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Journal of Resources and Ecology >

2023 , Vol. 14 >Issue 4: 706 - 716

DOI: https://doi.org/10.5814/j.issn.1674-764x.2023.04.003

Environmental Management of Mines

CFD Simulation of Wind Field Characteristics in Mining Areas: A Case Study of the Xinxing Coal Mine in Wuhai City, Inner Mongolia, China

  • QI Haoran , 1 ,
  • WANG Jingxue , 1, * ,
  • ZHAO Tingning , 1, * ,
  • HU Ping 1, 2 ,
  • LI Feng 3 ,
  • WANG Jinghua 4 ,
  • ZHANG Yan 1 ,
  • YAN Lei 4
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  • 1. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
  • 2. Yunnan Infrastructure Investment Co., Ltd, Kunming 650217, China
  • 3. Wuhai Xinxing Coal Co. Ltd, Wuhai, Inner Mongolia 016000, China
  • 4. School of Technology, Beijing Forestry University, Beijing 100083, China
*WANG Jingxue, E-mail: ;
ZHAO Tingning, E-mail:

QI Haoran, E-mail:

Received date: 2022-12-14

  Accepted date: 2023-04-30

  Online published: 2023-07-14

Supported by

Key Research and Development Program of China(2017YFC0504403)

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Abstract

The large amount of coal mining activity in the arid region of northwestern China leads to vegetation degradation and a greater probability of strong winds. The characteristics of windy fields directly affect the sand-dust transportation process. To capture the wind field characteristics in a mining area, numerical simulation of computational fluid dynamics (CFD) was conducted using the Reynolds-Averaged Naiver-Stokes (RANS) turbulence model based on open source software OpenFOAM，taking Xinxing Coal Mine in Wuhai, Inner Mongolia, China as an example. The field test results at several observation points in the mining area were adopted for validating the numerical results. The distributions of mean wind speed and turbulence kinetic energy in the mining area for the W, NW and E wind directions are discussed. The results show that the mean wind speed distributions of the mining area are greatly affected by the raised mountains or hills on the eastern and western sides. When the approaching wind comes from the W and E directions, the mean wind speed is relatively low behind the raised terrain due to the wake effect. However, the magnitude of turbulent kinetic energy shows a relatively large value. The wake effect of mountains or hills is less pronounced as the approaching wind comes from the NW direction. The reduced mean wind speed and increased turbulence kinetic energy are also observed near the low-lying pits in the mining area. The results of this study can provide a theoretical basis for the prevention and control of aeolian sand and its construction in mining areas.

Key words： CFD simulation; Reynolds-averaged Naiver-Stokes (RANS) turbulence model; mean wind speed; turbulence kinetic energy

Cite this article

QI Haoran , WANG Jingxue , ZHAO Tingning , HU Ping , LI Feng , WANG Jinghua , ZHANG Yan , YAN Lei . CFD Simulation of Wind Field Characteristics in Mining Areas: A Case Study of the Xinxing Coal Mine in Wuhai City, Inner Mongolia, China[J]. Journal of Resources and Ecology, 2023 , 14(4) : 706 -716 . DOI: 10.5814/j.issn.1674-764x.2023.04.003

