CN116882310A - Typhoon wind field calculation method and device based on hydrodynamics - Google Patents

Abstract

The invention provides a typhoon wind field calculation method and device based on hydrodynamics, wherein the method comprises the following steps: acquiring mountain data, and constructing a mountain model and a hydrodynamic calculation model according to the mountain data; simulating a hydrodynamic distribution model of each direction of a typical mountain according to the mountain model and the hydrodynamic calculation model, so as to obtain a wind speed simulation result; quantitatively analyzing the wind speed simulation result, and extracting typical mountain wind speed change characteristics; constructing a large-scale terrain correction factor calculation model suitable for typhoon disasters according to the characteristic of the variation of the wind speed of the typical mountain; and according to the mountain model, the hydrodynamic calculation model and the large-scale terrain correction factor calculation model, the typhoon wind field is finely simulated. The invention obtains a large-scale refined typhoon wind field, greatly improves the simulation speed and the space precision, and can rapidly evaluate the typhoon disaster surface high wind risk of large-scale complex terrains.

Description

A typhoon wind field calculation method and device based on fluid mechanics

Technical field

The present invention relates to the technical field of wind speed calculation and simulation, and in particular, to a typhoon wind field calculation method and device based on fluid mechanics.

Background technique

The current typhoon damage assessment methods are mostly based on the administrative unit scale, using the administrative unit as the statistical unit and the maximum process wind speed of the entire typhoon or other typhoon-related indicators as the intensity indicator. They do not target the areas actually affected by strong winds or heavy rains. Loss assessments are made based on actual exposure and actual intensity at the grid scale. Therefore, to a certain extent, there may be a large difference between loss estimates and actual losses.

Considering that the actual disaster occurrence process is a dynamically changing event, it is difficult to apply the post-event assessment results in early warning and disaster relief. For example, when a typhoon strikes, although its intensity has been reduced to a tropical storm level, its intensity is still a rare event that occurs once in more than 30 years in some areas. Therefore, the economic losses suffered by some areas are still huge.

In previous emergency relief cases for typhoon disasters, the areas where some typhoons caused heavy losses were not the areas close to the landing point with the highest intensity of disaster-causing factors. Instead, some of the intensity was lower than the intensity of the entire typhoon event. At the same time, the area The intensity is relatively rare in the local area. In the past weather forecasts, only the maximum wind speed in the center of the landing point was often used as the indicator of typhoon intensity. Such a forecasting method would cause the impact of wind speed after landing to be ignored during the typhoon process. Therefore, only calculating the probability of typhoon peak intensity occurrence is limited.

Contents of the invention

The technical problem to be solved by this invention is to provide a typhoon wind field calculation method and device based on fluid mechanics. Based on the fluid mechanics characteristics of typical mountains, a spatial distribution data set of hydrodynamic characteristic parameters of typical mountains in different wind directions is formed, thereby serving as The terrain correction factor model input of the refined typhoon wind field model can obtain a large-scale typhoon downscaled refined wind field, which greatly improves the simulation speed and efficiency. It can quickly assess the spatial distribution of typhoon and wind hazards, and provide disaster prevention and emergency management from the perspective of disaster prevention and emergency management. Pre-assessment of typhoon disaster losses is carried out from different angles, which plays a role in improving efficiency, saving energy and reducing emissions.

In order to solve the above technical problems, the technical solutions of the present invention are as follows:

In the first aspect, a typhoon wind field calculation method based on fluid mechanics, the method includes:

Obtain mountain data and construct typical mountain models and fluid mechanics calculation models based on the mountain data;

According to the mountain model and fluid mechanics calculation model, simulate the hydrodynamic wind speed distribution in each wind direction under a typical mountain to obtain wind speed simulation results;

Conduct quantitative analysis on the wind speed simulation results and extract wind speed change characteristics;

Construct a large-scale terrain correction factor calculation model suitable for typhoon disasters based on the wind speed change characteristics;

Based on the mountain model, fluid mechanics calculation model and large-scale terrain correction factor calculation model, combined with gradient wind field simulation and boundary layer model, the refined wind field of the typhoon is simulated.

Further, obtain mountain data and build a typical mountain model based on the mountain data, including:

According to the mountain data, the shape of the mountain is controlled by setting the mountain cross-section control curve equation, and the control curve parameters are set to control the slope of the mountain, and the plane curve is rotated to obtain a three-dimensional mountain;

The three-dimensional mountain is processed to obtain a closed geometric entity.

Further, the three-dimensional mountain is processed to obtain a closed geometric entity, including:

Convert the spatial surface of the three-dimensional mountain into an irregular triangular network, and extract the edge contour of each mountain;

Project the contour line onto the horizontal plane as the bottom surface, and connect the horizontal plane with the contour line to form a side elevation in the vertical direction;

Connect the bottom surface, side elevations and the top surface formed by the mountain itself, and then combine them to form a complete geometric solid model.

Further, obtain mountain data and construct a fluid dynamics calculation model based on the mountain data, including:

Convert the mountain model into a grid model;

Establish a wind tunnel according to the grid model;

Generate a regular grid within the cuboid formed by the wind tunnel, and divide the overall influence space into N small cuboids;

Generate a hexagonal subdivision grid from N small cuboids, refine the grid on the surface of the mountain so that the grid fits the shape of the mountain, and generate a coarse grid of large particles far away from the mountain;

Perform fluid dynamics calculations based on wind tunnels and subdivision grids.

