CN116882310A - Typhoon wind field calculation method and device based on hydrodynamics - Google Patents
Typhoon wind field calculation method and device based on hydrodynamics Download PDFInfo
- Publication number
- CN116882310A CN116882310A CN202310650583.1A CN202310650583A CN116882310A CN 116882310 A CN116882310 A CN 116882310A CN 202310650583 A CN202310650583 A CN 202310650583A CN 116882310 A CN116882310 A CN 116882310A
- Authority
- CN
- China
- Prior art keywords
- mountain
- wind
- typhoon
- model
- wind speed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 92
- 238000004088 simulation Methods 0.000 claims abstract description 58
- 238000012937 correction Methods 0.000 claims abstract description 56
- 238000000034 method Methods 0.000 claims abstract description 50
- 230000008859 change Effects 0.000 claims abstract description 46
- 238000009826 distribution Methods 0.000 claims abstract description 28
- 238000012360 testing method Methods 0.000 claims description 42
- 238000012545 processing Methods 0.000 claims description 22
- 230000008569 process Effects 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 14
- 238000010219 correlation analysis Methods 0.000 claims description 7
- 239000012530 fluid Substances 0.000 claims description 5
- 238000004519 manufacturing process Methods 0.000 claims description 5
- 239000002245 particle Substances 0.000 claims description 5
- 230000001788 irregular Effects 0.000 claims description 4
- 239000007787 solid Substances 0.000 claims description 4
- 230000000875 corresponding effect Effects 0.000 description 20
- 230000000694 effects Effects 0.000 description 19
- 238000012986 modification Methods 0.000 description 6
- 230000004048 modification Effects 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 230000001133 acceleration Effects 0.000 description 4
- 230000006978 adaptation Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 4
- 230000001276 controlling effect Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 3
- 230000002265 prevention Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 230000000903 blocking effect Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000009194 climbing Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005215 recombination Methods 0.000 description 2
- 230000006798 recombination Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 241000254032 Acrididae Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004040 coloring Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000003746 surface roughness Effects 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
- G01W1/06—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed giving a combined indication of weather conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Environmental & Geological Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Geometry (AREA)
- Pure & Applied Mathematics (AREA)
- Computing Systems (AREA)
- Fluid Mechanics (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Algebra (AREA)
- Mathematical Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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
Technical Field
The invention relates to the technical field of calculation and simulation of wind speed, in particular to a typhoon wind field calculation method and device based on hydrodynamics.
Background
The current typhoon loss evaluation method is mainly aimed at the scale of an administrative unit, the administrative unit is taken as a statistics unit, the maximum process wind speed of the typhoon in the whole field or other typhoon related indexes are taken as intensity indexes, and the loss evaluation is not carried out on the area which is actually affected by strong wind or storm and based on the actual exposure and the actual intensity of the grid scale. There are also situations where the loss estimate differs to some extent from the actual loss too much.
Considering that the actual disaster occurrence process is a dynamic change event, the post evaluation result is difficult to be practically applied in early warning and disaster relief. For example, when typhoons are in effect, although the intensity has fallen to the level of tropical storms, the intensity has historically been a rare occurrence over 30 years in some areas, and thus the economic losses suffered in some areas remain very large.
In the prior emergency disaster relief cases of typhoons, the areas with great loss caused by partial typhoons are not the areas close to the landing points and with the maximum disaster factor intensity, but some areas with lower intensity relative to the intensity of the typhoons, and meanwhile, the intensity is relative to the local less-frequent areas. In the prior weather forecast, only the central maximum wind speed at a landing point is often used as a typhoon intensity index for forecasting, and the influence of the wind speed after landing is ignored in the typhoon process due to the forecasting mode. Therefore, it is limited to calculate only the typhoon peak intensity occurrence probability.
Disclosure of Invention
The invention aims to solve the technical problem of providing a typhoon wind field calculation method and a typhoon wind field calculation device based on hydrodynamics, which form a spatial distribution data set of hydrodynamics characteristic parameters of typical mountain bodies with different wind directions based on hydrodynamics characteristics of the typical mountain bodies, so that the spatial distribution data set is used as terrain correction factor model input of a refined typhoon wind field model to obtain a large-scale typhoon down-scale refined wind field, the simulation speed and efficiency are greatly improved, the typhoon high-wind dangerous spatial distribution can be rapidly evaluated, and typhoon disaster loss pre-evaluation is respectively carried out from the aspects of disaster prevention and emergency management, thereby playing roles of improving efficiency, saving energy and reducing emission.
In order to solve the technical problems, the technical scheme of the invention is as follows:
in a first aspect, 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 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;
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.
Further, obtaining mountain data, and constructing a typical mountain model according to the mountain data, including:
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.
