Prediction Of Wind Loads Research Articles

Prediction of wind loading on masked angle members in lattice tower structures

Quantification of the wind loading is critical in lattice tower structure engineering. In this study, we use Computational Fluid Dynamics (CFD) to investigate the aerodynamic loading on two angle members of a lattice tower structure. The presence of two bluff bodies means that one of the angle members may be masked by the other and will thus undergo less wind loading, which is called the mask effect. In the current work, we were specifically interested in investigating this effect with respect to the angles of attack of the two angle members along with the inline and normal separation distance. The four parameters yield a large parameter space which is best tackled using a sophisticated sampling method such as Latin hypercube sampling. First, we validated our RANS simulation results against experiments and Large Eddy Simulation (LES). Then, we performed two-dimensional simulations on a large range of configurations to underline the impact of the input parameters on the output variables, which are the drag and lift coefficients. To produce a tool that can be applied by a structural engineer, the database created using the time-consuming CFD simulations was used to create a correlation between the input parameters and output variables. The functions used in the correlations were designed to respect the symmetries and limiting behavior in the problem. We then investigated the performance of four different cross-validated regression models to predict the drag and lift coefficients. Once created, the regression models produce a method that does not require CFD simulations to be run. The models’ accuracy represents a significant improvement in predicting wind loads on lattice towers. While further refinement is possible, the current results provide a solid basis for engineering design purposes.

Wind-Load Calculation Program for Rectangular Buildings Based on Wind Tunnel Experimental Data for Preliminary Structural Designs

In this study, we developed a wind load calculation program (WCP) capable of predicting wind loads with relative precision during the preliminary design phase. First, wind tunnel tests were conducted to identify the essential factors necessary for calculating wind loads and the variables influencing these factors. Square building shapes were considered, and the wind force coefficients and power spectral density were measured by combining four ground roughness values, eleven side ratios (D/B), four aspect ratios (H/BD), and wind directions ranging from 0° to 90°. The wind power coefficient and the spectral coefficient were formulated so that the wind load could be calculated according to various conditions. The WCP computations were based on the calculation of the load combination coefficient using the resonant wind load. Finally, the wind loads obtained from the wind tunnel tests were compared with those predicted by the WCP using an actual project model (inner-core (A) and outer-core (B) types). Building A yielded similar WCP and wind tunnel experimental responses when subjected to wind and laminar wind loads. Additionally, Building B yielded a larger error than that of Building A, but similar results were obtained when buildings were subjected to combination and laminar wind loads.

Assessment of computational fluid dynamic as a design tool for estimation of wind loads on unconventional skyscrapers in urban environment

AbstractComputational fluid dynamic (CFD) has not been widely accepted as a design tool in current wind‐resistant structural design practices due to its contentious accuracy. To promote the application of CFD in wind‐resistant structural design, the accuracy of CFD should be comprehensively validated. However, most previous validation studies were focused on isolated generic or regular‐shaped buildings. This paper evaluates the accuracy of large eddy simulation (LES) in predicting the wind loads on a 600‐m‐high supertall building with a complex appearance in a realistic urban area against wind tunnel test results. The aerodynamic characteristics obtained from the LES and the wind tunnel test are compared and analyzed in detail, including wind pressure and force coefficients, wind force spectra, base moments, and correlations of the wind loads. This study aims to assess the performance and potential as well as the strengths and weaknesses of CFD in predicting wind loads on high‐rise buildings in an urban environment and promote its application to the wind‐resistant design of skyscrapers.

Multiresolution dynamic mode decomposition approach for wind pressure analysis and reconstruction around buildings

AbstractAccurate wind pressure analysis on high‐rise buildings is critical for wind load prediction. However, traditional methods struggle with the inherent complexity and multiscale nature of these data. Furthermore, the high cost and practical limitations of deploying extensive sensor networks restrict the data collection capabilities. This study addresses these limitations by introducing a novel framework for optimal sensor placement on high‐rise buildings. The framework leverages the strengths of multiresolution dynamic mode decomposition (mrDMD) for feature extraction and incorporates a novel regularization term within an existing sensor placement algorithm under constraints. This innovative term enables the algorithm to consider real‐world system constraints during sensor selection, leading to a more practical and efficient solution for wind pressure analysis. mrDMD effectively analyzes the multiscale features of wind pressure data. The extracted mrDMD modes, combined with the enhanced constrained QR decomposition technique, guide the selection of informative sensor locations. This approach minimizes the required number of sensors while ensuring accurate pressure field reconstruction and adhering to real‐world placement constraints. The effectiveness of this method is validated using data from a scaled building model tested in a wind tunnel. This approach has the potential to revolutionize wind pressure analysis for high‐rise buildings, paving the way for advancements in digital twins, real‐time monitoring, and risk assessment of wind loads.

