基于GPU的非均匀网格可压缩格子玻尔兹曼模拟：从最佳实践到气动、气动声学和超音速流模拟
GPU-based compressible lattice Boltzmann simulations on non-uniform grids using standard C++ parallelism: From best practices to aerodynamics, aeroacoustics and supersonic flow simulations
AI总结：
本文提出了一种基于GPU的非均匀网格格子玻尔兹曼模拟框架，结合现代C++并行算法和保守网格细化，实现了高精度、高效率的气动、气动声学和超音速流模拟。
摘要：
尽管已有数十年的研究，创建在非均匀网格上准确、稳健且高效的格子玻尔兹曼方法（LBM）并实现无缝的GPU加速仍然具有挑战性。本文介绍了一种新的策略来解决这一挑战，通过整合简单而有效的组件：（1）现代C++的并行算法，（2）保守的单元中心网格细化，（3）局部边界条件，以及（4）稳健的碰撞模型。我们的框架支持多种晶格（D2Q9、D2Q13、D2Q21、D2Q37、D3Q27等），以适应各种流动条件。它包括具有多项式和数值平衡的碰撞模型，一个用于多原子行为的第二分布，一个类似Jameson的激波传感器，以及推广了Rohde的细化策略。该框架的精度和稳健性在多样化的基准测试中得到验证，包括驱动腔体流动、风生噪声、30P30N机翼气动学、无粘性Riemann问题以及在跨音速和超音速条件下粘性流过NACA机翼的流动。现代C++进一步使我们的框架能够达到GPU原生性能，同时确保高可移植性、模块化和易于实现。值得注意的是，弱压缩性LBM在非均匀网格上实现了最先进的GPU效率，而完全压缩性LBM在最计算密集的案例中受益于相当于数千个CPU核心的加速。我们的高级性能模型结合了邻居列表和异步时间步进效应，为在非均匀网格上进行LBM模拟的性能分解提供了新的见解。总体而言，本研究为可移植的树状LBM设定了新的标准，证明了通过精心选择的组件组合可以在各种流动条件下实现高性能、高精度和高稳健性。作为最终的证明概念，提出了自适应网格细化用于亚音速和超音速应用。
英文摘要：
Despite decades of research, creating accurate, robust, and efficient lattice Boltzmann methods (LBM) on non-uniform grids with seamless GPU acceleration remains challenging. This work introduces a novel strategy to address this challenge by integrating simple yet effective components: (1) parallel algorithms in modern C++, (2) conservative cell-centered grid refinement, (3) local boundary conditions, and (4) robust collision models. Our framework supports multiple lattices (D2Q9, D2Q13, D2Q21, D2Q37, D3Q27, etc) tailored to various flow conditions. It includes collision models with polynomial and numerical equilibria, a second distribution for polyatomic behavior, a Jameson-like shock sensor, and generalizes Rohde's refinement strategy. The framework's accuracy and robustness is validated across diverse benchmarks, including lid-driven cavity flows, Aeolian noise, 30P30N airfoil aerodynamics, inviscid Riemann problems, and viscous flows past a NACA airfoil in transonic and supersonic regimes. Modern C++ further enables our framework to reach GPU-native performance, while ensuring high portability, modularity, and ease of implementation. Notably, weakly compressible LBMs achieve state-of-the-art GPU efficiency on non-uniform grids, while fully compressible LBMs benefit from acceleration equivalent to thousands of CPU cores in the most compute-intensive cases. Our advanced performance models incorporate neighbor-list and asynchronous time-stepping effects, providing new insights into the performance decomposition of LB simulations on non-uniform grids. Overall, this study sets a new standard for portable, tree-based LBMs, demonstrating that a combination of well-chosen components can achieve high performance, accuracy, and robustness across various flow conditions. As a final proof-of-concept, adaptive mesh refinement is proposed for subsonic and supersonic applications.
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