A dynamic domain decomposition method for solving coupled multiphysics problems based on a distributed software agent environment

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The optimization of the efficient and flexible calculation process of complex three-dimensional coupled field problems, calculated by software agents, during run time is the goal of this project. Previous work shows, that the use of multiple software agents within a multi agent system using specialized algorithms of numerical field calculation such as the finite element method or boundary element method provide the efficient and flexible calculation. Therefore, the simulation is statically split on classes of problems like the included physics, its domains or available resources. The order of the separation is based on the strength of the expected coupling between the partial problems. This represents the usual approach to computations on distributed resources. The calculation of the partial problems as well as considering related couplings are done decentralized within the software agent system considering available computing resources, the capabilities of the calculation agent and their workload. A validation of the splitting - and thus also the coupling hierarchy is possible during runtime based on information, such as the convergence behaviour or the current state of partial calculations. Due to non-linear coupling between the partial problems, and due to the nondeterministic behaviour of every distributed cluster, a dynamic coupling hierarchy is numerically beneficial for the convergence of an iterative distributed calculation approach. Based on the existing agent system a dynamic consideration of the coupling is possible by an active coordination based on intelligent and learned decisions. Accordingly, the implementation of a learning system using a knowledge base for storable parameters of a coupled systems. This also includes the distributed, decentralized architecture where knowledge is not generated centrally, but on different units available only temporarily. Taken recommendations from the knowledge base into account is done within the numerical iterative solution procedure by adapting the calculation process. The final evaluation of the achieved results completes this project. It is done with special attention to the achieved optimization of coupled systems calculation calculated with decentralized and autonomously acting agents. An additional point is the comparability and gain achieved by the learning system.