AI Article Synopsis
- An adaptive-mesh-refinement (AMR) algorithm for the finite-difference lattice Boltzmann method (FDLBM) is introduced to improve computational efficiency by eliminating the need for complex tree data structures.
- The AMR uses pointer attributes to identify neighboring blocks, streamlining memory use and computation time, making it more suitable for parallel processing.
- The algorithm is validated through various flow scenarios, demonstrating its accuracy and effectiveness in handling vorticity-dominated flows without needing adjustments at grid boundaries.
Article Abstract
An adaptive-mesh-refinement (AMR) algorithm for the finite-difference lattice Boltzmann method (FDLBM) is presented in this study. The idea behind the proposed AMR is to remove the need for a tree-type data structure. Instead, pointer attributes are used to determine the neighbors of a certain block via appropriate adjustment of its children identifications. As a result, the memory and time required for tree traversal are completely eliminated, leaving us with an efficient algorithm that is easier to implement and use on parallel machines. To allow different mesh sizes at separate parts of the computational domain, the Eulerian formulation of the streaming process is invoked. As a result, there is no need for rescaling the distribution functions or using a temporal interpolation at the fine-coarse grid boundaries. The accuracy and efficiency of the proposed FDLBM AMR are extensively assessed by investigating a variety of vorticity-dominated flow fields, including Taylor-Green vortex flow, lid-driven cavity flow, thin shear layer flow, and the flow past a square cylinder.
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| http://dx.doi.org/10.1103/PhysRevE.89.033310 | DOI Listing |
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