Further, quantitative analysis is performed on the wind speed simulation results, and wind speed change characteristics are extracted, including:

Analyze the variables related to the shape of the mountain and the location of the test points that simulate the wind speed and the wind speed to determine whether the variables related to the shape of the mountain and the location of the test points that simulate the wind speed are related to the changes in the wind speed;

Three sets of test point data corresponding to the three areas were selected, as well as the mountain height, mountain slope and distance from the foot of the mountain, and correlation analysis was conducted between the test point height and the terrain correction coefficient eta.

Furthermore, based on the mountain model, fluid mechanics calculation model and large-scale terrain correction factor calculation model, combined with gradient wind field simulation and boundary layer model, the refined wind field of the typhoon is simulated, including:

Obtain the typhoon historical data provided in the optimal path data set based on interpolation processing;

Extract the typhoon center pressure, typhoon center longitude, and typhoon center latitude from the typhoon historical data;

According to the typhoon center pressure, typhoon center longitude, and typhoon center latitude, the typhoon center data at each moment is calculated.

Furthermore, based on the mountain model, fluid mechanics calculation model and large-scale terrain correction factor calculation model, combined with gradient wind field simulation and boundary layer model, the refined wind field of the typhoon is simulated, including:

Based on the interpolated typhoon center records, the typhoon center records at each moment are brought into the gradient wind field model for calculation to obtain the instantaneous wind speed spatial distribution;

Calculate the maximum value of the wind speed appearing in each grid at all times in the entire typhoon event and retain it to obtain the typhoon gradient process wind field;

The wind circle range that affects actual production and life is extracted as the simulation result of the typhoon gradient wind field.

The second aspect is a typhoon wind field calculation device based on fluid mechanics, including:

The acquisition module is used to obtain mountain data, and construct a typical mountain model and a fluid mechanics calculation model based on the mountain data; based on the typical mountain model and the fluid mechanics calculation model, simulate the hydrodynamic wind speed distribution in each wind direction under the typical mountain to obtain a wind speed simulation Results; quantitatively analyze the wind speed simulation results and extract wind speed change characteristics;

The processing module is used to construct a large-scale terrain correction factor calculation model suitable for typhoon disasters based on the wind speed change characteristics; based on the mountain model, fluid mechanics calculation model and large-scale terrain correction factor calculation model, combined with gradient wind field simulation, boundary layer model to simulate the refined wind field of typhoons.

In a third aspect, a computer includes:

one or more processors;

A storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method is implemented.

The above solution of the present invention at least includes the following beneficial effects:

The above solution of the present invention, based on the hydrodynamic characteristics of typical mountains, forms a spatial distribution data set of hydrodynamic characteristic parameters of typical mountains in different wind directions, which can be used as input to the terrain correction factor model of the refined typhoon wind field model to obtain large-scale Typhoon downscaling and refined wind fields greatly improve the simulation speed and efficiency. It can quickly assess the spatial distribution of typhoon and wind hazards, and conduct pre-assessment of typhoon disaster losses from the perspective of disaster prevention and emergency management, which has improved efficiency, saved energy and reduced emissions. role.

Description of the drawings

Figure 1 is a schematic flow chart of a typhoon wind field calculation method based on fluid mechanics provided by an embodiment of the present invention.

Figure 2 is a schematic diagram of a typhoon wind field calculation device based on fluid mechanics provided by an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a thorough understanding of the disclosure, and to fully convey the scope of the disclosure to those skilled in the art.

As shown in Figure 1, an embodiment of the present invention proposes a typhoon wind field calculation method based on fluid mechanics. The method includes the following steps:

Step 11: Obtain mountain data and construct a typical mountain model and fluid mechanics calculation model based on the mountain data;

Step 12: According to the typical mountain model and the fluid mechanics calculation model, simulate the hydrodynamic wind speed distribution in each wind direction under the typical mountain to obtain the wind speed simulation results;

Step 13: Conduct quantitative analysis on the wind speed simulation results and extract wind speed change characteristics;

Step 14: Construct a large-scale terrain correction factor calculation model suitable for typhoon disasters based on the wind speed change characteristics;

Step 15: Simulate the refined wind field of the typhoon based on the mountain model, fluid mechanics calculation model and large-scale terrain correction factor calculation model, combined with gradient wind field simulation and boundary layer model.

In the embodiment of the present invention, a spatial distribution data set of hydrodynamic characteristic parameters of typical mountains in different wind directions is formed based on the hydrodynamic characteristics of typical mountains, so as to be used as input to the terrain correction factor model of the refined typhoon wind field model to obtain a large-scale The downscaled and refined wind fields of large-scale typhoons can greatly improve the simulation speed and efficiency. It can quickly assess the spatial distribution of typhoon and wind hazards, and conduct pre-assessment of typhoon disaster losses from the perspective of disaster prevention and emergency management, which has improved efficiency and saved energy. The role of emission reduction.

In a preferred embodiment of the present invention, the above step 11 may include:

Step 111: According to the mountain data, the shape of the mountain is controlled by setting the mountain cross-section control curve equation, and the control curve parameters are set to control the slope of the mountain, and the plane curve is rotated to obtain the three-dimensional mountain;

Step 112: Process the three-dimensional mountain to obtain a closed geometric entity.

