3D MULTISCALE VECTORIAL SIMULATIONS OF RANDOM MODELS
Abstract
We present here a method using level sets as primary grains to generate 3D simulations of random models in the continuous space. Primary grains of any shape can be used as long as we are able to represent them by an implicit function. Simulations are generated using Boolean combinations of the primary implicit functions. This approach allows us to build complex combinations of simulations that we could not obtain with a pixel based method. Furthermore, vectorial simulations do not require a large amount of computer resources, which allows us to obtain fast generations of microstructures with very large sizes and numbers of primary grains.
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