A method and device for determining a scale ratio for a floating photovoltaic model test

Abstract

This application provides a method and apparatus for determining the scale ratio in floating photovoltaic (PV) model tests. The method acquires multi-dimensional characteristic parameters of the prototype floating PV system and the design environmental load spectrum. Based on the design environmental load spectrum and multi-dimensional characteristic parameters, it determines several key dynamic response indices and corresponding dominant physical mechanisms of the prototype floating PV system. According to each key dynamic response index and corresponding dominant physical mechanism, a corresponding similarity criterion is selected, and an objective function is constructed using the comprehensive deviation between the calculated values ​​and the corresponding ideal values ​​of each similarity criterion. An optimization decision model is constructed using multiple candidate scale ratio combinations as decision variables. This optimization decision model is solved under the constraint of the test facility capability parameters to obtain a set of target scale ratio combinations, which include geometric scale ratio, material property scale ratio, and load-time scale ratio. This method overcomes the problem of model dynamic response distortion caused by traditional single-criterion methods.

Description

Technical Field

[0001] This application relates to the field of marine engineering technology, and more specifically, to a method and apparatus for determining the scale ratio of floating photovoltaic model tests. Background Technology

[0002] Floating photovoltaic (PV) systems, a novel form of power generation that arranges PV modules on floating platforms, have gained widespread attention and application in lakes, reservoirs, and nearshore areas in recent years. Their operating environment is complex, often subjected to the coupled effects of various environmental loads such as wind, waves, and currents; therefore, system stability and safety are core concerns in engineering design.

[0003] In the model test research phase, determining the scale ratio is fundamental to the experimental design. Currently, in floating photovoltaic model tests, the selection of the scale ratio largely relies on engineering experience or references the commonly used scale range (usually between 1:50 and 1:150) of traditional marine floating structures (such as floating wind turbines, ships, or offshore platforms). The general approach is to initially select a scale ratio based on test conditions such as pool size and wave-generating capacity, referring to geometric similarity or Froude similarity criteria, and then make local adjustments based on the feasibility of model fabrication and measurement equipment.

[0004] However, existing methods for determining the scale ratio in floating photovoltaic model tests rely excessively on empirical judgment and fail to systematically integrate multiple constraints such as test conditions, measurement feasibility, model technology, and multi-physics field similarity. This results in a lack of objective optimization basis for the selected scale ratio, which easily leads to contradictions between scale effects and test feasibility. It cannot guarantee that the model test can reproduce the real dynamic response of the prototype system to the greatest extent under limited conditions, thus reducing the scientific value and engineering guidance significance of the test. Summary of the Invention

[0005] The purpose of this application is to provide a method and apparatus for determining the scale ratio of floating photovoltaic model tests, so as to overcome the problem of model dynamic response distortion caused by traditional single criterion methods.

[0006] Firstly, a method for determining the scale ratio in floating photovoltaic model experiments is provided, which may include: Obtain multi-dimensional characteristic parameters and the load spectrum of the design environment of the prototype floating photovoltaic system; Based on the design environmental load spectrum and multi-dimensional characteristic parameters, several key dynamic response indicators and corresponding dominant physical mechanisms of the prototype floating photovoltaic system were determined. Based on the key dynamic response indicators and the corresponding dominant physical mechanisms, the corresponding similarity criteria are selected, and the objective function is constructed by the comprehensive deviation between the calculated values ​​of each similarity criterion and the corresponding ideal values. An optimization decision model is constructed by using multiple sets of candidate scaling ratio combination schemes as decision variables. Solving the optimization decision model under the constraint of the test facility capacity parameters yields a set of target scaling ratio combinations, which include geometric scaling ratio, material property scaling ratio, and load-time scaling ratio.

[0007] In one possible implementation, the multidimensional feature parameters include the geometric and mass parameters, stiffness parameters, connection characteristic parameters, and mooring system characteristics of the prototype floating photovoltaic system. The design environmental load spectrum includes extreme or typical sea state parameters of the target sea area where the prototype floating photovoltaic system is located.