In the embodiment of the present invention, in terms of mountain model construction, a total of 12 hypothetical idealized mountain models for CFD (fluid mechanics) simulation were constructed. By setting the form of the mountain cross-section control curve equation (parabolic, cosine and Gaussian type) to control the shape of the mountain, set the control curve parameters to control the slope of the mountain (tanα=0.17~1, angle 10°~45°), and then rotate the plane curve to obtain a three-dimensional mountain. Compared with directly constructing a three-dimensional mountain model, it is easier to control the shape of the mountain with cross-sectional curve equations. The height and slope of the mountain can be directly adjusted, and the change pattern of wind speed along the hillside can directly correspond to the section curve. The three-dimensional space equations of the three types of mountain models are finally obtained. Where h is the height of the mountain at a certain point, H is the height at the top of the mountain, L is the horizontal distance from the top of the mountain to the foot of the mountain on either side, and the slope is defined as

In a preferred embodiment of the present invention, the above step 112 may include:

Step 1121, convert the spatial surface of the three-dimensional mountain into an irregular triangular network, and extract the edge contour of each mountain;

Step 1122: Project the contour line to the horizontal plane as the bottom surface, and connect the horizontal plane and the contour line to form a side elevation in the vertical direction;

Step 1123: Connect the bottom surface, side elevations and the top surface formed by the mountain itself, and then combine them to form a complete geometric solid model.

In the embodiment of the present invention, due to the limitation that the CFD solution requires the obstruction to be a closed entity, the spatial surface of the three-dimensional mountain is not a computable entity and needs to be further converted into a closed geometric entity.

In a preferred embodiment of the present invention, the above step 112 may include:

Step 1124, convert the mountain model into a grid model;

Step 1125, establish a wind tunnel according to the grid model;

Step 1126: Generate a regular grid within the cuboid formed by the wind tunnel, and divide the overall influence space into N small cuboids;

Step 1127, generate a hexagonal subdivision grid from N small rectangular bodies, refine the grid on the surface of the mountain so that the grid fits the shape of the mountain, and generate a coarse grid of large particles far away from the mountain;

Step 1128: Perform fluid mechanics calculations based on the wind tunnel and subdivision grid.

In the embodiment of the present invention, after the mountain model is obtained, it needs to be further converted into a grid model that Butterfly can parse. Usually, the butterfly entity is directly generated from the geographical entity. During the process, it is necessary to set the additional grid levels and grid quality on the mountain surface. Establishing and setting up a wind tunnel is similar to the wind tunnel test process. First, a wind tunnel needs to be established to provide a free initial wind speed that is not affected by terrain conditions. In Grasshopper, the wind tunnel is considered a cuboid, and its volume needs to completely encompass the geometric entities to be calculated. For the wind tunnel itself, it is necessary to set its distance from the front, rear, left, and right of the geometry to delineate the range that the wind force of the wind tunnel can affect. It is also necessary to set the range of influence of the wind tunnel, including the gradient wind reference height and surface roughness corresponding to the geometry itself. degree to simulate the attenuation of wind speed caused by friction in real situations. In addition, the wind speed and direction need to be set. In the present invention, the wind speed is set to 15m/s and 30m/s, and the wind direction blows horizontally in the y-axis direction. The initial multi-block grid is generated, and a regular grid is generated within the cuboid range formed by the wind tunnel, and the overall influence space is divided into N small cuboids. When generating a refined grid that fits the surface, we are more concerned about the wind speed distribution on the mountain surface. The wind speed at locations far away from the mountain within the scope of the wind tunnel can be roughly expressed. Therefore, the global refined solution is redundant, so we further generate six sides. It uses a shape-shaped subdivision grid to refine the grid on the mountain surface so that it fits the shape of the mountain, and generates a coarse grid of large particles far away from the mountain to reduce the amount of calculations. After the wind tunnel and subdivision grid are established, the differential equations of fluid mechanics will be solved. There are two types of commonly used differential equations, corresponding to different solvers. One is the temperature conduction solver, which is used for environmental temperature solutions. calculation; the other is the steady-state incompressible model, which is suitable for air flow calculations.

For the steady-state incompressible model solver, it is still necessary to further set up its control differential equation. Taking into account the turbulence phenomenon of the mountain, the turbulence model simulated by Reynolds average is selected as the input. In addition, due to the problem of multiple solutions to high-order differential equations, the model will continue to iterate to find the optimal solution. Therefore, it is stipulated that when the error during the solution process is less than 0.0001, the optimal solution is found and the iteration stops. Set the test points and sampling volume. Since what is being explored is the wind speed change pattern of the mountain, the test surface is defined as a position 10 meters above the surface of the mountain. Due to the large-scale nature of typhoon disasters and the fact that typhoon wind directions change at all times, test points are only set up on windward slopes, leeward slopes, and mountain shielding areas in the downwind direction facing the upwind and downwind directions. Calculated based on the actual air flow velocity, in order to ensure that the air flow has enough time to completely flow through the mountain, the total sampling volume is set to one sampling every five time steps, and the sampling is 300 times. Data output and visual expression. The CFD settlement result is a vector line (u, v) at each test point, which represents the wind direction and wind speed at that point. For further statistical analysis, the vector data needs to be further processed. Extract the wind speed. At the same time, visual expression can be performed, including coloring the subdivided grid according to the wind speed, drawing air flow lines, etc.

In a preferred embodiment of the present invention, the above step 13 may include:

Step 131, analyze the relevant variables of the mountain shape and the position of the test point to simulate the wind speed and the wind speed to determine whether the relevant variables of the mountain shape and the position of the test point to simulate the wind speed are related to the change of the wind speed;

Step 132: Select three sets of test point data corresponding to the three areas, mountain height, mountain slope, and distance from the foot of the mountain, and perform correlation analysis on the test point height and the terrain correction coefficient eta.

In the embodiment of the present invention, 15m/s and 30m/s are used as the initial wind speed input, and wind speed simulation is performed on cosine-shaped, parabolic-shaped, and Gaussian-shaped mountains with slopes of 10°, 20°, 30°, and 45° respectively. A total of 24 sets of CFD simulation results were obtained, with a total of more than 70,000 test point data. Select the valid data whose ordinate falls on the y-axis (positive wind direction) and are non-outliers within 50m to the left and right of the y-axis for further statistical processing, and draw a scatter plot, in which the cross-sectional shapes of the 12 mountain models are listed on the left. The two columns on the right are the test surface wind speed simulation results.