1 Introduction

In recent years, the mining of coal resources has effectively promoted economic development, but it has also caused many environmental problems in mining areas (Zhong et al., 2022). Wuhai City in Inner Mongolia is an important coal production base in the arid region of northwestern China. The large amount of coal mining activity in Wuhai City has caused a series of ecological and environmental problems in
the local area, such as vegetation degradation, heavy dust, sandstorms, land desertification and others, which urgently need to be resolved (Zhao et al., 2018). Compared with urban areas and plain areas with high vegetation cover, the terrain in mining areas is more complex with low vegetation cover, which leads to a greater probability of strong winds. The wind field in a mining area directly affects sand movement in the surrounding area. Examining the wind field characteristics in a mining area can optimize the local living and working environment, improve work safety, and provide theoretical support for the planning and design of the mining area (Ding, 2017).
To study the characteristics of the wind field in the atmospheric boundary layer, many scholars at home and abroad have carried out related research by means of field measurements (Xie et al., 2022; Xue, 2022), wind tunnel tests (Flay et al., 2019), and numerical simulations (Li et al., 2016). Field measurement is a direct and effective method for monitoring the actual wind field. For example, Flay et al. (2019) carried out full-scale measurements of wind speed using 3-cup wind speed sensors over complex terrain within the Belmont Regional Park near Wellington, which is representative of much of New Zealand’s hilly country. Xie et al. (2022) measured the wind speeds at different heights around bays using wind-cube laser radar. Xue (2022) conducted a statistical analysis of measured instantaneous wind speeds in the complex terrain around a bridge. By artificially generating and controlling the air flow, wind tunnel testing is an effective method for aerodynamic research based on similarity principles (Yuan et al., 2021). For example, Flay et al. (2019) made a hill model with a scale of 1: 2000 and used single-wire hot film anemometer probes to measure the wind speed around the hill. Guo and Hu (2022) made an island model with a scale of 1:500 and used cobra probes to measure the wind speed. In recent years, CFD simulation has become a popular research method for studying airflow characteristics (Kataoka et al., 2020). Based on field measurements and wind-tunnel modelling, Flay et al. (2019) used large eddy simulation (LES) to analyze the wind flow over the natural complex terrain of Belmont Hill, and demonstrated that the numerical simulations can closely match the measured results. Li et al. (2016) simulated the wind field characteristics around bridge sites with complex terrain in the southwestern mountainous areas of China, and discussed the blocking effect of mountains on airflow. Zhang (2019) investigated the effects of approaching wind speed, wind profile, and size of the terrain area on the formation of canyon wind through Reynolds-averaged Naiver-Stokes (RANS) numerical simulations. Considering that the wind field has a great influence on sand activity, many studies on the airflow field have been conducted to investigate the wind erosion and sand transportation characteristics in the local environment (Tan et al., 2016). For example, Tan et al. (2016) studied the three dimensional wind flow patterns of star-shaped dunes under different airflow effects by CFD simulations. Information on the wind flow of dunes provides new insights into the geomorphology and dynamics of complex sand dunes.
Although the research using CFD simulations to investigate the wind fields in complex terrain has been increasing, studies of the wind field characteristics in mining areas using high-precision digital elevation model (DEM) information are very rare. Considering the advantage of CFD simulation in capturing the three-dimensional flow field pattern, this study adopted CFD simulation using the RANS turbulence model to analyze the wind field characteristics in Xinxing Coal Mine area in Wuhai, Inner Mongolia, China, based on open source software OpenFOAM (Menter et al., 2001). The field test results at several observation points were adopted for validating the numerical results. The wind field characteristics within the mine area for different wind directions were simulated. The distributions of mean wind speed and turbulence kinetic energy in different horizontal and vertical planes were analyzed. The influence of the surrounding mountains on the wind field in the mine area was also analyzed. The results of this study can provide a basis for taking measures to prevent and control the local strong winds and sand-dust movement in the mining area.

