A method, device and electronic equipment for predicting wind load on heliostat surface

Abstract

The present invention provides a method, device and electronic device for predicting the surface wind load of a heliostat, including: obtaining the structural parameters of the heliostat; constructing an initial three-dimensional model of the heliostat; setting the elevation angle of the heliostat, the incoming wind direction angle and the external flow field; setting the physical model of the solver to a large eddy simulation model and setting the above solver to an incompressible transient flow; setting the inlet and outlet of the external flow field to a velocity inlet and a pressure outlet respectively; setting the top, side wall and ground boundary conditions of the surface of the heliostat and the external flow field to a non-slip wall condition; creating a pulsating velocity of the velocity inlet; setting the iteration time of the pulsating velocity based on a preset simulation duration and Courant number; calculating and simulating the external flow field to obtain a simulation result; and determining the average wind pressure coefficient, pulsating wind pressure coefficient and extreme wind pressure coefficient of the heliostat according to the above simulation result. The method ensures the safety of the heliostat.

Description

Technical Field

[0001] The present invention relates to the technical field of wind load prediction, and in particular to a method, device and electronic equipment for predicting wind load on a heliostat surface. Background Art

[0002] A tower solar thermal power station is a power generation method that uses concentrated solar energy collection technology. Its core components are the absorber at the top of the tower and a large number of heliostats arranged around the tower. These heliostats reflect and concentrate sunlight onto the absorber at the top of the tower through a sophisticated optical tracking system, generating high-temperature heat energy, which in turn generates steam through a thermal energy conversion system to drive turbines to generate electricity. Tower solar thermal power stations are usually built in relatively open areas. Since there are no large enough shields around to ease air flow, the wind force in the site is often relatively large. Heliostats are light in weight, flexible, and very sensitive to wind loads. Their wind resistance is one of the important control indicators of heliostat design.

[0003] With the development of computing technology and the improvement of wind engineering theory, numerical calculations are increasingly being used in the study of heliostat wind resistance. Through computer software and high-performance computing resources, the behavior of heliostats in various wind environments can be simulated, including the distribution of wind loads, structural stress and deformation, etc., providing strong support for heliostat design.

[0004] However, the wind field simulated by the existing numerical calculation method has a large deviation from the actual wind environment, and fails to fully consider the turbulence characteristics at the entrance of the simulated wind field. It mainly uses the exponential rate form to represent the turbulence intensity at the entrance, and then expresses the definition of turbulent kinetic energy and turbulent dissipation rate according to the turbulence intensity and turbulence integral scale, and then specifies the wind speed profile expression, turbulent kinetic energy and turbulent dissipation rate expression to the entrance boundary condition. The steady-state RANS model is used to calculate the stable flow field, and then converted into a transient large eddy simulation model to calculate the average wind load and pulsating wind load. Although this method helps the convergence of the large eddy simulation model, the calculation error is large and fails to provide more reliable data support for the design of heliostats. In addition, most of the wind resistance research on heliostats focuses on the average wind load and pulsating wind load on the mirror surface, and fails to analyze the extreme wind load on the heliostat surface, thereby increasing the risk of local damage to the heliostat structure. Summary of the invention

[0005] The object of the present invention is to provide a method, device and electronic device for predicting the wind load on the surface of a heliostat, so as to alleviate the technical problem that the prior art fails to analyze the extreme wind load on the heliostat surface, thereby increasing the risk of local damage to the heliostat structure, thereby improving the safety of the heliostat structure.

[0006] In a first aspect, an embodiment of the present invention provides a method for predicting wind load on a heliostat surface, comprising: obtaining structural parameters of a heliostat; constructing an initial three-dimensional model of the heliostat according to the structural parameters; setting the elevation angle of the heliostat, the incoming wind direction angle, and the external flow field based on the initial three-dimensional model to obtain an intermediate three-dimensional model; creating an external flow field and meshing the external flow field based on the intermediate three-dimensional model to obtain a mesh of the external flow field, and reading the mesh of the external flow field; setting a solver to incompressible transient flow; and setting the inlet and outlet of the external flow field to a velocity inlet and a pressure outlet, respectively; and setting the surface of the heliostat and the top of the external flow field to the velocity inlet and the pressure outlet. , side wall and ground boundary conditions are set to no-slip wall conditions; based on the turbulent pulsating flow field generation method, the pulsating velocity of the velocity inlet is created; and, based on the preset simulation time and Courant number, the iteration time of the pulsating velocity is set; the laminar model, the unsteady first-order format and the standard SIMPLE algorithm are used to calculate the external flow field to obtain a stable initial flow field; based on the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the SIMPLE algorithm, the external flow field is continued to be calculated to obtain simulation results; according to the simulation results, the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the heliostat are determined.

[0007] In a preferred embodiment of the present invention, the step of determining the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the heliostat according to the above simulation results comprises: determining the average wind pressure coefficient and the pulsating wind pressure coefficient of a preset measuring point on the mirror surface of the heliostat according to the above simulation results; determining the extreme wind pressure coefficient according to the average wind pressure coefficient and the pulsating wind pressure coefficient.

[0008] In a preferred embodiment of the present invention, the step of determining the extreme wind pressure coefficient according to the average wind pressure coefficient and the fluctuating wind pressure coefficient includes: judging whether the fluctuating wind load corresponding to the fluctuating wind pressure coefficient presents Gaussian characteristics based on the Kolmogorov-Smirnov test; if the fluctuating wind load presents Gaussian characteristics, calculating the first peak factor of the fluctuating wind load according to the zero over-crossing rate of the fluctuating wind load based on the extreme value distribution method of the standardized Gaussian process; determining the extreme wind load coefficient according to the first peak factor, the average wind pressure coefficient and the fluctuating wind pressure coefficient; if the fluctuating wind load does not present Gaussian characteristics, calculating the second peak factor of the fluctuating wind load based on the coefficient of correlation between the skewness and kurtosis of the wind pressure distribution obtained by the improved Hermite model method and the fluctuating wind load calculation; determining the extreme wind load coefficient according to the second peak factor, the average wind pressure coefficient and the fluctuating wind pressure coefficient.

