CN110414059B - Radiation energy density simulation method of planar heliostat in tower type solar thermal power station - Google Patents
Radiation energy density simulation method of planar heliostat in tower type solar thermal power station Download PDFInfo
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Abstract
The invention discloses a radiation energy density simulation method of a planar heliostat in a tower type solar thermal power station, which belongs to the technical field of simulation of the tower type solar thermal power station and comprises the following steps: (1) defining an imaging plane according to the direction of the reflected light of the heliostat; (2) uniformly subdividing the three-dimensional space, and calculating heliostats with shadow and shielding relation with the heliostats; (3) a rasterized representation of an imaging plane; (4) calculating the radiant energy density distribution on an imaging plane; (5) the radiation energy density distribution on the imaging plane is projected onto the receiving surface by oblique parallel projection. The method is realized by utilizing a correlation method of graphics to accelerate the solving process and based on the GPU, and has the advantages of high efficiency and accuracy compared with the prior analytic method.
Description
Technical Field
The invention relates to the technical field of simulation of tower type solar thermal power stations, in particular to a method for simulating radiation energy density of a planar heliostat in a tower type solar thermal power station.
Background
The effective utilization of solar energy is an important way to solve the problems of energy shortage and environmental pollution. In the utilization of solar energy, a tower type solar energy system is an important direction for photo-thermal power generation. At present, two types of methods are mainly used for tower type solar energy system simulation: ray tracing and analysis models (Garcia, P., Ferriere, A., Bezian, J.J., 2008.Codes for solar flux calculation specified to central receiver systems applications: a comparative review. solar Energy 82, 189-
The ray tracing method can naturally take the shape of the heliostat, the shape of a receiver, a sun model, a tracking error and other factors into consideration to obtain an accurate simulation result. Although the ray tracing is more accurate and flexible for modeling the tower type solar energy system, the method is not suitable for large-scale heliostat field simulation due to the defects of low calculation efficiency and unstable peak value of the ray tracing.
The analytical model expresses the radiation energy density distribution on the receiver by using a mathematical formula, and is suitable for tasks such as mirror field layout optimization, focusing strategy optimization, mirror field radiation energy density distribution simulation and the like. Classical analytical models include HFLCAL and UNIZAR models.
HFLCAL is a heliostat field layout and optimization procedure (
P.，Pitz-Paal R.，Schmitz M.Visual HFLCAL—A Software Tool for Layout and Optimisation of Heliostat Fields[C]// Proceedings of 15th International SolarPACES Symposium, Berlin, September.2009: 15-18.). HFLCAL uses a simplified mathematical model to characterize the radiant energy density distribution, describing the radiant energy density distribution of heliostat spots as an isotropic gaussian distribution. The HFLCAL model has high calculation efficiency and is generally used for rapid simulation of the tower type solar system. The results of HFLCAL are not accurate since HFLCAL assumes circular symmetry of the radiation energy density distribution.
The UNIZAR model uses a convolution of a Gaussian kernel function with the effective reflection area to represent the radiant Energy density distribution at the receiver surface (Collado F., Gomez A., Turegano J. an analytical function for the flux dense to bright reflected from a heliostat J. Solar Energy, 1986, 37 (3): 215-. Albert improves the universality of the UNIZAR model by means of projection, firstly defines an imaging plane, then solves the radiation Energy density distribution on the imaging plane, and finally projects the Energy distribution on a receiving surface (S < n > -nchez-Gonz a lez, Albert, Santana D.solar flash distribution on central receivers: A project method from the imaging function J. reusable Energy, 2015, 74: 576-587.).
