1. Introduction
In the context of frequent extreme weather events and high-density urban development, the slender structures and extreme wind climate of high-rise buildings make them more prone to overall or local structural wind-induced vibrations, which can affect both structural safety and occupant comfort. Therefore, it is crucial to assess the vibration characteristics of high-rise building structures under lateral wind loads for building design.
Physical wind tunnel tests are generally the standard means for wind load assessment in practical engineering projects. Considering the costs and testing periods, however, wind tunnel tests are difficult to directly apply to the iterative design in the preliminary stage. Additionally, scaled aerodynamic models fail to fully consider the Fluid–Structure Interaction phenomenon in high-rise buildings, making architects cautious when considering typhoons or hurricanes. With the increasing maturity of computational fluid dynamics (CFD) methods and abundant server-based parallel computing resources, the construction industry has attempted to use CFD for building shape optimization in the design stage. Predicting surface wind pressure and the wind-induced vibration of the targeted building in a computer system allows for the rapid evaluation and optimization of reasonable building shapes and structural layouts in the preliminary design stage.
Large Eddy Simulation (LES), technology based on the finite volume method (FVM), is currently used to predict surface wind pressure and the wind-induced response of high-rise buildings in the field of structural wind engineering. To address the issues of accuracy and reliability of LES, many numerical simulation validation studies based on the Commonwealth Advisory Aeronautical Research Council (CAARC) standard tall building were conducted. These studies often involve wind pressure comparison and structural vibration analysis with wind tunnel test results. Compared to the Reynolds-averaged Navier–Stokes (RANS) model [
1], LES can solve the turbulent flow field characteristics around the building and predict the distribution of mean and fluctuating wind pressure on the building surface, so that it can provide a more accurate time-series wind load for structural wind-induced response. Zheng [
2] compared RANS and LES methods in simulating the effects of building facade geometric details on the flow field and wind pressure. The results showed that the LES method can more accurately capture changes in wind pressure on the windward facade, while the RANS method predicts stronger flow field disturbances. To improve LES computational efficiency, Wijesooriyaa [
3] proposed a hybrid RANS–LES solver for efficiently solving the effects of wind on non-standard geometric shapes of a 406 m slender tower. They analyzed the effects of different sub-grid scale models (SGS) on wind-induced structural vibrations and found that the WALE turbulence model can accurately handle near-surface turbulence features. These studies discussed above mainly focused on validating the accuracy of mean surface wind pressure, but limited investigation into surface fluctuating wind pressure. Recent research has shown that inflow turbulence generation affects the satisfied atmospheric boundary layer wind field characteristics and LES accuracy. Thordal [
4] adopted LES to simulate surface wind pressure distributions on a CAARC building under the influence of surrounding buildings. By generating reasonable turbulence inlet conditions and grid mesh techniques, they were able to accurately predict both mean and fluctuating wind pressure features on the building surface, with overall mean errors of base forces and torques compared to wind tunnel tests below 15%. Lamberti [
5] compared the effects of different incoming turbulence characteristics on fluctuating wind pressure simulation results. They analyzed factors such as incoming direction roughness length, turbulent kinetic energy, and turbulent integral length scales, and found that accurately quantifying the statistical characteristics of turbulent wind fields at building locations is crucial for structural wind load analysis, recommending consideration of uncertainty in inlet turbulence. Hu [
6] adopted two different inlet turbulence generation methods (NSRFG and CDRFG) on the wake feature simulation of the CAARC model. They found that inlet turbulence has a significant impact on the distribution of surface fluctuating wind pressure, with NSRFG better to simulate non-Gaussian features of surface fluctuating wind pressure, providing a reference for selecting peak factor values for building envelope wind pressure evaluation. Additionally, Deng [
7] also proposed a new artificial turbulence synthesis method to improve the accuracy of CAARC building surface fluctuating wind pressure simulations. They analyzed the atmospheric boundary layer spectral characteristics, temporal correlation, and spatial correlation. From recent LES research progress, it is clear that LES turbulence model selection, inlet turbulence generation, computational domain, and grid size can affect the accuracy of fluctuating wind loads prediction. There is still significant uncertainty in using LES, especially for complex grid meshing and city model parallel computing in practical engineering projects.
