Experimental data for code validation: Horizontal air jets in a semicircular fluidized bed of Geldart Group D particles

Abstract

Experiments were conducted with 6 mm plastic beads (Geldart Group D) in a semi‐circular, gas‐fluidized bed with side jets. Attention was paid to particle characterization and bed measurements, making the resulting dataset ideal for CFD‐DEM validation and uncertainty quantification. The bed was operated slightly above and below the minimum fluidization velocity, with additional fluidization provided by one of two pairs of opposing jets located above the distributor near the flat, front face of the unit. Care is taken to report material properties and bed conditions with either measured distribution functions or uncertainty bounds. High‐speed video imaging and particle tracking velocimetry are used to extract bin‐averaged velocity profiles, which are used to extract jet penetration depths. The time‐averaged mean and standard deviation of the bed pressure drop is also reported. Finally, the lower jets are also inserted into the bed until the opposing jets merge to form a spout‐like pattern. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2351–2363, 2018

Impact of Contact Scaling and Drag Calculation on the Accuracy of Coarse‐Grained Discrete Element Method

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“…• complex geometries, e.g., Xu et al [32], Fullmer et al [51], Jalali et al [52] • ordered pattern formation, e.g., Wu et al [53], Bakshi et al [33] product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof.…”

Very small‐scale, segregating‐fluidized‐bed experiments: A dataset for CFD‐DEM validation and uncertainty quantification

“…However, a much smaller fraction provide a full characterization of system inputs and measured outputs-that is, full evaluation of the uncertainty-as required to allow for rigorous validation and UQ. Furthermore, the potential for rigorous model assessment, including validation and UQ of CFD-DEM models, using the few reports that accounted for uncertainty for the complete range of experimental inputs 13,17 is curbed by a combination of (i) the staggering number of simulations required for a fully-sampled, forward propagation (UQ) analysis and (ii) the computational resources required to simulate the large number of particles in the corresponding systems. Regarding (i), the number of simulations required to fully sample the input uncertainty space depends on the type of each uncertain input, namely, aleatoric or epistemic.…”

“…While treating all uncertainty sources as epistemic may reduce the total number of simulations required to perform UQ, this treatment does not allow for the sampling of aleatoric uncertainty sources-for example, particle diameter-to be based on their associated probability and therefore does not fully capture the precise shape of the uncertainty envelope. Fullmer et al 17 17 experiments is approximately a day using 12 computational processor units (CPU) (totaling 288 CPU hours). 20 While this is a reasonable computational expense for a limited number of parametric studies, the total number of simulations required for a rigorous, forward propagation (full) UQ analysis of Fullmer et al's 17 system based on the nine epistemic and six aleatory uncertainties is a minimum of 73,100 simulations 1 -summing to 21 million CPU hoursa prohibitive computational expense.…”