2 Overview of the study area

Wuhai City (39°09′00″-39°31′12″N, 103°21′36″-107°03′00″E), Inner Mongolia is located in the arid region of northwestern China. It borders the west of the Ordos Plateau, the southeastern boundary of the Ulan Buh desert, the Alashan League and the Ikezhao city. It is an emerging industrial city in Inner Mongolia and one of the important cities in Yellow River Economic Belt (Hu, 2020). The climate in Wuhai is arid year-round, with little precipitation.
As shown in Fig. 1, the study area is located in Xinxing Coal Mine in Wuhai, Inner Mongolia, which is an open pit mine with a length (L) of 3 km in the east-west direction and a width (W) of 3.6 km in the north-south direction. Thus, the total area is about 10.6 km2. The maximum altitude difference (H) is about 320 m across the whole area. The lowest point of Xinxing Coal Mine in the height dimension is defined as Z=0. There are several important functional areas around Xinxing Coal Mine, including the coal transportation road (No. 1), large pit (No. 2), coal gangue area (No. 3), coal storage yard (No. 4) and spoil area (No. 5). The characteristics of the underlying surface and locations from the lowest point are described in Table 1. There are also several characteristic functional locations in Xinxing Coal Mine, labeled I, II and III in Fig. 1. I is a large-scale pit in the central part of the mine that is near the coal storage site; II is a hill surrounded by small pits in the northern part of the mine; and III is a flat office area.
Table 1 Description of important functional areas in Xinxing Coal Mine
No. Functional area Underlying surface characteristics Elevations relative to the lowest point (Z=0)
1 Coal transportation road Vegetation cover low to medium, sub-clay, wet Z1=120 m
2 Large pit Breccia crust, dry Z2=60 m
3 Coal gangue area Gravel, dry Z3=131 m
4 Coal storage yard Round gravel, wet Z4=152 m
5 Spoil area Rock block, dry Z5=148 m
Fig. 1 Geographical location of the study area

Note: L is the east-west length and W is the north-south length.

Figure 2 shows the topographic characteristics around Xinxing Coal Mine with a surrounding area of 350 km2 which is indicated by a yellow box. Considering that the topographic characteristics around the mining area in different wind directions are different, 12 sectors indicated by north (N), north-northeast (NNE), east-northeast (ENE), east (E), east-southeast (ESE), south-southeast (SSE), south (S), south-southwest (SSW), west-south west (WSW), west (W), west-northwest (WNW) and north-northwest (NNW) were considered in this study. The corresponding aerody namic roughness length z0 in each sector can be calculated based on the Smith-Carson equation as follows (Smith and Carson, 1977):
${{z}_{0}}=0.2\times \frac{{{(\text{ }\!\!\Delta\!\!\text{ }H)}^{2}}}{{{L}_{0}}}$
Fig. 2 Topographic characteristics around the Xinxing Coal Mine and aerodynamic roughness length z0
where△H is the topographic altitude difference within 16 km2, and L0 is a length parameter related to terrain (Li et al., 2006). According to the relief degree of the land surface around the study area, the terrain grade was divided into medium relief and mountain relief, and the value of L0 is 10 km. The aerodynamic roughness length z0 in the above sectors are denoted in Fig. 2.

3 Wind field measurements in the mining area

To monitor the wind field in the Xinxing Coal Mine, seven observation masts were set up at a height of 2m above the local ground in functional areas 1-5 in the mining area between February and July 2018, as shown in Fig. 1 (Hu, 2020). Cup anemometers were mounted on the masts at heights of 0.1 m, 0.2 m, 0.5 m, 1 m and 2 m. The wind speed was collected every 30 s and the total measurement duration of each height was 20 min. According to the recorded data in the mining area, the average wind speed and wind direction at the height of 2 m were obtained by processing the data from five observation points in the whole mining area, which are presented in Fig. 3. The results show that the occurrence frequency of winds in the W, NW and E directions are higher than in the other directions.
Fig. 3 Wind rose diagram of average wind speed at a height of 2 m in the mining area
Figure 4 shows the mean wind speed profiles in functional areas 1-5 for the approaching wind in the W direction based on the field measurement data. The measured results were fitted with the power-law model which is expressed as:
$\frac{{{U}_{z}}}{{{U}_{ref}}}={{\left( \frac{z}{{{z}_{ref}}} \right)}^{\alpha }}$
Fig. 4 Mean wind speed profiles in the different functional areas for the approaching wind in the W direction
where α represents the power-law index; z is height, zref is the reference height which equals 2 m from the ground, Uz and Uref represent the wind speeds at the heights of z and zref, respectively (Blocken et al., 2015; Sang, 2021). The observations of wind speed data at different heights reveal that the variations in wind speed are generally consistent with the power-law model.

