CRII:OAC: Machine Learning- Enhanced Multiscale Simulation of Fiber Composites

Many engineered materials such as fiber composites have a hierarchical structure that spans multiple length scales. The analysis and design of these materials rely on multiscale simulations whose computational costs significantly increase if the structure is large and if the material deformation depends on its loading history. These high costs prohibit computationally intensive studies such as uncertainty propagation and design optimization.

To tackle this challenge, the project employs recent advances in high performance computing and machine learning to accelerate multiscale simulations by orders of magnitude without compromising accuracy. The developed methods and tools are applicable to many materials systems and the testbed on fiber composites benefits a wide range of academic and industrial efforts since these materials are heavily used in, for example, the automobile and aerospace industries.

This work develops cyberinfrastructure foundations that will enable acceleration of multiscale simulations while (1) minimizing the information loss incurred in inter-scale communication, and (2) considering various uncertainty sources such as spatial variation of microstructural properties and morphologies. The project builds mechanistic machine learning (ML) models that emulate complex and history-dependent microstructural deformations that embody a broad range of nanoscale and mesoscale effects to ensure transferability.

The ML models are integrated with a message passing interface (MPI) design that leverages the hierarchical nature of the multiscale simulation to achieve two-level parallelism, both within and across the computational nodes of a compute cluster. The message passing is employed to manage inter-scale and intra-scale data transfer during multiscale simulation of a light-weight fiber composite whose microstructures spatially vary due to manufacturing uncertainties.

The simulations on composite materials aim to increase understanding of how their properties are affected by inter- and intra- microstructural uncertainties, constituent properties, deformation history, and microstructure morphology.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.