[0009] In a preferred embodiment of the present invention, the step of determining the above-mentioned extreme wind load coefficient according to the first peak factor, the above-mentioned average wind pressure coefficient and the above-mentioned pulsating wind pressure coefficient includes: determining the first maximum wind load coefficient and the first minimum wind load coefficient according to the above-mentioned first peak factor, the above-mentioned average wind pressure coefficient and the above-mentioned pulsating wind pressure coefficient; determining the above-mentioned extreme wind load coefficient according to the above-mentioned first maximum wind load coefficient and the above-mentioned first minimum wind load coefficient; the step of determining the above-mentioned extreme wind load coefficient according to the above-mentioned second peak factor, the above-mentioned average wind pressure coefficient and the above-mentioned pulsating wind pressure coefficient includes: determining the second maximum wind load coefficient and the second minimum wind load coefficient according to the above-mentioned second peak factor, the above-mentioned average wind pressure coefficient and the above-mentioned pulsating wind pressure coefficient; determining the above-mentioned extreme wind load coefficient according to the above-mentioned second maximum wind load coefficient and the above-mentioned second minimum wind load coefficient.

[0010] In a preferred embodiment of the present invention, before the step of creating the pulsating velocity of the velocity inlet based on the turbulent pulsating flow field generation method, the method includes: constructing wind speed spectra corresponding to the downwind image, the crosswind direction and the vertical wind direction respectively based on the Von Karman target wind speed spectrum; the step of creating the pulsating velocity of the velocity inlet includes: calculating the first amplitude of the sine function of the pulsating velocity and the second amplitude of the cosine function of the pulsating velocity according to the wind speed spectra corresponding to the downwind image, the crosswind direction and the vertical wind direction respectively; creating the pulsating velocity of the velocity inlet according to the sine function, the first amplitude of the sine function, the cosine function and the second amplitude of the cosine function.

[0011] In a preferred embodiment of the present invention, the step of creating the pulsating speed of the speed inlet according to the sine function, the first amplitude of the sine function, the cosine function and the second amplitude of the cosine function comprises: creating the pulsating speed of the speed inlet according to the following formula:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] in, is the cosine function above, is the above sine function, i=1,2,3, u1 is the downwind direction, u 2 is the above crosswind direction, u 3 is the vertical wind direction, j=1,2,3, x 1 is the spatial longitudinal direction, x 2 is the horizontal space, x 3 is the spatial vertical direction, t is the preset time, M is the number of discrete segments of the target spectrum, M=256, N is the number of discrete random frequencies of each discrete spectrum segment, N=100, is a random number that follows a three-way normal distribution, sign is a sign function, is the wind speed spectrum of the pulsating component i, is the angular wave number, Generates adjustment factors for the spatial correlation of the fluctuating wind field for the preset regulation, Generates adjustment factors for the time dependency of the fluctuating wind field for the preset regulation, , is the preset frequency; m is the mth energy spectrum component in the discrete spectrum, K m is the wave number of the mth component in the above discrete spectrum.

[0019] In a preferred embodiment of the present invention, before the step of calculating the first amplitude of the sine function of the pulsating velocity and the second amplitude of the cosine function of the pulsating velocity according to the wind speed spectra corresponding to the above-mentioned downwind image, the above-mentioned crosswind direction and the above-mentioned vertical wind direction, the above-mentioned method includes: calculating wind speed profile data based on the wind speed at a preset height and the ground roughness index; calculating turbulence intensity based on the preset horizontal turbulence intensity and the above-mentioned ground roughness index; calculating turbulence integral scale based on the preset hydraulic diameter, width and height of the above-mentioned external flow field; determining the above-mentioned wind speed spectrum according to the above-mentioned wind speed profile data, the above-mentioned turbulence intensity and the above-mentioned turbulence integral scale.

[0020] In a preferred embodiment of the present invention, the step of determining the wind speed spectrum according to the wind speed profile data, the turbulence intensity and the turbulence integral scale comprises: determining the wind speed spectrum according to the wind speed profile data, the turbulence intensity and the turbulence integral scale based on the following formula:

[0021]

[0022]

[0023]

[0024] in, is the wind speed spectrum along the wind direction, is the wind speed spectrum in the above-mentioned crosswind direction, is the wind speed spectrum of the vertical wind direction, is the above wind speed profile data, i=u, v, w, Iu is the above turbulence intensity in the downwind direction, I v is the turbulence intensity in the above-mentioned crosswind direction, I w is the turbulence intensity in the vertical wind direction, L u x is the above-mentioned turbulence integral scale in the downwind direction, L v x is the turbulence integral scale in the above-mentioned crosswind direction, L w x is the turbulence integral scale in the above vertical wind direction.

