🤖 AI Summary
This study addresses the challenge of achieving both accuracy and conservation in field variable transfer within black-box multiphysics coupling when source mesh information is unavailable. The authors propose a novel field transfer method based on stochastic approximation Galerkin projection, which, for the first time, integrates stochastic approximation into a black-box coupling framework. This approach enables asymptotic conservation and high accuracy without requiring access to the source mesh. By overcoming the limitations of conventional radial basis function and mesh intersection methods, and leveraging GPU parallel acceleration (NVIDIA A100), the proposed technique demonstrates superior performance on both standard domains and the LTX fusion reactor mesh—exhibiting lower conservation error, higher accuracy, and computational cost comparable to that of mesh intersection methods.
📝 Abstract
This paper introduces a new method for performing field transfer operations in black-box coupling, when source discretization information is not available. This approach uses a stochastic approximation of the Galerkin projection which leads to a method that asymptotically provides conservation. Error in the accuracy and conservation has been compared to the mesh intersection method and radial basis functions on a simple domain, as well as on meshes of the LTX fusion reactor. For all cases tested, our new method provides higher accuracy and less conservation error than radial basis functions and can be used for black-box coupling, unlike the mesh-intersection method. Additionally, we demonstrate the implementation and performance of our method on an NVIDIA A100 GPU, showing that the cost is competitive with the mesh intersection method.