Prospect of LES for predicting wind loads and responses of tall buildings: A validation study

Wind load evaluation on buildings using large-eddy simulation (LES) poses several critical challenges. The most notable ones include accurately reproducing the approaching atmospheric boundary layer flow conditions and modeling massively separated flows around buildings. This study investigates the capability of LES for predicting cladding and overall loads on a tall building by addressing these key challenges. The results from LES are systematically validated against boundary layer wind tunnel measurements. The validation process unfolds in three key stages. Firstly, the approaching wind profiles were reproduced in LES by integrating a recently developed synthetic inflow turbulence generator with an implicit ground roughness modeling technique. Then, the statistics of the cladding and global loads were compared with the experiment for representative wind directions. Finally, the performance of the LES for predicting wind-induced response is examined, assuming typical structural properties for the building. Overall, the results obtained from each validation step showed encouraging agreement with the experimental measurement. In light of the findings from the current study, it is evident that LES is progressively realizing its potential to be a practical wind load evaluation tool. Selected simulation cases used in this study are available at .

Aerodynamic mitigation of low-rise building with complex roof geometry

During strong wind events, building roofs are subjected to high wind uplift forces (suctions), which often lead to severe roofing component damage, or even roof total failure, flying debris, and water intrusion, hence, interior damages. Typical roof shapes (e.g., gable and hip) are generally designed using provision codes and standards to accurately estimate peak load impacting the roofs during wind events for design purposes. Complex roof geometry can be efficiently examined using wind tunnel testing and computational modeling to provide quantitative assessment for wind to narrow down the design alternatives and to examine the improvement gained from mitigation techniques. In this study, an isolated low-rise building with a complex roof shape is examined using large eddy simulation (LES) to numerically assess wind load prediction by validating it with wind tunnel results. This study presents two roof modification scenarios using parapets added to roof corners and ridgelines to displace the flow from the separation locations to reduce the wind impact on the roof. The current study aims to 1) evaluate wind load on an isolated low-rise building with complex roof geometry for various angles of attack and 2) mitigate the roof aerodynamically using parapets, added corners, and ridgeline to reduce the wind impact on the roof. The validation shows that both the mean and RMS of the pressure coefficients are in good agreement with the wind tunnel results. The research results suggest that parapets with 500 mm height located at the corner and edges of complex roof geometry can effectively reduce extreme corner suction by 29% and roof uplift by 5.6%.

Influence of the wind tunnel model characteristics on the loading and response of cable-net hyperbolic paraboloid roofs

Aerodynamic modelling affects the prediction of wind loads if they are derived from wind tunnel tests. Depending on the structural type, difference in applied loads may induce larger or smaller differences in the predicted structural response. For low rise buildings, the roof roughness is a crucial parameter that affects the wind action distribution and magnitude, and its impact on the structural response. This paper discusses the results from wind tunnel pressure measurements on eight geometries of hyperbolic paraboloid roofs, coming from experimental campaigns in three different wind tunnels and using different models. The pressure distributions are then used to evaluate vertical displacements of cable nets tensile structures, which are also compared with each other. Results show that significant differences in the measured pressures are only slightly reduced when vertical displacements are compared, that is inaccuracy of the load is only slightly mitigated when propagated to the response.

Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review

Machine learning (ML) techniques, which are a subset of artificial intelligence (AI), have played a crucial role across a wide spectrum of disciplines, including engineering, over the last decades. The promise of using ML is due to its ability to learn from given data, identify patterns, and accordingly make decisions or predictions without being specifically programmed to do so. This paper provides a comprehensive state-of-the-art review of the implementation of ML techniques in the structural wind engineering domain and presents the most promising methods and applications in this field, such as regression trees, random forest, neural networks, etc. The existing literature was reviewed and categorized into three main traits: (1) prediction of wind-induced pressure/velocities on different structures using data from experimental studies, (2) integration of computational fluid dynamics (CFD) models with ML models for wind load prediction, and (3) assessment of the aeroelastic response of structures, such as buildings and bridges, using ML. Overall, the review identified that some of the examined studies show satisfactory and promising results in predicting wind load and aeroelastic responses while others showed less conservative results compared to the experimental data. The review demonstrates that the artificial neural network (ANN) is the most powerful tool that is widely used in wind engineering applications, but the paper still identifies other powerful ML models as well for prospective operations and future research.