[0025] In a second aspect, an embodiment of the present invention further provides a device for predicting the surface wind load of a heliostat, comprising: a data acquisition module for acquiring structural parameters of the heliostat; an initial model construction module for constructing an initial three-dimensional model of the heliostat according to the structural parameters; a model construction module for setting the surface elevation angle, incoming wind direction angle and external flow field of the heliostat based on the initial three-dimensional model to obtain an intermediate three-dimensional model; based on the intermediate three-dimensional model, creating an external flow field and meshing the external flow field to obtain a mesh of the external flow field, and reading the mesh of the external flow field; and setting the solver to incompressible transient flow; and setting the inlet and outlet of the external flow field to a velocity inlet and a pressure outlet, respectively; and setting the inlet and outlet of the heliostat to a velocity inlet and a pressure outlet, respectively. The surface and the top, side walls and ground boundary conditions of the above-mentioned external flow field are set as no-slip wall conditions; a simulation module is used to create the pulsating velocity of the above-mentioned velocity inlet based on the turbulent pulsating flow field generation method; and, based on the preset simulation time and Courant number, the iteration time of the above-mentioned pulsating velocity is set; the above-mentioned external flow field is calculated using a laminar model, an unsteady first-order format and a standard SIMPLE algorithm to obtain a stable initial flow field; the above-mentioned external flow field is continued to be calculated based on the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the above-mentioned SIMPLE algorithm to obtain simulation results; according to the above-mentioned simulation results, the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the above-mentioned heliostat are determined.

[0026] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the heliostat surface wind load prediction method.

[0027] The embodiments of the present invention have the following beneficial technical effects:

[0028] The embodiment of the present invention provides a method, device and electronic device for predicting the wind load on the surface of a heliostat, comprising: obtaining structural parameters of a heliostat; constructing an initial three-dimensional model of the heliostat according to the structural parameters; setting the elevation angle of the heliostat, the incoming wind direction angle and the external flow field based on the initial three-dimensional model to obtain an intermediate three-dimensional model; creating an external flow field and meshing the external flow field based on the intermediate three-dimensional model to obtain a mesh of the external flow field, and reading the mesh of the external flow field; setting a solver to incompressible transient flow; and setting the inlet and outlet of the external flow field to a velocity inlet and a pressure outlet, respectively; and meshing the surface of the heliostat and the external flow field. The top, side wall and ground boundary conditions are set as non-slip wall conditions; based on the turbulent pulsating flow field generation method, the pulsating velocity of the velocity inlet is created; and, based on the preset simulation time and Courant number, the iteration time of the pulsating velocity is set; the laminar flow model, the unsteady first-order format and the standard SIMPLE algorithm are used to calculate the external flow field to obtain a stable initial flow field; the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the SIMPLE algorithm are used to continue to calculate the external flow field to obtain simulation results; according to the simulation results, the average wind pressure coefficient, pulsating wind pressure coefficient and extreme wind pressure coefficient of the heliostat are determined. This method is based on the accurate simulation of the turbulent characteristics of the wind speed inlet, creates the pulsating velocity of the velocity inlet, and then simulates the flow field of the heliostat, thereby analyzing the extreme wind load on the heliostat surface and ensuring the safety of the heliostat. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 A schematic flow chart of a method for predicting wind load on a heliostat surface provided by an embodiment of the present invention;

[0031] Figure 2 A schematic flow chart of another method for predicting wind load on a heliostat surface provided by an embodiment of the present invention;

[0032] Figure 3 A schematic structural diagram of a heliostat surface wind load prediction device provided by an embodiment of the present invention;

[0033] Figure 4A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0034] Icons: 31 - data acquisition module; 32 - initial model building module; 33 - model building module; 34 - simulation module; 41 - memory; 42 - processor; 43 - bus; 44 - communication interface. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0036] The wind field simulated by the existing numerical calculation method has a large deviation from the actual wind environment, and fails to fully consider the turbulence characteristics at the entrance of the simulated wind field. It mainly uses the exponential rate form to represent the turbulence intensity at the entrance, and then expresses the definition of turbulent kinetic energy and turbulent dissipation rate according to the turbulence intensity and turbulence integral scale, and then specifies the wind speed profile expression, turbulent kinetic energy and turbulent dissipation rate expression to the entrance boundary condition. The steady-state RANS model is used to calculate the stable flow field, and then converted into a transient large eddy simulation model to calculate the average wind load and pulsating wind load. Although this method helps the convergence of the large eddy simulation model, the calculation error is large and fails to provide more reliable data support for the design of heliostats. In addition, most of the wind resistance research on heliostats focuses on the average wind load and pulsating wind load on the mirror surface, and fails to analyze the extreme wind load on the heliostat surface, thereby increasing the risk of local damage to the heliostat structure.

[0037] Based on this, an embodiment of the present invention provides a method, device and electronic device for predicting the wind load on the surface of a heliostat, which ensures the safety of the heliostat. To facilitate understanding of the embodiment of the present invention, a method for predicting the wind load on the surface of a heliostat disclosed in an embodiment of the present invention is first introduced in detail.

[0038] Example 1

[0039] In this embodiment, Figure 1 A schematic flow chart of a method for predicting wind load on a heliostat surface provided by an embodiment of the present invention.

[0040] Depend on Figure 1 As can be seen, the method includes:

[0041] Step S101: obtaining structural parameters of a heliostat.

[0042] Step S102: constructing an initial three-dimensional model of the heliostat according to the structural parameters.

[0043] Step S103: Based on the initial three-dimensional model, the elevation angle of the heliostat, the incoming wind direction angle and the external flow field are set to obtain an intermediate three-dimensional model.

[0044] Step S104: Based on the above intermediate state three-dimensional model, an external flow field is created and meshed to obtain a mesh of the external flow field, and the mesh of the external flow field is read; and the solver is set to incompressible transient flow; and the inlet and outlet of the external flow field are set to a velocity inlet and a pressure outlet, respectively; and the surface of the heliostat and the top, side wall and ground boundary conditions of the external flow field are set to no-slip wall conditions.

[0045] In actual operation, the principle of establishing the external flow field should ensure that the blockage ratio is 3%-5%.

[0046] Furthermore, the grid of the external flow field and the wake grid settings are divided. Here, the purpose of encrypting the grid in the wake area is to capture the flow characteristics of the wake area and improve the accuracy of the simulation. Because this area mainly contains complex phenomena such as flow separation and turbulence, a more precise grid is required for calculation.

