Sand barrier performance determination method, device and equipment based on CFD and cellular automaton

Abstract

The application provides a sand barrier efficiency determination method, device and equipment based on CFD and cellular automata, and relates to the windproof and sand-fixing technology field of a desert photovoltaic power station. The method comprises the following steps: obtaining full flow field data around a to-be-evaluated sand barrier by simulation according to a CFD simulation model of the to-be-evaluated sand barrier; processing the full flow field data by using a POD snapshot method to generate POD second-order modal energy singular values of the to-be-evaluated sand barrier under different geometric characteristics. Based on the full flow field data, the actual sand-blocking efficiency of the to-be-evaluated sand barrier with each layout parameter is determined. According to the POD second-order modal energy singular values and the actual sand-blocking efficiency, a judgment result is generated; the judgment result represents the windproof and sand-fixing degree of the to-be-evaluated sand barrier. The method is used to improve the evaluation simulation accuracy of the sand barrier.

Description

Technical Field

[0001] This application relates to the field of windbreak and sand-fixing engineering technology for desert photovoltaic power stations. Specifically, it relates to a method, device, and equipment for determining the effectiveness of sand barriers based on CFD and cellular automata. Background Technology

[0002] Currently, constructing photovoltaic power stations in deserts and Gobi regions is an important way to develop clean energy. However, these areas are prone to frequent wind and sandstorms, which can cause damage to the surface of photovoltaic modules, scratches on glass covers, and modules being buried by sand, severely reducing the power generation efficiency and lifespan of the photovoltaic power station. Sand barriers, as the most commonly used mechanical sand control measure for photovoltaic power stations, achieve the purpose of windbreak and sand fixation by blocking the movement of windblown sand, reducing near-ground wind speed, and promoting sand accumulation. They are the core facilities for ensuring the stable operation of desert photovoltaic power stations.

[0003] In existing technologies, some CFD methods are used to simulate the flow field around sand barriers, but these methods only focus on the velocity distribution of the flow field and evaluate the sand barrier's sand-fixing effectiveness based on the velocity distribution. However, due to the variable wind and sand activity in desert areas, evaluating sand barriers based solely on the velocity distribution of the flow field results in poor accuracy in the assessment. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, and device for determining sand barrier performance based on CFD and cellular automata, so as to solve the above-mentioned problems existing in the prior art and obtain the charging remaining time with high accuracy.

[0005] Firstly, a method for determining sand barrier effectiveness based on CFD and cellular automata is provided, which may include: Based on the constructed CFD simulation model of the sand barrier to be evaluated, the full flow field data around the sand barrier to be evaluated is simulated and obtained; the POD snapshot method is used to process the full flow field data to generate the POD second-order mode energy singular values ​​of the sand barrier to be evaluated under different geometric features. Based on the full flow field data, the actual sand-blocking efficiency of the sand barriers to be evaluated for each layout parameter is determined. Based on the singular values ​​of the second-order mode energy of the POD and the actual sand-blocking efficiency, a judgment result is generated; the judgment result characterizes the degree of windbreak and sand fixation of the sand barrier to be evaluated.

[0006] Secondly, a sand barrier performance determination device based on CFD and cellular automata is provided, which may include: The first generation module is used to construct a CFD simulation model of the sand barrier to be evaluated; based on the CFD simulation model, it simulates and acquires the full flow field data around the sand barrier to be evaluated; and uses the POD snapshot method to perform modal decomposition and feature extraction on the full flow field data to generate POD second-order mode energy singular values ​​with different geometric features. The determination module is used to construct a dynamic model of dune migration based on cellular automata based on the full flow field data; and to determine the actual sand-blocking efficiency of the sand barrier to be evaluated for each layout parameter according to the dynamic model of dune migration. The second generation module is used to generate a judgment result based on the singular value of the second-order mode energy of the POD and the actual sand-blocking efficiency; the judgment result characterizes the degree of windbreak and sand fixation of the sand barrier to be evaluated.

[0007] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0008] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0009] This application provides a method, apparatus, and equipment for determining the effectiveness of sand barriers based on CFD and cellular automata. The method involves simulating and acquiring full flow field data around the sand barrier using a constructed CFD simulation model of the barrier to be evaluated. The POD snapshot method is used to process the full flow field data, generating singular values ​​of the second-order modal energy of the sand barrier under different geometric features. Based on the full flow field data, the actual sand-blocking efficiency of the sand barrier for each layout parameter is determined. A judgment result is generated based on the singular values ​​of the second-order modal energy of the POD and the actual sand-blocking efficiency; the judgment result characterizes the degree of windbreak and sand-fixing effect of the sand barrier. In this scheme, the POD snapshot method is used to extract deep features from the CFD flow field data, and the singular values ​​of the second-order modal energy of the POD are selected as the core quantitative evaluation parameter for sand barrier effectiveness. This parameter can accurately and stably characterize the disturbance characteristics of the sand barrier on the wind and sand flow field. The indoor evaluation of sand barrier effectiveness is achieved based on CFD simulation, eliminating the need for long-term field measurements or large-scale wind tunnel tests. Therefore, it provides universally applicable and computationally efficient quantitative evaluation parameters, thereby realizing the correlation between the geometric characteristics of sand barriers and the dynamic characteristics of the flow field, achieving deep coupling between flow field simulation and sand dune migration simulation, and greatly improving the accuracy of sand barrier evaluation and simulation. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating a method for determining sand barrier effectiveness based on CFD and cellular automata, provided for an embodiment of this application; Figure 2 A flowchart illustrating a method for determining sand barrier effectiveness based on CFD and cellular automata, provided for an embodiment of this application; Figure 3 A schematic diagram of a sand barrier performance determination device based on CFD and cellular automata provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] The sand barrier performance determination method based on CFD and cellular automata provided in this application embodiment can be applied to electronic devices, terminal devices, sand barrier performance determination devices or equipment based on CFD and cellular automata, or other devices or equipment capable of executing this embodiment, without limitation. In this embodiment, the execution subject is described as an electronic device.