CFD Prediction of Wind Loads on FPSO and Shuttle Tankers during Side-by-Side Offloading

This study entailed the estimation of wind loads performed using computational fluid dynamics (CFD) simulations for four typical offshore vessels and for a Floating Production, Storage, and Offloading (FPSO) and shuttle tanker in a side-by-side configuration on offloading. For all vessels, under the wind heading condition, four meshes were used to carry out the verification and validation (V&V) study to check the numerical uncertainty. The CFD simulation results for the aerodynamic coefficients were compared with wind tunnel tests from the Offloading Operability 2 JIP. All CFD simulation results show generally good agreement with the experimental data, and the overall trend of the coefficients are well captured. In addition, the effect on the gap sizes between the FPSO and shuttle tanker in the range of 4–30 m was examined. On this basis, the shielding effect was analyzed according to the size of the gap between the two ships.

A multi-fidelity machine learning framework to predict wind loads on buildings

Large-eddy simulations (LES) can provide accurate predictions of wind loads on buildings, but their high computational cost, and the need to explore all wind directions with a 10° resolution, limits their use in the design process. Reynolds-averaged Navier–Stokes (RANS) have a low computational cost, but their accuracy can be compromised by the turbulence model and by the model required to retrieve the pressure fluctuations, that ultimately determine the design loads. This study proposes a multi-fidelity machine learning framework that combines computationally efficient RANS, for a large number of wind directions, with more expensive LES, for a small number of wind directions, to provide accurate predictions of the root mean square pressure coefficient at a reasonable computational cost. The training set includes 5 wind directions with a 20° resolution; the test set contains the 5 intermediate wind directions. A bootstrap algorithm, used to generate an ensemble of models, provides confidence intervals that encompass the majority of the LES data for the test directions. These results demonstrate that multi-fidelity machine learning frameworks provide a route to balancing accuracy and computational cost in the prediction of complex turbulent flow quantities.

Optimising the computational domain size in CFD simulations of tall buildings

Recently, there has been a growing interest in utilizing computational fluid dynamics (CFD) for wind resistant design of tall buildings. A key factor that influences the accuracy and computational expense of CFD simulations is the size of the computational domain. In this paper, the effect of the computational domain on CFD predictions of wind loads on tall buildings is investigated with a series of sensitivity studies. Four distinct sources of domain error are identified which include wind-blocking effects caused by short upstream length, flow recirculation due to insufficient downstream length, global venturi effects due to large blockage ratios, and local venturi effects caused by insufficient clearance between the building and top and lateral domain boundaries. Domains based on computational wind engineering guidelines are found to be overly conservative when applied to tall buildings, resulting in uneconomic grids with a large cell count. A framework for optimizing the computational domain is proposed which is based on monitoring sensitivity of key output metrics to variations in domain dimensions. The findings of this paper help inform modellers of potential issues when optimizing the computational domain size for tall building simulations.

Artificial Neural Network-Based Prediction of Wind Pressure Coefficients on Buildings

Wind load acting on a building in presence of one or more interfering buildings usually varies when compared to isolated buildings. This effect of variations in wind loads due to presence of other interfering building is termed interference effect. This effect on wind loads is mainly governed by parameters such as geometry, orientation of the structures with respect to wind direction and upstream terrain conditions. The provisions made in design codes and standards with regard to the effect of interfering structures are inadequate for proper estimation of wind loads. As such, the structural designers opt for wind tunnel experiments to get accurate wind loading on these structures. Computational fluid dynamics (CFD) and artificial neural network (ANN) approaches are other alternative methods emerging in recent years. This paper discusses on the ANN approach to predict the pressure coefficients on building faces due to interference, which may be used further in calculating wind loads. Mean pressure coefficients on each face of principal building (building under investigation) are predicted and analyzed for 45o wind angle, in presence of an interfering building at different locations upstream of the principal building. There is a good agreement between the predicted results and those obtained using wind tunnel. The results show that ANN approach can serve as a better tool to predict wind loads at preliminary stage and make an initial estimation for different interfering cases compared to methods like CFD and experimental which are complex, time consuming and costlier.

