Wind environment is closely related to people's lives and has a great influence on the comfort and safety of the environment. This research examines the influence of trees on wind environment in pedestrian-level. By integrating the theories of CFD simulation method and trees benefits, six canopy morphologies (Spheroid, Cone, Inverted Cone, Cylinder, Ellipsoid, Cuboid) models are proposed and validated. The PHOENICS is used for numerical simulation (144 scenarios), and the data are analyzed using Photoshop and linear regression model. It is found that the influence of trees on wind is linearly correlated with the varying crown width, trunk height and plant spacing. The influence of tree on wind velocity can be expressed by IFwind (the wind reducing ability) and AZ (the area of downwind deceleration zones). The framework and the numerical simulation in this paper are intended to support and guide future studies of wind comfort and wind safety of trees in pedestrian-level, and to contribute to improved wind environmental quality in urban areas through reasonable tree planting.
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Predicting the influence of trees on wind environment in pedestrian-level through numerical simulation
https://doi.org/10.21203/rs.3.rs-4905258/v1
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Wind is an important component of atmospheric environment. Wind environment is closely related to people's lives and has a great influence on the comfort and safety of the environment. Suitable wind speed and ventilation are conducive to the diffusion of atmospheric pollutants, reduce urban heat island effect, improve urban air quality, and improve the thermal comfort of outdoor activities1–5. However, with the continuous urbanization, the appearance of a large number of artificial structures has affected the urban wind speed, wind direction and ventilation efficiency, which further worsens the urban heat island effect and air pollution6. In recent decades, the pedestrian’s outdoor thermal comfort has been critical for designing sustainable cities, which can be affected by wind flow in urban spaces4, 7. Wind speed, an important factor impacting thermal comfort, has been shown to have various impacts on pedestrians depending on the specific surrounding conditions8–11. Pedestrian-level wind environment in urban areas has a significant impact on the quality of urban dwellers' outdoor thermal comfort3, 12. The analysis and optimization of pedestrian-level wind environment is of great significance for improving urban living environment and constructing sustainable urban space. And therefore, the pedestrian-level wind environment has attracted the attention of academics.
Given the growing call for more vegetation in cities, it is important to study the wind resistant of urban trees in order to address outdoor natural ventilation problem in the landscape planning13. Previous studies have shown trees are more effective climate moderators than the other green elements14–16. Trees have always played a crucial role for human beings, providing several ecosystem services in urban areas. It can regulate the local microclimate environment of the city, alleviate the heat island effect, hinder and adsorb the particulate pollutants, absorb the gaseous pollutants and so on17–21. And the trees can alter the thermal-stress conditions by reducing wind-speed, changing wind-directions, and improving the comfort and health of the wind environment22–28. The main methods used to study the flow field around trees include in situ measurements, wind tunnel tests and numerical simulations. In recent years, the airflow field around trees has been widely simulated using Computational Fluid Dynamics (CFD) technology9, 22, 29–30. However, in most urban construction activities, designers only estimate whether the urban space in the scheme can meet the requirements of ventilation in summer and windproof in winter based on experience, and habitually use plants in aesthetic design and vertical design, but ignore their important role in improving wind environment, and there is a lack of assessment studies on the influence factors of trees on wind environment. Therefore, it is necessary to have a comprehensive understanding of the changing laws of the flow field around trees to guide urban tree planting, which is of great practical significance for building a comfortable and healthy urban wind environment, creating a liveable urban ecological environment and achieving sustainable urban development.