Further, processing the three-dimensional mountain to obtain a closed geometric entity, including:
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.
Further, obtaining mountain data, and constructing a hydrodynamic calculation model according to the mountain data, including:
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.
Further, quantitatively analyzing the wind speed simulation result, and extracting the wind speed change characteristics, including:
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.
Further, according to the mountain model, the hydrodynamic calculation model and the large-scale terrain correction factor calculation model, combining the gradient wind field simulation and the boundary layer model, simulating the refined wind field of typhoon, comprising:
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.
Further, according to the mountain model, the hydrodynamic calculation model and the large-scale terrain correction factor calculation model, the method combines gradient wind field simulation and boundary layer model to simulate the refined wind field of typhoon, and further comprises the following steps:
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.
In a second aspect, a typhoon wind farm computing device based on hydrodynamics includes:
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 the typical mountain according to the typical 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.
In a third aspect, a computer comprises:
one or more processors;
and a storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method.
In a fourth aspect, a computer readable storage medium has a program stored therein, which when executed by a processor, implements the method.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, based on the hydrodynamic characteristics of the typical mountain, the spatial distribution data set of hydrodynamic characteristic parameters of the typical mountain with different wind directions is formed, so that the spatial distribution data set is used as the terrain correction factor model input of a refined typhoon wind field model, a large-scale typhoon scale-down refined wind field is obtained, the simulation speed and efficiency are greatly improved, the typhoon high wind risk spatial distribution can be rapidly estimated, typhoon disaster loss pre-estimation is respectively carried out from the disaster prevention angle and the emergency management angle, and the effects of efficiency improvement, energy conservation and emission reduction are achieved.
Drawings
Fig. 1 is a schematic flow chart of a typhoon wind field calculation method based on hydrodynamics, which is provided by an embodiment of the invention.
Fig. 2 is a schematic diagram of a typhoon wind farm computing device based on hydrodynamics, provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention proposes a typhoon wind farm calculation method based on hydrodynamics, the method comprising the steps of:
step 11, acquiring mountain data, and constructing a typical mountain model and a hydrodynamic calculation model according to the mountain data;
step 12, simulating hydrodynamic wind speed distribution of each wind direction under the typical mountain according to the typical mountain model and the hydrodynamic calculation model to obtain a wind speed simulation result;
step 13, quantitatively analyzing the wind speed simulation result, and extracting wind speed change characteristics;
Step 14, constructing a large-scale terrain correction factor calculation model suitable for typhoon disasters according to the wind speed change characteristics;
and 15, simulating a refined wind field of typhoon according to the mountain model, the hydrodynamic calculation model and the large-scale terrain correction factor calculation model by combining the gradient wind field simulation and the boundary layer model.
According to the embodiment of the invention, the spatial distribution data set of the hydrodynamic characteristic parameters of the typical mountain with different wind directions is formed based on the hydrodynamic characteristics of the typical mountain, so that the spatial distribution data set is used as the terrain correction factor model input of the refined typhoon wind field model to obtain the large-scale typhoon down-scale refined wind field, the simulation speed and efficiency are greatly improved, the typhoon high-wind risk spatial distribution can be rapidly evaluated, and the typhoon disaster loss pre-evaluation is respectively carried out from the disaster prevention angle and the emergency management angle, thereby playing roles of improving the efficiency, saving energy and reducing emission.
In a preferred embodiment of the present invention, the step 11 may include:
step 111, controlling the mountain shape according to the mountain data by setting a mountain cross section control curve equation and controlling the mountain gradient by setting control curve parameters, and rotating a plane curve to obtain a three-dimensional mountain;
And step 112, processing the three-dimensional mountain to obtain a closed geometric entity.
In the embodiment of the invention, 12 hypothetical idealized mountain models for CFD (fluid mechanics) simulation are constructed on the mountain model construction in total by setting the mountainThe shape of the mountain is controlled by the form of a control curve equation (parabolic, cosine and Gaussian) of the body cross section, the control curve parameters are set to control the gradient of the mountain (tanalpha=0.17-1, and the angle is 10-45 degrees), and then the plane curve is rotated to obtain the three-dimensional mountain. Compared with the method for directly constructing the three-dimensional mountain model, the method has the advantages that the shape of the mountain is more simple and convenient to control by using the cross section curve equation, the height of the mountain and the gradient of the mountain can be directly adjusted, and the change rule of the wind speed along the mountain slope can be directly corresponding to the tangent plane curve. And finally obtaining three-dimensional space equations of three types of mountain models. Wherein H is the height of the mountain at a certain point, H is the height of the mountain top, L is the horizontal distance from the mountain top point to any side mountain foot point, and the gradient is defined as
In a preferred embodiment of the present invention, the step 112 may include:
step 1121, converting the space curved surface of the three-dimensional mountain into an irregular triangular net, and extracting the edge contour line of each mountain;
Step 1122, 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;
and 1123, connecting the bottom surface, the side elevation and the top surface formed by the mountain body, and taking and integrating to form a complete geometric solid model.