[0014] The terminal can be a user equipment (UE) such as a mobile phone, smartphone, laptop computer, digital broadcast receiver, personal digital assistant (PDA), or tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, or other processing device connected to a wireless modem, mobile station (MS), or mobile terminal. This terminal has the ability to communicate with one or more core networks via a radio access network (RAN).

[0015] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0016] Figure 1 This is a flowchart illustrating a method for determining sand barrier effectiveness based on CFD and cellular automata, provided as an embodiment of this application. Figure 1 As shown, the method may include: Step S101: Based on the constructed CFD simulation model of the sand barrier to be evaluated, simulate and obtain the full flow field data around the sand barrier to be evaluated; use the POD snapshot method to process the full flow field data and generate the POD second-order mode energy singular values ​​of the sand barrier to be evaluated under different geometric features.

[0017] For example, based on the constructed CFD simulation model of the sand barrier to be evaluated, the full flow field data around the sand barrier is simulated and obtained. The full data includes wind speed vector, pressure distribution, vorticity field, turbulence intensity, and sediment transport flux distribution data. The Proper Orthogonal Decomposition Snapshot Method (POD) is used to perform modal decomposition and feature extraction on the full flow field data, generating the POD second-order modal energy singular values ​​of the sand barrier under different geometric features, which serve as the core quantitative evaluation parameter for the sand barrier's effectiveness. The geometric features of the sand barrier to be evaluated include shape, height, width, wall thickness, and porosity parameters. The shape refers to one or more combinations of trapezoids, rectangles, and triangles. The POD second-order modal energy singular values ​​are used to characterize the core features of the sand barrier's disturbance to the wind and sand flow field.

[0018] Step S102: Based on the full flow field data, determine the actual sand-blocking efficiency of the sand barrier to be evaluated for each layout parameter.

[0019] For example, a cellular automata-based dynamic model of dune migration is constructed, which couples the full data of the flow field. The dynamic model of dune migration is used to simulate the entire process of dune initiation, movement and accumulation under the action of sand barriers. The actual sand-blocking efficiency of the sand barriers is quantitatively calculated based on the dynamic model of dune migration.

[0020] Step S103: Generate a judgment result based on the singular value of the second-order mode energy of POD and the actual sand-blocking efficiency; the judgment result characterizes the degree of windbreak and sand fixation of the sand barrier to be evaluated.

[0021] For example, multivariate, multi-group controlled simulation experiments were conducted to obtain the singular values ​​of the second-order modal energy of POD and the actual sand-blocking efficiency corresponding to sand barriers with different geometric features and layout parameters. A negative correlation mathematical model between the singular values ​​of the second-order modal energy of POD and the actual sand-blocking efficiency was established using a linear fitting method combined with residual correction. The stability and accuracy of the model were verified by the coefficient of determination, root mean square error, and prediction error rate. Finally, a converged negative correlation mathematical model was obtained. Based on the singular values ​​of the second-order modal energy of POD, the actual sand-blocking efficiency, and the negative correlation mathematical model, a judgment result was generated. The judgment result represents the degree of windbreak and sand fixation of the sand barrier to be evaluated.

[0022] Among them, the variables in the multivariate, multi-group controlled simulation experiment include the cross-sectional shape, height, width, porosity, spacing and row and column arrangement of the sand barrier to be evaluated. Each group of variables is set with at least 5 horizontal gradients, and the total number of controlled simulation experiments is no less than 30 groups.

[0023] Therefore, multivariate, multi-group controlled simulation experiments were conducted, taking into account the synergistic effects of multiple parameters such as the cross-sectional shape, height, porosity, spacing, and arrangement of sand barriers, and establishing the correlation between all parameters and the effectiveness of sand barriers. At the same time, combined with the layout of photovoltaic arrays and engineering costs, the geometric characteristics and spatial layout of sand barriers were comprehensively optimized, solving the problem that existing technologies only focus on a single parameter and cannot take into account both effectiveness and cost, thus achieving the optimal balance between windbreak and sand fixation effectiveness and engineering economic costs.

[0024] The method provided in this application embodiment simulates and acquires the full flow field data around the sand barrier based on a constructed CFD simulation model of the sand barrier to be evaluated. The POD snapshot method is used to process the full flow field data, generating singular values ​​of the POD second-order modal energy under different geometric features of the sand barrier. Based on the full flow field data, the actual sand-blocking efficiency of the sand barrier for each layout parameter is determined. A judgment result is generated based on the singular values ​​of the POD second-order modal energy and the actual sand-blocking efficiency; the judgment result characterizes the degree of windbreak and sand-fixing effect of the sand barrier. In this scheme, the POD snapshot method is used to extract deep features from the CFD flow field data, and the singular values ​​of the POD second-order modal energy are selected as the core quantitative evaluation parameter for sand barrier effectiveness. This parameter can accurately and stably characterize the disturbance characteristics of the sand barrier on the wind and sand flow field. Indoor evaluation of sand barrier effectiveness is achieved based on CFD simulation and cellular automata simulation, without the need for long-term field measurements or large-scale wind tunnel tests. Therefore, it provides universally applicable and computationally efficient quantitative evaluation parameters, thereby realizing the correlation between the geometric characteristics of sand barriers and the dynamic characteristics of the flow field, achieving deep coupling between flow field simulation and sand dune migration simulation, and greatly improving the accuracy of sand barrier evaluation and simulation.

