叶轮机械大规模CFD并行计算方法应用验证

In response to the computational demands of full-annulus models of turbomachinery in large-scale numerical simulations， combined with the periodic nature of blade passage models， this study introduces an innovative method of hierarchical geometric domain decomposition. This method effectively decomposes computational tasks， achieving load balancing across different computing cores and optimizing parallel computing efficiency. On high-performance computing platforms with capabilities of 300PFlops and 200PFlops， this method can complete the solution of full-annulus multi-stage compressor models with up to 180 million grid scales within 1~6 hours. Through the analysis of several typical turbomachinery cases， this research comprehensively validates the performance and computational accuracy of the proposed parallel method. The results demonstrate that the computational outcomes of each model are highly consistent with experimental data， and the calculation error does not exceed 6%， which ensures the accuracy of the calculation. In computational tasks of the scale of ten thousand cores， the employed parallel computing method achieves linear acceleration， and the parallel efficiency is more than 90%， proving its efficiency in large-scale computations. Additionally， this method also exhibits good portability and adaptability on domestic high-performance computing platforms， showing its broad applicability across different hardware architectures and the effectiveness.