Strategies to Improve 3D Simulation Accuracy Using 1D Simulation for CFB Boilers
This presentation by Uendo Lee presents a strategy for improving the accuracy of 3D simulation using 1D simulation for CFB boilers.
This presentation by Uendo Lee presents a strategy for improving the accuracy of 3D simulation using 1D simulation for CFB boilers.
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…
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 presentation by Frederik Zafiryadis at the 2022 Barracuda Virtual Reactor Users’ Conference discusses a computational particle fluid dynamics (CPFD) model for simulating the sugar cracking reaction in a pilot-scale riser.
This presentation by Sina Tebianian at the 2022 Barracuda Virtual Reactor Users’ Conference discusses the utilization of a pressurized gas-solid feeder to inject sawdust powders into a fixed and fluidized bed cold-flow unit.
This presentation by Ahmed Taha shares details regarding the Azure GPU computational resources in addition to recent results and the scaling capabilities of the Barracuda Virtual Reactor code on Azure.
This presentation by Sam Clark of CPFD Software discusses updates within the Barracuda Simulation software such as Tecplot, RLMCloud, Multi-GPU Acceleration, and other features.
This presentation by Rey Gomez discusses the applications of GPUs and how NVIDIA hardware is powering workloads from CFD to physics-informed neural networks (PINNs).