Technique to measure heats of reaction of Ti-B, Al-Ti-B, and Al-Ti-B4C powder blends

In this research, a modification to initiation aid ignition in bomb calorimetry that involves systemically blending levels of boron and potassium nitrate initiation aids with a bulk structural energetic elemental power blend is developed. A regression is used to estimate the nominal heat of reaction for the primary reaction. The technique is first applied to the synthesis of TiB2 as a validation study to see if close proximity to literature values can be achieved. The technique is then applied to two systems of interest, Al-Ti-B, and Al-Ti-B4C. In all three investigations, x-ray diffraction is used to characterize the product phases of the reactions to determine the extent and identity of the product phases and any by-products that may have formed as a result of adding the initiation aid. The experimental data indicates the technique approximates the heat of reaction value for the synthesis of TiB2 from Ti-B powder blends and the formation of TiB2 is supported by volume fraction analysis by x-ray diffraction. Application to the Al-Ti-B and Al-Ti-B4C blends show some correlation with variation of the initiation aid, with x-ray diffraction showing the formation of equilibrium products. However, these blends require further investigation to resolve more complex interactions and rule out extraneous variables.

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).