The steady streaming (SS) phenomenon is gaining increased attention in the microfluidics community, because it can generate net mass flow from zero-mean vibration. We developed numerical simulation and experimental measurement tools to analyze this vibration-induced flow, which has been challenging due to its unsteady nature. The validity of these analysis methods is confirmed by comparing the three-dimensional (3D) flow field and the resulting particle trajectories induced around a cylindrical micro-pillar under circular vibration. In the numerical modeling, we directly solved the flow in the Lagrangian frame so that the substrate with a micro-pillar becomes stationary, and the results were converted to a stationary Eulerian frame to compare with the experimental results. The present approach enables us to avoid the introduction of a moving boundary or infinitesimal perturbation approximation. The flow field obtained by the micron-resolution particle image velocimetry (micro-PIV) measurement supported the three-dimensionality observed in the numerical results, which could be important for controlling the mass transport and manipulating particulate objects in microfluidic systems.
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http://dx.doi.org/10.3390/mi9120668 | DOI Listing |
Sci Rep
January 2025
Laboratory of Engineering Profile, Satbayev University, Satbayev St. 22a, 050013, Almaty, Kazakhstan.
Several mechanisms were postulated to reduce drilling problems, improve hole cleaning characteristics, and keep the bit in good condition for the second usage. This study was conducted on Majnoon Field in southeastern Iraq to optimize the bit and drilling parameters. The results indicated that the 16" SFD75D bit proved the preferred bit for both vertical and deviated wells due to its directional capabilities.
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January 2025
University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 680-749, Republic of Korea.
This study employed large eddy simulation (LES) with the wall-adapting local eddy-viscosity (WALE) model to investigate transitional flow characteristics in an idealized model of a healthy thoracic aorta. The OpenFOAM solver pimpleFoam was used to simulate blood flow as an incompressible Newtonian fluid, with the aortic walls treated as rigid boundaries. Simulations were conducted for 30 cardiac cycles and ensemble averaging was employed to ensure statistically reliable results.
View Article and Find Full Text PDFAnal Chim Acta
February 2025
Department of Physical and Analytical Chemistry & Institute of Biotechnology of Asturias, University of Oviedo, c/Julián Clavería 8, 33006, Oviedo, Spain. Electronic address:
The COVID-19 outbreak was an important turning point in the development of a new generation of biosensing technologies. The synergistic combination of an immunochromatographic test (lateral flow immunoassays, LFIA) and signal transducers provides enhanced sensitivity and the ability to quantify in the rapid tests. This is possible due to the variety of nanoparticles that can be used as reporter labels.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Institute of Fluid Mechanics, University of Rostock, Rostock, Germany.
Purpose: To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.
View Article and Find Full Text PDFNeural Netw
January 2025
Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China; Intelligent Game and Decision Laboratory, China.
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric PDEs due to network limitations. To address this issue, we propose a Physics-Informed Neural Implicit Flow (PINIF) framework, which enables a meshless low-rank representation of the parametric spatio-temporal field based on the expressiveness of the Neural Implicit Flow (NIF), enabling a meshless low-rank representation.
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