Development of efficient topology optimization methods for thermoelastic structures made of additively manufactured anisotropic materials

Recent progress in additive manufacturing (AM) of advanced materials, e.g. metallic alloys and fiber-reinforced composites (FRCs), has transformed the way in which engineering applications are fabricated. AM enables the fabrication of unprecedentedly complex geometry structures with spatially continuously changing material properties to optimally fulfill a particular function in a short build cycle. This proposal is to devise an efficient topology optimization (TO) framework that can take full advantage offered by AM of metallic lattice structures and FRCs to create extremely lightweight high-end thermal resistant applications. Especially, the thermo-mechanical behavior of lattice structures and FRCs can be anisotropic, i.e., they behave differently in different material orientations. TO of those applications is thus highly challenging because of the strong couplings between thermal and mechanical fields and between the structural topology and the material orientation. Additionally, TO is an iterative process and is thus computationally expensive, which hampers its usage for industrial applications. To accelerate the optimization process, different modern computational techniques, viz., model order reduction, machine learning, and parallel computing will be integrated into the developed TO framework. On completion of this project, the devised TO framework will rationalize the usage of advanced materials in thermal resistant applications and many others.