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Article

Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China

1
School of Civil Engineering, Taizhou University, Jiaojiang, Taizhou 318000, China
2
State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China
3
Key Laboratory of Intelligent Lifeline Protection and Emergency Technology for Resident Aty, Wenzhou University of Technology, Wenzhou 150080, China
4
School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Submission received: 21 April 2022 / Revised: 15 May 2022 / Accepted: 19 May 2022 / Published: 21 May 2022
(This article belongs to the Special Issue Advances in Computational Fluid Dynamics: Methods and Applications)

Abstract

:
The southeast coastal region of China is frequently affected by typhoons. The observation station was chosen to be located on the roof of Wenzhou University’s architectural engineering building to collect real-time wind speed data during the landfalling of Typhoon Morakot to investigate the properties of the near-ground wind field of typhoons. The turbulence characteristics of the near-ground wind and its variation with time intervals are analyzed on the basis of real-time measured data. The results show that the turbulence intensity only changes with the mean wind speed under relatively low wind speeds. The gust factors exhibit a scattered distribution under low wind speeds and tend to cluster together when the wind speed exceeds 8 m/s. With increasing time intervals, the turbulence intensity and the gust factor gradually decrease. The relationship between turbulence intensity and gust factor is obtained by the measured data and then compared with the empirical formulas. The peak factor remains constant while the mean wind speed changes, but diminish as the time intervals rise. The turbulence integral scale of typhoons slightly increases with the increasing mean wind speed, and its value falls between 70 and 150.

1. Introduction

Wind-induced structure damage has received numerous attentions [1]. Wind disasters present larger secondary effects, a wide range of influences, and considerable adverse effects on social development [2,3]. It is critical to investigate the wind properties to improve the resistance wind performance of buildings and provide guidance for wind-resistant design [4].

1.1. Computational Fluid Dynamics

Wind tunnel tests, computational fluid dynamics (CFD), and field measurements can all be used to explore wind effects on structures. CFD is now used as a supplement to traditional wind engineering methodologies (wind tunnel tests and field measurements), such as the large-eddy simulation (LES) approach [5,6,7]. Daniels et al. [8] employed LES and wind tunnel tests to conduct verification research on the wind effect of urban super high-rise buildings. The variable wind loading predicted by LES on a building model agreed well with wind tunnel tests utilizing a new inflow turbulence generation method [9]. Huang et al. [10] used CFD to predict wind loads and wind flows around a building. Tamura et al. [11] investigated turbulent boundary layers over a smooth and rough two-dimensional hill using LES, followed by a discussion on the applicability of turbulence models. Cao et al. [12] used LES to analyze the turbulent boundary layer over two-dimensional hills with two different mountain types and roughness conditions, and they verified the numerical analysis by comparing experimental wind tunnel data. Hu et al. [13] used numerical simulation to propose a new kind of turbulent kinetic energy profile as the inlet boundary condition based on the SST K-ω turbulence model and analyzed the wind speed characteristics at different terrain heights. Daemei investigated a method for improving the drag coefficient performance of an aerodynamic modification [14,15]. Many researchers have used the CFD method to investigate the effect of terrain on wind properties at a bridge and obtained some significant results [16,17,18]. The CFD method was used to simulate the wind field characteristics at the deep gorge bridge site with high altitude and considerable temperature changes [16]. Peng et al. [19] investigated the wind interference characteristics of six two-row buildings at various distances (including Sx and Sy) by combining wind tunnel tests and numerical simulations. Using the CFD method, Deleon and Yassin et al. [20,21] examined the effect of complicated terrain on atmospheric turbulence flow. Although numerical simulations can yield good results, field measurements are still essential for understanding wind characteristics.