In tower solar system simulations, the parameters of the HFLCAL and UNIZAR models are functions of heliostat position versus time. The model parameters of the heliostats at different positions are different, and the model parameters of the heliostats on the same surface can also change at different moments. Callado proposes a single-point fitting method, which determines parameters of HFLCAL model and UNIZAR model by fitting the energy of spot peak (Collado F.J. one-point fitting of the flux dense produced by a heiiostat]Solar Energy,2010,84(4): 673-. Cruz proposes an interpolation method to solve the model parameters of heliostats, and uses the model parameters of a whole heliostat field described by 30 surfaces of a 541-surface heliostat (Cruz N.C.,
J.D.,Redondo J.L.,et al.A new methodology for building-up a robust model for heliostat field flux characterization[J]energies,2017,10(5): 730.). However, the method is only used for analyzing the field data at one moment, and modeling needs to be performed again at different moments.
The defects of the current analytic model mainly lie in that: the precision is low; the parameters are difficult to determine, and in practical application, the parameters need to be modeled by means of peak fitting and interpolation.
Disclosure of Invention
The invention aims to provide a method for simulating the radiant energy density of a planar heliostat in a tower type solar thermal power station, which can be used for simulating the radiant energy density of a large-scale tower type solar system, realizes acceleration by utilizing an algorithm and a technology of computer graphics, and has the advantages of high efficiency, accuracy and strong universality.
In order to achieve the purpose, the method for simulating the radiant energy density of the planar heliostat in the tower type solar thermal power station comprises the following steps:
(1) calculating the sunlight incidence direction according to the geographic position and the simulation time of the tower type solar thermal power station, adjusting the orientation of a heliostat in the thermal power station, and defining a virtual imaging plane according to the direction of light reflected by the heliostat;
(2) uniformly dividing a three-dimensional space into a series of uniformly distributed three-dimensional cuboid grids, calling each grid as a voxel, and storing the corresponding relation between the heliostat and the voxel; calculating voxels intersected with the light columns along the incident and reflection directions of the light rays, and storing the heliostats corresponding to the voxels into a shadow and shielding heliostat set;
preferably, for intersection between a light column reflected by a heliostat and a scene, four light rays emitted by the vertexes of the heliostat are used for replacing the light column and the scene to perform intersection judgment, and then the heliostat with a shadow and shielding relation with the heliostat is judged, namely, the heliostat with the shadow and shielding relation is calculated by respectively using the light rays emitted by the four vertexes of the heliostat through 3D-DDA, and an union set is obtained for the results.
(3) Defining an imaging plane of the heliostat on the surface of the receiver by the reflecting light direction of the heliostat, performing rasterization representation on the imaging plane, and dispersing the imaging plane into pixel points to represent an effective reflecting area of the heliostat on the imaging plane;
preferably, after obtaining the rasterized representation of the effective projection area on the imaging plane, an area correction process is added:
wherein:
rcorrectthe correction ratio representing the discrete result, i.e. the discrete pixel value on the image plane multiplied by the correction ratio；
SgeometryRepresenting an ideal projected area of the effective reflection area;
Sdispersethe uncorrected total area representing the discrete representation of the effective reflection area is solved by GPU specification calculation.
(4) The radiant energy density distribution is calculated on the imaging plane. The FDD of the planar heliostat light spot is the convolution of the effective reflection area of the heliostat and the FDD of the mirror surface infinitesimal light spot, wherein the FDD of the single mirror surface infinitesimal light spot can be regarded as a convolution kernel function, the effective reflection area of the mirror surface is a convolution function, and the calculation of discrete convolution uses the fast Fourier convolution calculation of a GPU version:
wherein:
Fimagerepresenting a radiant energy density distribution on an imaging plane;
b represents a rasterized representation of the effective reflection area of the mirror surface of the heliostat on the imaging plane;
c is the radiation energy density distribution of the heliostat infinitesimal light spots;
(5) projecting the radiant energy density distribution on the imaging plane onto the receiving surface by oblique parallel projection:
Freceiver(u,v)＝Fimage(fu(u,v),fv(u,v))cosθ
wherein:
Freceiverrepresenting a distribution of radiant energy density on the receiving surface;
(u, v) are coordinates of a point on the receiving surface in the plane of the receiving surface local coordinate system oUV;
(fu(u,v),fv(u, V)) are oU 'V' plane coordinates of a point on the imaging plane under the local coordinate system of the imaging plane, the point (u, V) and the point (f)u(u,v),fv(u, v)) the correspondence is an oblique parallel projection;
theta denotes the angle of the reflected ray normal to the receiving surface.
