Design of ultra-thin composite deployable shell structures through machine learning

Abstract

A data-driven computational framework is applied for the design of optimal ultra-thin Triangular Rollable and Collapsible (TRAC) carbon fiber booms. High-fidelity computational analyses of a large number of geometries are used to build a database. This database is then analyzed by machine learning to construct design charts that are shown to effectively guide the design of the ultra-thin deployable structure. The computational strategy discussed herein is general and can be applied to different problems in structural and materials design, with the potential of finding relevant designs within high-dimensional spaces.