1.2. Field Measurements

Field measurements are widely accepted as a reliable way of investigating wind characteristics such as wind speed, peak factor, and turbulence profiles. Li et al. [5] presented an analysis and discussion of wind effects on a high skyscraper during the passage of Typhoon Nida. The coherence of spectral power density and wind velocities were explored using wind records [22]. Li et al. summarized the wind field parameters during Typhoon “Fung-Wong.” [23]. The variable characteristics of the wind speed profiles and related pulsation parameters were investigated by a measurement system established on a mountain [24]. Dai et al. [25] analyzed the wind parameters of the Typhoon Bailu and discovered that the logarithmic model was more compatible with the recorded wind speed profile than other generally described profiles. A complete investigation of wind characteristics was conducted based on records during 10 typhoons [26]. He et al. [27] explored the data recorded during Typhoon Haima passage and calculated the wind-induced pressures and structural responses of the 600 m high PingAn Finance Center (PAFC) in Shenzhen. Wang et al. [28] examined wind field characteristics of Typhoon Morakot and revealed that the autocorrelation coefficient reduced as the time interval increased. The wind properties of several typhoons, including the peak factor and turbulence intensity, were statistically examined [29,30]. The full-scale wind field parameters were estimated and described using record data from a bridge’s structural health monitoring system [31]. Wang et al. [32] studied the characteristics of wind and discovered that the integral turbulence scales increased with the height and time intervals.
In this paper, up to 42 h of wind speed data from Typhoon Morakot was obtained by using two WJ3 anemometers installed on the roof of a building at Wenzhou University. Then, the wind characteristics were studied near the ground surface. The findings can be utilized as a data reference in the prevention of wind disasters and to expand the wind database in this region.

2. Introduction of Measurement Procedure and Method

2.1. Observation Site and Measurement Procedure

2.1.1. Introduction of Typhoon Morakot

As the eighth tropical storm of 2009, Typhoon Morakot formed over the western Pacific Ocean on 4 August and strengthened into a typhoon the next day. It made landfall around 23:00 on 7 August in Hualien County, Taiwan, with a maximum wind speed of force 13 (40 m/s). At 18:00 on 9 August, this typhoon made landfall in Fujian Province, China, and the maximum wind speed near the center at landfall was force 12 (33 m/s). On the evening of 9 August, the typhoon weakened to a tropical storm. Typhoon Morakot is characterized by great intensity, lengthy duration of influence, a large affected area, and slow translation speed. The path of Typhoon Morakot is plotted in Figure 1.

2.1.2. Introduction of Monitoring System

Wenzhou is located on the southeastern coast of mainland China, and a large number of typhoon formations in the northwest Pacific make landfall or affect Wenzhou yearly. Data show [33] that the number of typhoons making landfall in China increases yearly, with an average of 6.6 per year. The field measurement site is located on roof of the architectural engineering building at Wenzhou University. The height of the surrounding buildings is approximately 8 m. The location is bordered on the east and south by hills, and on the west and north by broad plains. The area is classified as a city suburb, and its roughness category is Class III according to the Chinese standard [34]. To avoid wind interference, two WJ-3 anemometers were installed on the 9 m high straight poles on the roof of the architectural engineering building. The horizontal distance and the height above the ground of two straight poles are 17 m and 30 m, respectively. The wind speed data were collected concurrently by the two anemometers, with a sample frequency of 20 Hz. Figure 2 depicts the anemometer configuration [28]:

2.2. Data Processing Method

The measured data up to 42 h were collected, beginning at 22:00 on 9 August 2009 and ending at 12:00 on 11 August 2009. In accordance with the specification [34], the sample was separated into 10 min standard time intervals.

2.2.1. Mean Wind Speed and Wind Direction

There are two time series in the date: wind speed and wind direction. The two components of horizontal wind speed are ux(t) and uy(t), calculated by Formulas (1) and (2) [35,36], respectively:
u y ( t ) = u ( t ) sin ϕ ( t )
u x ( t ) = u ( t ) cos ϕ ( t )
With a standard time interval of 10 min, the mean wind speed U and the horizontal wind direction φ are determined using the vector decomposition method, that is, Formulas (3) and (4) [35], respectively:
U = u x ( t ) ¯ 2 + u y ( t ) ¯ 2
cos φ = u x ( t ) ¯ / U
where u x ( t ) ¯ and u y ( t ) ¯ are the 10 min average wind speed of u x ( t ) and u y ( t ) , respectively.

2.2.2. Turbulence Intensity

Turbulence intensity is a scale characterizing the strength of atmospheric turbulence. This scale is calculated by dividing the standard deviation of variable wind speed by the longitudinal wind speed averaged during a time interval, i.e., [35]:
I i = σ i U ( i = u , v )
where σ i is the root mean square of the sum of fluctuating wind speeds u ( t ) ˜ , v ( t ) ˜ ; u ( t ) ˜ and v ( t ) ˜ are the longitudinal and lateral components of the fluctuating wind speed, respectively.
The fluctuating wind speed is calculated using Formulas (6) and (7) [36]:
u ( t ) ˜ = u x ( t ) cos φ + u y ( t ) sin φ U
v ( t ) ˜ = u x ( t ) sin φ + u y ( t ) cos φ
The longitudinal turbulence intensity is influenced by the time interval, and the longitudinal turbulence intensity over a certain time interval is [37]:
S D u ( T , t ) = i = 1 n u i 2 ( t ) / ( N 1 ) / U ( T )
where u i is the longitudinal fluctuating wind speed, T is 1 h, and U(T) is the one-hour averaged wind speed; t is the specific time interval variable, which is 1–3600 s in this paper; N = T/t.

