基 于 降 阶 模 型 和 梯 度 优 化 的 流 场 加 速 收 敛 方 法

Computational Fluid Dynamics（CFD）is an important method to solve fluid-related complex engineering problems. The increasing complexity of fluid mechanics research and engineering applications makes it imperative to explore a more efficient and accurate CFD algorithm. A method based on reduced-order model and gradient optimization is proposed to accelerate the convergence process of fluid dynamics solvers. In this method，the reduced-order model is constructed by the snapshots collected from the iterative process of CFD solvers，and the gradient optimization method is then used to minimize the residual of the reduced-order model. Finally，the flow field with lower residual is selected as the initial condition of the CFD solver to accelerate the convergence process of CFD. The method is applied to the CFD solver based on the finite volume method，and the effectiveness of the proposed method is studied for inviscid and turbulence fluids. The results show that the accelerated convergence method can significantly reduce the number of iterative steps by 2⁃3 times compared with the initial CFD method，exhibiting efficiency，robustness，and a wide application prospect in complex engineering problems.