Publikationen

König, Jörg; Chen, Minqian; Rösing, Wiebke; Boho, David; Mäder, Patrick; Cierpka, Christian
On the use of a cascaded convolutional neural network for three-dimensional flow measurements using astigmatic PTV. - In: Measurement science and technology, ISSN 1361-6501, Volume 31 (2020), number 7, 074015, 14 Seiten

Many applications in chemistry, biology and medicine use microfluidic devices to separate, detect and analyze samples on a miniaturized size-level. Fluid flows evolving in channels of only several tens to hundreds of micrometers in size are often of a 3D nature, affecting the tailored transport of cells and particles. To analyze flow phenomena and local distributions of particles within those channels, astigmatic particle tracking velocimetry (APTV) has become a valuable tool, on condition that basic requirements like low optical aberrations and particles with a very narrow size distribution are fulfilled. Making use of the progress made in the field of machine vision, deep neural networks may help to overcome these limiting requirements, opening new fields of applications for APTV and allowing them to be used by nonexpert users. To qualify the use of a cascaded deep convolutional neural network (CNN) for particle detection and position regression, a detailed investigation was carried out starting from artificial particle images with known ground truth to real flow measurements inside a microchannel, using particles with uni- and bimodal size distributions. In the case of monodisperse particles, the mean absolute error and standard deviation of particle depth-position of less than and about 1 [my]m were determined, employing the deep neural network and the classical evaluation method based on the minimum Euclidean distance approach. While these values apply to all particle size distributions using the neural network, they continuously increase towards the margins of the measurement volume of about one order of magnitude for the classical method, if nonmonodisperse particles are used. Nevertheless, limiting the depth of measurement volume in between the two focal points of APTV, reliable flow measurements with low uncertainty are also possible with the classical evaluation method and polydisperse tracer particles. The results of the flow measurements presented herein confirm this finding. The source code of the deep neural network used here is available on https://github.com/SECSY-Group/DNN-APTV.

https://doi.org/10.1088/1361-6501/ab7bfd

Zheng, Jincan; Liu, Runcong; Wang, Xiaodong; Xu, Guodong; Lyu, Ze; Kolesnikov, Yuri; Na, Xianzhao
An online contactless investigation of the meniscus velocity in a continuous casting mold using Lorentz force velocimetry. - In: Metallurgical and materials transactions, ISSN 1543-1916, Bd. 51 (2020), 2, S. 558-569

Monitoring the meniscus velocities of molten steel in continuous casting molds is critical for revealing the velocity field in the whole mold and consequently for process control and final product quality, however, the realization of contactless online measurement in an actual metallurgy environment is a highly challenging task. In this paper, we develop a special Lorentz force velocimetry (LFV) device to measure the local meniscus velocities of molten steel flow, and this device can adapt to harsh environments with high temperature, opaque liquid metal and surrounding complex electromagnetic noise. A series of laboratory experiments and calibrations were conducted to provide support for a follow-up in-plant test. The LFV device exhibits capability and feasibility for measuring the meniscus velocity in continuous casting molds during plant tests. On this basis, the velocity field and turbulent flow in a wide slab continuous casting mold are analyzed. The measured meniscus velocity is on the order of ˜ 10^-1 m/s, which is consistent with the results obtained via the nail-board approach.

https://doi.org/10.1007/s11663-019-01757-z

Lyu, Ze; Zheng, Jincan; Karcher, Christian; Ni, Ming-Jiu; Wang, Xiaodong
Experimental study of Lorentz force velocimetry for bubble detection under ambient magnetic field. - In: Conference proceedings, PAMIR 2019, Reims, (2019), S. 37

Kolesnikov, Yuri;
Experimental study of liquid metal film flow in a strong streamwise magnetic field. - In: Conference proceedings, PAMIR 2019, Reims, (2019), S. 10

Karcher, Christian; Lyu, Ze; Xu, Xiujie; Wang, Engang
Liquid metal droplet flow affected by a travelling magnetic field. - In: Conference proceedings, PAMIR 2019, Reims, (2019), S. 393-398