2.2.3. Gust Factor

The gust factor is one of the indicators to measure the fluctuation degree of natural wind, similar to turbulence intensity. It is determined as the ratio of the peak wind speed averaged over the gust duration t g to the mean wind speed averaged over the average time interval at the height z [36]:
G u ( t g ) = 1 + max ( u ( t g ¯ ) ) U
G v ( t g ) = max ( v ( t g ¯ ) ) / U
where max ( u ( t g ) ¯ ) and max ( v ( t g ) ¯ ) are the peak wind speed of the longitudinal and lateral fluctuating wind averaged over the gust duration t g  tg, respectively.

2.2.4. Relationship between Turbulence Intensity and Gust Factor

The turbulence intensity and gust factor are dependent. Based on typhoon record data, Ishizaki [38] derived the empirical link between the turbulence intensity gust factor, which is written as Formula (11):
G u = 1 + a I u b ln ( T / t g )
where T is the time interval, and tg is the gust duration. Ishizaki [38] proposed values of a = 0.5 and b = 1.0.
Based on the collected data, Choi et al. [39] updated Formula (11) and proposed a = 0.62 and b = 1.27. Cao et al. [40] fitted Formula (11) using the collected data of Typhoon Maemi, and the results obtained after fitting were a = 0.5 and b = 1.15.

2.2.5. Peak Factor

The peak factor represents the instantaneous intensity of fluctuating wind. It can also be expressed by the peak factor, as shown in Formula (12) [37]:
g u = ( U ^ t g U ) / σ u
where σ u is the standard deviation of the longitudinal fluctuating wind speed, and U ^ t g is the maximum wind speed averaged over the time interval t g .

2.2.6. Turbulence Integral Scale

The energetic eddies are evaluated as a research topic in wind engineering considering turbulence as a flow formed by the superposition of eddies of different scales. Kaimal [41] used the turbulence integral scale to characterize the spatial scale of turbulence, and its definition is as follows:
L i = 1 σ i 2 0 R i 1 i 2 ( x ) d x
where L i is the turbulence integral scale of the fluctuating wind speed in the u, v, and w directions; R i 1 i 2 ( x ) is the correlation function of the synchronous fluctuating wind speed at two points in space.
To obtain the formula of turbulence integral scale, Formula (7) is simplified based on Taylor’s hypothesis:
L i = U σ i 2 0 R i ( τ ) d τ
where L i is the turbulence integral scale of the fluctuating wind speed in the u, v, and w directions; R i ( τ ) is the autocorrelation function of the fluctuating wind speed.

3. Results

3.1. Mean Wind Speed and Direction

In this paper, wind direction is measured clockwise, with due north having a wind direction of 0°. Figure 3a,b show the 10 min averaged wind speed and direction against time curves at the east and west measuring points, respectively. The figures reveal that the two curves exhibit a close trend. The mean wind speed increased with time beginning at 22:00 on 9 August 2009 and reached a peak value at 14:00 on 10 August 2009. The maximum wind speeds were 11.75 and 10.99 m/s at the east and west measuring points, respectively. Afterward, the mean wind speed dropped over time. At roughly 14:00, the wind direction changed sharply. Figure 3a illustrates that the wind speed changed significantly at this time, indicating the gradual approach of the typhoon to the measuring point, and then the wind direction gradually changed to a large degree. Moreover, the wind speed of the two measuring points demonstrates that the mean wind speed at the east measuring point is slightly larger than that at the west measuring point, with a difference of 0.76 m/s at the peak point. The wind direction at the east measuring point is greater in degrees than that at the west measuring point, with a maximum difference of 22.87° between them.

