一种用于黑箱多尺度和多物理耦合的随机保守场转移方法
A Stochastic Conservative Field Transfer Method for Black-box Multiscale and Multiphysics Coupling
AI总结：
本文提出了一种用于黑箱多尺度和多物理耦合的随机保守场转移方法，通过随机近似实现高精度和低保守性误差，且在GPU上具有竞争力的计算成本。
摘要：
本文介绍了一种在黑箱耦合中进行场转移操作的新方法，当源离散化信息不可用时。该方法使用了伽辽金投影的随机近似，从而得到一种渐近提供保守性的方法。在简单领域以及LTX聚变反应堆的网格上，将精度和保守性的误差与网格相交方法和径向基函数进行了比较。在所有测试案例中，我们的新方法比径向基函数提供了更高的精度和更小的保守性误差，并可用于黑箱耦合，不同于网格相交方法。此外，我们展示了在NVIDIA A100 GPU上的实现和性能，表明成本与网格相交方法具有竞争力。
英文摘要：
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 GeForce RTX 4060 GPU, showing that the cost is competitive with the mesh intersection method.
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