There are many studies on wind environment based on tree characteristics. Zheng's study show that tree crowns, created by branches, leaves and twigs, can provide shade and reduce wind speed31. Lai et al.32 found that the wind load a tree withstood is mainly applied to its crown, whose morphology and structure directly affect the degree of wind load given a certain wind condition. Cao et al.33 and Zhang et al.34 analyzed the relationships between changes in tree characteristics and wind speed, such as crown displacement, windward area and porosity. Mayaud et al. investigated the effects of a single tree, a grass clump, and a shrub on turbulent wind flow and discovered that wind velocity can be reduced by up to 70% in the lee of vegetation35. Amani-Beni et al. have investigated the effects of wind loading on three dimensional tree models using numerical simulation with implications for urban design36. In some papers, the wind environment is studied by taking individual trees as examples. Five-year-old white fir trees (Abides concolor) were tested as the wind break model in Lee and Lee’s work23. The drag coefficients of four common subtropical trees (Ficus microcarpa, Mangifera indica, Michelia alba and Bauhinia blakeana) have been obtained by using wind tunnel measurements37. The overall airflow variation in the downwind zone of a single tree is usually represented by the airflow variation in the central axis of the tree, based on which studies have developed airflow field models around single plants35, 38. Some scholars are also very concerned wind speed on the leeward side of trees: Zeng et al. point out the trees can cause a decrease in wind speed on their leeward side and limit heat dissipation28. Cheng et al. found out the downwind deceleration zone based on the trend of wind speed in their experiments38. For wind environmental improvements, the tree species selection and arrangement is the important parameters to be considered by planners and designers. So it is worth to be investigated for a better understanding of the wind environment which determine pedestrian comfort. The analysis of wind behavior behind trees has become an important task to address at the planning stage of tree planting. Raman et al. pointed out that isolated trees are found providing better cooling than the loosely clustered ones, containing open spaces in between39. Single tree is the basic unit of urban green layouts. Different types of green layout can be formed by planting multiple single trees together. In order to research the effect of green layouts on wind environment, it is necessary to study the wind environment of single tree. However, the wind environment of single tree is still not fully understood, especially from the point of view of tree canopy morphologies.
The complexity of the architecture of plant canopies makes the flow phenomena within and around the canopies extremely complex and difficult to predict40. Many factors can affect wind mitigation including wind direction, tree type, stem height, tree height, trunk height, crown width, distance between trees41–42. In order to figure out how crown width, trunk heights, and plant spacing of single tree influence the wind velocity, the PHOENICS CFD numerical simulation was performed in this study. Through the study of varying canopy morphologies of trees, to obtain downwind speeds data of the pedestrian-level. The goal of the present work is threefold: (1) to explore the influence of crown width, trunk height and plant spacing on wind velocity; (2) to calculate the area of downwind deceleration zones of varying crown width, trunk height above ground, and plant spacing; (3) to find out the relationship between the influence of trees on wind (wind velocity and downwind deceleration zones) and varying tree’s parameters.
Study area. Shenyang is located in the south of northeast China and the middle of Liaoning Province. It is the capital city of Liaoning Province and the central hub city of Central Liaoning City Group, the seventh largest city group in China. It is located at 41.80° North Latitude and 123.43° East Longitude and has a total area of 12,948 km2. Shenyang is located in the monsoon area with high frequency and strong wind, which brings many negative effects to the daily life of city residents. The rapid urbanization has an influence on the climatic environment of Shenyang, especially the wind and thermal environment in the central urban area. As a result, the urban heat island effect is enhanced in Shenyang in summer, and haze weather often occurs in winter heating period, which greatly affects the daily life and health of urban residents43,44. In this paper, trees planted in Shenyang Agricultural University were selected as reference for numerical simulation of plant parameters. According to the survey data, there are 62 species of trees in Shenyang Agricultural University. The main tree species are Abies nephrolepis, Picea koraiensis, Larix kaempferi, Pinus koraiensis, Pinus tabuliformis, Juniperus chinensis, Taxus cuspidata, Robinia pseudoacacia, Gleditsia japonica, Acer truncatum, Quercus mongolica, Ginkgo biloba, Sophora japonica, Prunus davidiana, Prunus padus, Salix matsudana, Salix babylonica, Juglans mandshurica, Populus alba, Sophora japonica, Catalpa ovata, Ulmus pumila, Fraxinus mandshurica, and Syringa reticulata. These tree species have strong adaptability and good growth characteristics, and are widely used in urban greening.
