Pēteris Zvejnieks

Pēteris Zvejnieks
University of Latvia | LU · Department of Physics

Bachelor of Physics

About

7
Publications
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9
Citations
Introduction
Skills and Expertise

Publications

Publications (7)
Preprint
Full-text available
This paper presents the analysis of the particle-laden liquid metal wake flow around a cylindrical obstacle at different obstacle Reynolds numbers. Particles in liquid metal are visualized using dynamic neutron radiography. We present the results of particle tracking velocimetry of the obstacle wake flow using an improved version of our image proce...
Article
Full-text available
This paper demonstrates particle tracking velocimetry performed for a model system wherein particle-laden liquid metal flow around a cylindrical obstacle was studied. We present the image processing methodology developed for particle detection in images with disparate and often low signal- and contrast-to-noise ratios, and the application of our MH...
Article
Full-text available
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...
Preprint
Full-text available
This paper demonstrates particle tracking velocimetry performed for a model system wherein particle-laden liquid metal flow about a cylindrical obstacle was studied. We present the image processing methodology developed for particle detection in images with disparate and often low signal- and contrast-to-noise ratios, and the application of our MHT...
Article
Full-text available
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 bu...
Preprint
Full-text available
We showcase the dynamic mode decomposition (DMD) code developed for applications in two-phase flow analysis. Vertical bubble chain flow in a rectangular vessel filled with liquid gallium is studied without and with applied static horizontal magnetic field (MF) and DMD is applied to the velocity fields computed via volume of fluid simulations. Flow...
Preprint
Full-text available
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...

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Project (1)
Project
The purpose of this project is to establish fundamental understanding of magnetohydrodynamic (MHD) bubble flow in liquid metal within a range of industrially relevant conditions where it is prospective to use magnetic field for flow control and process optimization. Research is carried out in collaboration with Paul Scherrer Institut (PSI, Switzerland) and Helmholtz-Zentrum Dresden-Rossendorf (HZDR, Germany). Specifically, model miniaturized liquid metal systems with bubble chain flow are studied where low melting point metals like gallium and galinstan plus argon gas are used. This enables to study the effects of applying magnetic fields of various configurations for different gas flow rates to see how exactly magnetic control can be accomplished and flow properties tailored as necessary. The objective is the systematic study of the underlying physics and parameter space mapping. To this end, we utilize experimental methods such as dynamic X-ray and neutron radiography, as well as numerical simulations and data analysis. We have developed methods and code for image processing, object tracing and dynamic mode decomposition of simulation output for in-depth analysis and physical interpretation of observed system dynamics.