Preferably, the step of projecting the radiation energy density distribution of the imaging plane onto the receiving surface is as follows:
(5-1) obtaining the coordinates of the discrete pixel points on the receiving surface in a world coordinate system;
(5-2) obtaining coordinates of corresponding points on the imaging plane in a world coordinate system through oblique parallel projection;
(5-3) calculating the position of the corresponding point in the imaging plane according to the coordinate of the corresponding point in the world coordinate system;
(5-4) the radiation energy density of one point on the receiving surface is the radiation energy density of the corresponding point on the imaging plane multiplied by the cosine of the receiving surface, wherein the cosine of the receiving surface is the cosine value of the included angle between the reflected light ray and the normal direction of the receiving surface.
Preferably, when calculating the radiant energy density distribution of the corresponding point on the imaging plane, bilinear interpolation is used to calculate the energy value of the corresponding point, i.e. the radiant energy density of the point is represented by the weighted average of the radiant energy densities of the adjacent pixels of the sampling point.
Compared with the prior art, the invention has the beneficial effects that:
(1) four light rays emitted by the top points of the heliostats are used for replacing light beams to conduct intersection judgment with a scene, uniform grid subdivision is conducted on the scene, and the heliostats with shadow and shielding relation with the light rays are rapidly calculated by using a 3D-DDA algorithm in graphics;
(2) the effective reflection area of the heliostat is efficiently solved by using rasterization operation in a drawing production line, the area correction process is added after rasterization representation, and the simulation precision is improved by using a fitted convolution kernel function;
(3) the operation of effective projection area calculation, convolution calculation, oblique parallel projection and the like is realized based on the GPU, and the method has the advantage of high efficiency.
Drawings
FIG. 1 is a heliostat field designed to verify the accuracy of the proposed model in an embodiment of the invention;
FIG. 2 is a definition of an imaging plane and a local coordinate system of a receiving surface and the imaging plane in an embodiment of the invention;
fig. 3(a) is a schematic plan view of field grid division of a heliostat in an embodiment of the present invention, and fig. 3(b) is an array for storing a correspondence between heliostats and spatial voxels in an embodiment of the present invention;
FIG. 4 is a schematic diagram of effective reflection area calculation according to an embodiment of the present invention;
FIG. 5 is a flow chart of effective reflection area calculation according to an embodiment of the present invention;
FIG. 6 is an exemplary diagram of convolution calculations on an imaging plane in an embodiment of the present invention;
FIG. 7 is a comparison of contours of the HFLCAL model (first column) and the UNIZAR model (second column) in the case of the spring minute day at noon and the method of the embodiment of the present invention, each row corresponding to heliostat numbers 0, 4, 20, and 24 of FIG. 1, respectively;
fig. 8 is a comparison of contours of the HFLCAL model (first column) and the UNIZAR model (second column) in the case of the midday spring centuries and the method of the embodiment of the invention, each row corresponding to heliostats nos. 7, 9, 37 and 39 in fig. 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the following embodiments and accompanying drawings.
Examples
Referring to fig. 1 to 8, an experimental heliostat field is taken as an example, and the heliostat distribution of the heliostat field is shown in fig. 1. Table 1 below is a specific parameter for the site.
TABLE 1 relevant parameters of the experimental mirror field
The method for simulating the radiant energy density of the planar heliostat in the tower type solar thermal power station comprises the following steps:
(1) defining an imaging plane according to the direction of the reflected light of the heliostat
The imaging plane is a virtual plane passing through the center of the receiving surface and having the normal direction of the sunlight emergence direction. The main reason for defining the imaging plane is that the heliostat micro-element light spots can be approximately distributed in a circular symmetry manner on the imaging plane, and calculation is convenient.