[0047] Step S105: based on the turbulent pulsating flow field generation method, creating the pulsating velocity of the velocity inlet; and setting the iteration time of the pulsating velocity based on the preset simulation time and Courant number.

[0048] In this embodiment, whether the pulsating flow at the inlet boundary of the external flow field can strictly meet the statistical characteristics of the actual turbulence is the key to the accuracy of the large eddy simulation results. This application cites the DSRFG method in the Fourier synthesis method to create the pulsating velocity at the velocity inlet. The computational efficiency of the DSRFG method is one of its key advantages. By non-uniform discretization of the energy spectrum, the amount of calculation can be reduced while maintaining the accuracy and efficiency of the inlet turbulent wind field. This method is based on strict theoretical derivation and can generate turbulent pulsating wind speed fields that meet any form of power spectrum and anisotropy, and can strictly meet the fluid continuity conditions, thereby ensuring the stability of the large eddy simulation calculation, and is easy to parallelize.

[0049] Step S106: using a laminar flow model, an unsteady first-order format and a standard SIMPLE algorithm to calculate the external flow field to obtain a stable initial flow field.

[0050] Step S107: continue to calculate the external flow field based on the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the SIMPLE algorithm to obtain simulation results.

[0051] Step S108: according to the above simulation results, the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the above heliostat are determined.

[0052] In the embodiment of the present invention, the above average wind pressure coefficient, pulsating wind pressure coefficient and extreme wind pressure coefficient are respectively the representation forms of average wind pressure load, pulsating wind pressure load and extreme wind pressure load, so as to facilitate actual analysis.

[0053] An embodiment of the present invention provides a method for predicting wind load on a heliostat surface, comprising: obtaining structural parameters of a heliostat; constructing an initial three-dimensional model of the heliostat according to the structural parameters; setting the elevation angle of the heliostat, the incoming wind direction angle and the external flow field based on the initial three-dimensional model to obtain an intermediate three-dimensional model; creating an external flow field and meshing the external flow field based on the intermediate three-dimensional model to obtain a mesh of the external flow field, and reading the mesh of the external flow field; setting a solver to incompressible transient flow; and setting the inlet and outlet of the external flow field to a velocity inlet and a pressure outlet, respectively; and meshing the surface of the heliostat and the top and side of the external flow field. The wall and ground boundary conditions are set as non-slip wall conditions; based on the turbulent pulsating flow field generation method, the pulsating velocity of the velocity inlet is created; and, based on the preset simulation time and Courant number, the iteration time of the pulsating velocity is set; the laminar model, the unsteady first-order format and the standard SIMPLE algorithm are used to calculate the external flow field to obtain a stable initial flow field; the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the SIMPLE algorithm are used to continue to calculate the external flow field to obtain simulation results; according to the simulation results, the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the heliostat are determined. This method is based on the accurate simulation of the turbulent characteristics of the wind speed inlet, creates the pulsating velocity of the velocity inlet, and then simulates the flow field of the heliostat, thereby analyzing the extreme wind load on the heliostat surface and ensuring the safety of the heliostat.

[0054] Example 2

[0055] Based on the above embodiments, Figure 2 A schematic flow chart of another method for predicting wind load on the surface of a heliostat provided by an embodiment of the present invention.

[0056] Depend on Figure 2 As can be seen, the method includes:

[0057] Step S201: obtaining structural parameters of a heliostat.

[0058] Step S202: constructing an initial three-dimensional model of the heliostat according to the structural parameters.

[0059] Step S203: Based on the initial three-dimensional model, the elevation angle of the heliostat, the incoming wind direction angle and the external flow field are set to obtain an intermediate three-dimensional model.

[0060] Step S204: Based on the above intermediate state three-dimensional model, an external flow field is created and meshed to obtain a mesh of the external flow field, and the mesh of the external flow field is read; and the solver is set to incompressible transient flow; and the inlet and outlet of the external flow field are set to a velocity inlet and a pressure outlet, respectively; and the surface of the heliostat and the top, side wall and ground boundary conditions of the external flow field are set to no-slip wall conditions.

[0061] Step S205: creating the pulsating velocity of the velocity inlet based on the turbulent pulsating flow field generation method; and setting the iteration time of the pulsating velocity based on the preset simulation time and Courant number;

[0062] Step S206: using a laminar flow model, an unsteady first-order format and a standard SIMPLE algorithm to calculate the external flow field to obtain a stable initial flow field;

[0063] Step S207: continue to calculate the external flow field based on the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the SIMPLE algorithm to obtain simulation results.

[0064] Step S208: according to the above simulation results, determine the average wind pressure coefficient and the pulsating wind pressure coefficient of the preset measuring points on the mirror surface of the above heliostat.

[0065] Step S209: Determine the extreme wind pressure coefficient according to the average wind pressure coefficient and the pulsating wind pressure coefficient.

[0066] Among them, before the step of creating the pulsating velocity of the above-mentioned velocity inlet based on the turbulent pulsating flow field generation method, the above-mentioned method includes: based on the Von Karman target wind speed spectrum, constructing wind speed spectra corresponding to the downwind image, the crosswind direction and the vertical wind direction respectively; the step of creating the pulsating velocity of the above-mentioned velocity inlet includes: according to the wind speed spectra corresponding to the downwind image, the crosswind direction and the vertical wind direction respectively, calculating the first amplitude of the sine function of the above-mentioned pulsating velocity and the second amplitude of the cosine function of the above-mentioned pulsating velocity; according to the above-mentioned sine function, the first amplitude of the above-mentioned sine function, the above-mentioned cosine function and the second amplitude of the above-mentioned cosine function, creating the pulsating velocity of the above-mentioned velocity inlet.