Numerical Simulation by PHOENICS. In this paper, PHOENICS is selected to simulate the effect of trees on wind environment. Ecological Housing Technology Assessment Manual of China (2003) stipulates that at the pedestrian height, that is, 1.5 m above the surface, the wind speed can only be considered comfortable when it is less than 5 m/s, and it cannot be considered comfortable when it exceeds this value45. Assessment Standard for Green Building of China stipulates that the wind speed in the pedestrian area around the building should be less than 5m/s46. In addition, the studies also found that people's sitting, standing and walking behaviors are relatively comfortable when the ambient wind speed is between 0−5.0m/s, while people feel uncomfortable when the ambient wind speed exceeds 5m/s7. Therefore, the inlet wind speed of simulation is set at 5m/s. At present, two equation models, k-ε model and RNG k-ε model, are the most widely used in the numerical simulation of outdoor wind environment. The simulation results of these two models are close to the actual wind field47. Among them, the overall accuracy of RNG k-ε model is higher, and is chosen for the study of this paper. In this study, the calculation area is set as 210m×210m×50m, the tree is located in the center of the calculation area, the inlet wind direction is set as N, and the inlet wind speed is 5m/s.
Previous studies have simplified tree canopies into ellipsoid, cone, cylinder, cuboid, square, rectangular and other geometries to analyze the effect of trees on the flow field48,49. Liu et al. stated that a better understanding of the airflow around a single plant is fundamental and important in the study of the flow field across multiple plants 48. Based on the tree species in site investigation, tree canopy morphologies were simplified into six types in this paper: Spheroid, Cone, Inverted Cone, Cylinder, Ellipsoid, and Cuboid (Fig. 1). In the early period, the tree parameters of Shenyang Agricultural University were measured and found that the tree average height of 8 m accounted for a large proportion of trees, and the average DBH was 0.25m. Therefore, in the simulation experiment, the height of a single tree was set to 8 m, and the DBH was set to 0.25m. The 3D plant modeling is carried out by using Rhino software. Then the model is imported into PHOENICS and the canopy model parameters are set as "porous media parameter module FOLIAGE", regardless of the internal structure of the canopies. This paper explores three scenarios of the influence of trees on wind environment (Table 1). S1 (48 sub-scenarios): The tree height, trunk height, and DBH are set to fixed value, so as to explore the influence of varying crown widths on wind velocity; S2 (54 sub-scenarios): The DBH and crown width are set to fixed value, so as to explore the influence of varying trunk heights on wind velocity; S3 (42 sub-scenarios): The tree height, trunk height, DBH, and crown width are set to fixed value, so as to explore the influence of varying plant spacing of two trees on wind velocity.
Through numerical simulation, wind velocity distribution map of varying scenarios can be obtained (Fig. 2). The map can show the effect of trees on wind velocity weakening and direction changing. In order to describe the pedestrian-level influence trend of trees on wind, wind velocity distribution map is divided into 10 levels of downwind deceleration zones
Z0, Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9 (Table 2). A range from Z0 to Z9 indicates that the trees is weakening the wind velocity more and more, so the weights Z0 to Z9 are calculated using Analytic Hierarchy Process. The downwind deceleration zones have a elliptical shape from the top view. This study used Photoshop histograms to calculate the pixels of each Zn and then converted to the area. According to the area and weight of Zn, the influence value of trees on wind velocity (IFwind) in each scenario was calculated.
Where Zn is the area of downwind deceleration zone, WZn is the weight of the downwind deceleration zone.
