Scaling of Interface Variability in Aerospace Composite Structures: A Multi-scale Stochastic Approach from Local Identification to Panel Response

Abstract

Quantifying random spatial variations in composite interface properties is an essential prerequisite for the high-fidelity propagation of material properties variabilities through structural numerical models. This study proposes a multi-scale stochastic framework designed to bridge the gap between local interfacial characterization at the coupon scale and the global response of industrial aerospace panels, focusing specifically on the skin-stringer bond line. Building upon previous stochastic modeling and identification methodologies for Mode I fracture, we first extend the likelihood-free Bayesian calibration to Mode II configurations to characterize interfacial stochastic parameters using experimental coupon data. To address the scale-up challenge, we employ a simulation-based Stochastic Finite Element Method (SFEM) to contrast two distinct probabilistic strategies -- spatially uniform variations (Paradigm 2) and spatially correlated random fields (Paradigm 3) -- against the traditional industrial B-basis standard (Paradigm 1). The numerical results reveal a dual mechanism in uncertainty propagation depending on the targeted airframe design criterion. For global performance, such as the ultimate load capacity, a significant scale effect leads to an uncertainty attenuation phenomenon; statistical averaging smooths out local heterogeneities, demonstrating that a computationally efficient spatially uniform approach is sufficient. Conversely, for local integrity, such as the threshold and spatial localization of early damage initiation, the inclusion of spatially correlated random fields becomes indispensable to capture premature degradation driven by local material weak spots. This balanced probabilistic treatment offers a route toward test derisking and predictive uncertainty management in the design of damage-tolerant aerospace composite airframes.