Another challenge of current LES is the calculation of dynamic wind-induced response, which involves considering the interaction between atmospheric flow fields and structural vibrations (Fluid–Structure Interaction, FSI). Hou and Frison [
8,
9] conducted research on CAARC multi-degree-of-freedom aeroelastic wind tunnel tests. However, there are problems in aeroelastic wind tunnel tests, such as high test cost and model scale. There are one-way and two-way FSI methods, according to the data transfer mechanisms. Regarding the two-way FSI, the surface wind pressure and structural dynamics of building using CFD and structural mechanical solvers are calculated simultaneously. The CFD wind pressure data is loaded into the finite element structural model in each simulation integration time step to calculate structural vibration displacement characteristics. Next, the structural deformation information is fed back to the CFD solver and affects the flow field calculation. The one-way FSI calculation principle is simpler and does not consider the influence of structural deformation on the CFD results of the building flow field, thus improving computational efficiency. Compared to one-way FSI, two-way FSI can consider the negative aerodynamic damping effect of high-rise buildings under strong wind vortex-induced vibrations. It can theoretically be equivalent to multi-degree of freedom aero-elastic model in physical wind tunnel tests. Braun [
10] were the first to use two-way FSI technology to conduct numerical simulations of the aerodynamic and aerodynamic elastic behavior of the CAARC model. They compared surface wind pressure simulations and structural vibration characteristics under different incoming flow reduction wind speed conditions and found that two-way FSI could reproduce consistent average and root-mean-square displacement in the along-wind direction with wind tunnel experiments. However, only the uniform incoming wind conditions are considered and the influence of incoming turbulence intensity on crosswind displacement characteristics are not discussed. To improve the computational efficiency of two-way FSI, Huang [
11] proposed a parallel FSI method based on socket parallel architecture for CAARC building models. They first integrated the surface wind pressure on each floor and then transmitted integral forces and moments from each floor to the finite element model, thereby improving FSI computational efficiency. Feng [
12] conducted a two-way FSI of a kilometer-height building via ANSYS Workbench and compared structural dynamic responses with and without consideration of aerodynamic elasticity. Péntek [
13] considered the additional mass damper’s influence on structural wind-induced displacement using an FSI method. Yan [
14] proposed an efficient two-way FSI technique based on an equivalent concentrated mass system. By comparing with other FSI equivalent methods and aerodynamic elasticity test data, it was found that this method, combined with LES, can efficiently capture and simulate the vortex-induced resonance phenomenon of CAARC high-rise buildings. This method provides a relatively efficient two-way FSI simulation method for engineering applications. Given the high computational cost of two-way FSI, Hasama [
15] proposed a one-way FSI method based on a multi-degree-of-freedom spring-mass structural simplified model. They found that the method can effectively simulate the displacement in both the along-wind and crosswind directions at the top of the building. Zhang [
16] performed wind-induced vibration analysis on the CAARC model using a one-way FSI method and found that the average wind pressure results on the building surface agreed well with wind tunnel experiments, but the fluctuating wind pressures are easily affected by inflow turbulence. By artificially considering aerodynamic damping in the one-way FSI calculation, they were able to effectively simulate the crosswind displacement response under different wind speed conditions. Wijesooriya [
17] established a spring-mass system structural model and an efficient one-way FSI boundary wind pressure data transfer mechanism, and they were able to meet the computational requirements under different wind speed conditions in practical engineering applications. It is evident from the above studies that two-way FSI can account for negative aerodynamic damping under the influence of vortex-induced resonance. However, due to the high computational cost, it is challenging to apply these techniques during the engineering design phase. On the other hand, simplified computational methods for one-way FSI generally fail to replicate complex modes of vibrations with acceptable levels of accuracy.
In recent years, the Lattice Boltzmann Method (LBM) has gradually gained attention in the field of wind engineering due to its high parallel computing efficiency and natural transient solving characteristics. Unlike traditional CFD codes based on FVM, the LBM is based on algorithms designed from particle collisions and kinetic energy theory, focusing on the solution of microscopic velocity distribution functions at the mesoscopic scale. The LBM computational framework easily enables large-scale parallel computing and has relative advantages in dealing with complex boundary conditions and grid mesh. Schröder [
18] conducted an LBM case to validate the wind flow around a typical square block at low Reynolds numbers (Re 2000~8000), and found that the LBM can effectively simulate the flow separation around the building and the unsteady flow separation in the downstream wake region. Wang [
19] also conducted an LBM validation study based on the flow field measurements of low Reynolds number square arrays. They analyzed the characteristics of turbulent wind fields around the building under different incoming wind directions and found that LBM with GPU-based large-scale computation can achieve real-time reliable simulations with millions of grid points. Han [
20] conducted a detailed comparison between the LBM–LES and FVM–LES results of single rectangular buildings. They compared the LBM’s ability to resolve flow fields and its computational speed under different grid resolutions with the FVM method. The results showed that LBM has a higher computational efficiency and is eight times faster than the FVM method under the same computational setup. Camps [
21] compared and validated the results of LBM and FVM simulations for flow around a single building. They found that both methods were able to correctly capture the typical flow field characteristics around the building. They also found that the LBM method, when implemented on a GPU parallel computing architecture, was able to achieve more efficient simulations. Due to this efficient simulation capability, the LBM method has been applied to large-scale simulations of urban building clusters for wind fields and pollutant diffusion [
22,
23,
24,
25]. Buffa [
26] used the LBM–LES method to simulate and validate the surface wind pressure on a scaled model of a rectangular high-rise building. They conducted a detailed analysis of the influence of inflow turbulence conditions, wall functions, and grid resolutions on the simulation results. The results showed that the LBM method can reasonably simulate the average wind pressure distribution features on the building surface, but further validation is needed for the fluctuating wind pressure and wind-induced vibrations.
Based on the current research status, it can be surmised that the application research on structural wind load simulations based on LBM–LES is still in its infancy, and there is an urgent need to conduct validation analysis of wind pressure simulations and wind-induced vibrations. The innovation of this article lies in that it focuses on the LBM–LES surface wind pressure and base force simulations and validations for the CAARC standard high-rise building models in the field of wind engineering, and attempts to apply the LBM method to efficient FSI computation. The differences between one-way and two-way FSI results under different reduced wind velocity conditions are compared. This article discusses detailed information on the LBM building wind pressure simulation and computational costs, providing valuable references for the application of the LBM method in wind-resistant structure design.
The paper is organized as follows. Firstly,
Section 2 introduces the methodology and principle used in this article, including the LBM, LES turbulence model, and FSI. Next,
Section 3 details the wind tunnel tests and numerical model of the CAARC model.
Section 4 introduces the simulated turbulent atmospheric boundary layer wind fields using LBM, the grid accuracy discussion, and wind fields around the building under multiple wind directions, and the mean wind pressure and base moment verification results are also discussed.
Section 4 also details the one-way and two-way FSI results under two different reduced wind velocity conditions. Conclusions are given in
Section 5.