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Lattice Boltzmann Method (LBM) simulations for turbulent flows over fractal and non-fractal obstacles are presented. The wake hydrodynamics are compared and discussed in terms of flow relaxation, Strouhal numbers and wake length for different Reynolds numbers. Three obstacle topologies are studied, Solid (SS), Porous Regular (PR) and Porous Fractal (FR). In particular, we observe that the oscillation present in the case of the solid square can be annihilated or only pushed downstream depending on the topology of the porous obstacle. The LBM is implemented over a range of four Reynolds numbers from 12,352 to 49,410. The suitability of LBM for these high Reynolds number cases is studied. Its results are compared to available experimental data and published literature. Compelling agreements between all three tested obstacles show a significant validation of LBM as a tool to investigate high Reynolds number flows in complex geometries. This is particularly important as the LBM method is much less time consuming than a classical Navier–Stokes equation-based computing method and high Reynolds numbers need to be achieved with enough details (i.e., resolution) to predict for example canopy flows.

This paper provides an analysis of the numerical performance of a hybrid computational fluid dynamics (CFD) solver for 3D natural convection. We propose to use the lattice Boltzmann equations with the two-relaxation time approximation for the fluid flow, whereas thermodynamics is described by the macroscopic energy equation with the finite difference solution. An in-house parallel graphics processing unit (GPU) code is written in MATLAB. The execution time of every single step of the algorithm is studied. It is found that the explicit finite difference scheme is not as stable as the implicit one for high Rayleigh numbers. The most time-consuming steps are energy and collide, while stream, boundary conditions, and macroscopic parameters recovery are executed in no time, despite the grid size under consideration. GPU code is more than 30 times faster than a typical low-end central processing unit-based code. The proposed hybrid model can be used for real-time simulation of physical systems under laminar flow behavior and on mid-range segment GPUs.