[0025] Figure 2 A flowchart illustrating a method for determining sand barrier effectiveness based on CFD and cellular automata provided in this application is shown below. Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the method is described in detail below, and the method includes: Step S201: Based on the constructed CFD simulation model of the sand barrier to be evaluated, simulate and obtain the full flow field data around the sand barrier to be evaluated.

[0026] In one example, the training process of the CFD simulation model includes: performing unstructured hybrid mesh generation on the sand barrier and surrounding wind and sand flow field area to be evaluated, and using boundary layer mesh refinement near the wall to obtain the divided mesh; if the divided mesh passes the mesh independence verification, the target mesh scale is determined; in response to the first setting operation, boundary conditions are set; the boundary conditions include velocity inlet, pressure outlet, non-slip wall surface of the sand barrier, and rough ground surface, and the inlet wind speed of the velocity inlet is set as a multi-condition variable wind speed based on multi-year meteorological data of the desert area where the photovoltaic power station is located; in response to the second setting operation, the turbulence model is set; the turbulence model adopts the SSTk-ω model and is coupled with the Eulerian-Lagrange two-phase flow model of sand particle transport; based on the target mesh scale, boundary conditions, and turbulence model, the CFD simulation model of the sand barrier to be evaluated is constructed.

[0027] For example, based on the constructed CFD simulation model of the sand barrier to be evaluated, the full flow field data around the sand barrier to be evaluated is simulated and obtained; the full flow field data includes wind speed vector, pressure distribution, vorticity field, turbulence intensity and sand transport flux distribution data.

[0028] Optional, refined CFD simulation model construction includes: performing unstructured hybrid mesh generation on the sand barrier and surrounding wind and sand flow field area to be evaluated, and using boundary layer mesh refinement near the wall to obtain the generated mesh. The optimal target mesh scale is determined after the generated mesh passes the irrelevance verification.

[0029] In response to the first setting operation, boundary conditions are set, including velocity inlet, pressure outlet, non-slip sand barrier surface, and rough ground surface. The inlet wind speed of the velocity inlet is set as a multi-condition variable wind speed based on multi-year meteorological data of the desert area where the photovoltaic power station is located. Multi-condition variable wind speed means that in the simulation, instead of simulating only a fixed wind speed, multiple representative and different wind speed values ​​are set and calculated separately.

[0030] In response to the second setup operation, a turbulence model is set up using the SSTk-ω model, coupled with an Eulerian-Lagrange two-phase flow model for sand particle transport to simulate the motion of the aeolian two-phase flow. The sand transport flux is calculated using the aeolian two-phase flow sand transport formula. Finally, based on the target grid scale, boundary conditions, and turbulence model, a CFD simulation model of the sand barrier to be evaluated is constructed.

[0031] Optionally, during the preliminary design phase of the sand barrier to be evaluated at the photovoltaic power station, the effectiveness of different sand barrier schemes can be simulated. Under the premise of meeting the requirements for windbreak and sand fixation, the following formula can be used to calculate the engineering cost:

[0032] in, The total project cost, Cost per unit volume of material For the volume of sand barrier material, Construction cost per unit length The length of the sand barrier construction is given. Therefore, based on the engineering cost calculation formula, the engineering cost can be obtained, thereby minimizing the material and construction costs of the sand barrier project and achieving the optimal balance between windbreak and sand fixation effectiveness and engineering economic cost.

[0033] Step S202: Process the full flow field data using the POD snapshot method to generate the POD second-order mode energy singular values ​​of the sand barrier to be evaluated under different geometric features.

[0034] In one example, S202 includes: collecting three-dimensional flow field snapshots at different time intervals; constructing a high-dimensional flow field snapshot matrix based on the three-dimensional flow field snapshots; the time interval is determined according to the convergence of the flow field; calculating the time mean field of the high-dimensional flow field snapshot matrix; centering the high-dimensional flow field snapshot matrix based on the time mean field to obtain a centered snapshot matrix with the mean field removed; and performing singular value decomposition on the centered snapshot matrix to obtain the singular values ​​of the dominant modes, mode coefficients, and corresponding mode energies of the flow field.

[0035] For example, three-dimensional flow field snapshots are collected at equal time intervals to construct a high-dimensional flow field snapshot matrix. The snapshot time interval is determined based on the flow field convergence, and the number of snapshots includes the number of multiple different geometric features, for example, the number of snapshots is not less than 800. The time average field of the high-dimensional flow field snapshot matrix is ​​calculated, and the time average field is subtracted from the high-dimensional flow field snapshot matrix to center the high-dimensional flow field snapshot matrix, resulting in a centered snapshot matrix that eliminates the average field and removes the interference of the steady-state components of the flow field. The centering formula is as follows: , in, This is the original snapshot matrix. The time-averaged field matrix, This is a centralized snapshot matrix.

[0036] Singular value decomposition (SVD) is performed on the centered snapshot matrix to obtain the singular values ​​of the dominant modes, modal coefficients, and corresponding modal energies of the flow field. The SVD formula is as follows: ,in For singular value diagonal matrices, the modal energy ratio formula is used. The second-order mode of POD was identified as the core mode characterizing the flow field disturbance of the sand barrier to be evaluated. For the first The energy percentage of the first mode, For the first Singular values ​​of the first mode, The total modal order is given, and the energy singular value σ2 of the second-order mode of POD is extracted as the core quantitative evaluation parameter.

