INTEGRATED MULTISCALE COMPUTATIONAL MODELS OF ALUMINUM-BASED MATRIX NANOCOMPOSITES REINFORCED WITH CERAMIC PARTICLES

Particle-reinforced aluminum matrix nanocomposites offer significant potential for advanced structural applications due to their enhanced stiffness, strength, and wear resistance. However, the simultaneous optimization of mechanical performance and damage tolerance remains a critical challenge, primarily due to the complex interplay between reinforcement content, particle morphology, spatial distribution, and interfacial behavior. In this study, an integrated multiscale computational framework is proposed to systematically investigate and optimize the mechanical behavior of ceramic particle–reinforced aluminum composites. The framework combines representative volume element (RVE)-based finite element modeling, molecular dynamics (MD) simulations, and phase-field approaches within a unified predictive architecture. Microstructural descriptors such as particle volume fraction, size distribution, morphology, agglomeration statistics, and interfacial properties are explicitly quantified and incorporated into statistically controlled RVE ensembles. The results demonstrate that nanoscale dispersion quality and interfacial mechanics govern the transition between strengthening and embrittlement. The proposed approach enables the construction of structure–property design maps that identify optimal reinforcement windows maximizing strengthening while limiting brittleness and defect sensitivity. This study establishes computational modeling as a viable alternative to extensive experimental campaigns for the design of aluminum-based nanocomposites.