The extended quadrature method of moments (EQMOM) for the solution of population balance equations (PBE) is implemented in the open-source computational fluid dynamic (CFD) toolbox OpenFOAM as part of the OpenQBMM project. The moment inversion procedure was designed (Nguyen et al., 2016) to maximize the number of conserved moments in the transported moment set. The algorithm is implemented in a general structure to allow the addition of other kernel density functions defined on R+, and arbitrary kernels to describe physical phenomena involved in the evolution of the number density function. The implementation is verified with a set of zero-dimensional cases involving aggregation and breakage problems. Comparison to the rigorous solution of the PBE provides validation for these cases. The coupling of the EQMOM procedure with a CFD solver to address aggregation and breakage problems of non-inertial particles is validated against experimental measurements in a Taylor-Couette reactor from the literature. Keywords Extended quadrature method of moments, Log-normal kernel density function, Population balance equation, Aggregation and breakage, OpenFOAM, OpenQBMM Disciplines Complex Fluids | Computer-Aided Engineering and Design | Engineering Physics | Fluid Dynamics Comments This is a manuscript of an article published as Passalacqua, Alberto, Frédérique Laurent, E. Madadi-Kandjani, J. C. Heylmun, and R. O. Fox. "An open-source quadrature-based population balance solver for OpenFOAM." Abstract The extended quadrature method of moments (EQMOM) for the solution of population balance equations (PBE) is implemented in the open-source computational fluid dynamic (CFD) toolbox OpenFOAM as part of the Open-QBMM project. The moment inversion procedure was designed (Nguyen et al., 2016) to maximize the number of conserved moments in the transported moment set. The algorithm is implemented in a general structure to allow the addition of other kernel density functions defined on R + , and arbitrary kernels to describe physical phenomena involved in the evolution of the number density function. The implementation is verified with a set of zerodimensional cases involving aggregation and breakage problems. Comparison to the rigorous solution of the PBE provides validation for these cases. The
Thanks to the unique molecular fingerprints in the mid-infrared spectral region, absorption spectroscopy in this regime has attracted widespread attention in recent years. Contrary to commercially available infrared spectrometers, which are limited by being bulky and cost-intensive, laboratory-on-chip infrared spectrometers can offer sensor advancements including raw sensing performance in addition to use such as enhanced portability. Several platforms have been proposed in the past for on-chip ethanol detection. However, selective sensing with high sensitivity at room temperature has remained a challenge. Here, we experimentally demonstrate an on-chip ethyl alcohol sensor based on a holey photonic crystal waveguide on silicon on insulator-based photonics sensing platform offering an enhanced photoabsorption thus improving sensitivity. This is achieved by designing and engineering an optical slow-light mode with a high group-index of n g = 73 and a strong localization of modal power in analyte, enabled by the photonic crystal waveguide structure. This approach includes a codesign paradigm that uniquely features an increased effective path length traversed by the guided wave through the to-be-sensed gas analyte. This PIC-based lab-on-chip sensor is exemplary, spectrally designed to operate at the center wavelength of 3.4 μm to match the peak absorbance for ethanol. However, the slow-light enhancement concept is universal offering to cover a wide design-window and spectral ranges towards sensing a plurality of gas species. Using the holey photonic crystal waveguide, we demonstrate the capability of achieving parts per billion levels of gas detection precision. High sensitivity combined with tailorable spectral range along with a compact form-factor enables a new class of portable photonic sensor platforms when combined with integrated with quantum cascade laser and detectors.
This work presents a vision of future water resources hydrodynamics codes that can fully utilize the strengths of modern high-performance computing. The advances to computing power, formerly driven by the improvement of central processing unit processors, now focus on parallel computing and, in particular, the use of graphics processing units (GPUs). However, this shift to a parallel framework requires refactoring the code to make efficient use of the data as well as changing even the nature of the algorithm that solves the system of equations. These concepts along with other features such as the precision for the computations, dry regions management, and input/output data are analyzed in this paper. A 2D multi-GPU flood code applied to a large-scale test case is used to corroborate our statements and ascertain the new challenges for the next-generation parallel water resources codes.
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