Iterative projection method for unsteady Navier-Stokes equations with high Reynolds numbers
Xiaoming Zheng, Kun Zhao, Jiahong Wu, Weiwei Hu, Dapeng Du

TL;DR
This paper introduces a novel iterative projection method for solving unsteady Navier-Stokes equations at high Reynolds numbers, achieving weak divergence-free velocities with enhanced stability and efficiency over traditional methods.
Contribution
The paper presents a new iterative projection technique incorporating BDF2 discretization, skew-symmetric convection, and iterative projections, improving accuracy and convergence for high Reynolds number flows.
Findings
Effectively handles high Reynolds numbers with few iterations
Achieves weak divergence-free velocity in practice
Outperforms traditional explicit convection methods
Abstract
A new iterative projection method is proposed to solve the unsteady Navier-Stokes equations with high Reynolds numbers. The convectional projection method attempts to project the intermediate velocity to the divergence free space only once per time step. However, such a velocity is not genuinely divergence free in general practice, which can yield large errors when the Reynolds number is high. The new method has several important features: the BDF2 time discretization, the skew-symmetric convection in a semi-implicit form, two modulating parameters, and the iterative projections in each time step. A major difficulty in the proof of iteration convergence is the nonlinear convection. We solve this problem by first analyzing the non-convective scheme with a focus on the spectral properties of the iterative matrix, and then employing a delicate perturbation analysis for the convective…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFluid Dynamics and Turbulent Flows · Advanced Numerical Methods in Computational Mathematics · Model Reduction and Neural Networks