4 CFD simulation of the wind field in the mining area

4.1 Geometric model, computational domain, boundary conditions and meshes

In this study, the DEM data with resolution of 20 cm in the mining area were obtained by aerial photography using an unmanned aerial vehicle (UAV). To simulate the flow characteristics in low-lying pits in the mining area precisely, the surrounding terrains were considered, including large areas of mountains and plains as shown in the yellow box in Fig. 2, which is the approach adopted in the study of Chen et al (2022). The information for the surrounding terrains with resolution of 15 m was obtained from the website of Geospatial Data Cloud (http://www.gscloud.cn/). Figure 5 shows the three dimensional geometric model of the study area, which was built using Global Mapper and Auto-desk 3DS Max software (Yi, 2021).
Fig. 5 Three dimensional geometric model
Figure 6 shows the schematic diagram of the computational domain and boundary conditions for the approaching wind in the W direction. The computational domain is 5 L long, 5 W wide and 4 H high, where L, W and H are the dimensions of the mining area as described in Section 2. The distance from the inlet boundary to the mining area is 1.5 L. Considering the approaching aerodynamic roughness length z0 in the different wind directions as shown in Fig. 2, the atmospheric boundary layer mean wind speed profiles with power-law index values of α=0.2 and 0.25 were imposed at the inlet boundary. The outlet boundary with gauge pressure of 0 was imposed at a downstream distance of 2.5 L from the mining area. The symmetric boundary conditions were applied on two side boundaries at a distance of 2 W from the target area. The symmetric boundary condition was also imposed on the top boundary. A no-slip wall was prescribed on the bottom boundary of the computational domain (Tang, 2017; Yi, 2021).
Fig. 6 Schematic diagram of the computational domain and boundary conditions for the approaching wind in the W direction

Note: H is the altitude difference in Xinxing Coal Mine.

The computational mesh was generated by mesh generators including blockMesh and snappyHexMesh. BlockMesh was used to build the computational mesh with blocks, while SnappyHexMesh was adopted to refine the terrain surface mesh around the Xinxing Coal Mine area on the basis of the base mesh. To examine the grid independency, three groups of meshes including the coarse, normal and fine types were generated. Figure 7 shows the computational normal mesh for the approaching wind in the W wind. The fundamental grid size in the x, y and z directions was 4.19 m×4.18 m×4.26 m, and the grid near the target area was refined with a refinement level of 4-5 to improve the accuracy of the solution. The total number of meshes in the whole domain was close to 8 million. Keeping the grid strategy unchanged, the corresponding grid size was adjusted to half for the coarse type and double for the fine type. The total numbers of grids were about 4 and 13 million for the coarse and fine types, respectively.
Fig. 7 Computational normal mesh for the approaching wind in the W direction