[0037] Optionally, the energy proportion of each mode can be calculated as follows: the energy proportion of each mode is the ratio of the square of the singular value of a single mode to the sum of the squares of the singular values ​​of all modes. The sum of the squares of the singular values ​​of all modes refers to the sum of the squares of the singular values ​​of each mode. According to the calculation results, the energy proportion of the second-order mode of POD is not less than 25%. As the core mode characterizing the disturbance of the wind and sand flow field by the sand barrier to be evaluated, the change of the singular value of the second-order mode of POD can accurately reflect the change of the sand barrier's sand-blocking effectiveness.

[0038] Therefore, by using the POD snapshot method to extract deep features from the full flow field data, the singular value of the second-order mode energy of POD was selected as the core quantitative evaluation parameter of the sand barrier effectiveness. This parameter can accurately and stably characterize the disturbance characteristics of the sand barrier on the wind and sand flow field, and is significantly negatively correlated with the actual sand blocking efficiency of the sand barrier. This solves the problem of the lack of standardized and refined quantitative evaluation indicators in existing technologies, and realizes a scientific, objective and accurate evaluation of the sand barrier effectiveness.

[0039] Step S203: Based on the full flow field data, determine the actual sand-blocking efficiency of the sand barrier to be evaluated for each layout parameter.

[0040] In one example, S203 includes: constructing a cellular automata-based dynamic model of dune migration based on the full flow field data; and determining the actual sand-blocking efficiency of the sand barrier to be evaluated for each layout parameter based on the dynamic model of dune migration.

[0041] In one example, a dynamic model of dune migration based on cellular automata is constructed based on the full flow field data. This includes the following steps: dividing the sand barrier to be evaluated and the surrounding wind-blown sand migration area into a grid to generate square cells of equal scale; generating a sand grain initiation frictional wind speed threshold at each square cell based on the full flow field data and a modified Baigno sand grain initiation wind speed formula; determining the actual frictional wind speed at each square cell based on the full flow field data; and simulating the dune migration process at preset time steps based on the sand grain initiation frictional wind speed threshold at each square cell, the actual frictional wind speed, and preset cell state transition rules, updating the cell state once per time step.

[0042] For example, a dynamic model of dune migration based on cellular automata coupled with full flow field data is constructed. The construction process includes setting cellular migration rules based on full flow field data and physical characteristics of sand barriers. This model is used to simulate the entire process of dune initiation, migration, and accumulation under the action of sand barriers, and to quantify the actual sand-blocking efficiency of sand barriers.

[0043] Optionally, the method for constructing and simulating a dynamic model of dune migration coupled with full flow field data is as follows: 1. The sand barrier to be evaluated and the surrounding wind and sand migration area are divided into grids to generate square cells of equal scale. The size of the cells is determined to be 0.5cm to 2cm according to the sand particle size and simulation accuracy. The cell states include four types: stable state, dynamic state, moving state, and accumulated state.

[0044] 2. Based on the complete flow field data, including wind speed vector, turbulence intensity, and sediment transport flux distribution data obtained from the CFD simulation model, and combined with the modified Baigno sand grain initiation wind speed formula, the sand grain initiation friction wind speed threshold at each square cell is generated; the modified Baigno sand grain initiation wind speed formula is as follows: , The conditions for the transition of a cell from a stable state to a dynamic state are pre-defined, that is, the cell state transition rules are pre-defined, whereby... The wind speed threshold for initiating friction between sand particles is given by A and B, which are empirical coefficients. The density of sand grains, air density, It is the acceleration due to gravity. The average particle size of the sand is... The viscosity coefficient of air motion is used. The physical blocking coefficient and sand particle collision recovery coefficient of the sand barrier to be evaluated are also introduced to quantitatively reflect the blocking effect of the sand barrier on sand particle transport. Empirical coefficients A and B are adjusted based on the sand particle size, soil moisture, and ground roughness of the desert region where the photovoltaic power station is located. The adjustment formula is as follows: , , in , These are the corrected empirical coefficients. For soil moisture, For surface roughness, , To correct the coefficients, the cell state transition rules are made to better reflect the actual wind and sand environment.

[0045] The sand dune migration process is simulated using a preset time step. The cell state is updated once at each time step. The amount of sand accumulated downstream of the sand barrier and the amount of sand transported by unobstructed sand are statistically analyzed within the simulation period. The sand blocking efficiency is then calculated using the formula. Calculate the actual sand-blocking efficiency of the sand barrier to be evaluated, where, This represents the actual sand-blocking efficiency. This represents the amount of sand accumulated downstream of the sand barrier. For the total sediment transport, the simulation period should be no less than three times the complete transport cycle of the wind-blown sand flow.

[0046] Therefore, the constructed cellular automata-based dynamic model of dune migration couples the actual three-dimensional transient flow field data obtained from CFD simulation, rather than empirical wind speed formulas. It also introduces the physical blocking coefficient of sand barriers and a modified Baigno sand grain initiation wind speed formula, making the cellular state transition rules more closely aligned with the actual aeolian environment. This allows for accurate simulation of the entire process of sand grain initiation, transport, and accumulation, resulting in a sand-blocking efficiency closer to the actual value. This solves the problem of disconnect between flow field simulation and dune migration simulation in existing technologies, achieving deep coupling between the two and improving simulation accuracy. Furthermore, the indoor evaluation of sand barrier effectiveness based on CFD simulation and cellular automata simulation eliminates the need for long-term field measurements or large-scale wind tunnel tests, shortening the evaluation cycle from over a year using traditional methods to just a few days and reducing evaluation costs by over 80%. Simultaneously, the refined simulation model and the cellular automata model coupled with flow field data make the evaluation results more closely resemble the engineering scenario of a real desert photovoltaic power station, with a prediction error rate of <4%, far lower than the error level of traditional wind tunnel testing methods.

