Fluidization Engineering
Fluidization Engineering, Second Edition, by Kunii and Levenspiel, expands on its original scope to encompass these new areas and introduces reactor models specifically for these contacting regimes.
Fluidization Engineering, Second Edition, by Kunii and Levenspiel, expands on its original scope to encompass these new areas and introduces reactor models specifically for these contacting regimes.
Also available on Youtube. About This Presentation Presented by Raj Singh, Technip Energies at the 2022 Barracuda Virtual Reactor Users’ Conference. Summary Computational modeling plays an increasingly important role in…
This report from Los Alamos National Lab (LANL) documents the KIVA-II computer program for the numerical calculation of transient, two- and three-dimensional, chemically reactive fluid flows with sprays.
This paper by Smagorinsky establishes some of the foundational equations for understanding particle-fluid physics.
Also available on Youtube. About This Presentation Presented by Sibashis Banerjee of Tronox at the 2022 Barracuda Virtual Reactor Users’ Conference. Summary This talk will cover the use of modeling…
This presentation by Song Wang at the 2022 Barracuda Virtual Reactor Users’ conference discusses Encina’s fluidized bed catalytic pyrolysis reaction system which converts post-consumer plastics to valuable products.
This presentation by Martin Weng discusses how the Barracuda Virtual Reactor was used to de-risk the modification of a German cement plant.
This application model uses Barracuda Virtual Reactor to simulate sand and water flowing through a downhole sand screen.
This application model extends CPFD Software’s industrial-scale cement calciner model to simulate co-firing with municipal sludge.
This application model uses Barracuda Virtual Reactor to simulate proppant transport and settling in a single hydraulic fracture, including the interaction of a realistic proppant size distribution with slickwater. The validated Barracuda results also provide training data for a Graph Neural Network reduced-order model (GNN-ROM), which closely reproduces the predicted bed profiles at a fraction of the computational cost.