3.2. Turbulence Intensity

Figure 4 shows the relationship between the turbulence intensity at the east and the west measuring points with time. The results show that the curves between turbulence intensity in the longitudinal or lateral direction and the mean wind speed at the two measuring points exhibit a close trend. When the wind speed is less than 8 m/s, the longitudinal and lateral turbulence intensities decrease with the increasing wind speed. The reduction rate, however, drops until the wind speed surpasses 8 m/s, at which point the longitudinal and lateral turbulence intensities no longer change with the mean wind speed. At measuring point 1 and 2, the ratios of longitudinal turbulence intensity to lateral turbulence intensity are 1:0.92 and 1:0.90, respectively. Wang et al. [42] studied Typhoon Meari and found that the ratio of longitudinal turbulence intensity to lateral turbulence intensity at a height of 40 m was 1:0.9, which is close to the results of the current study.
Figure 5 shows the variation in the longitudinal and lateral turbulence intensities with the time interval at measuring point 1 under different wind speeds to study the influence of the time interval on turbulence intensity. The results show that the turbulence intensity reduces as the wind speed increases. The turbulence intensity reduces gradually as thre time interval changes, and the reduction rate rises. Ultimately, the longitudinal and lateral turbulence intensities approach 0.2 and 0.1, respectively.

3.3. Gust Factor

Figure 6 shows the relationship between the longitudinal and lateral gust factors and the mean wind speed at the two measuring points. The results show that the curves between the longitudinal and lateral gust factors and the mean wind speed at the two measuring points exhibit a close trend. When the wind speed is less than 8 m/s, the distribution of the gust factors is relatively scattered. The gust factors tend to cluster together when the wind speed surpasses 8 m/s. The distribution of lateral gust factors is generally more scattered compared to that of longitudinal gust factors, and the longitudinal gust factor is greater than the lateral gust factor. At the east and the west measuring points, the longitudinal and lateral gust factors have mean values of 1.94/2.00 and 0.88/0.97, respectively.
The change in the gust factor is affected by the length of the time interval. A short time interval tg affects max ( u ( t g ) ¯ ) and max ( v ( t g ) ¯ ) , and a long time interval T affects U. This paper uses T = 3600 s based on the research findings of Ashcroft [43] and Krayer et al. [44] to study the impact of short time intervals on the gust factor. Figure 7 depicts the variation in the longitudinal and lateral gust factors with time intervals under different wind speeds. The results reveal that the gust factor decreases with the increase in the time interval. The longitudinal and lateral gust factors eventually approach 1.5 and 0, respectively. Under the same wind speed, the longitudinal gust factor is usually much bigger than the lateral gust factor.

3.4. Relationship between Turbulence Intensity and Gust Factor

Figure 8 depicts the link between the longitudinal turbulence intensity and the gust factor at the east and west measuring points. The gust factor becomes large with an increasingly scattered distribution with rising turbulence intensity. Formula (11) was used to create the curve that fits the measured data. The east measuring point has fitting parameters a = 0.66 and b = 1.30, and the west measuring point has fitting parameters a = 0.69 and b = 1.32. The comparison of the empirical curves of Ishizaki [38], Choi [39], and Cao et al. [40] reveals that the results obtained using the empirical formula of Choi [39] are more consistent with the measured data in this paper.

3.5. Peak Factor

Figure 9 shows the variation in the peak factor with the 10 min averaged wind speed under a time interval of 3 s at measuring point 1. The results show that the peak factor remains constant as the mean wind speed changes, which is consistent with the research findings of Wang et al. [42]. The long-time interval T = 3600 s is taken, and the length of the short time interval tg is changed to study the peak factor and to investigate the influence of the time interval variation on the peak factor. Figure 10 reveals the fluctuation in the peak factor with the time interval under different wind speeds. The results demonstrate that the peak factor decreases with the increase in the time interval, and the peak factor substantially varies under different wind speeds. The comparison of the measured results with the empirical results of Durst [45] reveals the following: the peak factor is greater than that in the empirical result of Durst under low wind speeds and short and long time intervals; the variation in the measured peak factor with the time interval is smaller than that in the empirical test of Durst under high wind speeds.

3.6. Turbulence Integral Scale

Turbulence integral scale is related to the terrain roughness length, the height above the ground, the mean wind speed, and the climatic conditions. Figure 11a and Figure 12a depict the correlation between the longitudinal and lateral turbulence integral scales and the 10 min averaged wind speed. The figures show that as the mean wind speed increases, the longitudinal and lateral turbulence integral scales rise, though not dramatically. This phenomenon may be due to the rapidly broken-down large-scale eddies in the near-ground layer into a series of small-scale eddies, thus resulting in a fluctuating wind field. The integral turbulence scales generally tend to cluster together in areas with low wind speeds; however, when the mean wind speed rises, the distribution becomes scattered. The probability density of the longitudinal and lateral turbulence integral scales is shown in Figure 11b and Figure 12b, respectively. Most integral turbulence scales fall between 70 and 150, and the distribution is relatively scattered with clear asymmetry, which has broadly matched the results of Wang et al. [42].