In the embodiment of the present invention, because the CFD calculation requires the occlusion object to be a limitation of a closed entity, the spatial curved 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 step 112 may include:
step 1124, converting the mountain model into a grid model;
step 1125, building a wind tunnel according to the grid model;
step 1126, generating a regular grid in the cuboid range formed by the wind tunnel, and dividing the whole influence space into N small cuboids;
step 1127, generating hexagonal subdivision grids by N small cuboids, thinning the grids on the surface of the mountain, enabling the grids to be attached to the shape of the mountain, and generating large-particle coarse grids at positions far from the mountain;
in step 1128, fluid mechanics calculations are performed based on the wind tunnels and the fine mesh.
In the embodiment of the invention, after the mountain model is obtained, the mountain model needs to be further converted into the Butterfly resolvable grid model, and a Butterfly entity is usually directly generated by a geographic entity. In the process, the additional layer level and the quality of the grid on the surface of the mountain are required to be set. A wind tunnel is established and set, and similar to the wind tunnel test process, the wind tunnel is firstly required to be established to provide free initial wind speed under the condition of no influence of terrain. In Grasshopper, the wind tunnel is considered a cuboid whose volume needs to completely encompass the geometric entity to be calculated. For the wind tunnel, the distance between the wind tunnel and the geometrical body is required to be set, so that the range of the influence of the wind force of the wind tunnel is defined, and the wind tunnel influence range is also required to be set, wherein the wind tunnel influence range comprises the gradient wind reference height and the surface roughness corresponding to the geometrical body, so that the attenuation caused by friction force of the wind speed in the real situation is simulated. In addition, wind speed and wind direction are required to be set. The wind speed is set to be 15m/s and 30m/s, and the wind direction is horizontally blown in along the y-axis direction. Generating a plurality of initial grids, generating a regular grid in the formed cuboid range of the wind tunnel, and dividing the whole influence space into N small cuboids. The fine grid of the attached surface is generated, the wind speed distribution of the mountain surface is more concerned, the wind speed of the position far away from the mountain in the wind tunnel range can be roughly expressed, and therefore, the fine grid is redundant for global fine resolution, the hexagonal fine grid is further generated, the grid is thinned on the mountain surface, the mountain shape is attached to the fine grid, the large-particle coarse grid is generated at the position far away from the mountain, and the calculated amount is reduced. After the wind tunnel and the subdivision grid are established, the differential equation of the fluid mechanics is solved, and two common differential equation sets are used for corresponding to different resolvers, namely a temperature conduction resolvers for solving the environmental temperature; the other is a steady-state incompressible model, which is suitable for airflow resolution.
For a steady-state incompressible model solver, a control differential equation of the steady-state incompressible model solver still needs to be further set, and a turbulence model which is simulated by the average Reynolds number is selected as input in consideration of the turbulence phenomenon of a mountain. In addition, since the higher order differential equation has a problem of multiple solutions, the model can be iterated continuously to find the optimal solution, so that the optimal solution is considered to be found when the error in the solving process is smaller than 0.0001, and the iteration is stopped. And setting test points and sampling quantity, wherein the test surface is defined as a 10-meter position on the surface of the mountain due to the fact that the change rule of the wind speed of the mountain is explored. Because of the large-scale characteristics of typhoon disasters, the typhoon direction changes at any time, and test points are only arranged in mountain shielding areas facing the windward slope, the leeward slope and the leeward slope of the upwind direction and the downwind direction. Calculating according to the actual air flow flowing speed, and setting the total sampling amount to be sampled once every five time steps for 300 times in order to ensure that the air flow has enough time to completely flow through the mountain. And (3) outputting data, visually expressing, and extracting the wind speed of each test point by using a CFD settlement result which is a vector line (u, v) at the position of the test point, wherein the wind direction of the test point and the wind speed of the test point are represented, and the vector data are required to be further processed for further statistical analysis. Meanwhile, visual expression can be carried out, including coloring of the subdivision grid according to the wind speed, drawing of air flow lines and the like.
In a preferred embodiment of the present invention, the step 13 may include:
step 131, analyzing the related variable of the mountain shape and the position of the test point of the simulated wind speed with 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 132, 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.