Therefore, it can be found from the simulation results that under the two input wind speed conditions, the wind speed changes significantly as the mountain height climbs and decreases. When climbing to the top of the mountain, the wind speed corresponding to the three types of mountains increases significantly, and When the total length of the mountain is the same, the higher the mountain height (the greater the mountain slope), the more obvious the wind speed increase effect at the top of the mountain is. At the foot of the windward slope and leeward slope, the wind speed decelerates, and the deceleration amplitude is related to the slope. The greater the slope, the more obvious the deceleration is. When the slope of the mountain exceeds 16° as defined in the "Load Code for Building Structures", the trend of wind speed changes with the mountain does not change, which is similar to the overall wind speed pattern of the mountain with a gentle slope, indicating that the slope does not change the trend of wind speed. Most of them affect the degree of increase in wind speed.

On the slope surface, the wind speed distribution is relatively close near the top of the mountain, and the windward slope and leeward slope show a symmetrical shape. Outside their range, the wind speed distribution between the two is different: the wind speed shows an overall increasing trend from the foot of the windward slope to the top of the mountain, only the Gaussian mountain shows a trend of first decreasing and then increasing when the slope is larger. The wind speed at the tail of the leeward slope mostly shows an upward trend, that is, the wind speed on the leeward slope first decreases and then increases, but in most cases the change amplitude is small, and the change is only obvious when the slope exceeds 30°.

Overall, the distribution pattern of wind speed is very similar to the shape of the mountain itself, that is, the terrain correction factor does not simply change continuously and linearly with the distance from the mountain top, but changes with the shape of the mountain. To sum up, the general changing trend of wind speed affected by mountain undulations is that the wind speed slows down at the foot of the windward slope, then increases to the maximum at the top of the mountain, and slows down from the top of the mountain to near the foot of the leeward slope.

For flat areas with small undulations behind the mountains, deceleration often occurs due to the blocking effect of the mountains. While detecting changes in wind speed on the slope, this invention also measures changes in the flat area and wind speed behind the mountain. The results show that in the direction of wind flow, as the slope of the mountain increases, the deceleration range behind the leeward slope of the mountain is also called an increasing trend. However, due to the squeeze and turbulence of the crosswind slope, the lateral distance of the deceleration range is related to the width of the mountain, while the width of the mountain and the squeeze range of the wind slope are related to the shape of the mountain. In the downwind direction, the deceleration effect starts from the foot of the mountain and extends outward. It continues to decelerate within a certain range near the foot of the leeward slope, and then slowly recovers. The average range is 7 times the height of the mountain, where it is separated from the influence of the mountain, that is, it returns to the initial conditions. Free wind speed.

Quantitative analysis was conducted on 24 sets of wind speed simulation results based on CFD, and wind speed change characteristics were extracted from a statistical perspective. The simulated wind speeds at the foot of the windward slope, the foot of the leeward slope, and the top of the mountain were selected respectively, and the ratio to the initial free wind speed (i.e., the terrain correction coefficient η) was calculated for each area.

First, a correlation analysis was conducted on the relevant variables that determine the shape of the mountain, as well as the position of the test point to simulate the wind speed and the wind speed, to determine whether these variables are relevant to the change in wind speed, that is, to determine which terrain-related indicators affect the change in wind speed in the mountain. The first choice is to select three groups of test point data (128 each) corresponding to the three areas for correlation analysis with mountain height, mountain slope, distance from the foot of the mountain, test point height and terrain correction coefficient eta. Considering that the actual impact of the mountain on wind speed does not depend on the absolute height, the height of the test point is compared with the full height of the mountain, and the relative height of the test point is used as one of the dependent variables.

The correlation analysis results between various variables are shown in Table 1:

All correlation coefficient analysis results are significant at the 0.01 level. It can be seen from the correlation coefficient analysis results that the relative height of the test point can represent two indicators: the height of the mountain and the height of the test point. The correlation coefficient between the distance from the test point to the edge of the mountain and the relative height of the test point is 1. This is due to the shape of the mountain and the slope Under certain conditions, in the two-dimensional coordinate system where the mountain cross-section is located, the distance from the edge is the X-axis coordinate of the point, and the Y-axis coordinate corresponding to the height of the test point has a one-to-one functional relationship with it. In the same way, the correlation coefficient between mountain height and slope is also 1, so the relative height of the mountain slope and the test point can be an equivalent substitute for other related indicators. Therefore, it is finally determined that the relevant index used for terrain correction factor fitting is the mountain slope and the test point. Relative height.

First, the mountaintop area is analyzed. Since the "Load Code for Building Structures" stipulates in detail the calculation formula for the terrain correction factor of the mountaintop, therefore, by comparing the CFD simulation results with the model fitting results, it can be concluded that the slope is less than 0.6. Within this slope interval, the model calculation results are too small, and the average value of each group of test data is 1.2 times the simulated value. Therefore, within this slope interval, a correction coefficient needs to be added to the original calculation model. As the slope further increases, the CFD simulation results show that the wind speed increase trend slows down and does not increase linearly with the slope. When the mountain slope reaches 1, the existing fitting formula can more accurately reflect the wind speed change trend at the top of the mountain.

Further, the terrain correction factor coefficients simulated at the foot of the windward slope and leeward slope were curve-fitted with the mountain slope and the relative height of the test point respectively, and attempts were made to use linear models, quadratic polynomials, logarithmic models, exponential models, power function models, etc. Regression analysis is performed to obtain the fitting formula. The results are shown in Table 2, in which the linear model has the best fitting effect, which is 0.702 at the foot of the windward slope and 0.721 at the foot of the leeward slope.