Influence of crown widths on wind velocity. According to the wind velocity distribution maps, with the increase of crown width, there was a strong positive correlation between crown width and IFwind (Fig. 3a). Among them, Ellipsoid (R2 = 0.98848) is the strongest correlation between crown width and IFwind, while Cylinder (R2 = 0.90431) is the weakest. On the whole, the min IFwind appeared at 1m (0.5 m) crown width and the max IFwind appeared at 8m (4.0 m) crown width. When the crown width is 1m (0.5 m), IFwind ranges from 0.02 (Cone) to 0.60 (Cuboid); crown width is 2m (1.0 m), IFwind ranges from 4.43 (Inverted Cone) to 9.87 (Spheroid); crown width is 3 m (1.5 m), IFwind ranges from 2.19 (Cuboid) to 12.09 (Cylinder); crown width is 4 m (2.0 m), IFwind ranges from 11.19 (Cone) to 16.86 (Cylinder); crown width is 5m (2.5 m), IFwind ranges from 17.08 (Cone) to 35.70 (Cylinder); crown width is 6m (3.0 m), IFwind ranges from 24.59 (Ellipsoid) to 36.03 (Spheroid); crown width is 7 m (3.5 m), IFwind ranges from 30.66 (Ellipsoid) to 58.32 (Spheroid); crown width is 8 m (4.0 m), IFwind ranges from 35.08 (Ellipsoid) to 76.24 (Spheroid).
According to the wind velocity distribution maps. Wherever each sub-scenario contains several downwind deceleration zones, these zones are continuous. There was a strong positive correlation between crown width and the area of downwind deceleration zones (AZ) (Fig. 3b). Linear relationship analysis model shows that the strongest positive correlation between crown width and AZ is Ellipsoid (R2 = 0.99054), and the weakest is Cylinder (R2 = 0.78106). On the whole, the min AZ appeared at 1 m (0.5 m) crown width and the max AZ appeared at 8 m (4.0 m) crown width. When the crown width is 1 m (0.5 m), AZ ranges from 0.96 m2 (Cone) to 39.87 m2 (Cuboid); crown width is 2 m (1.0 m), AZ ranges from 215.02 m2 (Cuboid) to 462.95 m2 (Spheroid); crown width is 3 m (1.5 m), AZ ranges from 73.02 m2 (Cuboid) to 372.17 m2 (Ellipsoid); crown width is 4 m (2.0 m), AZ ranges from 248.54 m2 (Cuboid) to 617.64 m2 (Inverted Cone); crown width is 5 m (2.5 m), AZ ranges from 352.49 m2 (Cone) to 631.16 m2 (Cylinder); crown width is 6 m (3.0 m), AZ ranges from 475.16 m2 (Cylinder) to 1128.13 m2 (Inverted Cone); crown width is 7 m (3.5 m), AZ ranges from 607.49 m2 (Ellipsoid) to 1766.09 m2 (Inverted Cone); crown width is 8 m (4.0 m), AZ ranges from 784.52 m2 (Cylinder) to 2084.62 m2 (Inverted Cone).
Influence of trunk heights on wind velocity. Wind velocity distribution maps show that with the increase of trunk height, the corresponding IFwind value also changes. Trunk heights of Spheroid, Cone, Cylinder, Ellipsoid, and Cuboid are negatively correlated with IFwind values (Fig. 4a). Among them, Spheroid (R2 = 0.94455) is the strongest correlation between trunk height and IFwind, while Cone (R2 = 0.01252) is the weakest. Trunk height of Inverted Cone (R2 = 0.83173) is positively correlated with IFwind values. For every tree canopy morphology, the distribution of min and max IFwind is irregular. When the trunk height is 0.0 m, IFwind ranges from 7.01 (Cuboid) to 42.21 (Cylinder); trunk height is 0.5 m, IFwind ranges from 6.06 (Cuboid) to 47.24 (Cylinder); trunk height is 1.0 m, IFwind ranges from 4.64 (Cuboid) to 51.72 (Cylinder); trunk height is 1.5 m, IFwind ranges from 2.69 (Cuboid) to 37.01 (Cylinder); trunk height is 2.0 m, IFwind ranges from 2.24 (Cuboid) to 33.92 (Cylinder); trunk height is 2.5 m, IFwind ranges from 1.44 (Cuboid) to 30.41 (Cylinder); trunk height is 3.0 m, IFwind ranges from 0.37 (Cuboid) to 25.43 (Cylinder); trunk height is 3.5 m, IFwind ranges from 0 (Cuboid) to 26.23 (Cone); trunk height is 4.0 m, IFwind ranges from 0 (Cuboid) to 25.55 (Cone).