In the embodiment of the invention, 15m/s and 30m/s are used as initial wind speed input, wind speed simulation is carried out on the residual chord type, parabolic type and Gaussian type mountain bodies with the gradients of 10 degrees, 20 degrees, 30 degrees and 45 degrees respectively, 24 groups of CFD simulation results are obtained, and the total quantity of the data of the test points is about seventy thousand. Effective data with the ordinate falling on the y axis (positive wind direction) and abnormal values within 50m of the y axis are selected for further statistical processing, and a scatter diagram is drawn, wherein the left side is the cross section shape of 12 mountain models, and the right side is the wind speed simulation result of the test surface.
Therefore, according to simulation results, under the condition of two input wind speeds, the wind speeds are obviously changed along with climbing and descending of the mountain body, when the climbing reaches the vicinity of the mountain top point, the wind speeds corresponding to three mountain bodies are obviously increased, and when the mountain bodies are the same in whole length, the higher the mountain body height (the larger the mountain body gradient), the more obvious the wind speed acceleration effect of the mountain top point is. At the mountain feet of the windward slope and the leeward slope, the wind speed has deceleration change, the deceleration amplitude is related to the gradient, and the larger the gradient is, the more obvious the deceleration is. When the gradient of the mountain exceeds 16 degrees divided by building structure load standard, the trend of wind speed along with the change of the mountain is unchanged, and the wind speed is similar to the law of the whole wind speed of the mountain with a slower gradient, which indicates that the gradient does not change the trend of the wind speed, and more influences the increasing degree of the wind speed.
On the slope surface, the wind speed distribution is relatively close to the position near the peak point, and the windward slope and the leeward slope are in symmetrical forms. Outside its range, the two wind speed distributions are different: the wind speed is increased from the mountain foot of the windward slope to the mountain top, and only the Gaussian mountain body has a tendency of increasing after decreasing under the condition of larger gradient. The wind speed at the tail part of the lee slope mostly shows an upward trend, namely the wind speed of the lee slope surface shows a change of decreasing before increasing, but the change range is smaller under most conditions, and the change is obvious only when the gradient exceeds 30 degrees.
Overall, the wind speed distribution is very similar to the shape of the mountain itself, i.e. the terrain correction factor does not simply change linearly with the mountain apex distance, but with the change in shape of the mountain. In summary, the general trend of the wind speed affected by the fluctuation of the mountain is to slow down the slope toe of the windward slope, and then to the peak point, the wind speed is increased to the maximum, and the wind speed is slowed down from the peak point to the vicinity of the slope toe of the leeward slope.
For a flat area with smaller relief degree behind a mountain, the influence of the shielding effect of the mountain is received, and the deceleration phenomenon is often generated. The invention detects the slope wind speed change and calculates the change of the flat area and the wind speed behind the mountain, and the result shows that the speed reduction range behind the mountain lee slope also is called increasing trend along with the increase of the slope of the mountain in the wind direction outflow direction. But is affected by the compression and turbulence of the side slopes, the lateral distance of the deceleration range is related to the mountain width, and the mountain width and the wind-sensing slope compression range are related to the mountain shape. In the downwind direction, the deceleration effect starts from the mountain foot point and extends outwards, the deceleration is continued in a certain range close to the mountain foot of the leeward slope, and then the speed is slowly recovered, and the average range is 7 times of the mountain height to separate from the influence of the mountain, namely the free wind speed in the initial condition is returned.
And quantitatively analyzing 24 groups of wind speed simulation results based on CFD, and extracting wind speed change characteristics from a statistical angle. And respectively selecting the simulated wind speeds of the windward slope toe, the leeward slope toe and the mountain top area, and calculating the ratio (namely the terrain correction coefficient eta) of the initial free wind speed for each area.
Firstly, carrying out correlation analysis on the correlation variables for determining the mountain shape and the positions of test points for simulating the wind speed and the wind speed so as to determine whether the variables are suitable for being correlated with the wind speed change, namely determining which topography correlation indexes influence the mountain wind speed change. And firstly, selecting three groups (128) of test point data corresponding to 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. Considering that the influence of an actual mountain on wind speed does not depend on absolute height, the height of a test point is compared with the total height of the mountain, and the relative height of the test point is used as one of dependent variables.
The results of the correlation analysis between the variables are shown in table 1:
all correlation coefficient analysis results were significant at the 0.01 level. According to the analysis result of the correlation coefficient, the relative height of the test point can represent two indexes of the mountain height and the test point height, and the correlation coefficient between the distance from the test point to the mountain edge and the relative height of the test point is 1, because the X-axis coordinate of the point is the distance from the edge in the two-dimensional coordinate system where the mountain cross section is located under the condition that the mountain shape and the gradient are determined, and the Y-axis coordinate corresponding to the test point height is in one-to-one corresponding functional relation with the X-axis coordinate. Similarly, the correlation coefficient between the mountain height and the gradient is 1, so that the relative height of the mountain gradient and the test point can be equivalently replaced by other correlation indexes, and the correlation indexes for fitting the terrain correction factors are finally determined to be the relative height of the mountain gradient and the test point.