Table 2 Terrain correction factor formula fitting results

In view of the limitations of the current "Building Structural Load Code" in the construction and application of typhoon wind fields, the terrain correction factor model based on CFD simulation results has the following main changes within the mountain range:

1. For the mountain top: The wind speed change characteristics of the mountain top can be reflected by the existing correction method within its applicable scope, but the overall value is small. So for the mountain top point, increase the magnification factor.

2. For the foot of the mountain: at the bottom of the windward slope, the overall value is too large because the turbulence and blocking effects are not taken into account. On the leeward slope, due to the neglect of the deceleration effect, the terrain correction factor is also too large. Therefore, the terrain correction factor is no longer set to 1 at the foot of the windward slope and the leeward slope. Instead, a calculation model related to the mountain is fitted to make the location of the acceleration phenomenon more realistic and the deceleration effect to be reflected.

3. Provide a more detailed correction plan for mountains with a slope greater than 16°. Since the wind speed change trends are similar, there are many mountains with larger slopes within the typhoon coverage area. Therefore, the slope is used as one of the variables that affects the terrain correction factor. During the calculation, mountains outside the national standard range also have corresponding parameter calculation models.

4. The wind speed change trend on the slope is not linear, and the difference in wind speed is affected by different mountain shapes. The calculation method of the terrain correction factor on the slope is changed to vertical interpolation to highlight the difference in the impact of different mountain shapes on the wind speed, and is closer to reality. Wind speed distribution.

For the area behind the mountain: The specification does not stipulate a terrain correction coefficient for the area behind the mountain. The range of terrain influence is only related to the width of the mountain, that is, a correction plan is only provided for uplifted terrain. The actual neutralization simulation results show that in the flat terrain behind the leeward slope of the mountain, taking into account the blocking effect of the mountain and local turbulence, the wind speed maintains a deceleration trend at a certain distance. Considering that the lateral distance that crosswind slopes can affect is smaller than that of the entire mountain, and that the mutual influence between mountains is often greater than the influence of crosswind slopes on a large scale, only changes in wind speed in the downwind direction are considered, and the deceleration trend is maintained to 3 times the height of the mountain, and then continues to be affected by the shielding effect of the mountain within the range of 5-7 times the height of the mountain, showing a decelerating trend compared to the initial wind speed, but slowly returns to no influence as the horizontal distance increases.

Based on the above analysis and horizontal comparison, in order to match the rules of the CFD simulation results with the large-scale terrain correction factor model, combined with the terrain correction factor model in the "Load Standard for Building Structures", the specific calculation model of the terrain correction factor is supplemented and improved as shown in Table 3 :

Table 3 Calculation method of correction factor (η) for large-scale complex terrain based on CFD

In addition to the mountainous terrain described in Table 3, other undulating terrains such as canyons and basins refer to the recommended values of the "Load Code for Building Structures". However, taking into account the interaction between multiple mountains and the turbulence characteristics reflected in CFD, transition areas are set up between different terrains. At this point, a more detailed terrain correction factor calculation model suitable for typhoon wind fields has been constructed.

This invention explains the modeling method of 12 idealized typical mountain models and the construction of the CFD wind speed settlement process. After building the required model, use 15m/s and 30m/s as the initial wind speed input to simulate the wind speed changes of 12 types of mountains, and obtain the wind speed change value of a single mountain under ideal conditions. The wind speed value increases significantly at the top of the mountain, and weakens significantly at the foot of the windward slope and the foot of the leeward slope. Statistical analysis was conducted on the results of more than 40,000 test points on all mountains and 384 test points in special areas. The terrain factors that affect wind speed changes are well correlated with the slope and height of the mountain, and the factor changes can be better described by linear models. . The mountain shape parameters and terrain correction factors were used to perform curve fitting to construct a terrain factor calculation model for each key position of the mountain, and a vertical interpolation method was used on the mountain slope to supplement the wind speed distribution in the plain behind the mountain caused by the mountain's shielding effect. Calculation model of deceleration effect, thereby constructing a large-scale terrain correction factor calculation model suitable for typhoon disasters.

In a preferred embodiment of the present invention, the above step 15 may include:

Step 151: Obtain the typhoon historical data provided in the best path data set based on interpolation processing;

Step 152: Extract the typhoon central pressure, typhoon central longitude, and typhoon central latitude from the typhoon historical data;

Step 153: Calculate the typhoon center data at each moment based on the typhoon center pressure, typhoon center longitude, and typhoon center latitude.

In the embodiment of the present invention, the present invention combines a parameterized typhoon wind field model, a boundary layer model, and a monitoring area terrain correction factor based on CFD simulation results to construct a refined typhoon wind field model. The typhoon center record simulates the refined wind field of historical typhoons based on the CMA optimal path data set. The measured data of meteorological stations were used to verify the spatial accuracy of the wind speed simulation results, and the simulation effect of the refined typhoon wind field model was tested by horizontal comparison with other typhoon wind field models. Within the full scope of the typhoon disaster, the simulation results were statistically analyzed by region, and the simulation effects of the refined wind field model in the face of different stages of typhoons and different regional characteristics were analyzed.

