A wind farm simulation method and apparatus

Abstract

This application provides a wind field simulation method and apparatus, comprising: performing a physical simulation of the wind field; performing a numerical simulation of the wind field; verifying the acceleration ratio of the three-dimensional average wind speed in different regions using the three-dimensional average wind speed in different regions; determining the three-dimensional average wind speed corresponding to the wind field simulation point; obtaining a cross-power spectrum matrix of the fluctuating wind speed based on the characteristics of the three-dimensional fluctuating wind speed in different regions; inputting the three-dimensional average wind speed corresponding to the wind field simulation point into the cross-power spectrum matrix of the fluctuating wind speed to obtain the three-dimensional fluctuating wind speed corresponding to the wind field simulation point; and superimposing the three-dimensional average wind speed and the three-dimensional fluctuating wind speed corresponding to the wind field simulation point to obtain the wind field corresponding to the wind field simulation point. This method can effectively reduce the cost required for wind field prediction and improve simulation accuracy.

Description

Technical Field

[0001] This application relates to the technical field of wind field simulation, and in particular to a wind field simulation method and apparatus. Background Technology

[0002] With the development of power grid construction, transmission lines need to cross typical mountainous terrains such as canyons and peaks. As long-span flexible structures, transmission lines are highly sensitive to wind loads. If the wind loads given by the atmospheric boundary layer wind field in existing design codes are used during the design process, the wind-induced response of transmission lines in mountainous terrain may be underestimated. Therefore, studying wind field simulation methods and parameter selection in different regions of mountainous terrain is of great significance for the wind-resistant design of transmission lines.

[0003] Existing technologies primarily employ field measurements, numerical wind field simulation, and physical wind field simulation to study the three-dimensional wind field characteristics under mountainous terrain. However, field measurements are time-consuming and difficult to arrange, and are mostly used as verification methods for numerical and physical wind field simulations. Physical wind field simulations are limited by factors such as the number of measurement points, typically only obtaining wind speed data at key locations in the model, making it difficult to reproduce the wind speed field under the entire mountainous terrain. To reproduce the entire mountainous terrain, a large number of measurement points need to be arranged, resulting in high setup costs. While large eddy simulation (LES), based on numerical wind field simulation, can obtain the time histories of fluctuating wind speeds at various points within the computational domain under mountainous terrain, its high computational cost remains a difficult technical problem to solve in LES.

[0004] Therefore, how to reduce the cost of wind field simulation is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, embodiments of this application provide a wind field simulation method and apparatus, which aim to reduce the cost of wind field simulation.

[0006] In a first aspect, embodiments of this application provide a wind field simulation method, including:

[0007] A physical simulation of the wind field was conducted to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0008] We conducted numerical simulations of the wind field to obtain the three-dimensional average wind speed acceleration ratio in different regions;

[0009] The acceleration ratio of the three-dimensional average wind speed in the different regions was verified using the three-dimensional average wind speed in the different regions.

[0010] In response to the verification result being passed, the three-dimensional average wind speed corresponding to the wind field simulation point is determined based on the location of the wind field simulation point. The three-dimensional average wind speed corresponding to the wind field simulation point is determined based on the acceleration ratio of the three-dimensional average wind speed in different regions.

[0011] Based on the characteristics of the three-dimensional fluctuating wind speed in the different regions, the cross power spectrum matrix of fluctuating wind speed is obtained;

[0012] Input the three-dimensional average wind speed corresponding to the wind field simulation point into the fluctuating wind speed cross power spectrum matrix to obtain the three-dimensional fluctuating wind speed corresponding to the wind field simulation point.

[0013] The wind field corresponding to the simulated wind field point is obtained by superimposing the three-dimensional average wind speed and the three-dimensional fluctuating wind speed.

[0014] Optionally, the step of performing wind field physics simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions includes:

[0015] A mountain model is established based on the mountain contour parameters and the model scale ratio;

[0016] The mountain model was placed in an atmospheric boundary layer wind tunnel for physical simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0017] Optionally, the step of performing wind field numerical simulation to obtain the three-dimensional average wind speed acceleration ratio in different regions includes:

[0018] Based on the mountain contour parameters, a mountain model with mesh division is drawn;

[0019] The meshed mountain model is input into the fluid dynamics calculation model to determine the mountain's inlet boundary conditions, wall functions, and turbulence model;

[0020] Using the entrance boundary conditions, wall functions, and turbulence model of the mountain, a numerical simulation of the wind field was conducted to obtain the three-dimensional average wind speed acceleration ratio in different regions.

[0021] Optionally, obtaining the cross-power spectrum matrix of fluctuating wind speed based on the characteristics of the three-dimensional fluctuating wind speed in the different regions includes:

[0022] Based on the three-dimensional fluctuating wind speed in the different regions, the fluctuating wind field characteristics are extracted, including turbulence intensity, turbulence integral scale, self-power spectrum function, spatial correlation function and point coherence function;

[0023] Based on the characteristics of the pulsating wind field, the cross-power spectrum matrix of the pulsating wind speed is obtained.

[0024] Optionally, the step of inputting the three-dimensional average wind speed corresponding to the wind field simulation point into the fluctuating wind speed cross-power spectrum matrix to obtain the three-dimensional fluctuating wind speed corresponding to the wind field simulation point includes:

[0025] Input the three-dimensional average wind speed corresponding to the wind field simulation point into the fluctuating wind speed cross power spectrum matrix to obtain the wind field simulation matrix;

[0026] After performing Joss decomposition on the wind field simulation matrix, the three-dimensional pulsating wind speed corresponding to the wind field simulation point is obtained according to the harmonic superposition method.