According to the wind velocity distribution maps, the AZ changes with the increase of trunk height. Trunk heights of Inverted cone (R2 = 0.96664), Cone (R2 = 0.71474), and Cylinder (R2 = 0.07178) are positively correlated with AZ (Fig. 4b). Trunk heights of Cuboid (R2 = 0.90109), Spheroid (R2 = 0.48754), and Ellipsoid (R2 = 0.43103) are negatively correlated with AZ. For every tree canopy morphology, the distribution of min and max AZ is irregular. When the trunk height is 0.0 m, AZ ranges from 163.63 m2 (Ellipsoid) to 377.36 m2 (Cylinder); trunk height is 0.5 m, AZ ranges from 164.13 m2 (Ellipsoid) to 450.25 m2 (Cone); trunk height is 1.0 m, AZ ranges from 162.37 m2 (Ellipsoid) to 499.05 m2 (Cone); trunk height is 1.5 m, AZ ranges from 157.36 m2 (Cuboid) to 507.28 m2 (Cone); trunk height is 2.0 m, AZ ranges from 142.14 m2 (Cuboid) ~ 601.64 m2 (Cone); trunk height is 2.5 m, AZ ranges from 95.06 m2 (Cuboid) to 556.33 m2 (Cone); trunk height is 3.0 m, AZ ranges from 24.47 m2 (Cuboid) to 544.26 m2 (Cone); trunk height is 3.5 m, AZ ranges from 0 m2 (Cuboid) to 615.00 m2 (Cone); trunk height is 4.0 m, AZ ranges from 0 m2 (Cuboid) to 587.33 m2 (Cone).
Influence of plant spacing on wind velocity. Wind velocity distribution maps show that with the increase of plant spacing, the corresponding IFwind value also changes. Plant spacing of Inverted cone (R2 = 0.20524), Spheroid (R2 = 0.18993), and Cone (R2 = 0.03381) are positively correlated with IFwind values (Fig. 5a). Plant spacing of Ellipsoid (R2 = 0.60740), Cylinder (R2 = 0.11324), and Cuboid (R2 = 0.03384) are negatively correlated with IFwind values. For every tree canopy morphology, the distribution of min and max IFwind is irregular. When the plant spacing is 3 m, IFwind ranges from 7.06 (Ellipsoid) to 18.16 (Cylinder); plant spacing is 6 m, IFwind ranges from 8.77 (Ellipsoid) to 24.22 (Cylinder); plant spacing is 9 m, IFwind ranges from 7.51 (Ellipsoid) to 20.81 (Cylinder); plant spacing is 12 m, IFwind ranges from 7.36 (Ellipsoid) to 19.89 (Cylinder); plant spacing is 15 m, IFwind ranges from 6.52 (Ellipsoid) to 19.42 (Cylinder); plant spacing is 18 m, IFwind ranges from 5.38 (Ellipsoid) to 19.32 (Cylinder); plant spacing is 21 m, IFwind ranges from 5.84 (Ellipsoid) to 19.01 (Cylinder).
Wind velocity distribution maps show that a two-tailed feature appears in downwind deceleration zones. The AZ increased first and then decreased with the increase of planting spacing. Plant spacing of Cylinder (R2 = 0.83393), Spheroid (R2 = 0.56241), Inverted cone (R2 = 0.33146), Cone (R2 = 0.24663), and Cuboid (R2 = 0.11994) are positively correlated with AZ (Fig. 5b). Plant spacing of Ellipsoid (R2 = 0.33901) are negatively correlated with AZ. For every tree canopy morphology, the distribution of min and max AZ is irregular. When the plant spacing is 3 m, AZ ranges from 201.35 m2 (Ellipsoid) to 367.82 m2 (Cylinder); plant spacing is 6 m, AZ ranges from 269.25 m2 (Cuboid) to 461.37 m2 (Cylinder); plant spacing is 9 m, AZ ranges from 243.10 m2 (Ellipsoid) to 478.14 m2 (Cylinder); plant spacing is 12 m, AZ ranges from 241.17 m2 (Ellipsoid) to 506.31 m2 (Cylinder); plant spacing is 15 m, AZ ranges from 212.48 m2 (Ellipsoid) to 529.94 m2 (Cylinder); plant spacing is 18 m, AZ ranges from 182.06 m2 (Ellipsoid) to 547.07 m2 (Cylinder); plant spacing is 21 m, AZ ranges from 188.62 m2 (Ellipsoid) to 540.79 m2 (Cylinder).