It should be noted that because the terrain factors corresponding to various geomorphic units such as mountains (including windward slopes, leeward slopes, etc.), canyons, basins, etc. are calculated in different ways, for each DEM raster, the geomorphic unit to which it belongs is first interpreted. Due to the large fluctuations in the monitoring area, there are continuous high mountains in the southwest, and there are river valleys between this area and the mountains in the northwest. The southeast of the monitoring area is rolling hills, and the northeast is a large plain with a small number of hills. Therefore, the conventional The classification of landforms, such as absolute altitude, slope, etc., is prone to misjudgment or omission. Therefore, by calculating relative elevation values, the present invention first extracts mountains and mountain lines, and then subdivides other landforms. The specific steps are: calculate the average elevation value of adjacent grids centered on the grid one grid at a time, and calculate the difference between the grid elevation value and the average elevation value of the neighborhood as the judgment criterion 1; within the range of the surrounding 3 neighborhoods Calculate the average elevation value as judgment criterion 2; within the surrounding 5 neighborhoods, calculate the average elevation difference between the upper, lower, and left neighbors centered on the grid point as judgment criteria 3 and 4. The mountainous area and mountain contours are obtained by combining the thresholds of all judgment criteria. The mountain contours serve as the boundary between mountains and other landforms. After determining the range of the mountain, continue to distinguish the slopes, intermountain basins, mountain passes, etc. within the mountain range. It needs to be explained that for different wind directions, whether the direction of the canyon and mountain pass is consistent with the wind direction determines the change pattern of wind speed in the canyon.

On this basis, take the due east direction as the direction and the due west direction as the main wind direction, and set a main wind direction at each interval, a total of 8 directions. The range on both sides of the main wind direction (i.e. the main wind direction) is regarded as the influence range of the main wind direction. After the wind is determined, the terrain factor calculation model corresponding to the terrain is selected according to the type of terrain where each grid is located. The intersection between other terrain in the upwind direction and the mountain, that is, the mountain contour line is the foot of the windward slope, the range from the foot of the windward slope to the top point corresponding to the grid is the windward slope, and the intersection between other terrain in the downwind direction and the mountain is the leeward slope. At the foot of the mountain, the leeward slope is between it and the top of the mountain, and the influence area is within 7 times the height of the mountain outside the foot of the mountain. And each canyon and mountain pass whose trend is consistent with the downwind direction is selected as a special correction area in that wind direction, and the 8-directional terrain correction factor of the monitoring area is calculated.

The maximum value of the terrain correction factor in the monitoring area occurs in the mountainous area in the southwest of the monitoring area. The terrain in this area is highly undulating, and the mountain height and slope are both high in the province. The northwest region is a hilly area. The overall altitude of the mountains is not high, but river valleys and canyons are relatively concentrated. The acceleration effects of canyons and river valleys and the deceleration effects of closed terrain such as basins coexist. The eastern region is a mixed area of coastal plains and hills. The overall value of the terrain correction factor is not high, but the variation range is large. The northern region is an alluvial plain with few hills and mountains. The terrain correction factor is the most average. It is also the area with the smallest impact of terrain on wind speed changes in the monitoring area. The acceleration and deceleration effects are not obvious.

In a preferred embodiment of the present invention, the above step 15 may include:

Step 154: Based on the interpolated typhoon center records, the typhoon center records at each moment are brought into the gradient wind field model for calculation to obtain the instantaneous wind speed spatial distribution;

Step 155: Calculate the maximum value of the wind speed appearing in each grid at all times in the entire typhoon event and retain it to obtain the typhoon gradient process wind field;

Step 156: Extract the wind circle range that affects actual production and life as the simulation result of the typhoon gradient wind field.

In the embodiment of the present invention, in order to simulate the historical typhoon wind field, the typhoon records in the monitoring area from 1949 to 2019 provided in the CMA best path data set are first filtered and filtered. According to the requirements of the Holland wind field model, the typhoon central pressure, typhoon central longitude, and typhoon central latitude in the records are extracted. Since the gradient wind field model is calculated based on the typhoon center data at each moment, when the typhoon center moves faster, the calculation range of the wind field calculation model will be omitted, so it is necessary to first record the initial typhoon at the 6-hour event interval. Perform interpolation processing.

Through the analysis of the CMA best path data set records, it can be seen that the relationship between the position of the typhoon center at time t and time t-1 can be better fitted through linear fitting. The fitted trend line The error is small. The changes in the central air pressure of typhoons generally show a trend of first decreasing and then increasing in time series, which is consistent with the generation and development process of typhoons, that is, they are weak when they first form, then develop and strengthen, and finally weaken and disappear. However, it is difficult to quantify this trend from a time series, so we still analyze the quantitative relationship between before and after moments. After trying, we found that polynomial fitting can better reflect the changes in the pressure at the center of the typhoon. The average values after fitting for multiple typhoons are close to 1.

Based on the interpolated typhoon center records, the typhoon center records at each moment are brought into the Holland gradient wind field model for calculation, and the instantaneous wind speed spatial distribution is obtained. The calculation formula is as follows:

RMW＝-18.04lnΔp+110.22;

B＝1.38-0.00184Δp+0.00309RMW;

Calculate the maximum value of the wind speed appearing in each grid at all times in the entire typhoon event and retain it to obtain the typhoon gradient process wind field. In the periphery of the typhoon, due to the weak influence of the low-pressure center, the wind force caused by the typhoon center cannot reach the disaster level. Therefore, the wind speed (10.8m/s) corresponding to the level 6 wind (strong wind) is used as the threshold to extract the The wind circle range that affects actual production and life is used as the simulation result of the typhoon gradient wind field, and the outer areas of the typhoon are calculated to avoid data redundancy.

While calculating the wind speed hourly, based on the spatial angular relationship between the typhoon center and the location of the grid, the instantaneous wind direction angle corresponding to the maximum wind speed is calculated when the maximum wind speed occurs. Since the wind direction of typhoons changes at all times, the wind direction can reflect the wind direction characteristics in the corresponding period by taking a certain range of values. In order to adapt to the correction of near-surface wind speed, the wind direction is finally classified into 8 wind directions.