Fig. 2 shows a corresponding relationship between an imaging plane and a planar receiving surface, UVW is a local coordinate system of the receiving surface, an origin of coordinates is a center of the receiving surface, V is located on an intersection line of the receiving surface and a horizontal plane, U is located on the receiving surface and perpendicular to V, and W is located in a normal direction of the receiving surface. U ' V ' W ' is a local coordinate system of the imaging plane, V ' is located on the intersection of the imaging plane and the horizontal plane, U ' is located on the imaging plane and perpendicular to V ', and W ' is located normal to the imaging plane and parallel to the direction of the reflected light rays of the heliostat.
(2) Uniformly subdividing the three-dimensional space, and calculating the heliostat with shadow and shielding relation with the heliostat
The present embodiment uses a regular grid to divide the field. As shown in fig. 3, fig. 3(a) is a schematic plan view of mirror field division, the mirror field is divided into 6 voxels, which are numbered 0-5; and 5 heliostats with the numbers of I-V are arranged in the heliostat field. Due to the rotation of the heliostat and other operations, the bounding box of the heliostat needs to be considered when calculating the intersection relationship between the heliostat and the voxel. A single voxel may correspond to 0, 1, or more heliostats, and a heliostat may also be in multiple voxels. As shown in fig. 3(b), the correspondence between heliostats and voxels is stored in an index array. Considering the efficient implementation of the GPU, the index relationship is stored in two one-dimensional arrays. The Match array stores heliostat indexes corresponding to all voxels in sequence, and the Index array stores the initial positions of corresponding voxel Index data in the Match array. Index [ i ] denotes the starting position of voxel i in the Match array, where voxel i intersects heliostats in the Match with indices Index [ i ] to Index [ i +1] -1, e.g., voxel 2 intersects heliostats in the Match with indices 2 to 3, Match [2] is 2, and Match [3] is 3, so voxel 2 intersects heliostat 2 and heliostat 3.
In this embodiment, four light rays emitted by the vertexes of the heliostat replace light beams to perform intersection judgment with a scene, so as to judge the heliostat having a shadow and shielding relationship with the heliostat, that is, the light rays emitted by the four vertexes of the heliostat are respectively calculated by 3D-DDA to obtain a heliostat having a shadow and shielding, and a union set is obtained for the result.
(3) Rasterized representation of an imaging plane
As shown in fig. 4, in solving for H2In the case of an effective reflection region on an imaging plane, first, a projection H 'of a heliostat on the imaging plane is calculated'2. Shadow and occlusion may exist in the projection area of the heliostat, and H is obtained2After projection on the imaging plane, the projection of the heliostat with shadow and occlusion relationship needs to be calculated. For heliostat H with shadow relation1It is necessary to calculate the projection H 'of the heliostat in the plane of the heliostat along the incident light direction'1Followed by H'1H' projected from heliostat plane onto imaging plane1. For heliostat H with possible shielding relation3A heliostat H is required3H 'projected onto an image plane along the direction of outgoing light'a。
For heliostat H2The calculation of the effective reflection area is shown in fig. 5. H2The effective reflection area solution is mainly divided into the following two steps:
(3-1) on the imaging plane, calculating the heliostat H2Is a projection region H'2Judging the center coordinate and H 'of each pixel'2For a point within the region, the value of the corresponding pixel is set to 1, otherwise it is set to 0;
(3-2) for heliostat H in which shadow and occlusion relationship may exist1And H3Separately, the projected areas H' on the imaging plane are determined1And H'3The pixel value in the corresponding area is set to 0.