[0008] In one possible implementation, based on the design environmental load spectrum and multi-dimensional characteristic parameters, several key dynamic response indices and corresponding dominant physical mechanisms of the prototype floating photovoltaic system are determined, including: The design environmental load spectrum and multi-dimensional characteristic parameters are input into a numerical simulation model that includes fluid dynamics, structural dynamics and mooring coupling algorithms to obtain the dynamic response data of the prototype floating photovoltaic system under extreme or typical working conditions calculated by the numerical simulation model. Based on the analysis of the spectral characteristics, spatial distribution and correlation with load of the obtained dynamic response data, the dynamic response data that plays a controlling role in the safety and performance of the prototype floating photovoltaic system are identified as key dynamic response indicators. Based on the physical force types of each key dynamic response index, the corresponding dominant physical mechanism is determined.

[0009] In one possible implementation, the key dynamic response indicators include at least: the low-frequency drift motion amplitude of the entire array, the motion response amplitude at wave frequency, the high-order elastic vibration stress of the flexible photovoltaic panel, and the maximum dynamic tension of the mooring system. The dominant physical mechanisms include at least the following: quasi-static wind and flow loads, wave inertial force and diffraction force, the coupling effect of structural elastic restoring force and hydrodynamics, and the nonlinear restoring force of the mooring system.

[0010] In one possible implementation, based on the key dynamic response indicators and the corresponding dominant physical mechanisms, a corresponding similarity criterion is selected. An objective function is constructed using the comprehensive deviation between the calculated values ​​of each similarity criterion and the corresponding ideal values. An optimization decision model is then built using multiple candidate scaling ratio combinations as decision variables, including: Based on the type of the dominant physical mechanism, match the corresponding similarity criteria for each key dynamic response index; For each group of candidate scaling ratio combinations, based on the included geometric scaling ratio, material property scaling ratio, and load-time scaling ratio, the predicted values ​​of the similarity criteria corresponding to each key dynamic response index are calculated; the deviation between the predicted values ​​of each similarity criterion and the corresponding ideal values ​​is calculated; after assigning weight coefficients to each deviation, a weighted summation or weighted square sum algorithm is used to obtain a comprehensive scalar value, which is then used as the comprehensive similarity evaluation value of the candidate scheme, and minimizing the comprehensive similarity evaluation value is set as the objective function; The multiple candidate scaling ratio combinations are defined as decision variables, the test facility capability parameters are used as constraints, and the objective function is used to form an optimization decision model. The solution of the optimization decision model is the optimal scaling ratio combination that minimizes the objective function value.

[0011] In one possible implementation, similarity criteria are matched to each key dynamic response index based on the type of the corresponding dominant physical mechanism, including: For the translational and rotational responses of rigid bodies with gravity as the dominant physical mechanism, the Froude similarity criterion is selected. To evaluate the deformation, vibration, and dynamic stress response at connection points of components where structural elastic restoring force is the dominant physical mechanism, the Cauchy similarity criterion is selected. For the vibration response of risers or slender components with fluid periodic vortex-induced action as the dominant physical mechanism, the Struhal similarity criterion is selected. For a low-frequency coupled response that is significantly affected by both gravity and elastic force, the Froude criterion and the Cauchy criterion are simultaneously correlated.

[0012] In one possible implementation, the test facility capability parameters are included as constraints: The maximum geometric dimensions to be simulated in the experiment are constrained by the size of the test pool; the maximum wave height and flow velocity to be simulated in the experiment are constrained by the capacity of the wave generator and flow system; and the minimum force and motion measurement requirements to be simulated in the experiment are constrained by the accuracy of the sensors.

[0013] Secondly, a scale determination device for floating photovoltaic model experiments is provided, the device comprising: The acquisition unit is used to acquire multi-dimensional characteristic parameters of the prototype floating photovoltaic system and the load spectrum of the design environment. The determination unit is used to determine several key dynamic response indicators and corresponding dominant physical mechanisms of the prototype floating photovoltaic system based on the design environmental load spectrum and multi-dimensional characteristic parameters. The selection unit is used to select the corresponding similarity criteria based on each key dynamic response index and the corresponding dominant physical mechanism; The building unit is used to construct the objective function by the comprehensive deviation between the calculated values ​​of each similarity criterion and the corresponding ideal values, and to construct an optimization decision model by using multiple sets of candidate scaling ratio combinations as decision variables; The solver unit is used to solve the optimization decision model under the constraint of the test facility capability parameters, and obtain a set of target scaling ratio combinations, which include geometric scaling ratio, material property scaling ratio and load-time scaling ratio.

[0014] 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.

[0015] 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.