In order to verify the simulation effect of typhoon wind fields under long-term series and compare the spatial distribution results of hazards under different data sources, in order to conduct a more comprehensive analysis of monitoring the hazards of typhoon winds, the present invention uses the same extreme value algorithm to calculate typhoon wind speed based on weather station data. Annual calculation, the specific steps are as follows:

(1) Set the peripheral wind speed of the typhoon gradient wind field to 13.8m/s (level 7 wind), use the simulation results of the gradient wind field, regard the corresponding range of the wind circle radius as the typhoon's influence range, and cut the upper limit of the range to 1000Km.

(2) For each meteorological station, count the start and end times of each typhoon that affects it, extract the daily maximum wind speed of the station during that time period, and calculate the maximum daily maximum wind speed during the start and end times.

(3) Use the kriging method to spatially interpolate the site wind speed. Based on the interpolated wind speed, extract the daily maximum wind speed of each grid point in annual units as the annual extreme value of the typhoon disaster and wind disaster factor intensity, and use Gumbel The model calculates site wind speeds for typical return periods.

As shown in Figure 2, an embodiment of the present invention also provides a typhoon wind field calculation device 20 based on fluid mechanics, including:

The acquisition module 21 is used to obtain mountain data, and construct a mountain model and a fluid mechanics calculation model based on the mountain data; according to the typical mountain model and the fluid mechanics calculation model, simulate the hydrodynamic wind speed distribution in each wind direction under the typical mountain to obtain wind speed simulation Results; quantitatively analyze the wind speed simulation results and extract wind speed change characteristics;

The processing module 22 is used to construct a large-scale terrain correction factor calculation model suitable for typhoon disasters based on the wind speed change characteristics; based on the mountain model, fluid mechanics calculation model and large-scale terrain correction factor calculation model, combined with gradient wind field simulation, boundary layer model to simulate the refined wind field of typhoons.

Optionally, obtain mountain data and build a typical mountain model based on the mountain data, including:

According to the mountain data, the shape of the mountain is controlled by setting the mountain cross-section control curve equation, and the control curve parameters are set to control the slope of the mountain, and the plane curve is rotated to obtain a three-dimensional mountain;

The three-dimensional mountain is processed to obtain a closed geometric entity.

Optionally, process the three-dimensional mountain to obtain a closed geometric entity, including:

Convert the spatial surface of the three-dimensional mountain into an irregular triangular network, and extract the edge contour of each mountain;

Project the contour line onto the horizontal plane as the bottom surface, and connect the horizontal plane with the contour line to form a side elevation in the vertical direction;

Connect the bottom surface, side elevations and the top surface formed by the mountain itself, and then combine them to form a complete geometric solid model.

Optionally, obtain mountain data and construct a fluid dynamics calculation model based on the mountain data, including:

Convert the mountain model into a grid model;

Establish a wind tunnel according to the grid model;

Generate a regular grid within the cuboid formed by the wind tunnel, and divide the overall influence space into N small cuboids;

Generate a hexagonal subdivision grid from N small cuboids, refine the grid on the surface of the mountain so that the grid fits the shape of the mountain, and generate a coarse grid of large particles far away from the mountain;

Perform fluid dynamics calculations based on wind tunnels and subdivision grids.

Optionally, quantitatively analyze the wind speed simulation results and extract wind speed change characteristics, including:

Analyze the variables related to the shape of the mountain and the location of the test points that simulate the wind speed and the wind speed to determine whether the variables related to the shape of the mountain and the location of the test points that simulate the wind speed are related to the changes in the wind speed;

Three sets of test point data corresponding to the three areas were selected, as well as the mountain height, mountain slope and distance from the foot of the mountain, and correlation analysis was conducted between the test point height and the terrain correction coefficient eta.

Optionally, simulate the refined wind field of typhoons based on the mountain model, fluid mechanics calculation model and large-scale terrain correction factor calculation model, combined with gradient wind field simulation and boundary layer model, including:

Obtain the typhoon historical data provided in the optimal path data set based on interpolation processing;

Extract the typhoon center pressure, typhoon center longitude, and typhoon center latitude from the typhoon historical data;

According to the typhoon center pressure, typhoon center longitude, and typhoon center latitude, the typhoon center data at each moment is calculated.

Optionally, based on the mountain model, fluid mechanics calculation model and large-scale terrain correction factor calculation model, combined with gradient wind field simulation and boundary layer model, simulate the refined wind field of the typhoon, including:

Based on the interpolated typhoon center records, the typhoon center records at each moment are brought into the gradient wind field model for calculation to obtain the instantaneous wind speed spatial distribution;

Calculate the maximum value of the wind speed appearing in each grid at all times in the entire typhoon event and retain it to obtain the typhoon gradient process wind field;

The wind circle range that affects actual production and life is extracted as the simulation result of the typhoon gradient wind field.

It should be noted that this device is a device corresponding to the above method. All implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

An embodiment of the present invention also provides a computer, including: a processor and a memory storing a computer program. When the computer program is run by the processor, the method as described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

Embodiments of the present invention also provide a computer-readable storage medium that stores instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each specific application, but such implementations should not be considered to be beyond the scope of the present invention.

Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be described again here.