[0067] Further, the step of creating the pulsating speed of the speed inlet according to the sine function, the first amplitude of the sine function, the cosine function and the second amplitude of the cosine function includes:

[0068] The pulsating velocity for the velocity inlet above is created using the following formula:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] in, is the cosine function above, is the above sine function, i=1,2,3, u1 is the above downwind direction, u 2 is the above crosswind direction, u 3 is the vertical wind direction, j=1,2,3, x 1 is the spatial longitudinal direction, x 2 is the horizontal space, x 3 is the spatial vertical direction, t is the preset time, M is the number of discrete segments of the target spectrum, M=256, N is the number of discrete random frequencies of each discrete spectrum segment, N=100, is a random number that follows a three-way normal distribution, sign is a sign function, is the wind speed spectrum of the pulsating component i, is the angular wave number, Generates adjustment factors for the spatial correlation of the fluctuating wind field for the preset regulation, Generates adjustment factors for the time dependency of the fluctuating wind field for the preset regulation, , is the preset frequency; m is the mth energy spectrum component in the discrete spectrum, K m is the wave number of the mth component in the above discrete spectrum.

[0076] In actual operation, before the step of calculating the first amplitude of the sine function of the pulsating velocity and the second amplitude of the cosine function of the pulsating velocity according to the wind speed spectra corresponding to the above-mentioned downwind image, the above-mentioned crosswind direction and the above-mentioned vertical wind direction, the above-mentioned method includes: calculating wind speed profile data based on the wind speed at a preset height and the ground roughness index; calculating turbulence intensity based on the preset horizontal turbulence intensity and the above-mentioned ground roughness index; calculating turbulence integral scale based on the preset hydraulic diameter, width and height of the above-mentioned external flow field; determining the above-mentioned wind speed spectrum according to the above-mentioned wind speed profile data, the above-mentioned turbulence intensity and the above-mentioned turbulence integral scale.

[0077] The step of determining the wind speed spectrum according to the wind speed profile data, the turbulence intensity and the turbulence integral scale comprises: determining the wind speed spectrum according to the wind speed profile data, the turbulence intensity and the turbulence integral scale based on the following formula:

[0078]

[0079]

[0080]

[0081] in, is the wind speed spectrum along the wind direction, is the wind speed spectrum in the above-mentioned crosswind direction, is the wind speed spectrum of the vertical wind direction, is the above wind speed profile data, i=u, v, w, Iu is the above turbulence intensity in the downwind direction, I v is the turbulence intensity in the above-mentioned crosswind direction, I w is the turbulence intensity in the vertical wind direction, L u x is the above-mentioned turbulence integral scale in the downwind direction, L v x is the turbulence integral scale in the above-mentioned crosswind direction, L w x is the turbulence integral scale in the above vertical wind direction.

[0082] In this embodiment, the step of determining the extreme wind pressure coefficient according to the average wind pressure coefficient and the fluctuating wind pressure coefficient includes: judging whether the fluctuating wind load corresponding to the fluctuating wind pressure coefficient presents Gaussian characteristics based on the Kolmogorov-Smirnov test; if the fluctuating wind load presents Gaussian characteristics, calculating the first peak factor of the fluctuating wind load according to the zero over-crossing rate of the fluctuating wind load based on the extreme value distribution method of the standardized Gaussian process; determining the extreme wind load coefficient according to the first peak factor, the average wind pressure coefficient and the fluctuating wind pressure coefficient; if the fluctuating wind load does not present Gaussian characteristics, calculating the second peak factor of the fluctuating wind load according to the coefficient of correlation between the skewness and kurtosis of the wind pressure distribution obtained by the improved Hermite model method and the fluctuating wind load calculation; determining the extreme wind load coefficient according to the second peak factor, the average wind pressure coefficient and the fluctuating wind pressure coefficient.

[0083] Furthermore, the step of determining the extreme wind load coefficient according to the first peak factor, the average wind pressure coefficient and the pulsating wind pressure coefficient includes: determining the first maximum wind load coefficient and the first minimum wind load coefficient according to the first peak factor, the average wind pressure coefficient and the pulsating wind pressure coefficient; determining the extreme wind load coefficient according to the first maximum wind load coefficient and the first minimum wind load coefficient; the step of determining the extreme wind load coefficient according to the second peak factor, the average wind pressure coefficient and the pulsating wind pressure coefficient includes: determining the second maximum wind load coefficient and the second minimum wind load coefficient according to the second peak factor, the average wind pressure coefficient and the pulsating wind pressure coefficient; determining the extreme wind load coefficient according to the second maximum wind load coefficient and the second minimum wind load coefficient.

[0084] Specifically, the evaluation of the simulation results should include the average wind load on the heliostat mirror. The static wind pressure at each measuring point on the mirror surface can be obtained according to the simulation calculation results. In the present invention, the mirror wind load is expressed by the wind pressure coefficient, and the dimensionless definition of the static wind pressure coefficient is:

[0085]

[0086] in, is the wind static pressure coefficient at point i on the mirror surface, is the atmospheric density, and It is the static wind pressure of the windward and leeward sides of the heliostat relative to the measuring point, in Pa.

[0087] Furthermore, the average wind static pressure coefficient of the heliostat mirror surface during the superposition time can be obtained by superposition.

[0088]

[0089] in, Represents the average wind static pressure coefficient.

[0090] Furthermore, the evaluation of the simulation results should include the fluctuating wind load on the heliostat mirror. The wind pressure at each measuring point on the mirror surface can be obtained according to the simulation calculation results. In the present invention, the mirror surface wind load is represented by the wind pressure coefficient, and the dimensionless definition of the fluctuating wind pressure coefficient is:

[0091]

[0092] in, is the wind dynamic pressure coefficient at point i on the mirror surface, is the atmospheric density, and It is the wind dynamic pressure of the windward and leeward sides of the heliostat to the measuring point, in Pa.