[0016] This application provides a method and apparatus for determining the scaling ratio in floating photovoltaic model tests. The method acquires multi-dimensional characteristic parameters of the prototype floating photovoltaic system and the design environmental load spectrum. Based on the design environmental load spectrum and multi-dimensional characteristic parameters, it determines multiple key dynamic response indices and corresponding dominant physical mechanisms of the prototype floating photovoltaic system. According to each key dynamic response indices and corresponding dominant physical mechanism, it selects corresponding similarity criteria and constructs an objective function using the comprehensive deviation between the calculated values ​​and corresponding ideal values ​​of each similarity criterion. Using multiple sets of candidate scaling ratio combinations as decision variables, it constructs an optimization decision model. Under the constraint of test facility capability parameters, it solves the optimization decision model to obtain a set of target scaling ratio combinations, including geometric scaling ratio, material property scaling ratio, and load-time scaling ratio. This method, through a decision-making process of mechanism diagnosis-criterion mapping-constraint optimization, outputs a set of scaling ratio combinations that optimally balance multiple physical similarities under test conditions, overcoming the model dynamic response distortion problem caused by traditional single-criterion methods. Attached Figure Description

[0017] 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.

[0018] Figure 1 A flowchart illustrating a method for determining the scale ratio in a floating photovoltaic model test, provided in an embodiment of this application; Figure 2 This is a schematic diagram of a scale determination device for floating photovoltaic model testing provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] 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.

[0020] For ease of understanding, the terms used in the embodiments of this application are explained below: The Froude Similarity Criterion guarantees that the ratio of inertial force to gravity is equal in the model and the prototype. It is applicable to flow problems where gravity plays a dominant role.

[0021] The Cauchy Similarity Criterion guarantees that the ratio of inertial force to elastic force (or volumetric compressive force) is equal. It applies to compressible fluids (such as high-speed gas flow).

[0022] The Strouhal Similarity Criterion guarantees that the ratio of unsteady inertial forces to steady inertial forces is equal, and is used to describe the time-scale similarity in periodic or oscillating flows.

[0023] Existing methods for determining the scale ratio of floating photovoltaic model tests rely on a single similarity criterion and do not fully consider the coupling effects of multiple physics fields. This results in insufficient similarity between the model and other aspects such as wave, wind load, and structural dynamic response. Consequently, the test results cannot accurately reflect the behavior of the real system, limiting the effective application of model tests in the design of floating photovoltaic projects.

[0024] To address the aforementioned issues, this application provides a method for determining the scaling ratio in floating photovoltaic model experiments. This method overcomes the limitations of relying on a single similarity criterion in the traditional approach. By systematically identifying the key dynamic behaviors and physical origins of the prototype system, and constructing an optimization decision-making model that seeks the best balance between multi-objective similarity criteria and the rigid constraints of the experimental facilities, a set of scaling ratio combinations that can most realistically reflect the complex coupled dynamic response of the prototype is scientifically determined.

[0025] 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.

[0026] Figure 1 This is a flowchart illustrating a method for determining the scale ratio in a floating photovoltaic model experiment, provided as an embodiment of this application. Figure 1 As shown, the method may include: Step S110: Obtain the multi-dimensional characteristic parameters of the prototype floating photovoltaic system and the load spectrum of the design environment.

[0027] (1) Multidimensional characteristic parameters may include the geometric and mass parameters, stiffness parameters, connection characteristic parameters, and mooring system characteristics of the prototype floating photovoltaic system. Among them: Geometric and mass parameters include detailed geometric dimensions, mass, center of mass position, and moment of inertia of each component, such as the floating body, photovoltaic panels, and support frame, extracted or calculated from the 3D design model of the floating photovoltaic system. These parameters determine the inertial characteristics of the system.

[0028] Stiffness parameters, including the bending and torsional stiffness of the floating structure, the local stiffness of the photovoltaic panel and its supporting structure, and the stiffness of connecting components, obtained through finite element analysis or materials mechanics experiments, affect the elastic deformation and vibration characteristics of the system.

[0029] Connection characteristic parameters, including the mechanical behavior of connecting components between quantification modules (such as hinges, bolt fasteners, etc.), including their stiffness, damping, and motion constraints (such as the allowable range of relative rotation or translation) in different directions. This is crucial for the integrity and flexibility of the simulation array.

[0030] The mooring system characteristics are described in detail, including its layout (such as the distribution and angles of anchor chains and cables), and the axial stiffness, nonlinear damping characteristics of the mooring cables, and the anchoring conditions. This determines the system's restoring force and range of motion under environmental loads.