[0027] Secondly, embodiments of this application provide a wind field simulation device, comprising:

[0028] The physics simulation module is used to perform wind field physics simulations to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0029] The numerical simulation module is used to perform numerical simulations of wind fields and obtain the three-dimensional average wind speed acceleration ratio for different regions.

[0030] The verification module is used to verify the acceleration ratio of the three-dimensional average wind speed in the different regions using the three-dimensional average wind speed in the different regions.

[0031] The simulation averaging module is used to determine the three-dimensional average wind speed corresponding to the wind field simulation point based on the location of the wind field simulation point in response to the verification result being passed. The three-dimensional average wind speed corresponding to the wind field simulation point is determined according to the acceleration ratio of the three-dimensional average wind speed in different regions.

[0032] The matrix module is used to obtain the cross power spectrum matrix of the fluctuating wind speed based on the characteristics of the three-dimensional fluctuating wind speed in the different regions.

[0033] The simulation pulsation module is used to input the three-dimensional average wind speed corresponding to the wind field simulation point into the pulsating wind speed cross power spectrum matrix to obtain the three-dimensional pulsating wind speed corresponding to the wind field simulation point.

[0034] The overlay module is used to overlay the three-dimensional average wind speed and the three-dimensional fluctuating wind speed corresponding to the wind field simulation point to obtain the wind field corresponding to the wind field simulation point.

[0035] Optionally, the physical simulation module includes:

[0036] The model building unit is used to build a mountain model based on the mountain contour parameters and the model scale ratio;

[0037] The physical simulation unit is used to place the mountain model in an atmospheric boundary layer wind tunnel for physical simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0038] Optionally, the numerical simulation module includes:

[0039] The mesh drawing unit is used to draw a meshed mountain model based on the mountain contour parameters.

[0040] The first feature extraction unit is used to input the meshed mountain model into the fluid dynamics calculation model to determine the inlet boundary conditions, wall functions and turbulence model of the mountain.

[0041] The numerical simulation unit is used to perform wind field numerical simulation using the entrance boundary conditions, wall functions, and turbulence model of the mountain to obtain the three-dimensional average wind speed acceleration ratio of the different regions.

[0042] Thirdly, embodiments of this application provide an apparatus comprising a memory and a processor, the memory for storing instructions or code, and the processor for executing the instructions or code to cause the apparatus to perform the wind field simulation method described in any of the first aspects above.

[0043] Fourthly, embodiments of this application provide a computer storage medium storing code, wherein when the code is executed, a device running the code implements the wind field simulation method described in any of the first aspects above.

[0044] This application provides a wind field simulation method and apparatus. When executing the method, first, physical and numerical simulations of the wind field are performed. Then, the acceleration ratio of the three-dimensional average wind speed in different regions is verified using the three-dimensional average wind speed of those regions. Next, the three-dimensional average wind speed corresponding to the wind field simulation point is determined. Based on the characteristics of the three-dimensional fluctuating wind speed in different regions, a fluctuating wind speed cross-power spectrum matrix is ​​obtained. Then, the three-dimensional average wind speed corresponding to the wind field simulation point is input into the fluctuating wind speed cross-power spectrum matrix to obtain the three-dimensional fluctuating wind speed corresponding to the wind field simulation point. Finally, the three-dimensional average wind speed and the three-dimensional fluctuating wind speed corresponding to the wind field simulation point are superimposed to obtain the wind field corresponding to the wind field simulation point.

[0045] In this way, by obtaining the fluctuating wind speed characteristics and average wind speed at different measurement points through physical simulation, it can not only provide a basis for constructing the cross-power spectrum of fluctuating wind speed for spectral representation, but also serve as a verification method for wind speed numerical simulation. When the accuracy of wind speed numerical simulation can be guaranteed, it can also compensate for the shortcomings of physical simulation, such as low efficiency and limited measurement points in large-scale wind speed testing. Compared with traditional methods, it has advantages in computational efficiency and lower requirements for computer performance. While saving costs, it can effectively improve the accuracy of wind field simulation. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart of one method of the wind field simulation method provided in the embodiments of this application;

[0048] Figure 2 Another flowchart of the wind field simulation method provided in the embodiments of this application;

[0049] Figure 3 A schematic diagram of a three-dimensional twin-mountain model of the wind field simulation method provided in this application embodiment;

[0050] Figure 4 A comparison diagram of wind speed acceleration ratios at various measuring points in the wind field simulation method provided in the embodiments of this application;

[0051] Figure 5 A numerical simulation result of three-dimensional average wind speed along the line direction for the wind field simulation method provided in the embodiments of this application;

[0052] Figure 6 Figures showing the turbulence intensity and turbulence integral scale physical simulation results of the wind field simulation method provided in the embodiments of this application;

[0053] Figure 7 A three-dimensional fluctuating wind speed result diagram of the wind field simulation method provided in the embodiments of this application;

[0054] Figure 8 A three-dimensional wind speed result diagram of the wind field simulation method provided in the embodiments of this application;

[0055] Figure 9 This is a schematic diagram of a wind field simulation device provided in an embodiment of this application. Detailed Implementation

[0056] Existing research primarily employs field measurements, numerical wind field simulation, and physical wind field simulation to study the three-dimensional wind field characteristics under mountainous terrain. However, field measurements are time-consuming and difficult to arrange, and are mostly used as a verification method for numerical and physical wind field simulations. Physical wind field simulation is limited by factors such as the number of measurement points, and can usually only obtain wind speed data at key locations in the model, making it difficult to reproduce the wind speed field under the entire mountainous terrain. To reproduce the entire mountainous terrain, a large number of measurement points need to be arranged, resulting in high setup costs. While large eddy simulation (LES) based on wind field numerical simulation can obtain the time histories of fluctuating wind speeds at various points in the computational domain under mountainous terrain, its high computational cost remains a difficult technical problem to solve in LES.

