東華大學圖書館 |

Advanced Aerodynamic Analysis and Predictive Wind Load Modelling of Sympodial Trees /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced Aerodynamic Analysis and Predictive Wind Load Modelling of Sympodial Trees // Pengfei Lin.
作者:	Lin, Pengfei,
面頁冊數:	1 electronic resource (179 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
標題:	Strain gauges. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31810301
ISBN:	9798346823254

Advanced Aerodynamic Analysis and Predictive Wind Load Modelling of Sympodial Trees /
Lin, Pengfei,

Advanced Aerodynamic Analysis and Predictive Wind Load Modelling of Sympodial Trees /Pengfei Lin. - 1 electronic resource (179 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

Wind is a significant natural disturbance factor affecting many forested regions globally, with tree stability during storms being a critical issue in urban forested areas. Urban forests, integral to urban ecosystems, are particularly vulnerable to frequent coastal storms, which induced substantial economic losses and threaten ecological balance and human safety. Therefore, developing storm-resistant urban forests necessitates a deeper understanding of tree aerodynamics to mitigate risks and ensure sustainable urban development. Previous research has primarily focused on analyzing the aerodynamic characteristics of single tree/tree species or establishing empirical models based on limited experimental data, which often lack generalizability. Even within the same tree species, the aerodynamics of different trees can vary significantly due to differences in tree morphology. Therefore, a comprehensive analysis of the impact of morphology on the aerodynamic characteristics of trees is essential to properly plan urban greening to build a resilient forest. Based on this analysis, constructing tree reconfiguration models can also aid researchers in understanding the ability of trees to withstand wind storms. Furthermore, developing wind-induced load models can facilitate the rapid prediction of tree failure, providing valuable insights for early warning assessments of urban forests before suffering from wind storms.This research investigated the effect of tree morphology on the aerodynamics of sympodial trees from macroscopic and microscopic ways through wind tunnel tests, which laid the foundation on the developed tree reconfiguration model and deep learning model for predicting the drag coefficient of urban trees. First, the effect of different crown configuration on the reconfiguration ability of sympodial trees is macroscopically investigated through wind tunnel tests using natural trees. Then, a modelling technique in simulating the aerodynamics of natural tree is proposed and validated. Subsequently, the effect of microscopic tree parameters, i.e. branch angle and branch orders, on the aerodynamics of sympodial trees is accessed based on the experimental data of artificial sympodial trees. Meanwhile, the vibration characteristics of sympodial tree is deeply explored based on the experimental data of natural trees. Finally, a reconfiguration model and a deep learning model predicting the drag coefficient of sympodial trees are proposed based artificial intelligence techniques.The wind-induced reconfiguration performance of 9 sympodial trees with three different pruning techniques is examined by wind tunnel tests of natural trees, macroscopically and comprehensively revealing the effects of crown configurations on tree reconfiguration. The area reduction effect decreases with an increase in \uD835\uDC3F\uD835\uDC34\uD835\uDC3C1(the ratio of leave area to ground area) and \uD835\uDC3F\uD835\uDC34\uD835\uDC3C2(the ratio of leave area to frontal area). Streamlining effect is gradually enhanced with an increase in \uD835\uDF06\uD835\uDC50\uD835\uDC61(the ratio of crown height to stem height), but shows a trend of first increasing and then decreasing with the increase of \uD835\uDC3F\uD835\uDC34\uD835\uDC3C1and \uD835\uDC3F\uD835\uDC34\uD835\uDC3C2.To investigate the effect of microscopic tree parameters, i.e. branch angle and branch orders, on the aerodynamics of sympodial trees in controlled conditions, artificial sympodial tree model is established using 3D printing technique and artificial leaves. The drag coefficient and overturning moment coefficient of artificial tree model match well with those of natural trees. Meanwhile, the reconfiguration ability of artificial tree model can also provide reasonable variations compared with natural trees.

