1. A standard lattice Boltzmann model is used to simulate mesoscopic fluid flow, but it has compressible errors.
2. Three different models have been proposed and tested to eliminate the compressible effect and the limits in 2D problems.
3. This paper investigates the numerical accuracy of these models in complex geometry and the effect of structural complexity, with reference results obtained by FEM.
The article provides a comprehensive overview of lattice Boltzmann models for complex fractal geometry, discussing their advantages and limitations in comparison to traditional CFD methods such as FEM and FVM. The authors provide evidence from benchmark questions that support their claims about the effectiveness of these models in eliminating compressible errors, as well as providing guidance on how to best use them for incompressible flow simulations in complex structures.
The article is generally reliable and trustworthy, presenting both sides of the argument fairly and objectively. The authors provide evidence from benchmark questions to support their claims about the effectiveness of these models, as well as providing guidance on how to best use them for incompressible flow simulations in complex structures. Furthermore, they acknowledge potential risks associated with using these models, such as computational instability or slow convergence rates.
However, there are some areas where the article could be improved upon. For example, while the authors discuss potential risks associated with using these models, they do not explore counterarguments or present any alternative solutions that could be used instead of LBM for simulating incompressible flows in complex structures. Additionally, while they provide evidence from benchmark questions to support their claims about the effectiveness of these models, they do not provide any evidence for their claims regarding structural complexity or grid resolution effects on numerical accuracy. Finally, there is no mention of any promotional content or partiality within the article which could potentially bias readers’ opinions towards one side or another.