Computational fluids domain reduction to a simplified fluid network

The primary goal of this project is to demonstrate the practical use of data mining algorithms to cluster a solved steady-state computational fluids simulation (CFD) flow domain into a simplified lumped-parameter network. A commercial-quality code, “cfdMine” was created using a volume-weighted k-means clustering that that can accomplish the clustering of a 20 million cell CFD domain on a single CPU in several hours or less. Additionally agglomeration and k-means Mahalanobis were added as optional post-processing steps to further enhance the separation of the clusters. The resultant nodal network is considered a reduced-order model and can be solved transiently at a very minimal computational cost. The reduced order network is then instantiated in the commercial thermal solver MuSES to perform transient conjugate heat transfer using convection predicted using a lumped network (based on steady-state CFD). When inserting the lumped nodal network into a MuSES model, the potential for developing a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track temperatures near specific objects (such as equipment in vehicles).

FACTORS INFLUENCING MATERIAL LOSS DURING IRON ORE PELLET HANDLING

Iron ore concentrate pellets have the potential to fracture and abrade during transportation and handling, which produces unwanted fine particulates and dust. Consequently, pellet producers characterize the abrasion resistance of their pellets, using an Abrasion Index (AI), to indicate whether their products will produce unacceptable levels of fines. However, no one has ever investigated whether the AI correlates to pellet dustiness. During the course of this research, we investigated the relationship between AI and iron ore concentrate pellet dustiness using a wide range of industrial and laboratory pellet samples. The results showed that, in general, AI can be used to indicate high levels of dust. However, for good-quality pellets, there was no correlation between the two. Thus, dust generation from shipping and handling pellets will depend on the quantity of pellets handled and how much they are handled. These results also showed that the type of industrial furnace used to harden iron ore concentrate pellets may affect their fines generation and potential dustiness.