Journal of Wind Engineering and Industrial Aerodynamics最新文献

Turbulence coherence in mountainous areas necessitates meticulous evaluation due to the complex terrain blocking. This study focuses on the turbulence coherence of the non-uniform wind field in a canyon via the anemometers positioned along a bridge span. Based on a proposed multi-point clustering, two categories of strong winds impacted by a nearby ridge are identified. The turbulence coherence of the two strong winds is then investigated with an extra focus on the phase spectrum. Several complex coherence functions are extended from commonly employed real forms, including the simplified Krenk's formula proposed. The fitting results of them all suggest that parameters such as the decay factor of the two strong winds show a negative linear correlation with separation but their relationship with wind speed is not significant. For the phase spectrum, in addition to skew winds, the contribution of horizontal wind shear is first emphasized in the two strong winds with piecewise fittings. This paper also discusses single-point coherence between the along-wind and vertical turbulence components. It is found to be unusually positive in sign due to the influence of the local topography and a formula with a constant term is suggested that substantially improves the fitting compared to Solari's formula.

The assessment of buffeting response of long span bridges relies significantly on the aerodynamic admittance and spanwise coherence functions of buffeting forces acting on bridge decks. This study presents a novel approach to derive admittance and coherence functions of buffeting lift on bridge decks, utilizing wind tunnel measurement of forces on spanwise distributed segments. The approach is developed by establishing connections of the power spectrum and coherence function of segmental lift with the admittance and coherence functions of strip lift. This study also explores the direct estimation of the generalized buffeting forces on long span bridges from the segmental force, eliminating the need of extracting admittance and coherence functions of strip force. The methodology is firstly validated for a thin plate with theoretical force information, follow by its application to a twin-box bridge deck using wind tunnel data. The investigation assesses the influence of a pre-assumed coherence model on the identified admittance and coherence functions and its consequential impact on the generalized buffeting forces of long span bridges. The proposed approach offers a practical and efficient means to quantify buffeting forces acting on bridge decks with intricate configurations, such as truss sections, where surface pressure measurement may pose challenges.

This study presents the characteristics of external wind pressures of a multi-span light steel industrial building through wind pressure measurements conducted in a tornado simulator. The test model is designed as a three-span low-rise building with gable roofs according to the practical measured sizes and shapes of the light steel industrial building destroyed in the Shengze tornado, China (2021). The distance from the tornado-like vortex center to the building, building orientation, roof angle, and swirl ratio are considered as experimental parameters. The effects of the parameters on the most unfavorable peak and mean pressure coefficients of each surface are analyzed. The roof zoning specified in ASCE 7–16 was re-analyzed based on the characteristics of wind pressure coefficients, and the tornado design pressure coefficients were calculated. The results of the analysis illustrate that the wind-ward roof surface at a distance of around one vortex core radius from the center experience the most severe pressure coefficient under tornado, the most unfavorable zone of the pressure coefficients on the building roof changes with the building orientation, the model with a smaller roof angle has smoother pressure coefficient contours than that with a larger roof angle because of the cutting effect of the ridge, and the simulated tornado with the lower swirl ratio generate higher external pressure coefficients on the building model. Moreover, the roof zoning in ASCE 7–16 for the boundary layer wind is not precise for tornado, thus an updated roof zoning for the tornado design pressure coefficients is defined.

Tropical cyclones (or typhoons, hurricanes) may influence the power production and structural integrity of offshore wind turbines. However, understanding of tropical cyclone wind structure over turbine-relevant heights remains limited. Based on observations from a wind lidar and a meteorological mast at a planned offshore wind farm site, this paper investigates the marine wind characteristics, e.g., wind speed shear, wind direction veer, and turbulence intensity, over turbine-relevant heights in tropical cyclones. Statistical distributions of the marine wind characteristics are analyzed, and their variations with upstream fetch conditions and hub-height wind speeds are examined. Some noteworthy features of the marine wind structure in tropical cyclones are observed, such as the median wind veer rate of 0.018°/m and veer angle of 2.2° across the turbine rotor heights, different wind shear coefficients and wind veer rates for upper and lower rotors, and levelling off or decrease of wind shear coefficient and turbulence intensity with increasing wind speed at hub-height wind speed over 25 m/s. The outcome of this study is expected to provide useful information for design and operation of offshore wind turbines, marine platforms, and other ocean structures in tropical cyclone-prone regions.

Efficient tunnel ventilation is essential for ensuring construction safety and protecting personnel health during tunnel construction. This study proposes a demand-controlled ventilation (DCV) method on the basis of deep learning algorithm to both improve pollutant removal efficiency and reduce energy consumption. The DCV method utilizes a two-layer bidirectional long short-term memory algorithm (BiLSTM) to predict pollutant concentrations. The air volume is dynamically adjusted based on the gaseous pollutant removal requirements. The coefficient of ventilation performance (COVP) is proposed to evaluate the performance of two ventilation methods (DCV and constant air-volume ventilation (CAV)) through computational fluid dynamics (CFD) simulations. The results show that the DCV results in a lower maximum average CO concentration and higher removal efficiency in the heading area (372.3 mg/m³) than the CAV does (404.1 mg/m³). The fan's energy consumption of DCV is 64.6% lower than that of CAV during a 1000 s ventilation period. The COVPs for both methods exhibit temporal variation and achieves their maximums (2.25 for DCV and 0.741 for CAV) after reaching the constraint conditions (air volume threshold). The DCV method expedites pollutant elimination, reduces construction waiting period, and minimizes energy consumption, providing a novel application of a deep learning algorithm in construction engineering.