In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

In addition, it should be pointed out that in the device and method of the present invention, obviously, each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent solutions of the present invention. Furthermore, the steps for executing the above series of processes can naturally be executed in chronological order in the order described, but they do not necessarily need to be executed in chronological order, and some steps may be executed in parallel or independently of each other. For those of ordinary skill in the art, it can be understood that all or any steps or components of the method and device of the present invention can be implemented in any computing device (including processor, storage medium, etc.) or a network of computing devices in the form of hardware or firmware. , software or their combination, this can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

Therefore, the objects of the invention can also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general-purpose device. Therefore, the object of the present invention can also be achieved only by providing a program product containing a program code for implementing the method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium may be any known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, obviously, each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent solutions of the present invention. Furthermore, the steps for executing the above series of processes can naturally be executed in chronological order in the order described, but do not necessarily need to be executed in chronological order. Certain steps can be performed in parallel or independently of each other.

The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications can also be made. should be regarded as the protection scope of the present invention.

The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications can also be made. should be regarded as the protection scope of the present invention.

Claims (10)

1. A typhoon wind farm calculation method based on hydrodynamics, the method comprising:

acquiring mountain data, and constructing a typical mountain model and a hydrodynamic calculation model according to the mountain data;

simulating hydrodynamic wind speeds of all wind directions under a typical mountain according to the mountain model and the hydrodynamic calculation model so as to obtain a wind speed simulation result;

quantitatively analyzing the wind speed simulation result, and extracting wind speed change characteristics;

constructing a large-scale terrain correction factor calculation model suitable for typhoon disasters according to the wind speed change characteristics;

According to a mountain model, a hydrodynamic calculation model and a large-scale terrain correction factor calculation model, combining a gradient wind field simulation and a boundary layer model, and simulating a refined wind field of typhoons.

2. The hydrodynamic typhoon wind farm computing method according to claim 1, wherein obtaining mountain data and constructing a typical mountain model from the mountain data comprises:

controlling the shape of the mountain by setting a mountain cross section control curve equation according to the mountain data, setting control curve parameters to control the gradient of the mountain, and rotating a plane curve to obtain a three-dimensional mountain;

and processing the three-dimensional mountain body to obtain a closed geometric entity.

3. The hydrodynamic typhoon wind farm computing method according to claim 2, wherein processing the three-dimensional mountain to obtain a closed geometric entity comprises:

converting the space curved surface of the three-dimensional mountain into an irregular triangular net, and extracting the edge contour line of each mountain;

projecting the contour line to a horizontal plane as a bottom surface, and connecting the horizontal plane with the contour line to form a side elevation in the vertical direction;

the bottom surface and the side elevation are connected with the top surface formed by the mountain body, and the complete geometric solid model is formed after the combination.

4. A typhoon wind farm calculation method based on fluid mechanics according to claim 3, wherein obtaining mountain data and constructing a fluid mechanics calculation model from the mountain data comprises:

converting the mountain model into a grid model;

establishing a wind tunnel according to the grid model;

generating a regular grid in a cuboid range formed by the wind tunnel, and dividing the whole influence space into N small cuboids;

generating hexagonal subdivision grids by N small cuboids, thinning the grids on the surface of a mountain, enabling the grids to be attached to the shape of the mountain, and generating large-particle coarse grids at positions far away from the mountain;

and carrying out hydrodynamic calculation according to the wind tunnel and the subdivision grid.

5. The hydrodynamic typhoon wind farm calculating method according to claim 4, wherein quantitatively analyzing the wind speed simulation result and extracting a wind speed variation feature comprises:

analyzing the related variable of the mountain shape and the position of the test point of the simulated wind speed and the wind speed to determine whether the related variable of the mountain shape and the position of the test point of the simulated wind speed are related to the wind speed change;

and selecting three groups of test point data corresponding to the three areas, namely mountain height, mountain gradient and distance from a mountain foot point, and performing correlation analysis on the test point height and the terrain correction coefficient eta.

6. The hydrodynamic typhoon-based wind farm calculation method according to claim 5, wherein simulating a refined wind farm of typhoons according to a mountain model, a hydrodynamic calculation model and a large-scale terrain correction factor calculation model in combination with a gradient wind farm simulation, a boundary layer model comprises:

obtaining typhoon history data provided in the optimal path data set based on interpolation processing;

extracting typhoon center air pressure, typhoon center longitude and typhoon center latitude in the typhoon history data;

and calculating typhoon center data at each moment according to the typhoon center air pressure, the typhoon center longitude and the typhoon center latitude.

7. The method according to claim 6, wherein the method is based on a mountain model, a hydrodynamic calculation model, and a large-scale terrain correction factor calculation model, and combines a gradient wind field simulation and a boundary layer model to simulate a refined wind field of typhoon, and further comprising:

based on the typhoon center record after interpolation processing, carrying the typhoon center record at each moment into a gradient wind field model for calculation to obtain instantaneous wind speed space distribution;

Calculating the maximum value of the wind speed of each grid at all times in the typhoon event of the whole field and reserving the maximum value to obtain a typhoon gradient process wind field;

and extracting a wind ring range which affects actual production and life as a simulation result of the typhoon gradient wind field.

8. A hydrodynamic based typhoon wind farm computing device, comprising:

the acquisition module is used for acquiring mountain data and constructing a typical mountain model and a hydrodynamic calculation model according to the mountain data; simulating hydrodynamic wind speed distribution of each wind direction under a typical mountain according to the mountain model and the hydrodynamic calculation model to obtain a wind speed simulation result; quantitatively analyzing the wind speed simulation result, and extracting wind speed change characteristics;

the processing module is used for constructing a large-scale terrain correction factor calculation model suitable for typhoon disasters according to the wind speed change characteristics; according to a mountain model, a hydrodynamic calculation model and a large-scale terrain correction factor calculation model, combining a gradient wind field simulation and a boundary layer model, and simulating a refined wind field of typhoons.

9. A computer, comprising:

one or more processors;

storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1-7.

10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program which, when executed by a processor, implements the method according to any of claims 1-7.