3D-printed mini trees help with wind load response predictions

From mitigating heat to improving air quality, urban vegetation plays a vital role in metropolitan living. However, assessing the risk involved with the wind load response of trees is highly subjective, and their complex, nonuniform geometry makes developing a computational model difficult. To help improve wind load forecasting, Poh et al. compared simulations of tree aerodynamics to wind tunnel experiments using 3D-printed tree models.

The authors created models of three tree species common to Singapore at about 1/70 scale, taking into account characteristics of the trees, such as joint spacing and branching patterns. They measured model air resistances in response to winds of different velocities, which they compared to simulations of aerodynamic impacts.

Critically, they found the most important parameter necessary for accurately modeling tree resistance is its frontal silhouette area density, the ratio between its frontal silhouette area and its volume. This contrasts with the parameter traditionally used by arborists, the leaf area density, which leads to high discrepancies between model predictions and actual tree behavior.

Though the group acknowledges model limitations, such as the exclusion of tree flexibility and slenderness, they intend to extend the study to consider nearby features like neighboring trees and buildings. They also have an upcoming report on four additional tree species.

“We are confident that the simulation results can still provide scientific wind load information to inform urban forest planning and management policy by identifying landscape features associated with increased wind speed and turbulence,” said author Hee Joo Poh. “This would help to prioritize tree pruning activities by identifying situations where trees are more vulnerable to fast, turbulent flows.”

Source: “Wind load prediction on single tree with integrated approach of L-system fractal model, wind tunnel and tree aerodynamic simulation,” by Hee Joo Poh, Woei Leong Chan, Daniel J. Wise, Chi Wan Lim, Boo Cheong Khoo, Like Gobeawan, Zhengwei Ge, Yong Eng, Jia Xin Peng, Venugopalan S.G. Raghavan, Siddharth Sunil Jadhav, Jing Lou, Y. D. Cui, Heow Pueh Lee, Daniel Christopher Burcham, Daryl Lee, Kelvin Wenhui Li, and Irene Lee, AIP Advances (2020). The article can be accessed at https://doi.org/10.1063/1.5144628 .