[0057] The method provided in this application embodiment is executed by a computer device and is used to reduce the cost of wind field simulation.

[0058] Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] See Figure 1 , Figure 1 A flowchart of a wind field simulation method provided in this application embodiment includes:

[0060] Step S101: Perform wind field physical simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0061] The physical simulation of the wind field requires first determining several measurement points at key locations in the model; then, setting up equipment for physical wind field measurement at the measurement points; subsequently, obtaining the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different areas based on the measurement results of the measurement points; finally, the wind field of the entire mountainous area can be estimated based on the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different areas.

[0062] However, relying solely on this method for simulation typically only yields wind speed data at key locations within the model, making it difficult to reproduce the wind speed field across the entire mountainous terrain. Reproducing the entire mountainous terrain requires setting up numerous measurement points, which is costly.

[0063] Step S102: Perform a numerical simulation of the wind field to obtain the three-dimensional average wind speed acceleration ratio in different regions.

[0064] Numerical simulation of wind fields can begin by meshing the mountain model using mesh generation software; the meshing results are then input into a fluid dynamics calculation model; next, the inlet boundary conditions, wall functions, and turbulence model are determined; finally, numerical simulation of the flow field and wind speed is performed to obtain the three-dimensional average wind speed acceleration ratio in the mountainous area. However, relying solely on this method for simulation lacks validation of the calculation results.

[0065] Step S103: Use the three-dimensional average wind speed of the different regions to verify the acceleration ratio of the three-dimensional average wind speed of the different regions.

[0066] When a certain three-dimensional average wind speed and its acceleration ratio are located at the same point, they can be compared and calculated to verify the accuracy of the three-dimensional average wind speed acceleration ratio in step S102. Subsequent steps can only proceed if the verification is successful; otherwise, the above values ​​cannot be combined.

[0067] Step S104: In response to the verification result being passed, determine the three-dimensional average wind speed corresponding to the wind field simulation point based on the location of the wind field simulation point.

[0068] Wind field simulation points are specific points where wind field simulations need to be performed. Important nodes in mountainous areas that require wind field simulations can be pre-defined as wind field simulation points. The three-dimensional average wind speed corresponding to the wind field simulation point is determined based on the acceleration ratio of the three-dimensional average wind speed in the different regions.

[0069] Step S105: Based on the characteristics of the three-dimensional pulsating wind speed in the different regions, obtain the pulsating wind speed cross-power spectrum matrix.

[0070] The power spectrum of fluctuating wind speed reflects the distribution of wind speed energy in the frequency domain, and the von Karman power spectral density model is widely considered the most accurate functional expression. The Pwelch function can be used to extract the power spectrum of fluctuating wind speed, and the von Karman power spectral density model can be modified to obtain the target spectrum, ensuring that the target spectrum better reflects the power spectrum of fluctuating wind speed.

[0071] The pulsed wind speed cross-power spectrum matrix represents the information related to pulsed wind speed at any two points, including the self-power spectrum and spatial correlation. The diagonal elements are the self-power spectra of spatial points, and the remaining elements are the cross-power spectra of any two points.

[0072] As one possible implementation method, the characteristics of three-dimensional fluctuating wind speed include turbulence intensity, turbulence integral scale, self-power spectrum function, spatial correlation function, and point coherence function, as well as their values. Based on these characteristics, the cross-power spectrum matrix of fluctuating wind speed can be obtained.

[0073] Step S106: Input the three-dimensional average wind speed corresponding to the wind field simulation point into the pulsating wind speed cross power spectrum matrix to obtain the three-dimensional pulsating wind speed corresponding to the wind field simulation point.

[0074] After inputting the three-dimensional average wind speed corresponding to the wind field simulation point into the pulsating wind speed cross power spectrum matrix, Joss decomposition can be performed, and the three-dimensional pulsating wind speed can be simulated according to the harmonic superposition method to obtain the three-dimensional pulsating wind speed corresponding to the wind field simulation point.

[0075] Step S107: Superimpose the three-dimensional average wind speed and the three-dimensional fluctuating wind speed corresponding to the wind field simulation point to obtain the wind field corresponding to the wind field simulation point.

[0076] Superimposing the three-dimensional average wind speed and the three-dimensional fluctuating wind speed is essentially superimposing the results of processing the three-dimensional average wind speed acceleration ratio and the three-dimensional fluctuating wind speed. This combines the advantages of both physical and numerical wind field simulations, compensating for each other's shortcomings. This reduces costs on one hand and increases simulation accuracy on the other.

[0077] In summary, this embodiment uses physical simulation to obtain the fluctuating wind speed characteristics and average wind speed at measurement points in different regions. This not only provides a basis for constructing the cross-power spectrum of fluctuating wind speed for spectral representation methods but also serves as a verification method for wind speed numerical simulation. When the accuracy of wind speed numerical simulation can be guaranteed, it can also compensate for the shortcomings of physical simulation, such as low efficiency and limited measurement points, in large-scale wind speed testing. Compared with traditional methods, it has advantages in computational efficiency and lower requirements for computer performance. While saving costs, it can effectively improve the accuracy of wind field simulation.

[0078] In the embodiments of this application, the above Figure 1 There are multiple possible implementations of the steps described below, which will be introduced separately. It should be noted that the implementations given below are merely illustrative examples and do not represent all implementations of the embodiments of this application.