In order to concretize and digitally present the wind environment at the pedestrian-level, this paper conducted a numerical simulation study on six canopy morphologies of trees in multi-scenarios. It is found that the changes of wind velocity and downwind deceleration zones influence by trees. The IFwind and AZ values of trees with varying crown width, trunk height and plant spacing have obvious differences.
The influence of plants on the pedestrian level wind environment is the focus of scholars8,9,10,11,50,51,52. Visualization and parameterization of tree canopy morphologies is the basic setting of these researches53,54. In this paper, the simulation experiments of six canopy morphologies have enriched the types of similar numerical simulation of tree canopy shape. The wind environment simulation research on the whole green space shows the overall situation of wind environment15, 20, but the wind environment of "single tree", the basic element of green space, is still an important topic. The numerical characteristics of single tree wind environment need to be solved fundamentally for the diversified planting forms of greening. It is necessary to numeralize the wind environment of the tree, so as to further quantify the influence of the tree on wind velocity. Based on previous studies, this paper designed three scenarios (S1, S2, and S3), including 144 sub-scenarios, to simulate the pedestrian-level wind environment of individual trees and coupled planting. Wind velocity distribution map shows 10 sub-zones (Fig. 2), all of which are downwind deceleration zones27. According to the area of the sub-zones and the corresponding weight, the IFwind of the scenario can be calculated. This is different from the expression in previous studies, which used the multiple of tree height to represent the length of wind speed zone, while this paper used the area to represent27. "IFwind" is a new term proposed in this paper, which is intended to explain the influence of trees on wind velocity reduction in a quantitative way. In the experiment, the IFwind of six canopy morphologies changed with the varying of crown width, trunk height and plant spacing.
The focus of this paper is to use PHOENICS to simulate the effects of crown width (S1), trunk height (S2) and plant spacing (S3) on wind velocity. To interpret, the findings suggest that for the same tree canopy morphology, the crown width had a strong positive correlation with both IFwind and AZ. With the increase of tree crown width, the corresponding IFwind and AZ increased (Fig. 6a). Compared with previous studies, it is found that the influence of different canopy size on wind velocity is of more practical significance, thus supporting the selection of suitable crown width according to different wind speed reduction needs, which is more targeted. In addition, the trunk height is negatively correlated with the IFwind value, which is the same as the research result of Zhao et al. "There is a significant negative correlation between the trunk height from the ground and the wind environment"55, but the Inverted Cone canopy morphology in this paper is positively correlated with the IFwind value (Fig. 4a), which can be used as a supplement to this theory. The correlation between trunk height and AZ value was observed in two cases: the positive correlations are Cone, Inverted Cone, and Ellipsoid; the negative correlations are Spheroid, Cylinder, and Cuboid. However, whether the correlation is positive or negative, the maximum value of AZ appears in the median range of the trunk height (Fig. 6b): Spheroid (1.5m), Cone (3.5m), Inverted Cone (3.5m), Cylinder (2.0m), Ellipsoid (2.5m), and Cuboid (0.5m); The minimum value of AZ appears when the plant spacing is the largest or the smallest: Spheroid (4.0m), Cone (0.0m), Inverted Cone (0.0m), Cylinder (0.0m), Ellipsoid (3.5m), and Cuboid (4m). The Numerical simulation results of crown width and trunk height same to the results of Hosseinzadeh and Keshmiri’s51 viewpoint "Younger trees with crowns closer to the ground mitigate wind more. However, older trees with wider crowns are able to decrease wind more.” It also coincides with to the discussion of Huang et al.19 "the trunk height affects significantly the flow". Finally, the correlation between plant spacing and IFwind value is also in two cases: Spheroid, Cone, and Inverted Cone are positively correlated with IFwind; Cylinder, Ellipsoid, and Cuboid are negatively correlated with IFwind. The reason is caused by the influence of the wind environment generated by the canopy morphologies. However, they have a common feature, that is, the maximum value of IFwind appears in the median range of plant spacing: Spheroid (9m), Cone (6m), Inverted Cone (15m), Cylinder (6m),
| a - IFwind with crown width | |
| s - IFwind with trunk height | |
| c- IFwind with plant spacing | |
| Figure 6. The linear regression model of the IFwind with AZ | |
Ellipsoid (6m), and Cuboid (12m) (Fig. 6c). Most of the minimum values of IFwind occur when the plant spacing is closest or farthest: Spheroid (3m), Cone (3m), Inverted Cone (3m), Cylinder (3m), Ellipsoid (18m), and Cuboid (21m). Plant spacing is positively correlated with AZ values, except for Ellipsoid.
