  Quantifying the relationship between geometric descriptors of microstructure
and effective properties like permeability is essential for understanding and
improving the behavior of porous materials. In this paper, we employ a
previously developed stochastic model to investigate microstructure-property
relationships of nonwovens. First, we show the capability of the model to
generate a wide variety of realistic nonwovens by varying the model parameters.
By computing various geometric descriptors, we investigate the relationship
between model parameters and microstructure morphology and, in this way, assess
the range of structures which may be described by our model. In a second step,
we perform virtual materials testing based on the simulation of a wide range of
nonwovens. For these 3D structures, we compute geometric descriptors and
perform numerical simulations to obtain values for permeability as an effective
material property. We then examine and quantify the relationship between
microstructure morphology and permeability by fitting parametric regression
formulas to the obtained data set, including but not limited to formulas from
literature. We show that for structures which are captured by our model,
predictive power may be improved by allowing for slightly more complex
formulas.