[0093] Furthermore, the average wind dynamic pressure coefficient of the heliostat mirror surface during the superposition time can be obtained by superposition.

[0094]

[0095] in, Represents the average wind dynamic pressure coefficient.

[0096] Further, it is evaluated whether the fluctuating wind load on the heliostat mirror surface is Gaussian. The present invention adopts the Kolmogorov-Smirnov test, i.e., the KS test. That is, the KS test should include the creation of the hypothesis H0: the fluctuating wind pressure data comes from a normal distribution; the KS test should include the calculation of the empirical cumulative distribution function.

[0097]

[0098] in, represents the empirical cumulative distribution function, I is the indicator function, if the condition is met, that is, when , returns 1, otherwise returns 0.

[0099] The KS test should include the calculation of the theoretical cumulative distribution function:

[0100]

[0101] in, represents the theoretical cumulative distribution function, erf() is the cumulative distribution function of the standard normal distribution, and It is a parameter of normal distribution and can be obtained by the mean value and standard deviation of pulsating wind pressure data.

[0102] Furthermore, the KS test should include the calculation of the KS test statistic:

[0103]

[0104] Where D represents the maximum vertical distance between the empirical distribution and the theoretical distribution.

[0105] Furthermore, the KS test should include confirmation of the significance level , and obtain the preset value of the KS test based on the sample size and significance level through table lookup or statistical software .

[0106] Furthermore, the KS test concluded that:

[0107] like , it is considered that the pulsating wind pressure data does not obey the normal distribution.

[0108] like , it is believed that the pulsating wind pressure data may obey the normal distribution.

[0109] Furthermore, the evaluation of the simulation results should include the extreme wind loads on the heliostat mirror. The solution to the extreme wind load requires the determination of the peak factor first. If the fluctuating wind pressure data is Gaussian distributed, the peak factor of the Gaussian process is calculated by the Davenport method based on the extreme value distribution of the standardized Gaussian process, and the expression is as follows:

[0110]

[0111] in, , is the zero crossing rate per unit time, T is the length of the observation period, and the Euler-Mascheroni constant .

[0112] Furthermore, if the pulsating wind pressure data presents a non-Gaussian distribution, the corresponding peak factor is calculated using the improved Hermite model method polynomial that represents the non-Gaussian process as a Gaussian process. The calculation formula is as follows:

[0113]

[0114] Where g is the first peak factor based on Gaussian distribution, represents the second peak factor, and is a coefficient related to the skewness and kurtosis of the wind pressure distribution.

[0115] Furthermore, if the fluctuating wind load is Gaussian distributed, the first maximum wind load coefficient is expressed by the following formula:

[0116]

[0117] Furthermore, if the fluctuating wind load is Gaussian distributed, the first minimum wind load coefficient is expressed by the following formula:

[0118]

[0119] Furthermore, if the fluctuating wind load is not Gaussian distributed, the second maximum wind load coefficient is expressed by the following formula:

[0120]

[0121] Furthermore, if the fluctuating wind load is not Gaussian distributed, the second minimum wind load coefficient is expressed by the following formula:

[0122]

[0123] An embodiment of the present invention provides a method for predicting wind load on a heliostat surface, comprising: obtaining structural parameters of a heliostat; constructing an initial three-dimensional model of the heliostat according to the structural parameters; setting the elevation angle of the heliostat, the incoming wind direction angle and the external flow field based on the initial three-dimensional model to obtain an intermediate three-dimensional model; creating an external flow field and meshing the external flow field based on the intermediate three-dimensional model to obtain a mesh of the external flow field, and reading the mesh of the external flow field; setting a solver to incompressible transient flow; setting an inlet and an outlet of the external flow field to a velocity inlet and a pressure outlet, respectively; and setting the surface of the heliostat and the top, side wall and ground boundary conditions of the external flow field to be non-slip. wall conditions; based on the turbulent pulsating flow field generation method, create the pulsating velocity of the above-mentioned velocity inlet; and, based on the preset simulation time and Courant number, set the iteration time of the above-mentioned pulsating velocity; use the laminar model, unsteady first-order format and standard SIMPLE algorithm to calculate the above-mentioned external flow field to obtain a stable initial flow field; based on the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the above-mentioned SIMPLE algorithm, continue to calculate the above-mentioned external flow field to obtain simulation results; according to the above-mentioned simulation results, determine the average wind pressure coefficient and the pulsating wind pressure coefficient of the preset measuring point on the mirror surface of the above-mentioned heliostat; according to the above-mentioned average wind pressure coefficient and the pulsating wind pressure coefficient, determine the above-mentioned extreme wind pressure coefficient. This method is based on accurately simulating the turbulent characteristics of the wind speed inlet, creating the pulsating speed of the above speed inlet, and then simulating the flow field of the heliostat, so as to analyze the extreme wind load on the heliostat surface, obtain the average wind pressure coefficient and the pulsating wind pressure coefficient, and then obtain the extreme wind pressure coefficient of the heliostat, which further ensures the safety of the heliostat.

[0124] Example 3

[0125] Based on the above implementation, Figure 3 A schematic structural diagram of a heliostat surface wind load prediction device provided by an embodiment of the present invention.

[0126] Depend on Figure 3 As can be seen, the device includes:

[0127] The data acquisition module 31 is used to acquire the structural parameters of the heliostat.

[0128] The initial model building module 32 is used to build an initial three-dimensional model of the heliostat according to the above structural parameters.

