The intent of this project is to dramatically improve our ability to quantitatively characterize and predict real-time microstructure evolution of heterogeneous materials including alloys, ceramics, composites and granular media under both normal and extreme conditions, based on the morphological information contained in limited tomography data. We are motivated by the fact that achieving optimal material performance and design of novel materials rely on our ability to characterize and modify their physical properties and behaviors under different external conditions, which in turn rests on our accurate knowledge of how their complex bulk microstructures evolve under these conditions. Tomography reconstruction methods such as the filtered back-projection algorithm have been widely used to produce accurate digital representations of a static material microstructure given a sufficiently large number of projections of the material of interest, which significantly limits their application in characterizing dynamically evolving microstructures. We are thus motivated to find alternative methods to statistically characterize and predict in situ microstructure evolution with a minimal set of tomography data that can be obtained in a few independent measurements.
|Effective start/end date||9/15/13 → 8/31/17|
- National Science Foundation (NSF): $300,000.00
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