Firstly, analyzing a mountain top area, and because a calculation formula of a mountain top terrain correction factor is specified in building structure load specification, comparing a CFD simulation result with a model fitting result can obtain that in a section with a gradient less than 0.6, a model calculation result is smaller, and the average value of each group of test data is 1.2 times of a simulation value, so that in the gradient section, the correction factor needs to be added to an original calculation model. And as the gradient further increases, the CFD simulation result shows that the wind speed increasing trend is slowed down, the wind speed increasing trend does not increase in a linear relation along with the gradient, and when the mountain gradient reaches 1, the conventional fitting formula can accurately reflect the mountain peak wind speed changing trend.
And further performing curve fitting on terrain correction factor coefficients obtained by simulating mountain foot areas of a windward slope and a leeward slope and the relative heights of mountain slopes and test points respectively, and attempting regression analysis by using a linear model, a quadratic polynomial, a logarithmic model, an exponential model, a power function model and the like, thereby obtaining a fitting formula. The results are shown in Table 2, where the linear model fitting effect is best, 0.702 at the windward slope toe and 0.721 at the leeward slope toe.
Table 2 terrain correction factor formula fitting results
Aiming at the limitation of the current building structure load standard on typhoon wind field construction application, the terrain correction factor model based on the CFD simulation result mainly changes in the mountain range:
1. for mountain peaks: the wind speed change characteristic of the mountain peak can be reflected by the existing correction method in the application range, but the whole numerical value is smaller. Thus, for mountain peaks, the magnification scale factor is increased.
2. For mountain foot points: the turbulence and the blocking effect are not considered at the bottom of the windward slope, the overall numerical value is larger, and the terrain correction factor is also larger due to the neglect of the deceleration effect of the leeward slope. Therefore, the terrain correction factor is not valued to be 1 at the mountain foot points of the windward slope and the leeward slope, but is fitted with a calculation model related to the mountain body, so that the appearance position of the acceleration phenomenon is more practical, and the deceleration effect is reflected.
3. The method provides a more detailed correction scheme for mountain bodies with gradient larger than 16 degrees, and because the wind speed change trend is similar, the mountain bodies with larger gradient are not lack in the typhoon coverage range, the gradient is used as one of variables affecting the terrain correction factors and is participated in the calculation, so that the mountain bodies outside the national standard range also have corresponding parameter calculation models.
4. For the fact that the wind speed change trend on the slope is nonlinear, the difference of wind speeds is influenced by different mountain shapes, the calculation method of the terrain correction factors on the slope is changed into vertical interpolation, the difference of the influence of different mountain shapes on the wind speeds is highlighted, and the wind speed distribution is closer to the real wind speed distribution.
For the area behind the mountain: the terrain modification factor is not specified in the specification for the area behind the mountain, and the range of the terrain influence is only related to the mountain width, i.e. the modification scheme is provided only on the raised terrain. The actual simulation results show that the speed reduction trend of the mountain is kept at a certain distance by considering the shielding effect and the local turbulence of the mountain on the flat terrain behind the mountain lee slope. Considering that the lateral distance that the side wind slopes can affect is smaller than the whole mountain, and in a large scale range, the mutual influence among the mountain is often larger than the influence of the side wind slopes, therefore, only the change of the wind speed along the wind direction is considered, the deceleration trend is kept to 3 times of mountain height, and then the mountain is continuously influenced by the mountain shielding effect within the height range of 5-7 times to be in the deceleration trend compared with the initial wind speed, but the slow recovery is carried out to be free of influence along with the increase of the horizontal distance.
Based on the analysis and the transverse comparison, in order to match the law of the CFD simulation result with a large-scale terrain correction factor model, a specific computing model of a complete terrain correction factor is supplemented by combining the terrain correction factor model in building structure load standard, as shown in Table 3:
TABLE 3 calculation method of large-scale complex terrain correction factor (eta) based on CFD
In addition to the mountain terrain described in table 3, other undulating terrain such as canyons, basins, etc. are referenced to the recommended values of the building structure load Specification, but transition regions are provided between different terrains in view of the interplay between multiple mountains and the turbulence characteristics embodied by CFD. Thus, a more refined terrain correction factor calculation model suitable for typhoon wind farms is constructed.
