0000000000304203

AUTHOR

Martins Klevs

showing 3 related works from this author

MHT-X: Offline Multiple Hypothesis Tracking with Algorithm X

2021

An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python. The code is intended for scientific applications that do not require online processing. Directed graph framework is used and multiple scans with progressively increasing time window width are used for edge construction for maximum likelihood trajectories. The current version of the code was developed for applications in multiphase hydrodynamics, e.g. bubble and particle tracking, and is capable of resolving object motion, merges and splits. Feasible object associations and trajectory graph edge likelihoods are determined using weak mas…

Fluid Flow and Transfer ProcessesFOS: Computer and information sciencesbubble dynamicsComputer Vision and Pattern Recognition (cs.CV)neutron imagingComputational MechanicsComputer Science - Computer Vision and Pattern RecognitionFluid Dynamics (physics.flu-dyn)General Physics and AstronomyFOS: Physical sciencesPhysics - Fluid DynamicsAlgorithm Ximage processingtwo-phase flowMechanics of Materialsliquid metalX-ray radiography
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Dynamic mode decomposition of magnetohydrodynamic bubble chain flow in a rectangular vessel

2021

We demonstrate the first application of dynamic mode decomposition (DMD) to bubble flow with resolved dynamic liquid/gas boundaries. Specifically, we have applied DMD to the output of numerical simulations for a system where chains of bubbles ascend through a rectangular liquid metal vessel. Flow patterns have been investigated in the vessel and bubble reference frames. We show how gas flow rate and applied magnetic affect bubble wake flow and larger-scale flow structures within the liquid metal vessel by examining the velocity field mode statistics over trajectory time and total flow time as well as the computed mode velocity fields. The results of this proof-of-concept study indicate that…

Fluid Flow and Transfer ProcessesPhysicsLiquid metalMechanical EngineeringBubbleComputational MechanicsFluid Dynamics (physics.flu-dyn)FOS: Physical sciencesMechanicsPhysics - Fluid DynamicsWakeCondensed Matter PhysicsVolumetric flow ratePhysics::Fluid DynamicsFlow (mathematics)Mechanics of MaterialsDynamic mode decompositionVector fieldMagnetohydrodynamic drive
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Resolving gas bubbles ascending in liquid metal from low-SNR neutron radiography images

2021

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 p…

FOS: Computer and information sciencesLiquid metalTechnologyMaterials scienceQH301-705.5low signal-to-noise ratio (SNR)BubbleAcousticsNoise reductionQC1-999Computer Vision and Pattern Recognition (cs.CV)dynamic neutron imagingComputer Science - Computer Vision and Pattern Recognitionmetohydrodynamics (MHD)FOS: Physical sciencesImage processingdenoisingGeneral Materials ScienceSegmentationBiology (General)InstrumentationQD1-999Fluid Flow and Transfer ProcessesProcess Chemistry and TechnologyNeutron imagingTPhysicssegmentationGeneral EngineeringFluid Dynamics (physics.flu-dyn)Experimental dataPhysics - Fluid DynamicsEngineering (General). Civil engineering (General)Computer Science Applicationsimage processingtwo-phase flowChemistryliquid metalComputer Science::Computer Vision and Pattern RecognitionTwo-phase flowTA1-2040bubble flow
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