A parallel spiral-flow column photobioreactor (PSCP) composed of eight spiral-flow columns, and two pipe headers was designed for scale-up cultivation of microalgae to capture CO. To solve the disturbance of spiral flow fields among parallel columns, computational fluid dynamics (CFD) simulation was used to optimize the main structural parameters, such as the number and the height of microalgae solution outlet (MSO), to improve flow field structure and enhance the cells' light/dark cycle. The horizontal velocity in the direction of optical path and the turbulent kinetic energy (TKE) reached the peak values of 0.214 m/s and 5.28 m/s when MSO number was four and MSO height was 1.05 m. Meanwhile, the disturbance of the spiral flow field among parallel columns are minimum, and microalgae light/dark cycle frequency was 33.3% higher than that of conventional bubble column photobioreactor. Therefore, the biomass yield and CO fixation rate of microalgae increased by 81.5% and 100.5%, respectively.
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http://dx.doi.org/10.1016/j.scitotenv.2021.149314 | 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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