Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU.

翻译：近期人工智能（AI）与高性能计算（HPC）的进展为加速反应流模拟带来了潜在的颠覆性机遇。本研究提出一个开源计算流体力学（CFD）框架，整合机器学习（ML）与图形处理器（GPU）的优势，以展示其协同能力。在该框架中，包括ML加速化学积分、偏微分方程全隐式求解、热物性与输运特性计算在内的所有计算操作均在GPU上执行，从而消除CPU-GPU内存拷贝开销。通过内核函数优化与内核启动过程优化提升计算性能，并采用静态数据重组与动态数据分配等策略降低GPU内存占用。基于准直接数值模拟与大涡模拟建模方法，在两种湍流火焰基准测试中评估计算性能。值得注意的是，在保持与基于CPU/CVODE传统求解器相近精度的同时，GPU/ML加速方法在两个案例中均展现超过两个数量级的整体加速比。该结果凸显了通常需要数百个CPU的高保真有限速率化学湍流燃烧模拟，现已可在配备中端GPU的笔记本电脑等便携设备上实现。