A local mesh-refinement technique for incompressible flows
Computers and Fluids
Large scale parallel structured AMR calculations using the SAMRAI framework
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Grid adaptation for functional outputs: application to two-dimensional inviscid flows
Journal of Computational Physics
Managing complex data and geometry in parallel structured AMR applications
Engineering with Computers
Performance Evaluation of the Multi-language Helios Rotorcraft Simulation Software
HPCMP-UGC '08 Proceedings of the 2008 DoD HPCMP Users Group Conference
Local adaptive mesh refinement for shock hydrodynamics
Journal of Computational Physics
Automatic off-body overset adaptive Cartesian mesh method based on an octree approach
Journal of Computational Physics
Journal of Scientific Computing
Hi-index | 31.45 |
We develop locally normalized feature-detection methods to guide the adaptive mesh refinement (AMR) process for Cartesian grid systems to improve the resolution of vortical features in aerodynamic wakes. The methods include: the Q-criterion [1], the @l"2 method [2], the @l"c"i method [3], and the @l"+ method [4]. Specific attention is given to automate the feature identification process by applying a local normalization based upon the shear-strain rate so that they can be applied to a wide range of flow-fields without the need for user intervention. To validate the methods, we assess tagging efficiency and accuracy using a series of static vortex-dominated flow-fields, and use the methods to drive the AMR process for several theoretical and practical simulations. We demonstrate that the adaptive solutions provide comparable accuracy to solutions obtained on uniformly refined meshes at a fraction of the computational cost. Overall, the normalized feature detection methods are shown to be effective in driving the AMR process in an automated and efficient manner.