Although this paper presupposes 10 downwind deceleration zones, only Cuboid's trunk height at 0.0 m, 0.5 m, and 1.0 m includes all zones. The simulation results of other scenarios include only 1–9 zones. The findings seems indicate that no matter how many zones are included in the sub-scenario, these zones are continuous, and there is no disconnection of zones’ serial number. Moreover, in most cases, the area of these zones is progressively reduced as the serial number increases. When Cuboid had trunk heights of 0.0 m, 0.5 m, and 1.0 m, their ability to reduce wind velocity reached 87.6%, exceeding that of Mayaud et al.35 stated "that wind velocity can be reduced by up to 70% in the lee of vegetation", which may explained by the differences in inlet wind speed and canopy details.
This paper focuses on the influences of crown width (S1), trunk height (S2) and plant spacing (S3) on wind velocity of single trees in pedestrian-level. The relationship between the varying parameters and the IFwind/AZ values was discussed. So is there a correlation between the AZ and IFwind? To further validate this question, a linear analysis was performed. The test results reveal that except for the trunk height and plant spacing scenarios of
| a - crown width |
| b - trunk height |
| c- plant spacing |
| Figure 7. The linear regression model of the IFwind with AZ |
Cylinder, the AZ is positively correlated with IFwind in other scenarios (Fig. 7). Which provides additional evidence on the IFwind / AZ by trees.
Based on the above analysis, we can draw the following results: For most scenarios, the larger the canopy width, the larger its IFwind and AZ values; In case of the trunk height and planting spacing scenarios, both the IFwind and AZ have the maximum values, and after the maximum value appears, there is a trend of gradual decrease. The results show that the varying of crown width (S1), trunk height (S2) and plant spacing (S3) all have an influence on wind velocity, and the maximum and minimum values of IFwind and AZ as well as their changing trends are simulated. The influence of crown width on wind velocity is less influenced and limited by canopy morphology. However, the influence of trunk height and plant spacing on wind velocity is limited by crown morphology. Our research results enrich the canopy morphologies setting for numerical simulation of tree’s wind environment, and the influence of crown width, trunk height and plant spacing on wind environment also strongly prove our initial hypothesis. The conclusion of this study provides a reference for the diversified planting design of today.
This research examines the influence of trees on wind environment t in pedestrian-level. By integrating the theories of CFD simulation method and trees benefits, six canopy morphology spatial models are proposed and validated. The PHOENICS is used for numerical simulation (144 scenarios), and the data are analyzed using Photoshop and linear regression model. It is found that crown width, trunk height, and plant spacing influence on wind velocity. Furthermore, our findings suggest that IFwind and AZ can represent the influence value of trees on wind velocity. Because the crown width, trunk height and plant spacing all have an influence on the two indicators. With the findings, our study makes several theoretical and practical contributions.
