Drying of Corn Kernels: From Experimental Images to Multiscale Multiphysics Modeling
This work demonstrated the importance and feasibility of experimental image
to simulation workflow. The workflow is successfully applied to a food processing study, where multiphysics and multiscale modeling
based on 3D experimental image reconstruction contributes to the preservation of corn, one of the major food sources for the world population.
Corn kernels have a complex structure as they are composed of a pericarp layer outside and contain hard and soft endosperm and germ components. Corn kernels are harvested around 30% moisture content on dry basis and dried to about 12% moisture content using heated air. Drying helps to lower the water activity to increase their shelf life. If the drying is not controlled properly, the kernels develop stress cracks, which makes them prone to insect and microbial damage.
To study and control the drying process, multiscale and multiphysics simulation conducted on the real corn kernel geometry is necessary. While the complex corn kernel structure make it extremely challenging to model
accurately, this paper adapted an experimental image to simulation workflow.