Abstract
We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented—stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our previously achieved results. Significant improvements are observed as well as the capacity to reliably extract physically meaningful information from measurements performed under highly adverse imaging conditions. The showcased image processing solution and separate elements thereof are readily extendable beyond the present application, and have been made open-source.
Highlights
Gas bubble flow in liquid metal is encountered in a variety of industrial processes
We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-tonoise ratio
This is a problem from the process engineering and optimization perspective, and from the point of view of computational fluid dynamics (CFD) where it is of interest to improve effective models for bubble flow (Euler–Euler and Lagrangian) [2,32,33,34,35]
Summary
Gas bubble flow in liquid metal is encountered in a variety of industrial processes. Examples include liquid metal stirring, purification and continuous casting in metallurgy, liquid metal-based chemical reactors, and more. In addition to demonstrating performance for dynamic neutron imaging data sets from our measurements with a rectangular gallium vessel with bubble chain flow, we have performed direct experimental validation of the code by imaging a reference spherical body, both stationary and in motion, and have quantified the shape detection errors. A 20 mm × 20 mm × 30 mm rectangular brass reference body (stationary and moving, LAC at ICON is 0.90 cm−1) with a central spherical cavity (5 mm radius) was imaged at 100 FPS within a 120.06 mm square FOV (a sCMOS ORCA Flash 4.0 V2 camera with a 200 μm 6LiF/ZnS scintillator) to reproduce the imaging conditions for argon bubbles in liquid gallium. For each image sequence recording at the NEUTRA and ICON camera, dark current signals and neutron beam profile signals were recorded to be used for subsequent image normalization during pre-processing
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