Two-level adaptation for Adaptive Multipreconditioned FETI

Abstract This article introduces two strategies to reduce the memory cost of the Adaptive Multipreconditioned FETI method (AMPFETI) while preserving its capability to solve ill conditioned systems efficiently. Their common principle is to gather search directions into aggregates which are frequently adapted in order to achieve the best compromise between the decrease of the solver error and the computational resources employed. The methods are assessed on two weak scalability studies on highly heterogeneous problems up to 10,368 cores and half a billion of unknowns, and on two ill-conditioned industrial applications, related to the numerical homogenization of solid propellant and to the simulation of a multiperforated aircraft combustion chamber.