The invention describes a modeling method of 12 ideal typical mountain models and construction of a CFD wind speed settlement flow. After the required model is built, 15m/s and 30m/s are used as initial wind speed input, the wind speed change of 12 mountain bodies is simulated, and a single mountain body wind speed change value under ideal conditions is obtained. The wind speed value in the mountain top area is obviously increased, and the wind speed value in the mountain foot area of the windward slope and the mountain foot area of the leeward slope is obviously weakened. And carrying out statistical analysis on results of 4 tens of thousands of test points and 384 test points in a special area of all mountains, wherein the terrain factors influencing the wind speed change have better correlation with the gradient and the height of the mountains, and the factor change can be better described by a linear model. And performing curve fitting by using the mountain shape parameters and the terrain correction factors to construct a terrain factor calculation model of each key position of the mountain, and adopting a vertical interpolation method on the slope of the mountain to supplement a calculation model of a deceleration effect generated by the shielding effect of the mountain on the wind speed distribution of a plain behind the mountain, thereby constructing a large-scale terrain correction factor calculation model suitable for typhoon disasters.
In a preferred embodiment of the present invention, the step 15 may include:
step 151, obtaining typhoon history data provided in the optimal path data set based on interpolation processing;
step 152, extracting the typhoon center air pressure, the typhoon center longitude and the typhoon center latitude in the typhoon history data;
step 153, calculating typhoon center data of each moment according to the typhoon center air pressure, the typhoon center longitude and the typhoon center latitude.
In the embodiment of the invention, the parameterized typhoon wind field model, the boundary layer model and the monitoring area terrain correction factors based on the CFD simulation result are combined to construct the refined typhoon wind field model. A typhoon center based on the CMA optimal path data set records a refined wind field simulating historical typhoons. And (3) carrying out space precision verification on wind speed simulation results by using weather station actual measurement data, and carrying out transverse comparison with other typhoon wind field models to verify the simulation effect of the refined typhoon wind field model. In the whole influence range of typhoon disasters, the simulation results are counted in different areas, and the simulation effects of the refined wind field model facing different stages of typhoons and facing different area features are analyzed.
It should be noted that, because the calculation modes of the terrain factors corresponding to various landform units such as mountain (including windward slope, leeward slope, etc.), canyon, basin, etc. are different, each DEM grid firstly interprets the landform unit to which it belongs. Because the fluctuation degree in the monitoring area is large, the southwest part is a continuous mountain, a valley exists between the area and the northwest mountain, the southeast part of the monitoring area is a continuous hilly, and the northeast part is a large plain and has a small amount of hilly, so that the conventional landform division, such as absolute elevation, gradient and the like, is easy to misjudge or miss. Therefore, the invention calculates the relative elevation value to extract mountain and mountain line, thereby subdividing other landforms. The method comprises the following specific steps: calculating the average elevation value of adjacent grids taking the grid as the center by grids, and calculating the difference value between the elevation value of the grid and the average elevation of the neighborhood as a judgment standard 1; calculating an average elevation value in the range of the surrounding 3 neighborhood as a judgment standard 2; and calculating the average elevation difference values of the upper, lower, left and right neighborhood around the grid point in the range of the surrounding 5 neighborhood as judgment standards 3 and 4. And synthesizing the threshold values of all the judgment standards to obtain mountain areas and mountain contour lines, wherein the mountain contour lines are used as boundaries for dividing mountain and other landforms. After the mountain range is determined, the mountain slopes, mountain basins, mountain openings and the like are continuously distinguished in the mountain range, and whether the trend of canyons and mountain openings is consistent with the wind direction or not is required to be explained according to different wind directions, so that the wind speed change rule in the canyons is determined.
On the basis, the main wind direction is set to be 8 directions at intervals by taking the forward direction as the forward direction and taking the forward and western direction as the forward and western direction. 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 direction is determined, a terrain factor calculation model corresponding to the terrain is selected according to the type of the terrain where each grid is located. The intersection of other terrains in the upwind direction and the mountain, namely the mountain contour line is a windward slope mountain foot, the windward slope is arranged in the region from the windward slope mountain foot to the mountain top point corresponding to the grid, the intersection of other terrains in the downwind direction and the mountain is a leeward slope mountain foot, a leeward slope is arranged between the windward slope and the mountain top point, and an influence area is arranged in the range of 7 times of mountain height outside the downwind slope mountain foot. And screening canyons and mountains with the trend consistent with the downwind direction as the special correction area under the wind direction, so as to calculate the 8-direction terrain correction factors of the monitoring area.
The maximum value of the terrain correction factor of the monitored area appears in the mountain area in the southwest of the monitored area, the terrain fluctuation of the area is large, and the mountain height and the slope are high in the province. The northwest area is a hilly area, the whole mountain has low altitude, but the valleys and the canyons are concentrated, and the acceleration function of the canyons and the valleys and the deceleration function of the blocking terrains such as basin exist at the same time. The eastern area is a mixed area of coastal plain and hills, the overall value of the terrain correction factors is not high, but the range of the variation interval is larger. The northern area is a alluvial plain, less hills and mountains exist, the terrain correction factors are the most average, and meanwhile, the method is also an area with the least influence of the terrain in the monitored area on the wind speed change, and the acceleration and deceleration effects are not obvious.