[0079] See Figure 2 The figure is a flowchart of another wind field simulation method provided in the embodiments of this application, including:

[0080] Step S201: Perform a wind field physical simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0081] As one possible implementation, the step of performing wind field physics simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions includes:

[0082] Step S2011: Establish a mountain model based on the mountain contour parameters and model scale ratio.

[0083] The contour parameters of the mountain can be determined by simplifying the geometric parameters of the terrain or by using the elevation information of the actual terrain. A mountain model is then created based on the contour parameters and a suitable model scale. Adjustments can be made according to the size of the wind tunnel cross-section. Furthermore, the mountain model should be made of suitable materials, and its surface should simulate the roughness of an actual mountain surface.

[0084] Step S2012: Place the mountain model in an atmospheric boundary layer wind tunnel for physical simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0085] As one possible implementation, the atmospheric boundary layer wind tunnel can be determined based on the terrain type and adjusted in the physical simulation to obtain the wind speed corresponding to that terrain type. The three-dimensional average wind speed in different regions can be expressed as... And three-dimensional pulsating wind speed (u, v, w).

[0086] For example, generating the wind field for a cosine-shaped double mountain requires determining the mountain profile parameters. See also Figure 3 , Figure 3This is a schematic diagram of a three-dimensional twin-mountain model for the wind field simulation method provided in this application embodiment. In the figure, the length of a single mountain model is L, the width is D, and the height is H, with values ​​of 600m, 300m, and 100m, respectively. Figure 3 The diagram in Figure 'a' indicates that the direction of the incoming flow is parallel to the length of the mountain model. The lines in the diagram represent the layout of the four-tower, three-line power transmission line under this mountainous terrain. Figure 3 The cross-sectional profile of a single mountain model in b can be described using a cosine function in the local coordinate system:

[0087]

[0088] Where z is the representation of the cross-sectional profile in the local coordinate system, D is the width of a single mountain model, H is the height of a single mountain model, and x is the x-coordinate of a point on the cross-sectional profile.

[0089] The model was scaled to 1:500. ABS plastic sheets were used as the skeleton, and synthetic fibers were fixed to the surface of the plastic sheets with paint to simulate the surface roughness of the actual mountain, thus completing the solid model of the mountain.

[0090] A physical model of the mountain was placed in an atmospheric boundary layer wind tunnel for wind speed physical simulation. A passive simulation method combining a spire vortex generator, baffles, and rough elements was used to simulate a Type B wind speed in the wind tunnel. The direction of the incoming airflow was determined to be parallel to the mountain range, and the basic wind speed at a height of 10m was determined to be 33m / s. During the experiment, a Cobra turbulence anemometer was used to collect longitudinal, lateral, and vertical three-dimensional wind speed time history data at each measuring point to obtain the average wind speed in the three directions. And the fluctuating wind speeds in three directions (u, v, w).

[0091] Step S202: Perform a numerical simulation of the wind field to obtain the three-dimensional average wind speed acceleration ratio in different regions.

[0092] As one possible implementation method, the mountain model can be meshed using mesh generation software; the mesh generation results can then be input into the fluid dynamics calculation model; the inlet boundary conditions, wall functions, and turbulence model can then be determined; finally, the flow field wind speed numerical simulation calculation can be performed to obtain the three-dimensional average wind speed acceleration ratio of the mountain area.

[0093] For example, a cosine-shaped double-mountain model is drawn using geometric modeling software, and then imported into mesh generation software for mesh generation. The computational domain size is selected as 8 times the mountain length × 6 times the mountain base diameter × 6 times the mountain height. It is found that the blockage ratio is less than 3%, which can satisfy the sufficient development of the incoming flow. The mesh generation results are input into the fluid dynamics calculation model. The inlet boundary condition is determined to be a Class B wind speed, the reference wind speed at a height of 10m is 33m / s, the wall function is determined to be a non-equilibrium wall function considering the pressure gradient, and the turbulence model is determined to be a Realizable k-ε model. Numerical simulation of the flow field and wind speed is performed based on the fluid dynamics calculation model. The three-dimensional average wind speed in the mountainous area is obtained through numerical simulation of wind speed.

[0094] See Figure 4 , Figure 4 A comparison diagram of wind speed acceleration ratios at various measuring points in the wind field simulation method provided in this application embodiment. Taking the y=0 profile coinciding with the transmission line as an example, Figure 4 The longitudinal (y-axis direction) average wind speeds at x = 0m, 75m, 150m, 225m, and 300m are given. Horizontal (x-axis) average wind speed And vertical (z-axis direction) The variation of average wind speed along altitude. The 3D average wind speed profile at the corresponding location on the other mountain in the twin-mountain model can be obtained through symmetry. The wind speed values ​​on the average wind speed profiles in each direction can be calculated according to the corresponding legend. The dashed line in the figure can be regarded as the zero-scale line of the wind speed profile at the corresponding measuring point. When the wind speed profile is to the right of the dashed line, the wind speed is positive; when it is to the left, the wind speed is negative. The positive directions of the longitudinal, lateral, and vertical wind speeds are respectively... Figure 3 In a, the positive directions of the y-axis, x-axis, and z-axis are the same. Figure 4 Similarly, a wind speed profile for landform type B is given as a reference, where the values ​​for both horizontal and vertical wind speeds are 0.

[0095] from Figure 4 As can be seen, due to the influence of the mountainous terrain, the three-dimensional average wind speed at the y=0 profile has changed significantly compared to the incoming flow: the longitudinal wind speed has produced varying degrees of acceleration near the ground, and this acceleration effect is stronger at the sides and the top of the mountain, with the maximum longitudinal wind speed increasing by more than 50% compared to the incoming flow; the lateral and vertical average wind speeds at various points along the profile are mostly not zero, showing a clear three-dimensional effect, with the maximum lateral and vertical wind speeds being about 16% of the incoming flow.

