Convergence Acceleration of Fluid Dynamics Solvers Using a Reduced-Order Model

Abstract

A new method based on reduced-order modeling is presented for the purpose of accelerating the convergence of an iterative solver via solution prediction in the framework of a computational fluid dynamics solver. Using a number of flow solutions as snapshots at preconvergent iterations of the explicit time-marching solver, the reduced-order-modeling-based acceleration technique is used to project to a more accurate solution. The method is first applied to accelerate the convergence of an incompressible Navier–Stokes solver, and the classical two-dimensional lid-driven cavity flow case is revisited for this purpose. Next, the proposed convergence acceleration technique is used in the framework of a density-based computational fluid dynamics solver, and subsonic as well as transonic (inviscid) flows over the NACA 0012 airfoil are studied. Additionally, the performance of the proposed convergence acceleration technique is investigated for turbulent transonic flow over the RAE 2822 airfoil.