4.2 Turbulence model

The RANS turbulence model can produce the average flow field with high efficiency and it was therefore adopted to simulate the wind field in the mining area. The average governing equations are expressed as:
$\frac{\partial \left( \rho {{{\bar{u}}}_{i}} \right)}{\partial {{x}_{i}}}=0$
$\frac{\partial \left( \rho {{{\bar{u}}}_{i}} \right)}{\partial t}+\frac{\partial \left( \rho {{{\bar{u}}}_{j}}{{{\bar{u}}}_{i}} \right)}{\partial {{x}_{j}}}=-\frac{\partial \bar{p}}{\partial {{x}_{j}}}+\frac{\partial }{\partial {{x}_{i}}}\left[ \mu \left( \frac{\partial {{{\bar{u}}}_{i}}}{\partial {{x}_{j}}}+\frac{\partial {{{\bar{u}}}_{j}}}{\partial {{x}_{i}}} \right) \right]+\frac{\partial {{\tau }_{ij}}}{\partial {{x}_{j}}}$
where $\bar{u}_{i}$ is the average wind velocity in the i-th direction, $\bar{u}_{j}$ is the average wind velocity in the j-th direction, xi and xj are the computed coordinate directions in the i and j directions, $\bar{p}$ is average pressure; ρ is the density of the fluid, μ is dynamic viscosity, and τij is the time-averaged Reynolds stress which can be expressed as:
${{\tau }_{ij}}=-\rho \left( \overline{{{u}_{i}}{{u}_{j}}}-{{{\bar{u}}}_{i}}{{{\bar{u}}}_{j}} \right)$
For the closure of the governing equations, τij must be modelled. The k-ω SST turbulence model, which combines the advantages of k-ω model in the near-wall region and the k-ԑ model in the far-field calculation, was adopted in this study (Wang, 2020). The following additional equations were used to calculate turbulent kinetic energy k and turbulence specific dissipation rate ω:
$\frac{\partial \left( \rho {{{\bar{u}}}_{i}}k \right)}{\partial {{x}_{i}}}={{\tau }_{ij}}\frac{\partial {{{\bar{u}}}_{i}}}{\partial {{x}_{j}}}-{{\beta }^{*}}\rho \omega k+\frac{\partial }{\partial {{x}_{j}}}\left[ \left( \mu +{{\sigma }_{k}}{{\mu }_{t}} \right)\frac{\partial k}{\partial {{x}_{j}}} \right]$
$\begin{align} & \frac{\partial \left( \rho {{{\bar{u}}}_{i}}\omega \right)}{\partial {{x}_{i}}}=\frac{\gamma }{{{v}_{t}}}{{\tau }_{ij}}\frac{\partial {{{\bar{u}}}_{i}}}{\partial {{x}_{j}}}-\beta \rho {{\omega }^{2}}+ \\ & \frac{\partial }{\partial {{x}_{j}}}\left[ \left( \mu +{{\sigma }_{\omega }}{{\mu }_{t}} \right)\frac{\partial \omega }{\partial {{x}_{j}}} \right]+2(1-F)\rho {{\sigma }_{{{\omega }^{2}}}}\frac{1}{\omega }\frac{\partial k}{\partial {{x}_{j}}}\frac{\partial \omega }{\partial {{x}_{j}}} \\ \end{align}$
where μt is turbulence viscosity, F is a blending function to combine the k-ω and k-ԑ models, and the other model coefficients for the SST model collectively called $\phi$ can be determined based on the following equation:
$\phi ={{\phi }_{1}}F+{{\phi }_{2}}(1-F)$
where ${{\phi }_{1}}$ and ${{\phi }_{2}}$ are the corresponding parameters for the k-ω and k-ԑ models, respectively. The default values shown in Table 2 were adopted in this study.
Table 2 Default model coefficients for the k-ω SST model
Parameters β* β γ σk σω
${{\phi }_{1}}$ 0.09 0.075 0.556 0.85 0.5
${{\phi }_{2}}$ 0.09 0.0828 0.44 1 0.856

4.3 Grid independence verification

In order to verify the independence of the grids, the results obtained from the coarse, normal and fine meshes were compared comprehensively. Taking the examples of three functional areas (Nos. 1, 4 and 5), Fig. 8 shows the comparison of the mean wind speed profiles obtained from the three grid types. Note that the simulation results for different meshes are quite similar. The numerical results for other locations have the same tendency and are not shown here for brevity. Thus, the results from the fine mesh were finally selected for the subsequent analysis.
Fig. 8 Grid independence verification with the examples of three functional areas (No. 1, 4 and 5) in the mining area

4.4 Validation of CFD simulation results

To validate the accuracy of the CFD simulation results, the CFD results were compared with the measured results as discussed in Section 3. Figure 9 shows the comparison of the measured and simulated results at each functional area for the approaching wind in the W direction. The changing tendencies of simulated mean wind speed with height at the coal transportation road (No.1), large pit (No. 2), coal gangue area (No. 3) and spoil area (No. 5) are roughly the same as the measured data. Regarding the coal storage area (No. 4), the simulated mean wind speed is relatively greater than the measured data at the same height. This difference is because the terrain on the west side of the coal storage yard is high and steep, and the local vortex there is more complex than the vortices at other locations. Figure 10 shows the quantitative comparison of relative wind speeds between the field measurements and numerical simulations for the approaching wind in the W direction at the different functional areas. Most of the scatters obtained from the CFD simulation are within the range of a ±20% difference from the measured data, except for the coal gangue area (No. 4). These results indicated that the CFD results are generally acceptable.
Fig. 9 Mean wind speed profiles obtained from field measurements and numerical simulations for the approaching wind in the W direction. (a) Coal transportation road; (b) large pit; (c) coal gangue area; (d) coal storage yard; (e) spoil area
Fig. 10 Quantitative comparison of relative wind speeds between field measurements and numerical simulations for the approaching wind in the W direction