Lattice-Boltzmann-based large-eddy simulation of high-rise building aerodynamics with inlet turbulence reconstruction

A Lattice-Boltzmann-based Large-Eddy Simulation approach for wind load prediction on high-rise building is proposed and validated. An extension of the original incompressible Synthetic Eddy Method to reconstruct inlet turbulence is proposed within the Lattice-Boltzmann framework, including a low-noise frozen density variant. Extensive successful comparisons with experimental data are carried out, for both quantities defined on the building surface and in its wake. A detailed sensitivity analysis of the results with respect to inlet turbulence reconstruction, boundary conditions at the building surface and grid resolution is also provided. An almost unique set of comparisons with experimental data is presented, including mean and rms values, spectra, but also peak values of pressure at the building surface.

A Comparative Study of Wind Tunnel Tests and Design Codes in Evaluation of Along-Wind Effects on Towers

This study aims to estimate the wind loads acting on a tower structure by comparing and reviewing design codes and the results of wind tunnel tests. To this end, the modal properties of the tower were identified through short-term on-site measurements of the Busan Tower in Korea. The wind load acting on the tower was calculated using four design codes: KBC2009 (Korea), ASCE7-10 (USA), EUROCODE (Europe), and AIJ2004 (Japan). Additionally, force measurement tests and aeroelastic model tests were conducted for comparison. The results obtained indicated that the design wind velocity of each design code differed slightly, reflecting the individual characteristics of each country. The base shear force, base moment, and maximum displacement obtained from each design code were similar to those obtained in the wind tunnel tests. The magnitudes of the base moments and maximum displacements calculated by each design code were in the order of KBC > AIJ ≈ EUROCODE > ASCE7. The overall results indicate that each design code reasonably estimates the wind forces and the responses of the tower and also has an appropriate safety margin. The scatter in the predicted wind loads occurs primarily from the variations in the design wind velocity in the respective design codes.

A data-driven artificial neural network model for predicting wind load of buildings using GSM-CFD solver

Wind load prediction is critical to the design of buildings, especially skyscrapers, because wind-induced dynamic loads and vibrations can often be a major concern that dominates the design. This paper presents a real-time wind load predictor for buildings by training an artificial neural network, with big data generated from a well-established software package for computational fluid dynamics (CFD), known as GSM-CFD. The cross-section of a building is idealized as a rectangle with various aspect ratios. The attack angles and velocity of the wind are also treated as variables in a wide range for building design considerations. We firstly establish a large number of computational models of major possible situations, and intensive computations using the GSM-CFD solver are conducted to generate detailed flow fields. Due to the high Reynold numbers of the flow fields, large eddy simulation (LES) model has been adopted in the calculation process. Both drag and lift coefficients are then computed based on the pressure distribution around the surface of the rectangular blocks. The GSM-CFD enables us to generate a mass of data considering different aspect ratios, wind velocities and attacking angles. Finally, an artificial neural network is trained to predict the drag coefficients for any given aspect ratios of the rectangular cross-sections of building subject to any given attack angles of wind with various velocities. The trained neural network is verified by comparing the results of the neural network predictions and that of the numerical simulations. The accuracy based on the test data set is found about 3.17%, which is sufficient for engineering design purposes. Our trained neural network model is applicable to other problems with a flow around rectangles with aspect ratios from 0.2 to 1, air-flow velocity of 34 m/s, and attack angles from 0° to 90°. The prediction is practically in real-time.

Wind load prediction on single tree with integrated approach of L-system fractal model, wind tunnel, and tree aerodynamic simulation

