Improving Regularization Techniques for Incompressible Fluid Flows via Defect Correction
Document Type
Article
Publication Date
1-1-2021
Abstract
We propose and investigate two regularization models for fluid flows at higher Reynolds numbers. Both models are based on the reduced ADM regularization (RADM). One model, which we call DC-RADM (deferred correction for reduced approximate deconvolution model), aims to improve the temporal accuracy of the RADM. The second model, denoted by RADC (reduced approximate deconvolution with correction), is created with a more systemic approach. We treat the RADM regularization as a defect in approximating the true solution of the Navier-Stokes equations (NSE) and then correct for this defect, using the defect correction algorithm. Thus, the resulting RADC model can be viewed as a first member of the class that we call "LESC-reduced", where one starts with a regularization that resembles a Large Eddy Simulation turbulence model and then improves it with a defect correction technique. Both models are investigated theoretically and numerically, and the RADC is shown to outperform the DC-RADM model both in terms of convergence rates and in terms of the quality of the produced solution.
Publication Title
Computational Methods in Applied Mathematics
Recommended Citation
Labovsky, A.,
&
Erkmen, D.
(2021).
Improving Regularization Techniques for Incompressible Fluid Flows via Defect Correction.
Computational Methods in Applied Mathematics.
http://doi.org/10.1515/cmam-2021-0074
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15529