在 QUOKKA 代码中使用 GPU 加速的新型混合框架多组辐射流体力学数值方法

Chong-Chong HeANU, Benjamin D. WibkingMSU, Mark R. KrumholzANU
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摘要

辐射流体力学(RHD)的混合帧公式,即辐射量在惯性帧中计算,而物质量在运动帧中计算,具有优势,因为它们采用的算法可以将能量和动量保存到机器精度,并与自适应网格技术更自然地结合,因为与纯粹的运动帧方法不同,它们不会面临每次细化或粗化单元时辐射量必须改变帧的问题。然而,由于处理频率相关的相互作用和辐射边界的多普勒偏移的复杂性,在混合帧公式中实现多组 RHD 面临挑战。在本文中,我们介绍了一种新的多组 RHD 方法,它将混合帧公式与针对组内频率相关性的片式幂律近似整合在一起。这种方法确保了总能量和总动量的精确守恒,同时有效地管理了组边界的洛伦兹变换和组平均不透明度的评估。我们的方法利用了物质-辐射耦合的局域性,允许用大小为 $N_g + 1$ 的稀疏雅各布矩阵的简单方程来处理 $N_g 频率组的源决定,可以用 $O(N_g)$ 复杂度来反演。这就使得计算复杂度与 $N_g$ 成线性比例,并且不需要比纯流体力学更新更多的通信,从而使其在大规模并行和基于 GPU 的系统中非常高效。我们在 GPU 加速的 RHD 代码 QUOKKA 中实现了我们的方法,并证明它通过了广泛的数值测试。我们证明,在处理频率分辨率适中的快速变化不透明度时,与传统的不透明度平均方法相比,片式幂律方法具有显著优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel numerical method for mixed-frame multigroup radiation-hydrodynamics with GPU acceleration implemented in the QUOKKA code
Mixed-frame formulations of radiation-hydrodynamics (RHD), where the radiation quantities are computed in an inertial frame but matter quantities are in a comoving frame, are advantageous because they admit algorithms that conserve energy and momentum to machine precision and combine more naturally with adaptive mesh techniques, since unlike pure comoving-frame methods they do not face the problem that radiation quantities must change frame every time a cell is refined or coarsened. However, implementing multigroup RHD in a mixed-frame formulation presents challenges due to the complexity of handling frequency-dependent interactions and the Doppler shift of radiation boundaries. In this paper, we introduce a novel method for multigroup RHD that integrates a mixed-frame formulation with a piecewise powerlaw approximation for frequency dependence within groups. This approach ensures the exact conservation of total energy and momentum while effectively managing the Lorentz transformation of group boundaries and evaluation of group-averaged opacities. Our method takes advantage of the locality of matter-radiation coupling, allowing the source term for $N_g$ frequency groups to be handled with simple equations with a sparse Jacobian matrix of size $N_g + 1$, which can be inverted with $O(N_g)$ complexity. This results in a computational complexity that scales linearly with $N_g$ and requires no more communication than a pure hydrodynamics update, making it highly efficient for massively parallel and GPU-based systems. We implement our method in the GPU-accelerated RHD code QUOKKA and demonstrate that it passes a wide range of numerical tests. We demonstrate that the piecewise powerlaw method shows significant advantages over traditional opacity averaging methods for handling rapidly variable opacities with modest frequency resolution.
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