In this work, we adopt the integration of the L-system fractal tree generation, 3D printed wind tunnel modeling, and computational fluid dynamics (CFD) simulation approach to model the wind effect on a single tree. We compare the agreement between CFD simulations and wind tunnel measurements of rigid branched structures resembling trees. First, fractal tree mesh models based on species growth and branching patterns are developed to represent tree species for wind–tree modeling. Subsequently, a scaled-down fractal tree is generated with 3D-printing and subjected to tunnel testing with load cell and particle image velocimetry measurement data under the wind speed of 10 m/s and 15 m/s. Finally, CFD based on Reynolds-Average Navier–Stokes (RANS) simulation with a full closure model and Large Eddy Simulation (LES) using appropriate momentum sink and turbulence source terms for the volumetric tree is carried out. We use both the volume-average porous media and the volume-splitting discretized zones (split number 10 × 10 × 10) to reproduce the momentum sink effect in the numerical simulation. Three tree species, namely, Peltophorum pterocarpum (yellow flame), Khaya senegalensis (African mahogany), and Hopea odorata (ironwood), are tested, and a reasonable agreement of drag force prediction and velocity profiles is obtained when comparing the CFD simulation results with wind tunnel data. The RANS modeled drag force results exhibit 20% of over-prediction, while the normalized velocity profiles display a good match of velocity decay at the tree leeward sides. On the other hand, LES produces much better results with only 3% discrepancy with the experimental results. A comparison of experimental results among the tree species is also carried out. Due to the actual random wind direction, tree slenderness representation, and structural flexibility issues, the current methodology still has the limitation for validation with urban on-site measurement. Nonetheless, this integrated approach is the first step in establishing modeling tool applicability to examine the effect of the forest structure and composition on wind loads.

NUMERICAL METHOD STUDY ON ENVIRONMENTAL LOADS OF FLOATING PHOTOVOLTAIC POWER ARRAY

Numerical prediction on environmental loads for the first 40 MW floating photovoltaic power station built in China is carried out. Based on the verification from the comparison with the measured data in a wind tunnel, a CFD (Computational Fluid Dynamics) method is applied to study the wind load and the current load on a large-scale square array. CFD numerical investigation verifies it feasible to adopt a simplified floating body model instead of the original one. The computational results of the model monomer are in a good agreement with the wind tunnel measured data. The 3D full scale square array's simplified calculations based on a coarse grid are carried out, and the distributions of wind loading on the array under various wind direction angles are obtained. Through the analysis of the distribution of maximum wind loading at north wind, the strategy of 2.5D calculation is put forward and numerically verified. According to the computational results, an 18(row)×11(column) scale model array test plan is designed and performed in a wind tunnel. The measured results are in a good agreement with CFD results, thus a global numerical calculation algorithm is set up. The 2.5D calculation based on a fine grid provides correction coefficients to rectify the 3D computational result to obtain more accurate wind loads. The calculation method for current loads is similar to that of wind loads. The numerical computation and analysis on the wave load are performed by the potential flow method. The wave load variations, at regular waves with unit wave amplitude, along the row or the column are obtained. According to the variation, the whole wave load is estimated. The wave load on the array is predicted at the extreme condition of a fifty-year return period. This study provides a method for the numerical prediction of wind loading, current loading and wave loading on a floating photovoltaic power station. The researches provide a technical support for anchoring the calculation of floating square array and the system design of floating photovoltaic power stations.

The wind load prediction and comparative study of asmmetric semi-submersible lifting platform based on CFD

In order to accurately predict the wind load of asymmetric offshore structures, this paper studied the wind load of an asymmetric semi-submersible lifting platform based on the CFD software FINE/MARINE, and calculated the wind load of operation state, lifting state and survival state respectively, and compared the results with those of China Classification Society(CCS) specification. The results show that under the same wind speed, the change of wind direction angle has a great influence on the distribution of wind field around the platform. The specification calculation can not fully consider the influence of the shielding effect between the components of the superstructure, therefore, the results are larger than the numerical simulation value. In addition, the wind field distribution near the platform can be used to analyze the variation of wind load, which provides an effective method for wind load prediction and wind field analysis of asymmetric offshore structures.

The Wind Load Prediction of Asymmetric Semi-submersible Lifting Platform based on CFD

The Wind Load Prediction of Asymmetric Semi-submersible Lifting Platform based on CFD

Wind effects on long-span bridges: Probabilistic wind data format for buffeting and VIV load assessments

The technology development within the structural design of long-span bridges in Norwegian fjords has created a need for reformulating the calculation format and the physical quantities used to describe the properties of wind and the associated wind-induced effects on bridge decks. Parts of a new probabilistic format describing the incoming, undisturbed wind is presented. It is expected that a fixed probabilistic format will facilitate a more physically consistent and precise description of the wind conditions, which in turn increase the accuracy and considerably reduce uncertainties in wind load assessments. Because the format is probabilistic, a quantification of the level of safety and uncertainty in predicted wind loads is readily accessible. A simple buffeting response calculation demonstrates the use of probabilistic wind data in the assessment of wind loads and responses. Furthermore, vortex-induced fatigue damage is discussed in relation to probabilistic wind turbulence data and response measurements from wind tunnel tests.