This paper examines the streamline curvature effects on the aerodynamic pressure patterns induced by tornado-like flows on a low-rise structure. The analysis derives from the detailed scrutiny of a wind tunnel test campaign carried out at the WindEEE Dome tornado simulator. The simulated tornado-like flows were characterized by a swirl ratio such that the cores were characterized by multiple vortices. The tornado-like vortex was slowly translated across the chamber of the simulator. Surface pressure measurements were acquired on both the building model surface and on a circular ground plate around it, and synchronized with velocity measurements gathered from four Cobra probes installed in the proximity of the corners of the structure. These Cobra probes allowed the definition of the tornadic streamlines. Multiple nominally identical repeats were carried out to gather a robust estimation of conditionally-averaged pressure and velocity measurements based on the position of the tornado-like vortex. When comparing different cases characterized by similar conditions of upstream wind direction, the mean aerodynamic pressure patterns were revealed to be sensitive to the streamline curvature. Moreover, these are distinct than those generated from straight-line ABL winds, consistently calibrated upon the measurements from the Cobra probes in the upstream proximity of the building.

Understanding near-surface wind variations in urban environments is essential for a wide range of applications, including urban weather forecasting and wind impact assessment. In this study, we introduced a novel model for mean wind speed profiles within the urban roughness sublayer based on theoretical analysis. To calibrate the model's coefficients, we conducted large-eddy simulations of airflow over a variety of idealized urban configurations. The resulting expression effectively captures wind speed variations across different urban morphologies. Subsequently, a regression modeling was employed to identify the relationships between building morphological parameters and these coefficients. This highlights the pivotal role of using building morphology to predict near-surface velocity profiles. The proposed model also yields significantly more accurate wind speed predictions within the urban canopy layer than traditional exponential profiles. The findings in the present study lay a more robust foundation for assessing urban wind conditions and improving urban-scale weather forecasts.

In this manuscript, we propose an automatic classification and recognition method for extreme wind events based on Convolutional Neural Networks (CNNs) and combining the Shapelet Transform (ST) algorithm with the improved Gramian Angular Summation Field - Gramian Angular Difference Field (GASF-GADF) 2D images construction format. First, a CNN model suitable for wind speed time series 2D images classification and recognition among five mainstream CNNs (ResNet-50, ShuffleNet0.5 × , DenseNet-121, EfficientNet-B2, and EfficientNetV2-S) is preferred based on the basic Gramian Angular Field (GAF) method; then, the improved GASF-GADF images construction format is proposed, and the optimal CNN is used to compare the classification and recognition results based on other three image conversion methods: Markov Transition Field (MTF), GASF, GADF. Last, it is proposed to utilize the ST algorithm to extract the feature subsequence Shapelets of wind speed time series to further improve the classification and recognition effect on extreme wind events. The effectiveness and applicability of the proposed method were verified through three extreme wind event datasets in this paper.

The results show that the combination of Shapelets and the improved GASF-GADF images transformation method proposed in this paper can effectively enhance the classification and recognition of extreme wind events by CNNs. Among them, EfficientNetV2-S combined with the method proposed in this paper achieves 99.50%, 99.50% and 97.50% recognition Accuracy for thunderstorm, gust front and typhoon, respectively. Meanwhile, it still has good robustness for extreme wind events disturbed by noise.

The aerodynamic impact of surface roughness and trips on a circular cylinder was investigated. Surface roughness was caused by textile sleeves of seven distinct fabrics varying in roughness. These sleeves were configured with one or two seams strategically positioned to influence the flow dynamics over a Reynolds number range of $4\times 10^4⩽\text{Re}⩽1.3\times 10^5$. Measurements of the aerodynamic drag, lift, unsteady flow, and mean flow field were made. It is shown that seam(s) can reduce cylinder drag across the Reynolds number range. The parameters influencing this reduction include the number, position, configuration of seams, and fabric roughness. For a single seam, drag reduction of up to 45% and an increase in vortex shedding frequency were observed, relative to a smooth cylinder. In the case of fabrics with two symmetrical seams about the cylinder stagnation point, a significant reduction in the critical Reynolds number occurred, accompanied by approximately 35% drag reduction across a broad range of Reynolds numbers. Furthermore, an increase in freestream turbulence intensity caused a considerable reduction in both the critical Reynolds number and drag. These variations in the forces acting on the cylinder were related to the behavior of shear layers and wake.

As the demand for precise prediction of fatigue and collapse continues to grow, it is imperative to conduct research on wind load calculation methods for lattice structures using members as the fundamental unit. This study focuses on the tower body of an angle steel lattice structure with a rectangular cross-section. The aerodynamic characteristics of entire segments, column members in lattice structures, and a single angle steel were investigated, along with the aerodynamic interference effects on members within lattice structures. The research findings indicate that drag coefficients cannot universally substitute the SRSS coefficients of column members or a single angle steel across all wind directions. Therefore, considering that the drag coefficient of the column member or single angle steel surpasses 98% of their SRSS coefficients at a wind direction of 45°, a particular skewed wind load factor was defined, and its fitting formula was proposed. Additionally, a calculation method for wind loads on columns within lattice structures was developed, introducing the concepts of SRSS angle and aerodynamic interference factors on members. The comparison among the experimental data, fitting formula, and proposed calculation method, were conducted. To facilitate the practical application, the simplified formulas for both AIF and SRSS were recommended.