[0129] The model building module 33 is used to set the elevation angle of the heliostat, the incoming wind direction angle and the external flow field based on the initial three-dimensional model to obtain an intermediate three-dimensional model; create an external flow field and mesh the external flow field based on the intermediate three-dimensional model to obtain the mesh of the external flow field, and read the mesh of the external flow field; set the solver to incompressible transient flow; set the inlet and outlet of the external flow field to the velocity inlet and the pressure outlet respectively; and set the surface of the heliostat and the top, side wall and ground boundary conditions of the external flow field to no-slip wall conditions.

[0130] The simulation module 34 is used to create the pulsating velocity of the velocity inlet based on the turbulent pulsating flow field generation method; and to set the iteration time of the pulsating velocity based on the preset simulation time and Courant number; to calculate the external flow field using the laminar model, the unsteady first-order format and the standard SIMPLE algorithm to obtain a stable initial flow field; to continue to calculate the external flow field based on the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the SIMPLE algorithm to obtain simulation results; and to determine the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the heliostat according to the simulation results.

[0131] Among them, the data acquisition module 31, the initial model construction module 32, the model construction module 33 and the simulation module 34 are connected in sequence.

[0132] The heliostat surface wind load prediction device provided by the embodiment of the present invention has the same technical features as the heliostat surface wind load prediction method provided by the above embodiment, so it can also solve the same technical problems and achieve the same technical effects. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the device described above can refer to the corresponding process in the above method embodiment, and will not be repeated here.

[0133] Example 4

[0134] This embodiment provides an electronic device, including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of a method for predicting wind load on a heliostat surface.

[0135] See also Figure 4 A structural schematic diagram of an electronic device is shown, the electronic device includes: a memory 41 and a processor 42, the memory 41 stores a computer program that can be run on the processor 42, and the processor implements the steps provided by the above-mentioned heliostat surface wind load prediction method when executing the computer program.

[0136] like Figure 4 As shown, the device further includes: a bus 43 and a communication interface 44, a processor 42, a communication interface 44 and a memory 41 are connected via the bus 43; the processor 42 is used to execute executable modules stored in the memory 41, such as computer programs.

[0137] The memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 44 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0138] The bus 43 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0139] Among them, the memory 41 is used to store the program, and the processor 42 executes the program after receiving the execution instruction. The method executed by the heliostat surface wind load prediction device disclosed in any embodiment of the present invention can be applied to the processor 42, or implemented by the processor 42. The processor 42 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 42 or the instruction in the form of software. The above processor 42 can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 41, and the processor 42 reads the information in the memory 41 and completes the steps of the above method in combination with its hardware.

[0140] Furthermore, an embodiment of the present invention also provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by the processor 42, the machine-executable instructions prompt the processor 42 to implement the above-mentioned heliostat surface wind load prediction method.

[0141] The electronic device and computer-readable storage medium provided by the embodiments of the present invention have the same technical features, and therefore can solve the same technical problems and achieve the same technical effects.

[0142] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0143] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the aforementioned embodiments, those of ordinary skill in the art should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed by the present invention, or can easily conceive of changes, or make equivalent replacements for some of the technical features therein. Such modifications, changes or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention.

Claims

1. A method for predicting wind load on a heliostat surface, characterized in that: include: Obtaining structural parameters of the heliostat; constructing an initial three-dimensional model of the heliostat according to the structural parameters; Based on the initial three-dimensional model, setting the elevation angle of the heliostat, the incoming wind direction angle and the external flow field to obtain an intermediate three-dimensional model; Based on the intermediate three-dimensional model, an external flow field is created and meshed to obtain a mesh of the external flow field, and the mesh of the external flow field is read; and setting the solver to incompressible transient flow; and setting the inlet and outlet of the external flow field to velocity inlet and pressure outlet, respectively; and setting the surface of the heliostat and the top, side wall and ground boundary conditions of the external flow field to no-slip wall conditions; Based on the turbulent pulsating flow field generation method, the pulsating velocity of the velocity inlet is created; and based on the preset simulation time and Courant number, the iteration time of the pulsating velocity is set; The external flow field is calculated using a laminar flow model, an unsteady first-order format and a standard SIMPLE algorithm to obtain a stable initial flow field; Based on the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the SIMPLE algorithm, the external flow field is continuously calculated to obtain simulation results; According to the simulation results, the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the heliostat are determined.

2. The method for predicting wind load on the heliostat surface according to claim 1, characterized in that: According to the simulation results, the step of determining the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the heliostat comprises: Determining an average wind pressure coefficient and a pulsating wind pressure coefficient of a preset measuring point on the mirror surface of the heliostat according to the simulation result; The extreme wind pressure coefficient is determined according to the average wind pressure coefficient and the pulsating wind pressure coefficient.

3. The method for predicting wind load on the heliostat surface according to claim 2, characterized in that: The step of determining the extreme wind pressure coefficient according to the average wind pressure coefficient and the pulsating wind pressure coefficient comprises: Based on the Kolmogorov-Smirnov test, it is determined whether the fluctuating wind load corresponding to the fluctuating wind pressure coefficient is Gaussian; If the fluctuating wind load presents Gaussian characteristics, the first peak factor of the fluctuating wind load is calculated according to the zero over-crossing rate of the fluctuating wind load based on the extreme value distribution method of the standardized Gaussian process; the extreme value wind load coefficient is determined according to the first peak factor, the average wind pressure coefficient and the fluctuating wind pressure coefficient; If the fluctuating wind load does not present Gaussian characteristics, the second peak factor of the fluctuating wind load is calculated based on the coefficient of correlation between the skewness and kurtosis of the wind pressure distribution obtained based on the improved Hermite model method and the fluctuating wind load calculation; the extreme wind load coefficient is determined based on the second peak factor, the average wind pressure coefficient and the fluctuating wind pressure coefficient.

