Rubber trees in coastal habitats are exposed to a high degree of wind stress. An algorithm-hardware synergetic methodology was developed for investigating and predicting rubber tree phenotyping excited by strong winds. The framework includes (1) a custom-designed industrial fan that recreates a variable airflow field at wind speeds of 15, 30 and 45 m/s coupled with a terrestrial laser scanner and bundled motion sensors to acquire point clouds and vibration data; (2) a graphic model that approximates tree canopies based on foliage clumps with phenotypic traits that are derived from point clouds captured while trees are subjected to aerodynamic drag; and (3) the wind characteristic parameters of forest canopies were calculated by a developed forest-specialized k-ε turbulence model combining the constructed tree models and grid-scale subdivision of the wind fluid field. (4) A digital twin model that incorporates detailed tree phenotypic traits and considers plant mechanical characteristics was established, depicting the related wind-induced actions of target trees under various wind influences. The results show that tree crowns with spreading forms are prone to yield larger pendulum amplitudes than compact crowns, but trees directly exposed to wind exhibit greater crown volume reductions than trees in sheltered areas. Within tree canopies, a one-fold increase in inlet wind speed intensified crown compression (approximately 17 % decrease in crown volume), generated 2.1-fold pressure gradients and increased turbulence kinetic energy by approximately 60 %. Moreover, the entire scenario of the adaptation of experimental trees to wind perturbations was visually restored using digital twin techniques, serving as an integral behaviour dataset for further data-driven decision-making. In summary, this paper presents a comprehensive methodology that can decipher the phenotypic manifestations of trees' reactions to wind hazards, with potential applications in phenotyping or envirotyping studies designed to evaluate the wind resistance properties of rubber trees.

中文翻译：

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利用激光扫描和数字孪生技术对强风应力下的橡胶树进行表型分析的框架


沿海栖息地的橡胶树承受着高度的风应力。开发了一种算法-硬件协同方法，用于研究和预测由强风激发的橡胶树表型。该框架包括 （1） 一个定制设计的工业风扇，可在 15、30 和 45 m/s 的风速下重建可变气流场，并配有地面激光扫描仪和捆绑的运动传感器，以获取点云和振动数据;（2） 一个图形模型，该模型基于具有表型特征的树叶丛来近似树冠，这些树冠来自树木受到空气动力阻力时捕获的点云;（3） 森林冠层的风特征参数是通过开发的森林专用 K-ε 湍流模型计算的，该模型结合了构建的树木模型和风流体场的网格尺度细分。（4） 建立了一个数字孪生模型，该模型结合了详细的树木表型特征并考虑了植物机械特性，描绘了目标树木在各种风影响下的相关风诱导作用。结果表明，具有展开形式的树冠比紧凑的树冠更容易产生更大的钟摆振幅，但直接暴露在风中的树木比遮蔽地区的树木表现出更大的树冠体积减少。在树冠内，入口风速增加一倍，加剧了树冠压缩（树冠体积减少了约 17%），产生了 2.1 倍的压力梯度，湍流动能增加了约 60%。 此外，使用数字孪生技术直观地恢复了实验树适应风扰动的整个场景，作为进一步数据驱动决策的完整行为数据集。总之，本文提出了一种全面的方法，可以破译树木对风灾反应的表型表现，在旨在评估橡胶树抗风性能的表型或环境分析研究中具有潜在应用。