4. Discussion

Based on the wind speed data from two observation stations during the landfall of Typhoon Morakot, a detailed analysis of the near-surface turbulence characteristics of the typhoon and its variation with time intervals was carried out. Different wind speeds have a greater impact on the turbulence characteristics of typhoons near the surface layer. For the turbulence intensity, gust factor, and peak factor, the results obtained by applying the short and long time intervals are significantly different. The turbulence intensity, gust factor, and peak factor all decrease with the increasing time interval. Therefore, different calculation intervals have a greater impact on the turbulence characteristics in this area.
Furthermore, owing to the turbulence characteristics being influenced by elements such as measured height, surrounding landforms, and wind field types, the measured results of the typhoon’s near-surface wind characteristics vary by area. Therefore, numerous typhoon data along the southeast coast of China should be studied, common norms during multiple typhoons should be explored, and wind characteristics in various places should be enriched.

5. Conclusions

Based on the analysis of Typhoon Morakot in Wenzhou, the main findings of this study are summarized as follows:
(1)
The longitudinal and lateral turbulence vary with the wind speed. The ratios of longitudinal to lateral turbulence intensity at measuring points 1 and 2 are 1:0.92 and 1:0.90, respectively. The turbulence intensity gradually decreases, and the reduction rate rises with the change in the time interval. Ultimately, the longitudinal and lateral turbulence intensities approach 0.2 and 0.1, respectively.
(2)
The distribution of the gust factors is relatively scattered when the wind speed is less than 8 m/s. Conversely, they tend to cluster together with the wind speed exceeds 8 m/s. The distribution of lateral gust factors is more scattered compared to that of longitudinal gust factors, and the gust factor reduces as the time interval increases. Under the same wind speed, the longitudinal gust factor is much great than the lateral gust factor.
(3)
A curve is constructed to fit the measured data to obtain the link between the turbulence intensity and the gust factor. Measuring point 1 has fitting parameters a = 0.66 and b = 1.30, while measuring point 2 has fitting parameters a = 0.69 and b = 1.32.
(4)
When the time interval is 3 s, the observed peak factor remains constant as the mean wind speed fluctuates. The peak factor reduces with increased time intervals, and the peak factor substantially varies under different wind speeds.
(5)
With increasing mean wind speed, the longitudinal and lateral turbulence integral scales rise slightly. The turbulence integral scales generally tend to cluster together in areas with low wind speeds, but the distribution gradually becomes scattered as the mean wind speed increases.

Author Contributions

Conceptualization, Y.W. and Y.L.; methodology, Y.W.; software, Y.L.; validation, Y.L.; formal analysis, Y.W., B.F. and G.F.; investigation, C.Z.; resources, C.Z.; data curation, C.Z.; writing—original draft preparation, Y.W., X.W. and Y.L.; visualization, Y.L. and Q.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Zhejiang Province Natural Science Foundation Project (LY19E080022), the Natural Science Foundation of China (51508419, 51678455), and the Zhejiang Provincial Department of Education Project (The title of the Project: Wind Field Characteristic Monitoring and Wind Load Monitoring Research on Coastal Low-rise Buildings and the number: Y202147409).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Nomenclature

aa parameter of gust factor (11)
ba parameter of gust factor (11)
g u peak factor (12)
G u ( t g ) gust factor of the longitudinal direction (9)
G v ( t g ) gust factor of the lateral direction (10)
I i ( i = u , v ) turbulence intensity in the u, v directions (5)
L i turbulence integral scale of the fluctuating wind speed in i direction
max ( u ( t g ) ¯ ) maximum value of wind speed of the longitudinal fluctuating wind averaged over the gust duration tg (9)
max ( v ( t g ) ¯ ) maximum value of wind speed of the lateral fluctuating wind averaged over the gust duration tg (10)
Na parameter of the longitudinal turbulence intensity (8)
R i 1 i 2 ( x ) correlation function of the synchronous fluctuating wind speed (13)
R i ( τ ) autocorrelation function (14)
S D u ( T , t ) longitudinal turbulence intensity (8)
tthe specific time interval variable
tggust duration
Ttime interval
u ( t ) wind speed time series
ux(t)horizontal wind speed component in the x direction (1)
uy(t)horizontal wind speed component in the y direction (2)
u i longitudinal fluctuating wind speed (8)
u x ( t ) ¯ the 10 min average wind speed of u x ( t )
u y ( t ) ¯ the 10 min average wind speed of u y ( t )
u ( t ) ˜ the longitudinal component of the fluctuating wind speed (6)
Umean wind speed
U(T)one-hour averaged wind speed (8)
U ^ t g maximum wind speed averaged over the time interval t g (12)
v ( t ) ˜ the lateral component of the fluctuating wind speed (7)
φ wind direction
φvertical wind angle
ϕ ( t ) vertical wind direction angle time history
σ i root mean square of the sum of fluctuating wind speeds u ( t ) ˜ , v ( t ) ˜
σ u standard deviation of the longitudinal fluctuating wind speed (12)

