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Sensitivity Analysis and Accuracy of a CFD-TFM Approach to Bubbling Bed Using Pressure Drop Fluctuations

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, June 2017
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Title
Sensitivity Analysis and Accuracy of a CFD-TFM Approach to Bubbling Bed Using Pressure Drop Fluctuations
Published in
Frontiers in Bioengineering and Biotechnology, June 2017
DOI 10.3389/fbioe.2017.00038
Pubmed ID
Authors

Leonardo Tricomi, Tommaso Melchiori, David Chiaramonti, Micaël Boulet, Jean Michel Lavoie

Abstract

Based upon the two fluid model (TFM) theory, a CFD model was implemented to investigate a cold multiphase-fluidized bubbling bed reactor. The key variable used to characterize the fluid dynamic of the experimental system, and compare it to model predictions, was the time-pressure drop induced by the bubble motion across the bed. This time signal was then processed to obtain the power spectral density (PSD) distribution of pressure fluctuations. As an important aspect of this work, the effect of the sampling time scale on the empirical power spectral density (PSD) was investigated. A time scale of 40 s was found to be a good compromise ensuring both simulation performance and numerical validation consistency. The CFD model was first numerically verified by mesh refinement process, after what it was used to investigate the sensitivity with regards to minimum fluidization velocity (as a calibration point for drag law), restitution coefficient, and solid pressure term while assessing his accuracy in matching the empirical PSD. The 2D model provided a fair match with the empirical time-averaged pressure drop, the relating fluctuations amplitude, and the signal's energy computed as integral of the PSD. A 3D version of the TFM was also used and it improved the match with the empirical PSD in the very first part of the frequency spectrum.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 18%
Student > Master 2 18%
Professor 1 9%
Student > Doctoral Student 1 9%
Researcher 1 9%
Other 0 0%
Unknown 4 36%
Readers by discipline Count As %
Engineering 3 27%
Energy 1 9%
Chemical Engineering 1 9%
Unknown 6 55%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 August 2017.
All research outputs
#18,556,449
of 22,982,639 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#3,427
of 6,695 outputs
Outputs of similar age
#241,458
of 315,940 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#11
of 16 outputs
Altmetric has tracked 22,982,639 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,695 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.