[0047] Step S204: Generate a judgment result based on the singular value of the second-order mode energy of POD and the actual sand-blocking efficiency; the judgment result characterizes the degree of windbreak and sand fixation of the sand barrier to be evaluated.

[0048] In one example, S204 includes: based on the actual sand-blocking efficiency, using linear fitting combined with residual correction, establishing a negative correlation mathematical correlation model between the singular values ​​of the second-order modal energy of POD and the actual sand-blocking efficiency dataset; using the singular values ​​of the second-order modal energy of POD and the negative correlation mathematical correlation model, quantitatively evaluating and classifying the windbreak and sand-fixing effectiveness of the sand barrier to be evaluated, and generating a judgment result; the judgment result includes the effectiveness level.

[0049] For example, the least squares method is first used to perform linear fitting on the dataset containing actual sand-blocking efficiency to obtain an initial linear correlation model. ,in, The slope is the fitted slope. For the fitting intercept, The singular values ​​of the second-order modal energy of POD (i.e., σ² in the above steps) are then calculated; the fitting residuals are then calculated. By correcting the residuals through polynomial fitting, the final negative correlation mathematical model is obtained. The formula for the determination coefficient is: , The formula for root mean square error is: , The formula for prediction error rate is: , in, The coefficient of determination is used to evaluate the goodness of fit of the model. For the sample size, This represents the actual sand-blocking efficiency. The negative correlation mathematical correlation model is used to predict the sand-blocking efficiency based on the singular values ​​of the second-order mode of the input POD. The actual average sand-blocking efficiency is given. Based on the above formulas for the coefficient of determination, root mean square error (RMSE), and prediction error rate, the negative correlation mathematical model is validated, requiring a coefficient of determination R² ≥ 0.96, a root mean square error (RMSE) ≤ 3.5%, and a prediction error rate of [missing information]. <4%, thus obtaining a convergent negative correlation mathematical model.

[0050] Using the core quantitative evaluation parameter (i.e., the singular value of the second-order modal energy of POD) as input, the sand-blocking efficiency is predicted through a validated negative correlation mathematical correlation model. The windbreak and sand-fixing effectiveness of the sand barrier to be evaluated is quantitatively evaluated and classified, and the judgment result is generated, including the effectiveness level.

[0051] Optionally, the quantitative evaluation and grading standards for sand barrier effectiveness are as follows: The singular values ​​of the second-order mode energy of POD are divided into three ranges: low (S≤0.25), medium (0.25<S≤0.40), and high (S>0.40), which correspond to three efficiency levels of sand barrier blocking efficiency: excellent (η≥80%), medium (60%≤η<80%), and poor (η<60%).

[0052] Step S205: Based on the judgment results, optimize the geometric features and layout parameters of the sand barrier to be evaluated; the optimization of geometric features includes cross-sectional shape optimization, height-to-width ratio optimization, and porosity adaptation optimization; the optimization of layout parameters includes optimization of sand barrier row spacing and column spacing, optimization of the distance between the sand barrier to be evaluated and the photovoltaic array, and optimization of the arrangement of the sand barrier to be evaluated.

[0053] For example, based on the judgment results, the geometric features and layout parameters of the sand barrier to be evaluated are optimized. The optimization of the geometric features of the sand barrier to be evaluated includes the optimization of the cross-sectional shape, the optimization of the height-to-width ratio, and the optimization of the porosity; the optimization of the spatial layout parameters includes the optimization of the row spacing and column spacing of the sand barrier, the optimization of the spacing between the sand barrier to be evaluated and the photovoltaic array, and the optimization of the row and column arrangement and staggered arrangement of the sand barrier to be evaluated, etc., so as to achieve the best balance between windbreak and sand fixation and cost control.

[0054] Step S206: Obtain the actual wind and sand environment and photovoltaic array layout of the photovoltaic area; combine the actual wind and sand environment and photovoltaic array layout to generate a sand barrier layout scheme.

[0055] For example, the actual wind and sand environment and photovoltaic array layout of the photovoltaic area are obtained, and a sand barrier layout scheme is generated by combining the actual wind and sand environment and photovoltaic array layout. Therefore, by optimizing the geometric characteristics and layout parameters of the sand barrier to be evaluated based on the evaluation results, and combining the actual wind and sand environment and photovoltaic array layout of the photovoltaic power station, it is convenient to formulate a customized, low-cost, and high-efficiency sand barrier design and layout scheme.

[0056] Optionally, a long-term dynamic assessment of sand barrier effectiveness can also be performed. Specifically, based on the wind and sand environment evolution data of the photovoltaic power station area, the boundary conditions of the CFD simulation model are updated periodically, and the singular values ​​of the second-order mode energy of the POD are recalculated. The recalculated singular values ​​of the second-order mode energy of the POD are substituted into the negative correlation mathematical correlation model to predict the dynamic sand-blocking efficiency of the sand barrier to be assessed. Based on the dynamic sand-blocking efficiency, the windbreak and sand-fixing effectiveness of the sand barrier to be assessed is quantitatively evaluated and classified. Based on the assessment results, the sand barrier layout is adjusted or maintenance is carried out in a timely manner to ensure the long-term windbreak and sand-fixing effectiveness of the sand barrier.

[0057] The evaluation method proposed in this application is applicable to photovoltaic power stations in different desert and Gobi regions. It can adjust parameters according to the wind and sand environment, sand characteristics, and photovoltaic array layout of the power station's location, and has strong universality. At the same time, this method can be applied to the entire life cycle of sand barriers, including early design, scheme comparison, performance evaluation, layout optimization, and long-term dynamic maintenance. It can provide comprehensive technical support for the scientific design and efficient operation of sand barriers for desert photovoltaic power stations, and has extremely high engineering practicality.

