Data-constrained modelling (DCM) refers to a research and development methodology where the sample-specific data and information and a theoretical model are tightly integrated for quantitative modeling of a physical system . The DCM methodology has been developed by Dr Sam Yang Et Al at CSIRO since 2007 in relation to 3D characterization of material compositional microstructures and microstructure-based properties, and has been implemented as DCM software. The DCM software can also be used for 3D modelling of material properties, visualization of 3D volumetric data, converting between 3D data formats and export as Web3D/X3D format for online visualization. In contrast to the conventional image segmentation technique to derive microstructures from X-ray CT (computed tomography) images which imposes an arbitrary length-scale cut-off at the image pixel size scale, DCM incorporates fine structures below X-ray CT image resolution as voxel compositional partial volumes. Consequently, the fine length-scale information below image pixel size is preserved. Using DCM, consistent 3D compositional microstructures are obtained with different X-ray CT imaging resolution.
DCM has been successfully used in 3D microstructure characterization and analysis of aerospace paint primers, conventional and unconventional hydrocarbon reservoir rocks, coal, and other materials.
A number of DCM characterized material compositional microstructure data sets are available online. Such data sets make it possible for quantitative modeling of microstructure-based material properties, such as fluid transport, and electric properties.
For accurate characterization of microstructures, DCM would require multi-energy and quantitative X-ray CT. Care should be taken to minimize the factors which could influence the quantitative nature of the X-ray imaging.
Additional functionalities can be added to the DCM software using various plug-ins.