English

ISBN: 9798346823254Subjects--Topical Terms:

3563166
Strain gauges.

Advanced Aerodynamic Analysis and Predictive Wind Load Modelling of Sympodial Trees /
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520 $a Wind is a significant natural disturbance factor affecting many forested regions globally, with tree stability during storms being a critical issue in urban forested areas. Urban forests, integral to urban ecosystems, are particularly vulnerable to frequent coastal storms, which induced substantial economic losses and threaten ecological balance and human safety. Therefore, developing storm-resistant urban forests necessitates a deeper understanding of tree aerodynamics to mitigate risks and ensure sustainable urban development. Previous research has primarily focused on analyzing the aerodynamic characteristics of single tree/tree species or establishing empirical models based on limited experimental data, which often lack generalizability. Even within the same tree species, the aerodynamics of different trees can vary significantly due to differences in tree morphology. Therefore, a comprehensive analysis of the impact of morphology on the aerodynamic characteristics of trees is essential to properly plan urban greening to build a resilient forest. Based on this analysis, constructing tree reconfiguration models can also aid researchers in understanding the ability of trees to withstand wind storms. Furthermore, developing wind-induced load models can facilitate the rapid prediction of tree failure, providing valuable insights for early warning assessments of urban forests before suffering from wind storms.This research investigated the effect of tree morphology on the aerodynamics of sympodial trees from macroscopic and microscopic ways through wind tunnel tests, which laid the foundation on the developed tree reconfiguration model and deep learning model for predicting the drag coefficient of urban trees. First, the effect of different crown configuration on the reconfiguration ability of sympodial trees is macroscopically investigated through wind tunnel tests using natural trees. Then, a modelling technique in simulating the aerodynamics of natural tree is proposed and validated. Subsequently, the effect of microscopic tree parameters, i.e. branch angle and branch orders, on the aerodynamics of sympodial trees is accessed based on the experimental data of artificial sympodial trees. Meanwhile, the vibration characteristics of sympodial tree is deeply explored based on the experimental data of natural trees. Finally, a reconfiguration model and a deep learning model predicting the drag coefficient of sympodial trees are proposed based artificial intelligence techniques.The wind-induced reconfiguration performance of 9 sympodial trees with three different pruning techniques is examined by wind tunnel tests of natural trees, macroscopically and comprehensively revealing the effects of crown configurations on tree reconfiguration. The area reduction effect decreases with an increase in \uD835\uDC3F\uD835\uDC34\uD835\uDC3C1(the ratio of leave area to ground area) and \uD835\uDC3F\uD835\uDC34\uD835\uDC3C2(the ratio of leave area to frontal area). Streamlining effect is gradually enhanced with an increase in \uD835\uDF06\uD835\uDC50\uD835\uDC61(the ratio of crown height to stem height), but shows a trend of first increasing and then decreasing with the increase of \uD835\uDC3F\uD835\uDC34\uD835\uDC3C1and \uD835\uDC3F\uD835\uDC34\uD835\uDC3C2.To investigate the effect of microscopic tree parameters, i.e. branch angle and branch orders, on the aerodynamics of sympodial trees in controlled conditions, artificial sympodial tree model is established using 3D printing technique and artificial leaves. The drag coefficient and overturning moment coefficient of artificial tree model match well with those of natural trees. Meanwhile, the reconfiguration ability of artificial tree model can also provide reasonable variations compared with natural trees.
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650 4 $a Load. $3 3562902
650 4 $a Polytopes. $3 704968
650 4 $a Deep learning. $3 3554982
650 4 $a Wind power. $3 672558
650 4 $a Spectrum analysis. $3 520440
650 4 $a Landscape architecture. $3 541842
650 4 $a Aerodynamics. $3 532569
650 4 $a Storms. $3 520680
650 4 $a Fractals. $3 524457
650 4 $a Trees. $3 516384
650 4 $a Energy. $3 876794
650 4 $a Forests. $3 3560105
650 4 $a Reynolds number. $3 3681436
650 4 $a Geometry. $3 517251
650 4 $a Morphology. $3 591167
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710 2 $a Hong Kong University of Science and Technology (Hong Kong). $e degree granting institution. $3 3814653
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