[0058] Therefore, long-term dynamic evaluation of sand barrier effectiveness can promptly identify the decline in sand barrier effectiveness based on data on wind and sand environment evolution and the updated evaluation results of sand barrier status. It can also guide the layout adjustment, maintenance and replacement of sand barriers, ensuring the long-term windbreak and sand fixation effectiveness of sand barriers. This solves the problem of the lack of long-term dynamic evaluation methods in existing technologies and provides continuous protection for the long-term stable operation of desert photovoltaic power stations.

[0059] The method provided in this application embodiment simulates and acquires the full flow field data around the sand barrier to be evaluated based on a constructed CFD simulation model of the sand barrier to be evaluated. The full flow field data is processed using the POD snapshot method to generate singular values ​​of the POD second-order modal energy of the sand barrier under different geometric features. Based on the full flow field data, the actual sand-blocking efficiency of the sand barrier under each layout parameter is determined. A judgment result is generated based on the singular values ​​of the POD second-order modal energy and the actual sand-blocking efficiency; the judgment result characterizes the degree of windbreak and sand-fixing effect of the sand barrier to be evaluated. Based on the judgment result, the geometric features and layout parameters of the sand barrier to be evaluated are optimized; the optimization of geometric features includes cross-sectional shape optimization, height-to-width ratio optimization, and porosity adaptation optimization; the optimization of layout parameters includes optimization of row and column spacing of the sand barrier, optimization of the distance between the sand barrier to be evaluated and the photovoltaic array, and optimization of the arrangement of the sand barrier to be evaluated. The actual wind and sand environment of the photovoltaic area and the layout of the photovoltaic array are acquired; combined with the actual wind and sand environment and the photovoltaic array layout, a sand barrier layout scheme is generated. Therefore, it provides universally applicable and computationally efficient quantitative evaluation parameters, thereby realizing the correlation between the geometric characteristics of sand barriers and the dynamic characteristics of the flow field, achieving deep coupling between flow field simulation and sand dune migration simulation, and greatly improving the accuracy of sand barrier evaluation and simulation.

[0060] In one example, this application will be described in detail according to the following specific embodiments 1-3.

[0061] Taking a photovoltaic power station in a desert in Northwest China as the engineering background, the average annual wind speed in the area is 6.5 m / s, the average particle size of sand is 0.25 mm, the photovoltaic array is arranged in rows and columns with an array spacing of 3 m, and the original sand barrier is a rectangular sand barrier. The photovoltaic modules are buried by sand and the power generation efficiency is reduced. The method of this application is used to evaluate the effectiveness of the original sand barrier and optimize the sand barrier design scheme.

[0062] Example 1 Step 1: Construct a refined CFD simulation model: For the existing rectangular sand barrier, with geometric parameters of 20cm height, 15cm width, 0% porosity, and 2cm wall thickness, a 3D geometric model was constructed. Considering the topography of the photovoltaic power station, a computational domain of 50m length, 30m width, and 5m height was constructed. An unstructured hybrid mesh was used to divide the computational domain, with mesh refinement around the perimeter and near the wall of the sand barrier. The minimum mesh size was 0.5cm, and the optimal mesh size was determined after verifying mesh independence.

[0063] Boundary conditions were set as follows: the inlet was a velocity inlet, and the wind speed was set to the multi-year average wind speed of the region, 6.5 m / s; the outlet was a pressure outlet; the sand barrier surface was a non-slip wall, and the ground surface was a rough wall (roughness coefficient 0.01). The turbulence model adopted was the SSTk-ω model, coupled with an Eulerian-Lagrange two-phase flow model, and the sand particle size was set to 0.25 mm.

[0064] Submit CFD simulation calculations. After the calculations converge, obtain full data of the three-dimensional transient flow field around the sand barrier, including wind speed vector, pressure distribution, vorticity field, turbulence intensity, and sand transport flux distribution data.

[0065] Step 2: POD snapshot modal decomposition to extract core evaluation parameters: 1000 sets of three-dimensional flow field snapshots were collected at equal time intervals to construct a high-dimensional flow field snapshot matrix. The time-averaged field was calculated and centered to obtain a centered snapshot matrix. Singular value decomposition was performed on the centered snapshot matrix to solve for the energy proportion of each mode. The energy proportion of the second-order POD mode was 28%, which is the core mode. The singular value of the energy of the second-order POD mode of the original rectangular sand barrier was extracted to be 0.38.

[0066] Step 3: Construct a cellular automata model and calculate the actual sand-blocking efficiency: The sand barrier and the surrounding wind-blown sand migration area are divided into equal-scale cells of 1cm×1cm. The cell states are set as four types: stable state, dynamic state, moving state, and accumulated state. Based on CFD flow field data, combined with the modified Baigno sand particle initiation wind speed formula (corrected according to the sand particle size and ground roughness in this area), the cell state transformation rules are set, and a physical blocking coefficient of 0.9 (solid sand barrier) is introduced.

[0067] The dune migration process was simulated with a time step of 0.1s and a simulation period of 60s. The amount of sand accumulated downstream of the sand barrier was 62.5kg, and the total sand transport was 91.9kg. The actual sand blocking efficiency of the original rectangular sand barrier was calculated to be 68%.

[0068] Step 4: Substitute into the correlation model to complete the performance evaluation and ranking: The mathematical correlation model established in this application, which establishes a negative correlation between the singular value of the second-order modal energy of POD and the sand-blocking efficiency of sand barriers, is used: η = -0.232 × S + 0.925 (the model has R² = 0.97, RMSE = 3.2%, and prediction error rate of 3.8%, meeting the verification criteria). Substituting the singular value of 0.38 into the model, the predicted sand-blocking efficiency is calculated to be 69.2%, with an error of 1.2% compared to the actual sand-blocking efficiency, indicating high prediction accuracy.

