Development and validation of an offline multiscale topology optimization framework using interpolated constraint functions

Multiscale structural design is an emerging field within the aerospace community driven by the need for innovative structural concepts capable of fulfilling ever-expanding performance requirements. However, exploring novel material systems or architectures at the preliminary design stage can be inefficient due to potential changes in objectives, boundary conditions, and constraints. Such changes often necessitate a complete redesign at both the material and system levels, and can thus rapidly become intractable. To address this challenge, this paper presents a novel computational framework that seeks to reduce the barrier to entry for multiscale structural design by optimizing the structure sequentially across length scales. Specifically, a precomputed database of potential material-level responses is developed and subsequently passed to a system-level optimization process via a series of constraints. This database can be reused if the system-level problem changes, making it more suitable for the preliminary design stage. An optimized solution to a benchmark structural design problem is presented in the context of both the predicted mechanics of the problem as well as a solution obtained using a traditional structural design tool. A simplified design is then generated and compared against both an experimentally characterized 3D printed structure as well as high-fidelity finite element models, where it is shown that the proposed framework is capable of generating high-performance solutions.