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Figure 1. Route of Typhoon Morakot.
Figure 1. Route of Typhoon Morakot.
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Figure 2. Anemometers’ layout.
Figure 2. Anemometers’ layout.
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Figure 3. Variations in mean wind speed and wind direction with time: (a) Variation in mean wind speed with time; (b) Variation in wind direction with time.
Figure 3. Variations in mean wind speed and wind direction with time: (a) Variation in mean wind speed with time; (b) Variation in wind direction with time.
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Figure 4. Variation in turbulence intensity with mean wind speed: (a) Longitudinal turbulence intensity; (b) Lateral turbulence intensity.
Figure 4. Variation in turbulence intensity with mean wind speed: (a) Longitudinal turbulence intensity; (b) Lateral turbulence intensity.
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Figure 5. Variation in turbulence intensity with time interval: (a) Variation in longitudinal turbulence intensity with time interval; (b) Variation in lateral turbulent intensity with time interval.
Figure 5. Variation in turbulence intensity with time interval: (a) Variation in longitudinal turbulence intensity with time interval; (b) Variation in lateral turbulent intensity with time interval.
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Figure 6. Variation in gust factor with mean wind speed: (a) Longitudinal; (b) Lateral.
Figure 6. Variation in gust factor with mean wind speed: (a) Longitudinal; (b) Lateral.
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Figure 7. Variation in gust factor with time interval: (a) Variation in longitudinal gust factor with time interval; (b) Variation in lateral gust factor with time interval.
Figure 7. Variation in gust factor with time interval: (a) Variation in longitudinal gust factor with time interval; (b) Variation in lateral gust factor with time interval.
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Figure 8. Variation in turbulence intensity with mean wind speed: (a) East and (b) West.
Figure 8. Variation in turbulence intensity with mean wind speed: (a) East and (b) West.
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Figure 9. Variation in peak factor with mean wind speed.
Figure 9. Variation in peak factor with mean wind speed.
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Figure 10. Variation in peak factor with time interval.
Figure 10. Variation in peak factor with time interval.
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Figure 11. Longitudinal turbulence integral scale: variation with wind speed and distribution: (a) Variation in longitudinal turbulence integral scale with wind speed; (b) Distribution of longitudinal turbulence integral scales.
Figure 11. Longitudinal turbulence integral scale: variation with wind speed and distribution: (a) Variation in longitudinal turbulence integral scale with wind speed; (b) Distribution of longitudinal turbulence integral scales.
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Figure 12. Lateral turbulence integral scale: variation with wind speed and distribution: (a) Variation in lateral turbulence integral scale with wind speed; (b) Distribution of lateral turbulence integral scales.
Figure 12. Lateral turbulence integral scale: variation with wind speed and distribution: (a) Variation in lateral turbulence integral scale with wind speed; (b) Distribution of lateral turbulence integral scales.
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Wang, Y.; Li, Y.; Zhang, C.; Wang, X.; Fan, G.; Qi, Q.; Fu, B. Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China. Appl. Sci. 2022, 12, 5218. https://0-doi-org.brum.beds.ac.uk/10.3390/app12105218

AMA Style

Wang Y, Li Y, Zhang C, Wang X, Fan G, Qi Q, Fu B. Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China. Applied Sciences. 2022; 12(10):5218. https://0-doi-org.brum.beds.ac.uk/10.3390/app12105218

Chicago/Turabian Style

Wang, Yanru, Yongguang Li, Chuanxiong Zhang, Xu Wang, Guangyu Fan, Qianqian Qi, and Bin Fu. 2022. "Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China" Applied Sciences 12, no. 10: 5218. https://0-doi-org.brum.beds.ac.uk/10.3390/app12105218

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