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pro vyhledávání: '"Klevs, Mārtiņš"'
Complex fluid flows are important in many real-life problems. For an in-depth understanding, new and more elaborate methods of flow description are necessary. Often experimental and numerical data are accumulated in large quantities however only simp
Externí odkaz:
http://arxiv.org/abs/2410.16511
Autor:
Birjukovs, Mihails, Zvejnieks, Peteris, Lappan, Tobias, Klevs, Martins, Heitkam, Sascha, Trtik, Pavel, Mannes, David, Eckert, Sven, Jakovics, Andris
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
Externí odkaz:
http://arxiv.org/abs/2206.11033
Autor:
Birjukovs, Mihails, Trtik, Pavel, Kaestner, Anders, Hovind, Jan, Klevs, Martins, Gawryluk, Dariusz Jakub, Thomsen, Knud, Jakovics, Andris
Publikováno v:
Applied Sciences 2021, 11(20), 9710
We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation
Externí odkaz:
http://arxiv.org/abs/2109.04883
Publikováno v:
Physics of Fluids 33, 083316 (2021)
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
Externí odkaz:
http://arxiv.org/abs/2103.13291
Autor:
Zvejnieks, Peteris, Birjukovs, Mihails, Klevs, Martins, Akashi, Megumi, Eckert, Sven, Jakovics, Andris
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. Dire
Externí odkaz:
http://arxiv.org/abs/2101.05202
Akademický článek
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Autor:
Klevs, Mārtiņš
Šis darbs demonstrē dynamic mode decomposition (DMD) analīzes lietderību un potenciālu divfāzu plūsmas analīzei. Tiek demonstrēts izstrādāts DMD algoritms, kas ir labi piemērots lielu, sarežģītu skaitliskās modelēšanas un/vai eksper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2360::5c64ac630fc4028b5db142df32c805ec
https://dspace.lu.lv/dspace/handle/7/55957
https://dspace.lu.lv/dspace/handle/7/55957