  Metallic foams are crucial to many emerging applications, among them
shielding against hypervelocity impacts caused by micrometeoroids and orbital
debris. The variability of properties at feature-scale and mesoscale lengths
originating from the foam's inherently random microstructure makes predictive
models of their properties challenging. It also hinders the optimization of
components fabricated with such foams, an especially serious problem for
spacecraft design where the balance between cost and mass must also be balanced
against the catastrophic results of component failure. To address this problem,
we compute the critical transition length between the feature-scale, where
mechanical properties are determined by individual features, and the mesoscale,
where behavior is determined by ensembles of features. At the mesoscale,
distributions of properties -- with respect to both expectation value and
standard variability -- are consistent and predictable. The Kentucky Random
Structure Toolkit (KRaSTk) is applied to determine the transition from
feature-scale to mesoscale for computational volumes representing metallic
foams at a range of reduced densities. The transition is found to occur when
the side length of a cubic sample volume is ~10x greater than the
characteristic length. Comparing KRaSTk-computed converged stiffness
distributions with experimental measurements of a commercial metallic foam
found excellent agreement for both expectation value and standard variability
at all reduced densities. Lastly, we observe that the diameter of a
representative MMOD strike is ~30x shorter than the feature-scale to mesoscale
transition for the foam at any reduced density. Therefore, features will
determine response to hypervelocity impacts, rather than bulk (or even
mesoscale) structure.
