CPU-GPU Performance Comparison in Large Eddy Simulations of a Combustor Liner With Rapid Octree Meshing

Abstract Modern gas turbine engines operate at high temperatures, placing substantial thermal loads on critical components like the combustor liner and turbine blades. Effusion cooling, a technique that creates a protective coolant layer on hot surfaces, is widely employed to mitigate these thermal stresses and extend component life. This study explores the impact of effusion cooling on liner heat transfer through a detailed computational fluid dynamics (CFD)analysis of a three-benchmark gas turbine combustor validation. The simulations were conducted on both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) architectures using a newly developed GPU solver. Large Eddy Simulation (LES) was applied, with mesh generation handled by the Rapid Octree meshing technique, facilitating the creation of large, complex grids. The results were compared across CPU and GPU runs, demonstrating that GPU-based simulations achieved significant computational speed-ups. Optimized LES Numerics for GPU processing enabled substantial reductions in computational time, enhancing simulation efficiency without compromising accuracy.