilation. Current reconstruction algorithms are heuristic and typically based on minimizing the distance between the particles and the so-called lines of response (LoRs) joining the detection points, while accounting for spurious LoRs generated by scattering. Here we develop a probabilistic framework for the Bayesian inference and uncertainty quantification of particle positions from PEPT data. We formulate a likelihood by describing the emission of photons and their noisy detection as a Poisson process in the space of LoRs. We derive formulas for the corresponding Poisson rate in the case of cylindrical detectors, accounting for both undetected and scattered photons. We illustrate the formulation by quantifying the uncertainty in the reconstruction of the position of a single particle on a circular path from data generated by state-of-the-art Monte Carlo simulations. The results show how the observation time Δt can be chosen optimally to balance the need for a large number of LoRs with the requirement of small particle displacement imposed by the assumption that the particle is static over Δt. We further show how this assumption can be relaxed by inferring jointly the position and velocity of the particle, with clear benefits for the accuracy of the reconstruction.

show abstract

“…The radioactive decay of these particles emits positrons that are annihilated on or near the particles, resulting in γ-radiation that is picked up by an array of sensors, allowing for the triangulation of each particle position. Because of their high energy, the γ photons penetrate even very dense media, making it possible to visualize, for example, the flow of liquid metals [5] or of colloidal drugs in the bloodstream [13]. The triangulation process naturally recovers the full position of the particles, so the reconstructed trajectories are inherently three dimensional.…”

mentioning

confidence: 99%

Uncertainty quantification in positron emission particle tracking

Offner¹,

Manger²,

Vanneste³

2022

Preprint

View full text Add to dashboard Cite

Positron Emission Particle Tracking (PEPT) is an imaging method for the visualization of fluid motion, capable of reconstructing three-dimensional trajectories of small tracer particles suspended in nearly any medium, including fluids that are opaque or contained within opaque vessels. The particles are labeled radioactively, and their positions are reconstructed from the detection of pairs of back-to-back photons emitted by positron annihilation. Current reconstruction algorithms are heuristic and typically based on minimizing the distance between the particles and the so-called lines of response (LoRs) joining the detection points, while accounting for spurious LoRs generated by scattering. Here we develop a probabilistic framework for the Bayesian inference and uncertainty quantification of particle positions from PEPT data. We formulate a likelihood by describing the emission of photons and their noisy detection as a Poisson process in the space of LoRs. We derive formulas for the corresponding Poisson rate in the case of cylindrical detectors, accounting for both undetected and scattered photons. We illustrate the formulation by quantifying the uncertainty in the reconstruction of the position of a single particle on a circular path from data generated by stateof-the-art Monte Carlo simulations. The results show how the observation time ∆t can be chosen optimally to balance the need for a large number of LoRs with the requirement of small particle displacement imposed by the assumption that the particle is static over ∆t. We further show how this assumption can be relaxed by inferring jointly the position and velocity of the particle, with clear benefits for the accuracy of the reconstruction.

show abstract

Liquid Metal Flow Studied by Positron Emission Tracking

Cited by 5 publications

References 23 publications

Monte Carlo Model of the Large Modular Array for Positron Emission Particle Tracking

Monte Carlo Model of the Large Modular Array for Positron Emission Particle Tracking

A probabilistic framework for uncertainty quantification in positron emission particle tracking

Uncertainty quantification in positron emission particle tracking

Contact Info

Product

Resources

About