5 Results and discussion

In this section, the distributions of mean wind speed and turbulent kinetic energy in the mining area for the approaching winds in the W, WN and E directions are discussed. The simulation results for the different wind directions are presented successively in the following sections.

5.1 Wind field characteristics for the approaching wind in the W direction

Figure 11 shows the horizontal distributions of normalized mean wind speed and turbulence kinetic energy at different heights for approaching wind in the W direction. The referenced mean wind speed Uref was adopted for normalization. Two heights with values of Z=122 m and 160 m, which are the average heights of functional areas No.1-5 and the whole mining area, respectively, were selected for presentation. As shown in Fig. 11a and 11b, the mean wind speeds increase with increasing height. Due to the wake effect of the raised terrain in the southwestern part of the mining area, the mean wind speed in the northern region of the mining area is generally greater than that in the south. However, the magnitude of turbulent kinetic energy shows the opposite tendency. Taking the coal transportation road (No. 1) as an example, the mean wind speed is relatively lower and the turbulence kinetic energy is relatively higher than those of the other regions.
Fig. 11 Horizontal distributions of normalized mean wind speed and turbulence kinetic energy at different heights for the approaching wind in the W direction. (a) Z=122 m; (b) Z=160 m

Note: The dashed lines denote the positions of vertical planes through locations I-III. Z=0 refers to the area with a relative elevation of 0 within the study area. Z=122 and Z=160 are the heights of the two selected cloud maps.

Considering the topographic characteristics of locations I-III as described in the overview of the study area in Section 2, the vertical distributions of mean wind speed and turbulent kinetic energy of the above three vertical planes were analyzed. Figure 12 shows the vertical distributions of the normalized mean wind speed and turbulence kinetic energy of locations I-III in the along-wind direction for the approaching wind in the W direction. As shown in Fig. 12a, the mean wind speed is low near location I, which is a large-scale pit close to the coal storage site, because of the wake effect. The corresponding turbulence kinetic energy shows a slight increase near the bottom of the pit. Additionally, the significant wake region denoted by a low mean wind speed and a higher turbulence kinetic energy can be seen behind the hill in the upstream of location I. Regarding the flow fields around locations II and III as shown in Fig. 12b and 12c, no sudden changes in the mean wind speed or turbulence kinetic energy are observed.
Fig. 12 Vertical distributions of the normalized mean wind speed and turbulence kinetic energy in the along-wind direction for the approaching wind in the W direction. (a) Location I; (b) Location II; (c) Location III

5.2 Wind field characteristics for the approaching wind in the NW direction

The horizontal distributions of the normalized mean wind speed and turbulence kinetic energy at heights of Z=122 m and 160 m for the approaching wind in the NW direction are presented in Fig. 13a and 13b, respectively. Considering that the northwestern part of the mining area in the upstream region is mainly flat, the mean wind speed in the mining area is relatively stable and the overall magnitude is greater compared to those in the W direction as shown in Fig. 11. In the northeast and southwest of the mining area, the turbulent kinetic energy is relatively higher due to the influences of the mountains and hills.
Fig. 13 Horizontal distributions of the normalized mean wind speed and turbulence kinetic energy at different heights for the approaching wind in the NW direction. (a) Z=122 m; (b) Z=160 m

Note: The dashed lines denote the positions of vertical planes through locations I-III. Z=0 refers to the area with a relative elevation of 0 within the study area. Z=122 and Z=160 are the heights of the two selected cloud maps.