After obtaining the rasterized representation of the effective projection area on the imaging plane, an area correction process is added:
wherein:
rcorrectcorrection scales representing discrete results, i.e. image planesMultiplying the discrete pixel value by the correction ratio;
Sgeometryrepresenting an ideal projected area of the effective reflection area;
Sdispersethe uncorrected total area representing the discrete representation of the effective reflection area is solved by GPU specification calculation.
(4) Calculating a radiant energy density distribution on an imaging plane
The convolution calculation of the radiant energy density distribution is defined on the imaging plane, and FDD on the imaging plane is the convolution of the heliostat infinitesimal spot FDD with the effective reflection area. As shown in fig. 6, the inside of the dashed line represents the spot reflected by the heliostat on the imaging plane; the black solid line represents the effective reflection area of the heliostat on the imaging plane, the projection area is dispersed into mirror surface micro-elements, the circular area represents the light spot of the heliostat micro-elements, and the final energy light spot is the accumulation of the heliostat micro-element light spots, namely the convolution of the effective reflection area and the heliostat micro-element light spot.
(5) Projecting the radiation energy density distribution on the imaging plane onto the receiving surface by oblique parallel projection
In the world coordinate system, p represents a point on the imaging plane, and the corresponding coordinate is (p)x,py,pz) (ii) a p 'represents a point on the receiving surface, and the corresponding coordinate is (p'x,p′y,p′z)；r＝(rx,ry,rz) The direction of oblique parallel projection, namely the emergent direction of the light rays, is defined by an imaging plane, and r is the normal direction of the imaging plane; o denotes the origin of the coordinate system of the imaging plane and the receiving surface. In calculating the projection, the sunlight needs to be regarded as parallel light. From the straight line propagation of the light, the relationship between p and p' can be obtained:
p+tr＝p′
wherein t represents the distance between p and p', and t is calculated as follows:
t＝r·(p′-o)
by combining the two formulas, the corresponding relation between p and p' can be obtained:
p＝p′+r·(o-p′)·r
p' local coordinate system on receiving surfaceThe lower oUV plane has the coordinate of (u)r,vr) P is (u) as the coordinates of the oU 'V' plane in the local coordinate system of the imaging planei,vi). When solving the correspondence between two points, firstly, the local coordinate system needs to be mapped into the world coordinate system.
p′＝o+urU+vrV
p＝o+uiu′+viV′
(ui,vi) And (u)r,vr) The correspondence between the two points is as follows:
Freceivershowing FDD, F on the receiving surfaceimageIndicating FDD at the imaging plane. From the above derivation, the FDD distribution on the receiving surface can be obtained:
Freceiver(u,v)＝Fimage(fu(u,v),fv(u,v))cosθ
where θ represents the angle between the outgoing ray and the normal to the receiver surface.
After the coordinate corresponding relation between the receiving surface and the imaging plane is obtained through oblique parallel projection, the corresponding pixel coordinate obtained through calculation is decimal and is not a specific pixel coordinate, and the energy value of the point is obtained through bilinear interpolation of the energy values of adjacent pixels of the sampling point.
Evaluation indexes are as follows:
the evaluation indexes of the radiant energy density distribution modeling mainly comprise: total energy error, peak error, mean square error.
In this embodiment, the real result is an average result of 100 experiments performed by ray tracing, where the number of rays emitted by each heliostat infinitesimal is 1024, and the division granularity of the heliostat is 0.01 m.
The total energy error is the error in total energy between the fit result and the true result:
wherein E isrt_totalTotal energy, E, of ray tracing resultstotalIs the total energy of the fit.
The peak error is the error of the peak between the fitted result and the true result:
wherein, Crt_peakIs the peak of the ray tracing result, CpeakIs the peak of the fit.
The mean square error represents the difference of the fitted result and the real result energy density distribution:
wherein M, N denotes the resolution of the receiving surface, SsubDenotes the pixel area of the receiver surface, i.e. the area of each discrete cell, C (m, n) denotes the radiated energy density of the fitting result at (m, n), and Gt (m, n) denotes the radiated energy density of the ray result at (m, n).
