首页|University College London (UCL) Reports Findings in Machine Learning (Machine Le arning Assisted Experimental Characterization of Bubble Dynamics in Gas-Solid Fl uidized Beds)
University College London (UCL) Reports Findings in Machine Learning (Machine Le arning Assisted Experimental Characterization of Bubble Dynamics in Gas-Solid Fl uidized Beds)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of London, United Kingdom , by NewsRx editors, research stated, “This study introduces a machine learning (ML)-assisted image segmentation method for automatic bubble identification in g as-solid quasi-2D fluidized beds, offering enhanced accuracy in bubble recogniti on. Binary images are segmented by the ML method, and an in-house Lagrangian tra cking technique is developed to track bubble evolution.” Our news journalists obtained a quote from the research from University College London (UCL), “The ML-assisted segmentation method requires few training data, a chieves an accuracy of 98.75%, and allows for filtering out common sources of uncertainty in hydrodynamics, such as varying illumination conditions and out-of-focus regions, thus providing an efficient tool to study bubbling in a standard, consistent, and repeatable manner. In this work, the ML-assisted me thodology is tested in a particularly challenging case: structured oscillating f luidized beds, where the spatial and time evolution of the bubble position, velo city, and shape are characteristics of the nucleation-propagation-rupture cycle. The new method is validated across various operational conditions and particle sizes, demonstrating versatility and effectiveness. It shows the ability to capt ure challenging bubbling dynamics and subtle changes in velocity and size distri butions observed in beds of varying particle size. New characteristic features o f oscillating beds are identified, including the effect of frequency and particl e size on the bubble morphology, aspect, and shape factors and their relationshi p with the stability of the flow, quantified through the rate of coalescence and splitting events.”
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