In a preferred embodiment of the present invention, the step 15 may include:
step 154, 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;
step 155, calculating and reserving the maximum value of the wind speed of each grid at all times in the whole typhoon event to obtain a typhoon gradient process wind field;
step 156, extracting the range of the wind ring which affects the actual production and life as the simulation result of the typhoon gradient wind field.
In the embodiment of the invention, in order to simulate a historical typhoon wind field, typhoons in 1949-2019 monitoring areas provided in a CMA optimal path dataset are recorded and screened firstly. And extracting the typhoon center air pressure, the typhoon center longitude and the typhoon center latitude in the records according to the requirements of the Holland wind field model. Since the gradient wind field model is calculated according to the typhoon center data at each moment, when the typhoon center moves at a high speed, the calculation range of the wind field calculation model is omitted, so that interpolation processing is required to be performed on initial typhoon records at 6-hour event intervals.
Through analysis of CMA optimal path data set records, the relation between the position of the typhoon center at the time t and the time t-1 can be better fitted through linear fitting, and the fitted trend line has smaller error. The change of the typhoon central air pressure generally shows a trend of decreasing first and increasing second on a time sequence, accords with the generation and development process of typhoons, namely weaker in initial generation, and then enhances development, and finally weakens and disappears. However, since the trend is difficult to quantify on time sequence, the quantitative relation between the front moment and the rear moment is analyzed, and the change rule of the typhoon central air pressure can be reflected better through polynomial fitting. The average value of the typhoons after fitting is close to 1.
Based on the typhoon center record after interpolation processing, carrying the typhoon center record at each moment into a Holland gradient wind field model for calculation to obtain instantaneous wind speed spatial distribution, wherein the calculation formula is as follows:
RMW＝-18.04lnΔp+110.22；
B＝1.38-0.00184Δp+0.00309RMW；
and 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 the typhoon gradient process wind field. On the periphery of typhoon, because the influence of the low-voltage center is weaker, the wind power caused by the typhoon center cannot reach the disaster-causing level, and therefore, the wind ring range which can influence actual production and life is extracted as the simulation result of the typhoon gradient wind field by taking the wind speed (10.8 m/s) corresponding to six-level wind (strong wind) as a threshold value, and the calculation is stopped to avoid data redundancy for the region around typhoon.
And calculating the instantaneous wind direction angle corresponding to the maximum wind speed when the maximum wind speed occurs according to the spatial angle relation between the typhoon center and the grid position while calculating the wind speed time by time. Because the typhoon wind direction changes at the moment, the wind direction takes a certain range of values to reflect the wind direction characteristics in the corresponding time period. To accommodate near-surface wind speed correction, the wind direction is classified as 8 wind directions.
In order to verify the simulation effect of typhoon wind field under long time sequence and compare the dangerous space distribution results under different data sources so as to more comprehensively analyze the dangerous of the typhoon and the typhoon, the invention adopts the same extremum algorithm to calculate the annual typhoon wind speed based on weather station data, and comprises the following specific steps:
(1) The peripheral wind speed of the typhoon gradient wind field is set to be 13.8m/s (7-level wind), the wind circle radius corresponding range is regarded as the typhoon influence range by using the simulation result of the gradient wind field, and the upper limit of the range is intercepted to 1000Km.
(2) And counting the start-stop time of each meteorological station, which is influenced by each typhoon, extracting the daily maximum wind speed of the station in the time period, and calculating the maximum value of the daily maximum wind speed in the start-stop time period.
(3) And (3) performing spatial interpolation on the site wind speed by using a Kriging method, extracting the daily maximum wind speed of each grid point by taking a year as a typhoon disaster factor intensity years extreme value based on the interpolated wind speed, and calculating the site wind speed under a typical reproduction period by using a Gumbel model.
As shown in fig. 2, an embodiment of the present invention further provides a typhoon wind farm computing device 20 based on hydrodynamics, including:
an acquisition module 21 for acquiring mountain data and constructing a mountain model and a hydrodynamic calculation model from the mountain data; simulating hydrodynamic wind speed distribution of each wind direction under the typical mountain according to the typical 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 22 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.
Optionally, acquiring mountain data, and constructing a typical mountain model according to the mountain data, including:
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.
Optionally, processing the three-dimensional mountain to obtain a closed geometric entity, including:
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.
Optionally, acquiring mountain data, and constructing a hydrodynamic calculation model according to the mountain data, including:
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.
Optionally, quantitatively analyzing the wind speed simulation result, and extracting a wind speed variation feature, including:
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.