For σ in the gaussian kernel and s in the quasi-cauchy kernel, this embodiment solves by minimizing the loss function, which is defined as follows:
Loss＝α(|Cpeak-Crt_peak|)+(1-C)Errorrms
where Loss (-) is the value of the Loss function, CpeakRepresenting the peak of the fitting kernel, Crt_peakIndicating the peak value, Error, of the ray tracing resultrmsAnd the mean square error of the fitting kernel function and the ray tracing result is shown. α is used to balance the importance of peak error and mean square error, pairFor the convolution kernel, both the peak error and the fitting mean square error have an important effect on the accuracy of the model, and therefore the value is 0.5 in this embodiment.
The experimental results are as follows:
FIG. 7 is a spot fit result for a close-range heliostat at a horizontal range of 100m to 500m from the heliostat to the receiver, where each column corresponds to the results of the HFLCAL, UNIZAR and planar heliostat models, respectively, with the coordinate axis in m and the energy density in W/m2. As can be seen from fig. 7, the fitting effect of the HFLCAL is not good when the heliostat is close to the receiving surface, the light spot of the heliostat # 0 is approximately rectangular, the HFLCAL fits the light spot into an ellipse, the shape of the heliostat is ignored, the shape difference of the light spot is large, and the light spots of other heliostats also have the problem of inaccurate shape fitting. The result of the UNIZAR fitting is similar to the method of the present embodiment, and generally the planar heliostat model in the present embodiment is closer to the real result, for example, the light spot of heliostat # 4 is an ellipse, and the result of the UNIZAR fitting is more biased to a circular distribution.
For the light spot of the long-distance heliostat, the light spot fitting result of the plane heliostat model is obviously superior to HFLCAL and UNIZAR. Fig. 8 is a fitting result of spots of distant heliostats at horizontal distances between the heliostat and the receiver of 500m to 1000m, where each column corresponds to the results of HFLCAL, UNIZAR and planar heliostat models, respectively. The light spot of the planar heliostat model is basically consistent with the real result, the result diffusion of the HFLCAL and UNIZAR is obvious, for example, the difference between the contour lines of the HFLCAL and the UNZIAR and the real value is large in the fitting result of the heliostat # 39, and the result of the planar heliostat model is relatively consistent with the real result.
Table 2 below is the average error for the simulation of the mirror field by the HFLCAL, UNIZAR and planar heliostat models, which is the average of the simulated error percentage for all heliostats in the mirror field. For the peak error and the total energy error, the peak total energy error of the planar heliostat model is better than HFLCAL and UNIZAR, and the average peak error of the UNIZAR model is better than the planar heliostat model and HFLCAL. The planar heliostat model in this embodiment is superior to the HFLCAL and UNIZAR models for fitting the mean square error and loss function.
TABLE 2 mean error of mirror field simulation
Claims (8)
1. A radiation energy density simulation method of a planar heliostat in a tower type solar thermal power station is characterized by comprising the following steps:
(1) calculating the sunlight incidence direction according to the geographic position and the simulation time of the tower type solar thermal power station, adjusting the orientation of a heliostat in the thermal power station, and defining a virtual imaging plane according to the direction of light reflected by the heliostat;
(2) uniformly dividing a three-dimensional space into a series of uniformly distributed three-dimensional cuboid grids, calling each grid as a voxel, and storing the corresponding relation between the heliostat and the voxel; calculating voxels intersected with the light columns along the incident and reflection directions of the light rays, and storing the heliostats corresponding to the voxels into a shadow and shielding heliostat set;
(3) defining an imaging plane of the heliostat on the surface of the receiver by the reflecting light direction of the heliostat, performing rasterization representation on the imaging plane, and dispersing the imaging plane into pixel points to represent an effective reflecting area of the heliostat on the imaging plane;
(4) the radiant energy density distribution is calculated on the imaging plane:
wherein:
Fimagerepresenting a radiant energy density distribution on an imaging plane;
b represents a rasterized representation of the effective reflection area of the mirror surface of the heliostat on the imaging plane;
c is the radiation energy density distribution of the heliostat infinitesimal light spots;
(5) projecting the radiant energy density distribution on the imaging plane onto the receiving surface by oblique parallel projection:
Freceiver(u，v)＝Fimage(fu(u，v)，fv(u，v))cosθ
wherein:
Freceiverrepresenting a distribution of radiant energy density on the receiving surface;
(u, v) are coordinates of a point on the receiving surface in the plane of the receiving surface local coordinate system oUV;
(fu(u，v)，fv(u, V)) are oU 'V' plane coordinates of a point on the imaging plane under the local coordinate system of the imaging plane, the point (u, V) and the point (f)u(u，v)，fv(u, v)) the correspondence is an oblique parallel projection;
theta denotes the angle of the reflected ray normal to the receiving surface.