[0069] According to the efficiency rating standard, the original sand barrier has an outlier of 0.38, which is in the middle range, and a sand blocking efficiency of 68%, which is in the middle level. It cannot meet the windbreak and sand fixation requirements of the photovoltaic power station (which requires a sand blocking efficiency of ≥75%), so the sand barrier needs to be optimized.

[0070] Example 2 Step 1: Conduct a multivariate controlled simulation experiment to screen for optimal parameters: Using the cross-sectional shape, height, width, and arrangement of sand barriers as variables, 35 sets of comparative simulation experiments were conducted to select the optimal parameter combination: trapezoidal sand barrier (cross-sectional shape), height 25cm, upper base width 12cm, lower base width 25cm, porosity 0.05, staggered arrangement, sand barrier row spacing 2.5m, column spacing 2m, and sand barrier and photovoltaic panel spacing 1.5m.

[0071] Step 2: Evaluation of the effectiveness of the optimized sand barrier: For the optimized trapezoidal sand barrier, a refined CFD simulation model was constructed according to the method in Example 1. After modal decomposition using the POD snapshot method, the singular value of the second-order mode energy of POD was extracted to be 0.22. Substituting it into the correlation model, the predicted sand-blocking efficiency was calculated to be 87.2%.

[0072] A cellular automaton model coupled with the full flow field data was constructed. The simulation showed that the actual sand-blocking efficiency of the optimized sand barrier was 86.5%, with an error of only 0.7% compared to the predicted value, demonstrating high prediction accuracy. According to the efficiency level classification standard, the singular value of 0.22 for the optimized sand barrier belongs to the low range, and the sand-blocking efficiency of 86.5% belongs to the excellent level, meeting the windbreak and sand-fixing requirements of photovoltaic power stations.

[0073] Step 3: Cost-Effectiveness Balance Analysis: Compared with the original rectangular sand barriers, the optimized trapezoidal sand barriers have improved sand-blocking efficiency by 18.5%. At the same time, by optimizing the spacing and arrangement of the sand barriers, the amount of engineering materials used for the sand barriers has been reduced by 12%, and the construction cost has been reduced by 10%, achieving the optimal balance between windbreak and sand-fixing effectiveness and engineering economic cost.

[0074] The engineering application results are as follows: After the optimized trapezoidal sand barrier was applied in the actual photovoltaic power station, it effectively blocked the movement of wind and sand, completely solved the problem of sand burying the photovoltaic modules, and significantly reduced the wear and tear on the module surface. The annual power generation efficiency of the photovoltaic power station increased by 1.5%, and the annual cost of module cleaning and maintenance was reduced by about 250,000 yuan, achieving good engineering, economic and environmental benefits.

[0075] Example 3 One year after the optimized sand barrier application, based on the wind and sand environment evolution data of the area where the photovoltaic power station is located (annual average wind speed increased to 7.0 m / s) and the on-site inspection data of the sand barrier (slight wear on the sand barrier, porosity increased to 0.08), a long-term dynamic evaluation of the sand barrier effectiveness was carried out: The boundary conditions (inlet wind speed 7.0 m / s) and the geometric parameters of the sand barrier (porosity 0.08) of the CFD simulation model were updated, and the singular value of the second-order mode energy of POD was recalculated to be 0.24. Substituting the singular value of 0.24 into the correlation model, the current dynamic sand-blocking efficiency of the sand barrier is calculated to be 84.8%, which is still considered excellent and can meet the requirements for windbreak and sand fixation. Based on the assessment results, a sand barrier maintenance plan was formulated: the sand barriers will be inspected every 6 months, and worn parts will be repaired in a timely manner to ensure that the porosity of the sand barriers is controlled within 0.08, thus ensuring the long-term windbreak and sand fixation effectiveness of the sand barriers.

[0076] Corresponding to the above method, this application also provides a sand barrier performance determination device based on CFD and cellular automata, such as... Figure 3 As shown, the device includes: The first generation module 41 is used to construct a CFD simulation model of the sand barrier to be evaluated; based on the CFD simulation model, it simulates and obtains the full flow field data around the sand barrier to be evaluated; it uses the POD snapshot method to perform modal decomposition and feature extraction on the full flow field data to generate POD second-order mode energy singular values ​​with different geometric features. Module 42 is used to construct a dynamic model of dune migration based on cellular automata based on the full flow field data; and to determine the actual sand-blocking efficiency of the sand barrier to be evaluated for each layout parameter based on the dynamic model of dune migration. The second generation module 43 is used to generate a judgment result based on the singular value of the second-order mode energy of POD and the actual sand-blocking efficiency; the judgment result characterizes the degree of windbreak and sand fixation of the sand barrier to be evaluated.

[0077] The functions of each functional unit of the sand barrier performance determination device based on CFD and cellular automata provided in the above embodiments of this application can be implemented through the above-described method steps. Therefore, the specific working process and beneficial effects of each unit in the sand barrier performance determination device based on CFD and cellular automata provided in the embodiments of this application will not be repeated here.

[0078] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540.

[0079] Memory 530 is used to store computer programs; The processor 510 performs the above steps when executing the program stored in the memory 530.

[0080] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0081] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0082] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0083] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0084] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0085] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the sand barrier performance determination methods based on CFD and cellular automata described in the above embodiments.

[0086] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the sand barrier performance determination methods based on CFD and cellular automata described in the above embodiments.