[0096] Step S203: Use the three-dimensional average wind speed of the different regions to verify the acceleration ratio of the three-dimensional average wind speed of the different regions.

[0097] For example, the average wind speed obtained from the decomposition of wind tunnel physical simulation results is used to verify the simulated three-dimensional average wind speed. The ratio of the longitudinal wind speed at the measuring point to the longitudinal wind speed at the corresponding height of the incoming flow is defined as the wind speed acceleration ratio.

[0098] See Figure 5 , Figure 5 The figure shows the numerical simulation results of the three-dimensional average wind speed along the line direction for the wind field simulation method provided in the embodiments of this application. Figure 5 The results comparing the wind speed acceleration ratios of physical and numerical simulations at typical measuring points along the y=0 profile are presented. Figure 5 As can be seen, the numerical simulation results of wind speed are very close to the physical simulation results of wind speed, and the accuracy of the above numerical simulation calculation results of wind speed can be guaranteed.

[0099] Step S204: In response to the verification result being passed, determine the three-dimensional average wind speed corresponding to the wind field simulation point based on the location of the wind field simulation point.

[0100] The above steps are similar to those in Example 1, and will not be repeated here.

[0101] Step S205: Based on the characteristics of the three-dimensional pulsating wind speed in the different regions, obtain the pulsating wind speed cross-power spectrum matrix.

[0102] As one possible implementation method, the characteristics of three-dimensional fluctuating wind speed include turbulence intensity, turbulence integral scale, self-power spectrum function, spatial correlation function and point coherence function, as well as their values.

[0103] Turbulence intensity is used to describe the intensity of wind speed fluctuations, and is usually defined as the ratio of the standard deviation of fluctuating wind speed over a basic time interval to the mean wind speed, i.e.

[0104]

[0105] In the formula, I i (z) represents the turbulence intensity at height z, σ i (z) is the root mean square of the fluctuating wind speed at height z. Let z be the average wind speed at height z, and u, v, and w represent the downwind, crosswind, and vertical wind directions, respectively.

[0106] The turbulence integral scale characterizes the average scale of turbulent vortices in the airflow, and its magnitude determines the influence range of the fluctuating wind. The turbulence integral scale can be calculated using the direct integration method of the autocorrelation function.

[0107]

[0108] In the formula, L i R is the turbulent integral scale for fluctuating wind speed i. iiLet σ be the autocovariance of the fluctuating wind speed i, τ be the time interval, and σ be the autocovariance of the fluctuating wind speed i. i Let i be the root mean square of the fluctuating wind speed i. This represents the average wind speed.

[0109] The spatial correlation function considers the spatial correlation of fluctuating winds for two synchronously measured fluctuating wind speed time histories, a and b. The calculation process of the spatial correlation function includes:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] In the formula, coh ab (f) is the spatial correlation function of the two fluctuating wind velocities, where f is the wind field frequency and N is the frequency. ij S is the number of segments in the power spectrum, k is the number of segments selected, and S a (f), S b (f) is the power spectrum of fluctuating wind speed. and These are the common and orthogonal spectra of the two fluctuating wind speeds, respectively, which can be obtained through Fourier transform of the corresponding autocovariance and crosscovariance functions, R. a (τ) is the autocovariance function, R ab (τ) is the cross-covariance function, τ is the time interval, t is the time information corresponding to the fluctuating wind speed, and a k (t) represents the fluctuating wind speed time history a during time interval t in segment k. k (t+τ) represents the time history of fluctuating wind speed after time τ, b k (t+τ) The fluctuating wind speed time history b during the k-th segment t+τ.

[0117] In addition, based on the calculated spatial correlation function, the Davenport attenuation coefficient model was used for fitting to examine the influence of the measuring point spacing D on the attenuation coefficient, including:

[0118]

[0119] In the formula, Let i be the spatial correlation function between points i and j in space. Let x be the attenuation coefficient. i ,yi ,z i ), (x j ,y j ,z j Let be the positions of two points i and j in space. Let be the average wind speed at points i and j in space. ε represents the wind direction.

[0120] The correlation of fluctuating wind speeds in different directions at the same point is described by the cross-power spectral density function. At the same point, neither the longitudinal nor vertical fluctuating wind speeds are correlated with the lateral fluctuating wind speeds; only the correlation between the longitudinal and vertical fluctuating wind speeds (w) needs to be considered, including:

[0121]

[0122]

[0123] In the formula, Coh uw (ω n ) is the point correlation function, u * This is the field friction velocity, σ u and σ w The root mean squares of the fluctuating wind speeds in the u and v directions are respectively, A uw L is a function representing heterogeneous correlation. u The turbulent integral scale is the sum of the fluctuating wind speed u. S represents the average wind speed. u (f), S w (f) represents the power spectrum of the fluctuating wind speed, where f is the wind field frequency.

[0124] Therefore, considering the complex mountainous terrain with N target points, the three-dimensional fluctuating wind speed cross-power spectrum matrix can be expressed as:

[0125]

[0126] In the formula, S(ω) represents the cross-power spectrum matrix, SYM indicates symmetry with the lower left matrix element, and S jk (ω) represents the three-dimensional cross-power spectrum matrix of fluctuating wind speeds at points j and k in the terrain. This is because the lateral fluctuating wind speed has a very weak correlation with the fluctuating wind speeds in the other two directions.