Firstly, the influence of trees on wind is linearly correlated with the varying crown width, trunk height and plant spacing. The influence of tree on wind velocity can be expressed by IFwind and AZ, where IFwind value indicates the wind reducing ability of tree; AZ value shows the area of downwind deceleration zones. This result enriches the wind environment theory of trees. More importantly, the crown morphology affects the tree’s influence on wind velocity. Because there were varying degrees of positive and negative linear correlations between IFwind and trunk height / plant spacing, and between AZ and trunk height / plant spacing. This finding to modify the detail theory of the negative linear correlation between trunk height / plant spacing and wind velocity. Except for the scenarios of Cylinder’s trunk height and plant spacing, the AZ values in other scenarios where the crown width, trunk height and plant spacing are all positively correlated with the IFwind values. Additionally, the findings indicates that no matter how many downwind deceleration zones are included in the scenarios, these zones are continuous, and there is no disconnection of zones’ serial number. Moreover, in most cases, the area of these zones is progressively reduced as the serial number increases.
We propose a new targeted wind velocity measurement system of trees developed using PHOENICS. The framework and the numerical simulation in this paper are intended to support and guide future studies of wind comfort and wind safety of trees in pedestrian-level, and to contribute to improved wind environmental quality in urban areas through reasonable tree planting. Our research results also provide relevant data reference for landscape architects and urban planners, and can provide numerical reference for the reasonably use of tree species with their IFwind and AZ. Which is of great practical significance for building a comfortable and healthy urban wind environment, creating a liveable urban ecological environment and achieving sustainable urban development. The numerical simulation experiment is an exploratory attempt. The simulation of wind velocity of tree is carried out in a ideal laboratory environment. There are some differences between the experimental results and the real environment. Therefore, in the follow-up study, it is necessary to modify and improve the simulation data combined with the field measured data.
Competing interests
The authors declare no competing interests.
Funding
The research was supported by Research Fund project of Liaoning Provincial Department of Education (JYTMS20231295), Social Science Research Fund project of Liaoning Province (L23BDJ002), University level Research Fund project of Shenyang Agricultural University (X2022005).
Author Contribution
L.F. and Y.Z. wrote the main manuscript text, and H. Z. J. prepared figures. All authors reviewed the manuscript.
Data Availability
All data generated or analysed during this study are included in this article (and its supplementary information files).
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Table 1. Parameters of tree for simulation
|
Scenarios |
Parameters of tree (m) |
Tree canopy morphologies |
|||||
|
Spheroid |
Cone |
Inverted Cone |
Cylinder |
Ellipsoid |
Cuboid |
||
|
S1 |
Height |
8 |
|||||
|
Trunk height |
2 |
||||||
|
DBH |
0.25 |
||||||
|
Crown width |
1, 2, 3, 4, 5, 6, 7, 8 |
0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4 |
|||||
|
S2 |
Height |
6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10 |
|||||
|
Trunk height |
0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4 |
||||||
|
DBH |
0.25 |
||||||
|
Crown width |
6 |
3 |
|||||
|
S3 |
Height |
8 |
|||||
|
Trunk height |
2 |
||||||
|
Plant spacing of two trees |
3, 6, 9, 12, 15, 18, 21 |
||||||
|
DBH |
0.25 |
||||||
|
Crown width |
6 |
3 |
|||||
Table 2. Downwind deceleration zones
|
Zones |
Z0 |
Z1 |
Z2 |
Z3 |
Z4 |
Z5 |
Z6 |
Z7 |
Z8 |
Z9 |
|
Wind velocity (m/s) |
3.12 -3.44 |
2.81 -3.12 |
2.50 -2.81 |
2.19 -2.50 |
1.88 -2.19 |
1.56 -1.88 |
1.25 -1.56 |
0.94 -1.25 |
0.62 -0.94 |
0.31 -0.62 |
|
Colors in wind velocity distribution map |
|
|||||||||
|
Weight |
1.516% |
2.035% |
2.841% |
4.008% |
5.638% |
7.876% |
10.941% |
15.154% |
20.991% |
29.000% |
No competing interests reported.