Inverse Design of Spinodoid Cellular Structures With Tailored Mechanical-Hydro-Thermal Performances

In this study, a data-driven computational framework is developed for multi-objective inverse design of spinodoid cellular metamaterials with desired anisotropic properties, which enables us to simultaneously tailor the mechanical deformation, fluid transport and heat transfer performances. Cellular structures with various spinodal topologies are generated within the full design space, and multiple physical properties, including mechanical stiffness, hydraulic permeability and thermal conductivity tensors, are numerically evaluated via finite element analysis and computational fluid dynamics respectively. After having a sufficiently large database, regressional conditional generative adversarial network (cGAN)is used to construct the one-to-many mapping to represent inverse structure-property relations, from which multiple cellular structures can be generated by inputting the target mechanical-hydro-thermal properties. The preliminary results demonstrate that this new computational design framework is an effective tool to deal with stiffness-permeability-diffusivity synergy, expand the tunable scope of multiphysical performances, and tailor spinodoid cellular metamaterials with the desired multi-functionality.