[0127] The above S jk (ω) can be expressed as:

[0128]

[0129]

[0130]

[0131]

[0132] In the formula, This represents the cross-power spectrum of fluctuating wind speeds in the same direction between points j and k. and Let j and k represent the self-power spectra of points j and k in the i-direction, respectively. This represents the correlation between the fluctuating wind speeds at points j and k in the same direction. and The correlation between the longitudinal and vertical fluctuating wind speeds at points j and k is easily proven.

[0133] See Figure 6 , Figure 6 The turbulence intensity and turbulence integral scale physical simulation results of the wind field simulation method provided in this application embodiment are shown in the figure, which presents the three-dimensional turbulence intensity I at the y=0 profile. i (i = u, v, w) and turbulent integral scale L i The result is (i = u, v, w). The power spectral density function of the fluctuating wind speed along the transmission line in this terrain is:

[0134]

[0135]

[0136] In the formula, S i (f) is the self-power spectral density function of the fluctuating wind speed in the i-direction, L i Let σ be the turbulent integral scale for fluctuating wind speed i. i Let be the root mean square value of the fluctuating wind speed i. Let f be the average wind speed and f be the frequency. The spatial correlation function can be measured by the attenuation coefficient. Experimental results show that the attenuation coefficient c in the x-direction is... xi (i = u, v, w) are respectively:

[0137]

[0138]

[0139]

[0140] In the formula, D x Let be the distance between the two points in the x-direction. The experimental result for the attenuation coefficient in the z-direction is: c zu =7.0, c zv =5.2, c zw =3.7.

[0141] Step S206: Input the three-dimensional average wind speed corresponding to the wind field simulation point into the pulsating wind speed cross power spectrum matrix to obtain the three-dimensional pulsating wind speed corresponding to the wind field simulation point.

[0142] The three-dimensional average wind speed corresponding to the wind field simulation point is input into the pulsating wind speed cross power spectrum matrix, and then Joss decomposition can be performed. The three-dimensional pulsating wind speed is then simulated according to the harmonic superposition method to obtain the three-dimensional pulsating wind speed corresponding to the wind field simulation point.

[0143] As one possible implementation method, Joss decomposition can be expressed by the following formula:

[0144] S(ω)=H(ω)H(ω) *T ;

[0145] In the formula, S(ω) is the cross-power spectrum matrix, H(ω) is the lower triangular matrix of the cross-power spectrum matrix after Joss decomposition, *T denotes the conjugate transpose, and H(ω) can be expressed as:

[0146]

[0147] In the formula, H 11 (ω), H 21 (ω), H 22 (ω), H m1 (ω), H m2 (ω) and H mm (ω) represents the element in the decomposed lower triangular matrix, with the subscript number indicating the row and column.

[0148] When simulating three-dimensional fluctuating wind speed using the harmonic superposition method, the fluctuating wind speed can be simulated in the three directions of u, v and w respectively, and the fluctuating wind speed time histories u(t), v(t) and w(t) in the three directions can be obtained.

[0149] This process requires two summations of trigonometric series, which can be calculated using the following formula:

[0150]

[0151] Δω=ω u / M;

[0152] ω l = (l-1)Δω;

[0153]

[0154] In the formula, u j (t) represents the time history of the j-th fluctuating wind speed, H jk (ω l ) represents the elements in matrix H, Δω represents the angular frequency increment, and ω uθ is the upper limit of the angular frequency cutoff. jk (ω l Let be the phase angle between points j and m. Let M be a random phase angle uniformly distributed in [0, 2π], M be the frequency fraction, and Im[·] and Re[·] represent taking the real part and taking the imaginary part, respectively.

[0155] The conditions that need to be met to use this method include:

[0156]

[0157] t = (r-1)Δt, r = 1, 2, ..., N t ;

[0158] In the formula, N t The fractions of time are represented by Δt, Δω, and M, where M represents the fraction of frequency. u To define the upper limit of the angular frequency cutoff and avoid data distortion, the time is divided into equal parts N. t The fraction M, including frequency, should satisfy N. t ≥2M.

[0159] Step S207: Superimpose the three-dimensional average wind speed and the three-dimensional fluctuating wind speed corresponding to the wind field simulation point to obtain the wind field corresponding to the wind field simulation point.

[0160] As one possible implementation method, superposition can be performed using the following formula:

[0161]

[0162]

[0163]

[0164] In the formula, The mean wind speeds are longitudinal, vertical, and lateral, respectively. They do not change with time and are only related to spatial location. x, y, and z represent the spatial location of a point, while u, w, and v represent the longitudinal, vertical, and lateral fluctuating wind speeds, respectively, which are functions of time and space.

[0165] For example, see Figure 7 , Figure 7 The three-dimensional fluctuating wind speed result diagram of the wind field simulation method provided in this application embodiment is the three-dimensional fluctuating wind speed result of the mountain top measuring point along the route x=150m, calculated using the harmonic superposition method. The three-dimensional average wind speed data for this measuring point obtained through wind speed numerical simulation are: longitudinal wind speed 35m / s, lateral wind speed 1.4m / s, and vertical wind speed 3.2m / s. Adding these two values ​​together yields the three-dimensional wind speed at this measuring point. Figure 8 As shown, Figure 8A three-dimensional wind speed result diagram of the wind field simulation method provided in the embodiments of this application.

[0166] In summary, this embodiment uses a scaled-down model to perform physical simulations of wind speed to obtain the fluctuating wind speed characteristics and average wind speed at different measurement points. This not only provides a basis for constructing the cross-power spectrum of fluctuating wind speed for spectral representation methods but also serves as a verification method for wind speed numerical simulations. When the accuracy of wind speed numerical simulations can be guaranteed, it can also compensate for the shortcomings of physical simulations, such as low efficiency and limited measurement points, in large-scale wind speed testing.