[0087] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0092] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for determining sand barrier effectiveness based on CFD and cellular automata, characterized in that, include: Based on the constructed CFD simulation model of the sand barrier to be evaluated, the full flow field data around the sand barrier to be evaluated is simulated and obtained. The POD snapshot method was used to process the full flow field data to generate the POD second-order mode energy singular values ​​of the sand barrier under different geometric features to be evaluated. Based on the full flow field data, the actual sand-blocking efficiency of the sand barrier to be evaluated for each layout parameter is determined. The judgment result is generated based on the singular values ​​of the second-order mode energy of the POD and the actual sand-blocking efficiency; The judgment result characterizes the degree of windbreak and sand fixation of the sand barrier to be evaluated.

2. The method as described in claim 1, characterized in that, The POD snapshot method is used to process the full flow field data to generate POD second-order mode energy singularities with different geometric characteristics, including: Three-dimensional flow field snapshots are collected at different time intervals; a high-dimensional flow field snapshot matrix is ​​constructed based on the three-dimensional flow field snapshots; the time interval is determined according to the flow field convergence. Calculate the time average field of the high-dimensional flow field snapshot matrix, and center the high-dimensional flow field snapshot matrix according to the time average field to obtain a centered snapshot matrix with the average field removed. Singular value decomposition is performed on the centered snapshot matrix to obtain the singular values ​​of the dominant modes, mode coefficients and corresponding mode energies of the flow field.

3. The method as described in claim 1, characterized in that, Based on the complete flow field data, the actual sand-blocking efficiency of the sand barriers to be evaluated for each layout parameter is determined, including: Based on the full flow field data, a dynamic model of dune migration based on cellular automata is constructed. Based on the aforementioned sand dune migration dynamic model, the actual sand-blocking efficiency of the sand barriers to be evaluated for each layout parameter is determined.

4. The method as described in claim 3, characterized in that, Based on the complete flow field data, a dynamic model of dune migration based on cellular automata is constructed, including: Perform the following steps to construct a dynamic model of dune migration based on cellular automata: The sand barrier to be evaluated and the surrounding wind and sand migration area are divided into grids to generate square cells of the same scale. Based on the full flow field data, and combined with the modified Baigno sand grain initiation wind speed formula, the sand grain initiation friction wind speed threshold at each square cell is generated. Based on the full flow field data, the actual frictional wind speed at each square cell is determined; Based on the sand grain initiation friction wind speed threshold at each square cell, the actual friction wind speed, and the preset cell state transition rules, the dune migration process is simulated in preset time steps, and the cell state is updated once at each time step.

5. The method as described in claim 1, characterized in that, Based on the singular values ​​of the second-order mode energy of the POD and the actual sand-blocking efficiency, a judgment result is generated, including: Based on the actual sand-blocking efficiency, a negative correlation mathematical model between the singular values ​​of the second-order mode energy of POD and the actual sand-blocking efficiency dataset is established by using linear fitting combined with residual correction. Using the singular values ​​of the second-order modal energy of POD and the negative correlation mathematical correlation model, the windbreak and sand-fixing effectiveness of the sand barrier to be evaluated is quantitatively assessed and classified, and a judgment result is generated; the judgment result includes the effectiveness level.

6. The method according to any one of claims 1-5, characterized in that, The training process of the CFD simulation model includes: An unstructured hybrid grid is generated for the sand barrier and surrounding wind and sand flow field area to be evaluated, and a boundary layer grid is used for densification near the wall to obtain the divided grid; if the divided grid passes the grid independence verification, the target grid scale is determined. In response to the first setting operation, boundary conditions are set; the boundary conditions include velocity inlet, pressure outlet, non-slip sand barrier surface, and rough ground surface. The inlet wind speed of the velocity inlet is set as a multi-condition variable wind speed based on multi-year meteorological data of the desert area where the photovoltaic power station is located. In response to the second setting operation, a turbulence model is set; the turbulence model adopts the SSTk-ω model and is coupled with an Eulerian-Lagrange two-phase flow model for sand particle transport; Based on the target grid scale, the boundary conditions, and the turbulence model, a CFD simulation model of the sand barrier to be evaluated is constructed.

7. The method according to any one of claims 1-5, characterized in that, The method further includes: Based on the judgment results, the geometric features and layout parameters of the sand barrier to be evaluated are optimized; Obtain the actual wind and sand environment and photovoltaic array layout of the photovoltaic area; combine the actual wind and sand environment and the photovoltaic array layout to generate a sand barrier layout scheme; The optimization of the geometric features includes optimization of cross-sectional shape, optimization of height-to-width ratio, and optimization of porosity adaptation; the optimization of the layout parameters includes optimization of row spacing and column spacing of the sand barrier to be evaluated, optimization of the spacing between the sand barrier to be evaluated and the photovoltaic array, and optimization of the arrangement of the sand barrier to be evaluated.

8. A sand barrier performance determination device based on CFD and cellular automata, characterized in that, The device includes: The first generation module is used to construct a CFD simulation model of the sand barrier to be evaluated; based on the CFD simulation model, it simulates and acquires the full flow field data around the sand barrier to be evaluated; and uses the POD snapshot method to perform modal decomposition and feature extraction on the full flow field data to generate POD second-order mode energy singular values ​​with different geometric features. The determination module is used to construct a dynamic model of dune migration based on cellular automata based on the full flow field data; and to determine the actual sand-blocking efficiency of the sand barrier to be evaluated for each layout parameter according to the dynamic model of dune migration. The second generation module is used to generate a judgment result based on the singular value of the second-order mode energy of the POD and the actual sand-blocking efficiency; the judgment result characterizes the degree of windbreak and sand fixation of the sand barrier to be evaluated.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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