Dual-Phase Steels - From Micro to Macro Properties (EXASTEEL-2)

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In the EXASTEEL-2 project, experts on scalable iterative solvers, computational modeling in materials science, performance engineering, and parallel direct solvers are joining forces to develop new computational algorithms and implement software for a grand challenge problem from computational materials science. There is an increasing need for predictive simulations of the macroscopic behavior of complex new materials. In the EXASTEEL-2 project, this problem is considered for modern micro-heterogeneous (dual-phase) steels, attempting to predict the macroscopic properties of new materials from those on the microscopic level. It is the goal to develop algorithms and software towards a virtual laboratory for predictive material testing in silico. A bottleneck is the computational complexity of the multiscale models needed to describe the new materials, involving sufficiently accurate micromechanically motivated models on the crystalline scale. Therefore, new ultra-scalable nonlinear implicit solvers will be developed and combined with a highly parallel computational scale bridging approach (FE^2), intertwined with a consequent and permanent performance engineering, to bring the challenging engineering application of a virtual laboratory for material testing and design to extreme scale computing. We envisage a continuously increased transition from descriptive to predictive macroscopic simulations and take into account, to the best of our knowledge for the first time within a computational scale bridging approach, the polycrystalline nature of dual phase steels including grain boundary effects at the microscale.Our goals could not be reached without building on the algorithm and software infrastructure from EXASTEEL-1. We will complete the paradigm shift, begun in the EXASTEEL-1 project, from Newton-Krylov solvers to nonlinear methods (and their composition) with improved concurrency and reduced communication. By combining nonlinear domain decomposition with multigrid methods we plan to leverage the scalability of both implicit solver approaches for nonlinear methods.Although our application is specific, the algorithms and optimized software will have an impact well beyond the particular application. Nonlinear implicit solvers are at the heart of many simulation codes, and our software building blocks PETSc, BoomerAMG, PARDISO, and FEAP are all software packages with a large user base. The advancement of these software packages is explicitely planned for in the work packages of this project.The project thus adresses computational algorithms (nonlinear implicit solvers and scale bridging), application software, and programming (PE, hybrid programming, accelerators).