[0167] The above are some specific implementations of the wind field simulation method provided in the embodiments of this application. Based on this, this application also provides a corresponding device. The device provided in the embodiments of this application will be described below from the perspective of functional modularity.

[0168] See Figure 9 The diagram shows the structure of the wind field simulation device 900, which includes a physical simulation module 901, a numerical simulation module 902, a verification module 903, a simulation averaging module 904, a matrix module 905, a simulation pulsation module 906, and a superposition module 907.

[0169] The physics simulation module 901 is used to perform wind field physics simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0170] The numerical simulation module 902 is used to perform wind field numerical simulation and obtain the three-dimensional average wind speed acceleration ratio in different regions.

[0171] The verification module 903 is used to verify the acceleration ratio of the three-dimensional average wind speed in the different regions using the three-dimensional average wind speed in the different regions.

[0172] The simulation averaging module 904 is used to determine the three-dimensional average wind speed corresponding to the wind field simulation point based on the location of the wind field simulation point in response to the verification result being passed. The three-dimensional average wind speed corresponding to the wind field simulation point is determined according to the acceleration ratio of the three-dimensional average wind speed in different regions.

[0173] Matrix module 905 is used to obtain the cross power spectrum matrix of fluctuating wind speed based on the characteristics of the three-dimensional fluctuating wind speed in the different regions.

[0174] The simulation pulsation module 906 is used to input the three-dimensional average wind speed corresponding to the wind field simulation point into the pulsating wind speed cross power spectrum matrix to obtain the three-dimensional pulsating wind speed corresponding to the wind field simulation point.

[0175] The overlay module 907 is used to overlay the three-dimensional average wind speed and the three-dimensional fluctuating wind speed corresponding to the wind field simulation point to obtain the wind field corresponding to the wind field simulation point.

[0176] As one possible implementation, the physical simulation module 901 includes:

[0177] The model building unit is used to build a mountain model based on the mountain contour parameters and the model scale ratio;

[0178] The physical simulation unit is used to place the mountain model in an atmospheric boundary layer wind tunnel for physical simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

[0179] As one possible implementation, the numerical simulation module 902 includes:

[0180] The mesh drawing unit is used to draw a meshed mountain model based on the mountain contour parameters.

[0181] The first feature extraction unit is used to input the meshed mountain model into the fluid dynamics calculation model to determine the inlet boundary conditions, wall functions and turbulence model of the mountain.

[0182] The numerical simulation unit is used to perform wind field numerical simulation using the entrance boundary conditions, wall functions, and turbulence model of the mountain to obtain the three-dimensional average wind speed acceleration ratio of the different regions.

[0183] As one possible implementation, the matrix module 905 includes:

[0184] The second feature extraction unit is used to extract the characteristics of the fluctuating wind field based on the three-dimensional fluctuating wind speed in the different regions. The characteristics of the fluctuating wind field include turbulence intensity, turbulence integral scale, self-power spectrum function, spatial correlation function and point coherence function.

[0185] The matrix unit is used to obtain the cross power spectrum matrix of the fluctuating wind speed based on the characteristics of the fluctuating wind field.

[0186] As one possible implementation, the analog pulsation module 906 includes:

[0187] The simulation matrix unit is used to input the three-dimensional average wind speed corresponding to the wind field simulation point into the fluctuating wind speed cross power spectrum matrix to obtain the wind field simulation matrix.

[0188] The simulated pulsation unit is used to perform Joss decomposition on the wind field simulation matrix and obtain the three-dimensional pulsating wind speed corresponding to the wind field simulation point according to the harmonic superposition method.

[0189] This application also provides corresponding devices and computer storage media for implementing the solutions provided in this application.

[0190] The device includes a memory and a processor. The memory is used to store instructions or code, and the processor is used to execute the instructions or code to enable the device to perform the wind field simulation method described in any embodiment of this application.

[0191] The computer storage medium stores code, and when the code is run, the device running the code implements the wind field simulation method described in any embodiment of this application.

[0192] In the embodiments of this application, the terms "first" and "second" (if they exist) are used only as name identifiers and do not represent the order of first and second.

[0193] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0194] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0195] The above description is merely an exemplary implementation of this application and is not intended to limit the scope of protection of this application.

