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Metallic foams are crucial to many emerging applications, among themshielding against hypervelocity impacts caused by micrometeoroids and orbitaldebris. The variability of properties at feature-scale and mesoscale lengthsoriginating from the foam's inherently random microstructure makes predictivemodels of their properties challenging. It also hinders the optimization ofcomponents fabricated with such foams, an especially serious problem forspacecraft design where the balance between cost and mass must also be balancedagainst the catastrophic results of component failure. To address this problem,we compute the critical transition length between the feature-scale, wheremechanical 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 andstandard variability -- are consistent and predictable. The Kentucky RandomStructure Toolkit (KRaSTk) is applied to determine the transition fromfeature-scale to mesoscale for computational volumes representing metallicfoams at a range of reduced densities. The transition is found to occur whenthe side length of a cubic sample volume is ~10x greater than thecharacteristic length. Comparing KRaSTk-computed converged stiffnessdistributions with experimental measurements of a commercial metallic foamfound excellent agreement for both expectation value and standard variabilityat all reduced densities. Lastly, we observe that the diameter of arepresentative MMOD strike is ~30x shorter than the feature-scale to mesoscaletransition for the foam at any reduced density. Therefore, features willdetermine response to hypervelocity impacts, rather than bulk (or evenmesoscale) structure.

Stochastic mechanical modeling of metallic foams to determine onset of mesoscale behavior