【目的】研究不同来流风下树木的风致响应特性,分析树叶数量和湍流度剖面对树枝和树干加速度概率分布特征和树干加速度极值的影响,为防护林的维护和管理提供参考。【方法】基于满足主要相似要求的树木缩尺气弹模型,设计8种不同树叶数量的树冠形态,参照工程科学数据集ESDU 85020中的全尺寸理论风场,在风洞中构建能够覆盖平坦、开阔和城郊地貌的4类风场,通过测量风速、树枝和树干加速度,开展树木风致加速度概率分布特征及极值研究。【结果】以基于对应80%累积概率的偏度和峰度值作为关键点,确定树干加速度高斯分布的判别准则。树干加速度概率分布多数符合高斯分布特征,树枝加速度概率分布不符合高斯分布特征。树枝加速度对应80%累积概率的偏度和峰度值随湍流度的增加而显著增加。通过对比广义极值理论Gumbel法、广义Pareto法、峰值因子法3种计算方法,广义Pareto法对树干顺风向加速度极值的拟合效果最好。二次多项式可以较好地拟合树干顺风向极限加速度与平均风速的关系,树干顺风向极限加速度随平均风速的增加而增加,随湍流度的增加而增加。【结论】基于树木气弹模型风洞试验研究,得到了能够反映全尺寸下树枝和树干加速度的概率分布特征和极值特征。

【Objective】This study aims to investigate the wind-induced response characteristics of trees across different terrains. It specifically examines to analyze how leaf quantity and turbulence profiles affect the acceleration probability distribution and extremes of the branches and trunks. By analyzing these factors, the study aims to provide valuable references for maintaining and managing protective forests. Understanding how environmental variables impact the aerodynamic behavior of trees can aid in designing better protective measures against wind damage.【Method】An aeroelastic model tree, designed to meet primary similarity requirements, served as the research foundation. Eight distinct crown shapes, varying in leaf quantity, were meticulously crafted to replicate realistic tree structures. These models were tested in wind tunnel terrains, constructed based on the Engineering Sciences Data Unit (ESDU) 85020 framework. Four terrain were replicated in the wind tunnel to measure wind-induced responses. Wind speed, along with branch and trunk accelerations, were recorded to explore the probability distribution and extremes of these accelerations. This comprehensive setup ensured a thorough investigation of how different wind conditions and tree structures affect wind-induced responses.【Result】Skewness and kurtosis values at the 80% cumulative probability were used to establish criteria for determining the Gaussian distribution of trunk acceleration. The number of leaves had a limited effect on the skewness and kurtosis values for trunk acceleration in the along wind and cross wind directions but had a strong effect on branch acceleration. The trunk accelerations in both wind directions generally followed a Gaussian distribution, while branch accelerations did not. Skewness and kurtosis values for branch acceleration significantly increased with turbulence intensity. Trunk acceleration samples in the along-wind direction approximately exhibited a bell shape, with a high peak and heavy tails. The Generalized Pareto distribution fited the central part of the sample data best, followed by the Gaussian distribution, while the Gumbel distribution fited the worst. Similarly, the Generalized Pareto distribution best fitted the tail data, followed by the Gaussian and Gumbel distributions. This indicated that the Generalized Pareto distribution was more effective for fitting the probability distribution of sample data than the Gaussian and Gumbel distributions. Among the three calculation methods, Generalized Extreme Value (Gumbel) method, Generalized Pareto method, and Peak Factor method, the Generalized Pareto method best fited extreme values of trunk accelerations in the along-wind direction. A quadratic polynomial effectively modeled the relationship between extreme trunk accelerations in the along wind direction and average wind speed. Trunk acceleration increaseed with both average wind speed and turbulence levels.【Conclusion】Based on wind tunnel tests with an aeroelastic model tree, this study provides insights into the probability distribution characteristics and extreme characteristics of branch and trunk accelerations at full scale.