Optionally, according to the mountain model, the hydrodynamic calculation model and the large-scale terrain correction factor calculation model, combining the gradient wind field simulation and the boundary layer model, simulating the refined wind field of typhoon 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.
Optionally, according to the mountain model, the hydrodynamic calculation model and the large-scale terrain correction factor calculation model, combining the gradient wind field simulation and the boundary layer model, simulating the 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.
It should be noted that the apparatus is an apparatus corresponding to the above method, and all implementation manners in the above method embodiment are applicable to this embodiment, so that the same technical effects can be achieved.
Embodiments of the present invention also provide a computer including: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art after reading this description of the invention.
The object of the invention can thus 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. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said 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. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310650583.1A CN116882310A (en) | 2023-06-04 | 2023-06-04 | Typhoon wind field calculation method and device based on hydrodynamics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310650583.1A CN116882310A (en) | 2023-06-04 | 2023-06-04 | Typhoon wind field calculation method and device based on hydrodynamics |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116882310A true CN116882310A (en) | 2023-10-13 |
Family
ID=88259306
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310650583.1A Pending CN116882310A (en) | 2023-06-04 | 2023-06-04 | Typhoon wind field calculation method and device based on hydrodynamics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116882310A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117197383A (en) * | 2023-11-03 | 2023-12-08 | 成都流体动力创新中心 | Terrain extension method, equipment and medium based on characteristic dimension of complex terrain |
| CN118410744A (en) * | 2024-07-01 | 2024-07-30 | 国网浙江省电力有限公司经济技术研究院 | Method, device and equipment for monitoring wind load of transmission line wire |
-
2023
- 2023-06-04 CN CN202310650583.1A patent/CN116882310A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117197383A (en) * | 2023-11-03 | 2023-12-08 | 成都流体动力创新中心 | Terrain extension method, equipment and medium based on characteristic dimension of complex terrain |
| CN117197383B (en) * | 2023-11-03 | 2024-02-09 | 成都流体动力创新中心 | Terrain extension method, equipment and medium based on characteristic dimension of complex terrain |
| CN118410744A (en) * | 2024-07-01 | 2024-07-30 | 国网浙江省电力有限公司经济技术研究院 | Method, device and equipment for monitoring wind load of transmission line wire |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111582755B (en) | Mountain torrent disaster comprehensive risk dynamic assessment method based on multi-dimensional set information | |
| CN116882310A (en) | Typhoon wind field calculation method and device based on hydrodynamics | |
| CN110427857B (en) | Power transmission line geological disaster analysis method based on remote sensing data fusion | |
| CN110598290B (en) | Method and system for predicting future hydropower generation capacity of basin considering climate change | |
| CN111507375B (en) | Urban waterlogging risk rapid assessment method and system | |
| CN105205466B (en) | A kind of energy carbon emission amount remote sensing estimation method based on night lights image | |
| CA3154014A1 (en) | Climate-based risk rating | |
| Friedland et al. | Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions | |
| Zhang et al. | Changes in precipitation extremes over eastern China simulated by the Beijing Climate Center Climate System Model (BCC_CSM1. 0) | |
| Deng et al. | Pattern of spatio-temporal variability of extreme precipitation and flood-waterlogging process in Hanjiang River basin | |
| CN108646319A (en) | A kind of heavy showers forecasting procedure and system in short-term | |
| CN111079999A (en) | Flood disaster susceptibility prediction method based on CNN and SVM | |
| CN115115262A (en) | Flood risk disaster assessment method | |
| CN117556197B (en) | Typhoon vortex initialization method based on artificial intelligence | |
| CN112132341B (en) | Flood risk prediction method based on rainstorm process | |
| CN112132795A (en) | Electric tower disaster risk assessment method and system based on LiDAR point cloud | |
| CN117152919A (en) | Meteorological disaster early warning system based on multi-source heterogeneous comprehensive database | |
| CN117807917B (en) | Loss function construction method and system based on scene flood disasters | |
| CN114091240B (en) | Method for researching long-range persistence of wind speed by using wind measuring tower data | |
| CN113935658A (en) | Mountain land utilization change evaluation method based on terrain gradient | |
| Liu et al. | Field measurement study of intense thunderstorm outflows characteristics based on 356 m high meteorological tower | |
| CN116596410A (en) | Typhoon storm surge disaster vulnerability evaluation method based on combined weighting method | |
| Zeng et al. | Evaluating the effects of topographical factors on the precipitation simulated by kilometer-scale versus quarter-degree dynamical downscaling models in eastern China | |
| CN114563771A (en) | Double-threshold laser radar cloud layer detection algorithm based on cluster analysis | |
| Yan et al. | A novel integrated urban flood risk assessment approach based on one-two dimensional coupled hydrodynamic model and improved projection pursuit method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |