###### Abstract
Recent work has shown the capability of LBM in accurately capturing heat transfer phenomena in porous media [9] and simulating fluid-structure interactions [4]. These advancements in modelling complex physics contribute to more realistic and comprehensive numerical results. LBM has emerged as a powerful tool for enhancing the quality of numerical results in CFD applications. Its ability to handle complex geometries, simulate multi-scale and multiphase flows, efficient parallelization, and incorporate complex physics makes it a promising choice for researchers. The emergence of computational fluid dynamics (CFD) solutions using the Lattice Boltzmann method (LBM) has led to advancements in meshingless approaches in CFD simulations [6; 10]. While LBM is often referred to as a mesh-free method, it is essential to note that it does utilize lattice structures, such as Cartesian grids, for simulation purposes. Therefore, \"meshingless\" is more appropriate in describing the method's characteristic of not requiring a body-fitting mesh during CFD solutions. In meshingless CFD simulations, lattice structures are present around the geometry in the flow field, with the size of the lattice depending on the virtual wind tunnel size or flow area for internal/external flow analysis. The resolution of the Cartesian grids is determined by a given value at the resolved scale. Refinement algorithms can control the solution, enabling different methods based on near-static walls and solution adaptivity. This solution-adaptive meshinglessapproach is beneficial for addressing challenges related to complex geometries, moving structures in the flow field, and fluid-structure interaction [11; 12]. The absence of mesh generation and the capability to solve adaptively make meshingless techniques, especially LBM, increasingly popular in CFD [10]. Traditional CFD methods based on meshing techniques have limitations, as they heavily rely on the quality of the mesh, which can be time-consuming to generate. Also, handling changes in the domain topology, such as problems involving moving parts or fluid-structure interaction, can be challenging. The LBM overcomes many of these drawbacks and offers a promising alternative, as it is based on a particle-based, fully Lagrangian approach and does not require a mesh for discretization. Commercial CFD software like XFlow (Next Limit Technologies) utilizes LBM to simulate complex problems such as aerodynamics, aero-acoustics, and fluid-structure interaction [13]. The simulation of wake effects in straight-bladed wind turbines presents a challenge due to the shedding and trailing vortices system. Adaptive wake refinement techniques can be employed to capture the wake by refining the lattice structures around the blades in both coarse and fine resolutions, allowing for the investigation of the aerodynamic performance of the blades. XFlow incorporates an adaptive wake refinement feature based on the vorticity field in the lattice elements, triggering automatic refinement when a threshold value is reached [11]. Previous studies have examined the performance of vertical axis wind turbines (VAWTs) using various simulation methods. Ref. [3] compared wind tunnel tests and three-dimensional numerical analysis of a Darrieus VAWT, finding that 2D simulations performed significantly better than 3D simulations, primarily due to large tip vortices in the real turbine. Ref. [14] utilized LBM to simulate a 2D incompressible flow over Savonius VAWTs with different shapes, successfully implementing bounce-back boundary conditions at large lattice sizes to simulate curved boundaries. Experimental measurements and computational simulations have also been employed to understand the flow structures and performance of VAWTs. Ref. [2] used large eddy simulation (LES) with a dynamic Smagorinsky subgrid-scale model to accurately predict the performance of VAWTs with fixed- and variable-pitch mechanisms. Ref.