Accelerate the convergence of turbulent flows simulation: A novel progressive locally power-law preconditioning method. (15th June 2022) - Derazgisoo, S.M.; Akbarzadeh, P.; Lehdarboni, A. Askari
Highlight: A novel progressive locally power-law preconditioning method (LPLPM) is employed to analyze turbulent flows. The turbulence model coupled with the governing equations is the BSL k − ω turbulent model. The preconditioning factors are automatically adapted using velocity-field sensors from the computational domain. A sensitivity evaluation, including the numerical and turbulent parameters, is conducted to study the performance of the LPLPM. LPLPM is also compared with the other preconditioning methods, including SPM, MPM, and ACM. LPLPM enhances the rate of convergence considerably compared to other preconditioning approaches. Abstract: In this study, an extended progressive preconditioning technique called the locally power-law preconditioning method (LPLPM) is employed for analyzing unsteady and steady turbulent flows for the first time. This technique's effectiveness and convergence rate are compared to the standard and Malan's preconditioning methods, along with the artificial compressibility approach. In all methods, a finite volume and the four-stage local time Runge-Kutta schema are applied to discretize the space and the time derivative terms of governing equations, respectively. In the LPLPM, the preconditioning matrix is automatically adapted using flow-field velocity sensors via a power-law relation. The turbulence model coupled with the governing equations is the BSL- k − ω model. To investigate the efficiency and accuracy of the LPLPM, different test cases are performed for flows over S809, NACA0012, and ONERA airfoils at various Reynolds numbers (1.0 × 10 6 to 5.25 × 10 6 ) and angles of attack (0 to 16 (deg)). A sensitivity evaluation is performed to investigate the impacts of numerical variables on the solution. The results reveal that the LPLPM is accurate, robust, and computationally efficient in simulating turbulent flows and significantly improves the convergence rate up to 71% and 43% reductions in the iteration number for simulating steady and unsteady turbulent flows, respectively.