An efficient geometry-adaptive mesh refinement framework and its application in the immersed boundary lattice Boltzmann method

Abstract

This work presents an adaptive mesh refinement (AMR) method. This AMR is based on a pointless representation of octrees, i.e. hash table. An individual hash table is used for each level of refinement to avoid conflicts of key values. Cases for two complex geometries are presented to analyse the performance of the AMR framework with different data structures and strategies. Then, the immersed boundary-lattice Boltzmann method (IB-LBM) is implemented as an example to evaluate the performance of the proposed AMR framework in computational fluid dynamics (CFD) applications. The integrated solver is validated and its performance is analysed through several cases. Considerable reductions of more than 65% in computational time are achieved when the hash table is adopted instead of the binary tree. Moreover, hash table offers more benefits to 3D cases with larger numbers of nodes. The AMR framework presented in this study is simple to implement with the standard C++ libraries and time-efficient for computational fluid dynamics (CFD) applications, which is released as open-source software and can be used by researchers with their CFD solvers.