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Xu, Lin; Wang, Engang; Karcher, Christian; Deng, Anyuan; Xu, Xiujie; Han, Zefeng
Effect of vertical-combine EMBr on steel/slag interface in the funnel shape mold of thin slab continuous casting. - In: International journal of applied electromagnetics and mechanics, ISSN 1875-8800, Bd. 61 (2020), S. S85-S92

https://doi.org/10.3233/JAE-209106
Kazerooni, Hamid; Thieme, Alexander; Schumacher, Jörg; Cierpka, Christian
Electron spin-vorticity coupling in pipe flows at low and high Reynolds number. - In: Physical review applied, ISSN 2331-7019, Bd. 14 (2020), 1, S. 014002-1-014002-9

Spin-hydrodynamic coupling is a recently discovered method to directly generate electricity from an electrically conducting fluid flow in the absence of Lorentz forces. This method relies on a collective coupling of electron spins - the internal quantum-mechanical angular momentum of the electrons - with the local vorticity of a fluid flow. In this work, we experimentally investigate the spin-hydrodynamic coupling in circular- and noncircular-capillary pipe flows and extend a previously obtained range of Reynolds numbers to smaller and larger values, 20 < Re < 21500, using the conducting liquid-metal alloy (Ga,In)Sn as the working liquid. In particular, we provide experimental evidence for the linear dependence of the generated electric voltage with respect to the bulk-flow velocity in the laminar regime of the circular pipe flow as predicted by Matsuo et al. [Phys. Rev. B. 96, 020401 (2017)]. Moreover, we show analytically that this behavior is universal in the laminar regime regardless of the cross-sectional shape of the pipe. Finally, the proposed scaling law by Takahashi et al. [Nat. Phys. 12, 52 (2016)] for the generated voltage in turbulent circular pipe flows is experimentally evaluated at Reynolds numbers higher than in previous studies. Our results verify the reliability of the proposed scaling law for Reynolds numbers up to Re = 21500 for which the flow is in a fully developed turbulent state.



https://doi.org/10.1103/PhysRevApplied.14.014002
Blahout, Sebastian; Reinecke, Simon R.; Kazerooni, Hamid; Kruggel-Emden, Harald; Hussong, Jeanette
On the 3D distribution and size fractionation of microparticles in a serpentine microchannel. - In: Microfluidics and nanofluidics, ISSN 1613-4990, Bd. 24 (2020), 3, 22, S. 1-10

https://doi.org/10.1007/s10404-020-2326-7
Moller, Sebastian; Resagk, Christian; Cierpka, Christian
On the application of neural networks for temperature field measurements using thermochromic liquid crystals. - In: Experiments in fluids, ISSN 1432-1114, Bd. 61 (2020), 4, 111, S. 1-21

This study presents an investigation regarding the applicability of neural networks for temperature measurements using thermochromic liquid crystals (TLCs) and discusses advantages as well as disadvantages of common calibration approaches. For the characterization of the measurement technique, the dependency of the color of the TLCs on the temperature as well as on the observation angle and, therefore, on the position within the field of view of a color camera is analyzed in detail. In order to consider the influence of the position within the field of view on the color, neural networks are applied for the calibration of the temperature measurements. In particular, the focus of this study is on analysis of the error of temperature measurement for different network configurations as well as training methods, yielding a mean absolute deviation and a mean standard deviation in the range of 0.1 K for instantaneous measurements. On the basis of a comparison of this standard deviation to that of two further calibration approaches, it is shown that neural networks are suited for temperature measurements via the color of TLCs. Finally, the applicability of this measurement technique is illustrated at an exemplary temperature measurement in a horizontal plane of a Rayleigh-Bénard cell with large aspect ratio, which clearly shows the emergence of convective flow patterns by means of the temperature field.



https://doi.org/10.1007/s00348-020-2943-7
Otto, Henning; Resagk, Christian; Cierpka, Christian
Optical measurements on thermal convection processes inside thermal energy storages during stand-by periods. - In: Optics, ISSN 2673-3269, Bd. 1 (2020), 1, S. 155-172

Thermal energy storages (TES) are increasingly important for storing energy from renewable energy sources. TES that work with liquid storage materials are used in their most efficient way by stratifying the storage fluid by its thermal density gradient. Mixing of the stratification layers during stand-by periods decreases the thermal efficiency of the TES. Tank sidewalls, unlike the often poorly heat-conducting storage fluids, promote a heat flux from the hot to the cold layer and lead to thermal convection. In this experimental study planar particle image velocimetry (PIV) measurements and background-oriented schlieren (BOS) temperature measurements are performed in a model experiment of a TES to characterise the influence of the thermal convection on the stratification and thus the storage efficiency. The PIV results show two vertical, counter-directed wall jets that approach in the thermocline between the stratification layers. The wall jet in the hot part of the thermal stratification shows compared to the wall jet in the cold region strong fluctuations in the vertical velocity, that promote mixing of the two layers. The BOS measurements have proven that the technique is capable of measuring temperature fields in thermally stratified storage tanks. The density gradient field as an intermediate result during the evaluation of the temperature field can be used to indicate convective structures that are in good agreement to the measured velocity fields.



https://doi.org/10.3390/opt1010011
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
Brockmann, Philipp; Kazerooni, Hamid; Brandt, Luca; Hussong, Jeanette
Utilizing the ball lens effect for astigmatism particle tracking velocimetry. - In: Experiments in fluids, ISSN 1432-1114, Bd. 61 (2020), 2, 67, S. 1-19

https://doi.org/10.1007/s00348-020-2900-5
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
Dubovikova, Nataliia; Karcher, Christian; Resagk, Christian
Electromagnetic effects on the salt crystallization process within the turbulent pipe flow. - In: Conference proceedings, PAMIR 2019, Reims, (2019), S. 51

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