Claims

1. A wind field simulation method, characterized in that, The method includes: A physical simulation of the wind field was conducted to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions. A numerical simulation of the wind field is conducted to obtain the three-dimensional average wind speed acceleration ratio in different regions. This includes: drawing a meshed mountain model based on the mountain contour parameters; inputting the meshed mountain model into a fluid dynamics calculation model to determine the inlet boundary conditions, wall functions, and turbulence model of the mountain; and using the inlet boundary conditions, wall functions, and turbulence model of the mountain to conduct a numerical simulation of the wind field and obtain the three-dimensional average wind speed acceleration ratio in the different regions. The acceleration ratio of the three-dimensional average wind speed in the different regions was verified using the three-dimensional average wind speed in the different regions. In response to the verification result being passed, the three-dimensional average wind speed corresponding to the wind field simulation point is determined based on the location of the wind field simulation point. The three-dimensional average wind speed corresponding to the wind field simulation point is determined based on the acceleration ratio of the three-dimensional average wind speed in different regions. Based on the characteristics of the three-dimensional fluctuating wind speeds in the different regions, a fluctuating wind speed cross-power spectrum matrix is ​​obtained, including: extracting fluctuating wind field characteristics based on the three-dimensional fluctuating wind speeds in the different regions, wherein the fluctuating wind field characteristics include turbulence intensity, turbulence integral scale, self-power spectrum function, spatial correlation function, and point coherence function; and obtaining the fluctuating wind speed cross-power spectrum matrix based on the fluctuating wind field characteristics; wherein, the fluctuating wind speed spectrum power spectral density function along the transmission line is: ， ； ， ； In the formula, S i (f) is the self-power spectral density function of the fluctuating wind speed in the i-direction, L i Let i be the turbulent integral scale for the fluctuating wind speed i. i Let be the root mean square value of the fluctuating wind speed i. where f is the average wind speed and f is the frequency; The spatial correlation function is determined based on the attenuation coefficient, specifically the attenuation coefficient in the x-direction. c xi ( They are respectively: ； ； ； In the formula, D x Let c be the distance between two points in the x-direction. The experimental results for the attenuation coefficient in the z-direction are: zu =7.0、c zv =5.2、c zw =3.7; Input the three-dimensional average wind speed corresponding to the wind field simulation point into the fluctuating wind speed cross power spectrum matrix to obtain the three-dimensional fluctuating wind speed corresponding to the wind field simulation point. The wind field corresponding to the simulated wind field point is obtained by superimposing the three-dimensional average wind speed and the three-dimensional fluctuating wind speed.

2. The method according to claim 1, characterized in that, The wind field physical simulation was performed to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions, including: A mountain model is established based on the mountain contour parameters and the model scale ratio; The mountain model was placed in an atmospheric boundary layer wind tunnel for physical simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

3. The method according to claim 1, characterized in that, The step of inputting the three-dimensional average wind speed corresponding to the wind field simulation point into the fluctuating wind speed cross-power spectrum matrix to obtain the three-dimensional fluctuating wind speed corresponding to the wind field simulation point includes: Input the three-dimensional average wind speed corresponding to the wind field simulation point into the fluctuating wind speed cross power spectrum matrix to obtain the wind field simulation matrix; After performing Joss decomposition on the wind field simulation matrix, the three-dimensional pulsating wind speed corresponding to the wind field simulation point is obtained according to the harmonic superposition method.

4. A wind field simulation device, characterized in that, The device includes: The physics simulation module is used to perform wind field physics simulations to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions. The numerical simulation module is used to perform numerical simulations of wind fields and obtain the three-dimensional average wind speed acceleration ratio for different regions. The verification module is used to verify the acceleration ratio of the three-dimensional average wind speed in the different regions using the three-dimensional average wind speed in the different regions. The simulation averaging module is used to determine the three-dimensional average wind speed corresponding to the wind field simulation point based on the location of the wind field simulation point in response to the verification result being passed. The three-dimensional average wind speed corresponding to the wind field simulation point is determined according to the acceleration ratio of the three-dimensional average wind speed in different regions. The matrix module is used to obtain the cross power spectrum matrix of the fluctuating wind speed based on the characteristics of the three-dimensional fluctuating wind speed in the different regions. The simulation pulsation module is used to input the three-dimensional average wind speed corresponding to the wind field simulation point into the pulsating wind speed cross power spectrum matrix to obtain the three-dimensional pulsating wind speed corresponding to the wind field simulation point. The overlay module is used to overlay the three-dimensional average wind speed and the three-dimensional fluctuating wind speed corresponding to the wind field simulation point to obtain the wind field corresponding to the wind field simulation point. The numerical simulation module includes: The mesh drawing unit is used to draw a meshed mountain model based on the mountain contour parameters; The first feature extraction unit is used to input the meshed mountain model into the fluid dynamics calculation model to determine the inlet boundary conditions, wall functions and turbulence model of the mountain. The numerical simulation unit is used to perform wind field numerical simulation using the entrance boundary conditions, wall functions and turbulence model of the mountain, and to obtain the three-dimensional average wind speed acceleration ratio of the different regions. The matrix module includes: extracting fluctuating wind field characteristics based on the three-dimensional fluctuating wind speeds of different regions, wherein the fluctuating wind field characteristics include turbulence intensity, turbulence integral scale, self-power spectrum function, spatial correlation function, and point coherence function; and obtaining the fluctuating wind speed cross-power spectrum matrix based on the fluctuating wind field characteristics; wherein the fluctuating wind speed spectrum power spectral density function along the transmission line is: ， ； ， ； In the formula, S i (f) is the self-power spectral density function of the fluctuating wind speed in the i-direction, L i Let i be the turbulent integral scale for the fluctuating wind speed i. i Let be the root mean square value of the fluctuating wind speed i. where f is the average wind speed and f is the frequency; The spatial correlation function is determined based on the attenuation coefficient, specifically the attenuation coefficient in the x-direction. c xi ( They are respectively: ； ； ； In the formula, D x Let c be the distance between two points in the x-direction. The experimental results for the attenuation coefficient in the z-direction are: zu =7.0、c zv =5.2、c zw =3.

7.

5. The apparatus according to claim 4, characterized in that, The physical simulation module includes: The model building unit is used to build a mountain model based on the mountain contour parameters and the model scale ratio; The physical simulation unit is used to place the mountain model in an atmospheric boundary layer wind tunnel for physical simulation to obtain the three-dimensional average wind speed and three-dimensional fluctuating wind speed in different regions.

6. A device, characterized in that, The device includes a memory and a processor, the memory being used to store instructions or code, and the processor being used to execute the instructions or code to cause the device to perform the wind field simulation method according to any one of claims 1 to 3.

7. A computer storage medium storing code, wherein when the code is executed, a computer storage device executing the code implements the wind field simulation method according to any one of claims 1 to 3.

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