Figure 14 shows the vertical distributions of the normalized mean wind speed and turbulence kinetic energy of locations I-III in the along-wind direction for the approaching wind in the NW direction. In Fig. 14a, the mean wind speed of location I is higher than the corresponding value for the W wind direction as shown in Fig.12a. As Fig. 14b shows, the mean wind speed and turbulence kinetic energy around location II have large values due to the variations of the surrounding terrain. The distributions of mean wind speed and turbulent kinetic energy near location Ⅲ, which is a flat office area, are relatively stable, as indicated in Fig. 14c.
Fig. 14 Vertical distributions of the normalized mean wind speed and turbulence kinetic energy in the along-wind direction for the approaching wind in the NW direction. (a) Location I; (b) Location II; (c) Location III

5.3 Wind field characteristics for the approaching wind in the E direction

Figure 15 shows the horizontal distributions of the normalized mean wind speed and turbulence kinetic energy at heights of Z=122 m and 160 m for the approaching wind in the E direction. Compared with the W and NW wind dire ctions as shown in Figs. 11 and 13, the mean wind speed in the whole mining area is at a lower level due to the influence of the mountains on the eastern side (Fig. 15). Clearly, the mean wind speeds around the large pit (No. 2) and the spoil area (No. 5), which are quite close to the eastern mountains, are very low. In general, the mean wind speed and turbulent kinetic energy fluctuate strongly throughout the whole region.
Fig. 15 Horizontal distributions of the normalized mean wind speed and turbulence kinetic energy at different heights for the approaching wind in the E direction. (a) Z=122 m; (b) Z=160 m

Note: The dashed lines denote the positions of vertical planes through locations I-III. Z=0 refers to the area with a relative elevation of 0 within the study area. Z=122 and Z=160 are the heights of the two selected cloud maps.

The vertical distributions of the normalized mean wind speed and turbulence kinetic energy of locations I~III in the along-wind direction for the approaching wind in the E direction are presented in Fig. 16. Similar to the flow field distribution for the W direction (Fig. 11a), a low mean wind speed and high turbulence kinetic energy are observed in location Ⅰ (Fig. 16a). Due to the wake effect of raised terrain in the eastern region of the mining area, the mean wind speed is relatively low and the turbulence kinetic energy is relatively high at location Ⅱ (Fig. 16b). The mean wind speed near location Ⅲ (Fig. 16c) is obviously lower than those for the W and NW wind directions, while the turbulent kinetic energy does not change very much.
Fig. 16 Vertical distributions of the normalized mean wind speed and turbulence kinetic energy in the along-wind direction for the approaching wind in the E direction. (a) Location I; (b) Location II; (c) Location III

6 Conclusions

In this study, the CFD simulation was conducted and used to analyze the wind field characteristics of the Xinxing Mining Area in Wuhai City for three typical wind directions. The distributions of the mean wind speed and turbulence kinetic energy in specific horizontal and vertical planes are discussed comprehensively, and four main conclusions were obtained.
(1) The precise geometric model in the CFD simulation can be generated using the DEM data. The CFD results generally agree with the results of wind field measurements.
(2) When the approaching wind comes from the W direction, the mean wind speed in the northern region of the mining area is generally greater than that in the south due to the wake effect of raised terrain in the southwestern region of the mining area. However, the magnitude of turbulent kinetic energy shows the opposite tendency. The data show that the mean wind speed is low near the large-scale pit that is close to the coal storage site because of the wake effect.
(3) As the approaching wind comes from the NW direction, the terrain in the upstream region is mostly flat. As a result, the overall mean wind speed distribution in the mining area is relatively uniform and high, except for the areas with mountains or hills.
(4) For the approaching wind in the E direction, the mean wind speed in the whole mining area is at a lower level due to the influence of the mountains on the eastern side. Furthermore, the mean wind speed and turbulent kinetic energy show large fluctuations throughout the whole region.

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