2. The method of claim 1, wherein the heliostat is a planar heliostat.
3. The method for simulating the radiant energy density of a planar heliostat in a tower-type solar thermal power station according to claim 1, wherein in the step (2), four light rays emitted from the vertexes of the heliostat replace light columns to intersect with the scene, so that the heliostat having a shadow and shielding relation with the heliostat is judged, namely, the heliostat having the shadow and the shielding is calculated by respectively using 3D-DDA for the light rays emitted from the four vertexes of the heliostat, and the union set is calculated for the results.
4. The method for simulating the radiant energy density of a planar heliostat in a tower-type solar thermal power plant according to claim 1, wherein in the step (3), 0 or 1 is used to indicate whether a pixel is an effective projection area of the heliostat on an imaging plane, and the heliostat H is calculated on the imaging plane2Is a projection region H'2Judging the center coordinate and H 'of each pixel'2For a point within the region, the value of the corresponding pixel is set to 1, otherwise to 0.
5. The method for simulating the radiant energy density of a planar heliostat in a tower-type solar thermal power plant according to claim 1, wherein in step (3), after obtaining the rasterized representation of the effective projection area on the imaging plane, an area correction process is added:
wherein:
rcorrecta correction value representing a discrete result, i.e., a discrete pixel value on the image plane multiplied by the correction value;
Sgeometryrepresenting an ideal projected area of the effective reflection area;
Sdispersethe uncorrected total area representing the discrete representation of the effective reflection area is solved by GPU specification calculation.
6. The method of claim 1, wherein in step (4), the discrete numerical convolution of the effective projection area and the radiation energy density distribution of the heliostat infinitesimal spot is calculated using a fast fourier convolution of a GPU version.
7. The method for simulating the radiation energy density of a planar heliostat in a tower-type solar thermal power plant according to claim 1, wherein in the step (5), the step of projecting the radiation energy density distribution of the imaging plane onto the receiving surface is as follows:
(5-1) obtaining the coordinates of the discrete pixel points on the receiving surface in a world coordinate system;
(5-2) obtaining coordinates of corresponding points on the imaging plane in a world coordinate system through oblique parallel projection;
(5-3) calculating the position of the corresponding point in the imaging plane according to the coordinate of the corresponding point in the world coordinate system;
(5-4) the radiation energy density of one point on the receiving surface is the radiation energy density of the corresponding point on the imaging plane multiplied by the cosine of the receiving surface, wherein the cosine of the receiving surface is the cosine value of the included angle between the reflected light ray and the normal direction of the receiving surface.
8. The method for simulating the radiant energy density of a planar heliostat in a tower-type solar thermal power plant according to claim 7, wherein in the step (5-4) of calculating the radiant energy density distribution of the corresponding point on the imaging plane, the energy value of the corresponding point is calculated by using bilinear interpolation, that is, the radiant energy density of the point is represented by weighted average of the radiant energy densities of adjacent pixels of the sampling point.
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