4. The method for predicting the wind load on the heliostat surface according to claim 3, characterized in that: The step of determining the extreme wind load coefficient according to the first peak factor, the average wind pressure coefficient and the pulsating wind pressure coefficient comprises: Determine a first maximum wind load coefficient and a first minimum wind load coefficient according to the first peak factor, the average wind pressure coefficient and the pulsating wind pressure coefficient; Determining the extreme wind load coefficient according to the first maximum wind load coefficient and the first minimum wind load coefficient; The step of determining the extreme wind load coefficient according to the second peak factor, the average wind pressure coefficient and the pulsating wind pressure coefficient comprises: Determine a second maximum wind load coefficient and a second minimum wind load coefficient according to the second peak factor, the average wind pressure coefficient and the pulsating wind pressure coefficient; The extreme wind load coefficient is determined according to the second maximum wind load coefficient and the second minimum wind load coefficient.

5. The method for predicting the wind load on the heliostat surface according to claim 4, characterized in that: Based on the turbulent pulsating flow field generation method, before the step of creating the pulsating velocity of the velocity inlet, the method includes: Based on the Von Karman target wind speed spectrum, the wind speed spectra corresponding to the downwind direction, crosswind direction and vertical wind direction are constructed; The step of creating the pulsating speed of the speed inlet comprises: Calculate a first amplitude of the sine function of the pulsating speed and a second amplitude of the cosine function of the pulsating speed according to the wind speed spectra corresponding to the downwind image, the crosswind direction, and the vertical wind direction, respectively; A pulsating velocity of the velocity inlet is created based on the sine function, the first amplitude of the sine function, the cosine function, and the second amplitude of the cosine function.

6. The method for predicting wind load on the heliostat surface according to claim 5, characterized in that: The step of creating a pulsating speed of the speed inlet according to the sine function, the first amplitude of the sine function, the cosine function and the second amplitude of the cosine function comprises: The pulsating velocity of the velocity inlet is created according to the following formula: in, is the cosine function, is the sine function, i=1,2,3, u1 is the downwind direction, u2 is the crosswind direction, u3 is the vertical wind direction, j=1,2,3, x1 is the longitudinal direction of the space, x2 is the transverse direction of the space, x3 is the vertical direction of the space, t is the preset time, M is the number of discrete segments of the target spectrum, M=256, N is the number of discrete random frequencies of each discrete spectrum segment, N=100, is a random number that follows a three-way normal distribution, sign is a sign function, is the wind speed spectrum of the pulsating component i, is the angular wave number, Generates adjustment factors for the spatial correlation of the fluctuating wind field for the preset regulation, Generates adjustment factors for the time dependency of the fluctuating wind field for the preset regulation, , is the preset frequency; m is the mth energy spectrum component in the discrete spectrum, K m is the wave number of the mth component in the discrete spectrum.

7. The method for predicting the wind load on the heliostat surface according to claim 5, characterized in that: Before the step of calculating the first amplitude of the sine function of the pulsating speed and the second amplitude of the cosine function of the pulsating speed according to the wind speed spectra corresponding to the downwind image, the crosswind direction, and the vertical wind direction, the method includes: Calculate wind speed profile data based on wind speed and ground roughness index at a preset height; Calculating turbulence intensity based on a preset horizontal turbulence intensity and the ground roughness index; Calculating a turbulence integral scale based on a preset hydraulic diameter, width, and height of the external flow field; The wind speed spectrum is determined according to the wind speed profile data, the turbulence intensity and the turbulence integral scale.

8. The method for predicting wind load on the heliostat surface according to claim 7, characterized in that: The step of determining the wind speed spectrum according to the wind speed profile data, the turbulence intensity and the turbulence integral scale comprises: The wind speed spectrum is determined based on the following formula according to the wind speed profile data, the turbulence intensity and the turbulence integral scale: in, is the wind speed spectrum along the wind direction, is the wind speed spectrum in the crosswind direction, is the wind speed spectrum in the vertical wind direction, is the wind speed profile data, i=u, v, w, Iu is the turbulence intensity along the wind direction, I v is the turbulence intensity in the crosswind direction, I w is the turbulence intensity in the vertical wind direction, L u x is the turbulence integral scale along the wind direction, L v x is the turbulence integral scale in the crosswind direction, L w x is the turbulence integral scale in the vertical wind direction.

9. A heliostat surface wind load prediction device, characterized in that: include: A data acquisition module, used to obtain structural parameters of the heliostat; An initial model building module, used to build an initial three-dimensional model of the heliostat according to the structural parameters; A model building module is used to set the elevation angle of the heliostat, the incoming wind direction angle and the external flow field based on the initial three-dimensional model to obtain an intermediate three-dimensional model; based on the intermediate three-dimensional model, create an external flow field and mesh the external flow field to obtain a mesh of the external flow field, and read the mesh of the external flow field; and setting the solver to incompressible transient flow; and setting the inlet and outlet of the external flow field to velocity inlet and pressure outlet respectively; and setting the surface of the heliostat and the top, sidewall and ground boundary conditions of the external flow field to no-slip wall conditions; A simulation module, for creating the pulsating velocity of the velocity inlet based on a turbulent pulsating flow field generation method; and for setting the iteration time of the pulsating velocity based on a preset simulation duration and Courant number; The external flow field is calculated using a laminar flow model, an unsteady first-order format and a standard SIMPLE algorithm to obtain a stable initial flow field; The external flow field is continuously calculated based on the Smagorinsky-Lilly subgrid model of large eddy simulation, the unsteady second-order format iteration method and the SIMPLE algorithm to obtain simulation results; and the average wind pressure coefficient, the pulsating wind pressure coefficient and the extreme wind pressure coefficient of the heliostat are determined according to the simulation results.

10. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the heliostat surface wind load prediction method according to any one of claims 1 to 8.

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