[0031] (2) The design environmental load spectrum is obtained through statistical analysis of long-term historical observation data (such as buoy and satellite remote sensing data) of the target sea area, and is used to characterize extreme or typical sea state parameters, including: The wind load spectrum includes the joint probability distribution of wind speed (such as Weibull distribution parameters), prevailing wind direction, and wind speed time history or spectral density function.

[0032] Wave load spectrum uses wave spectra (such as JONSWAP spectrum, PM spectrum) to describe the distribution of wave energy at different frequencies and to define key parameters such as effective wave height, spectral peak period, and main wave direction.

[0033] Ocean current load spectrum provides velocity and direction profile data at different water depths to reflect the vertical distribution structure of ocean currents.

[0034] Step S120: Based on the design environmental load spectrum and multi-dimensional characteristic parameters, determine several key dynamic response indicators and corresponding dominant physical mechanisms of the prototype floating photovoltaic system.

[0035] First, the acquired multi-dimensional feature parameters are concretized and digitized to establish a computational model that accurately reflects the physical characteristics of the prototype system. Specifically: The design environmental load spectrum (such as extreme wind, wave, and current combinations) and multi-dimensional characteristic parameters are input into a numerical simulation model that incorporates fluid dynamics, structural dynamics, and mooring coupling algorithms. This yields dynamic response data of the prototype floating photovoltaic system under extreme or typical conditions calculated by the numerical simulation model. Specifically, the acquired multi-dimensional characteristic parameters are concretized and digitized to establish a computational model that accurately reflects the physical characteristics of the prototype system. By using geometric and mass parameters, the three-dimensional shape, mass, and moment of inertia of each component of the floating photovoltaic system model are precisely set in order to establish accurate dynamic equations.

[0036] By using stiffness parameters, the elastic properties of the floating body, supporting structure, and photovoltaic panel are defined, and a finite element model or equivalent stiffness model that can simulate structural deformation and vibration is constructed.

[0037] By utilizing connection characteristic parameters, connection conditions (such as hinges and spring-damped elements) can be set between modules in the model to simulate actual constraints, stiffness, and energy dissipation.

[0038] By utilizing the characteristics of the mooring system, an anchor chain or cable model is established, and its arrangement, stiffness, and nonlinear damping are accurately configured.

[0039] At this point, the multi-dimensional feature parameters have been fully integrated into the structural definition of the numerical simulation model.

[0040] Subsequently, based on the analysis of the spectral characteristics, spatial distribution, and load correlation of the obtained dynamic response data, the dynamic response data that plays a controlling role in the safety and performance of the prototype floating photovoltaic system were identified as key dynamic response indicators. Specifically, the obtained design environmental load spectrum was used as an external excitation and applied to the constructed numerical model that already included the system's inherent characteristics, thereby driving the model to calculate the dynamic response. Specifically, the time history or spectral data of environmental loads such as wind, waves, and currents (from the design environmental load spectrum) were used as input loads and applied to the model. Through time-domain or frequency-domain coupled dynamic analysis, the complete dynamic response time history data, including the system's motion (displacement, velocity, acceleration), structural internal forces / stresses, and mooring tensions under the combined action of wind, waves, and currents, were solved. The response time history data were then analyzed to identify key dynamic response indicators: (1) Extreme value analysis: Statistically analyze the maximum value, minimum value, standard deviation, etc. of each response quantity, and identify the response that exceeds the engineering safety threshold or the design allowable value as the key dynamic response index.

[0041] (2) Spectrum analysis: Perform a Fourier transform on the response time history and observe its spectrum. Identify the responses corresponding to significant resonance peaks that appear in the main frequency band of the environmental load (such as wave frequency) or the natural frequency of the system as key dynamic response indicators. This is because resonance responses often have large amplitudes, posing a threat to structural fatigue and safety.

[0042] (3) Correlation analysis: Analyze the spatiotemporal correlation between a specific response (such as mooring tension peak) and a specific environmental load component (such as large wave).

[0043] Based on the above analysis, the key dynamic response indicators identified include at least: the low-frequency slow drift amplitude of the array as a whole, the six-degree-of-freedom motion response (roll, pitch, heave, etc.) at wave frequencies, the high-order elastic vibration stress of the flexible photovoltaic panel or connecting structure, and the maximum dynamic tension and fatigue damage equivalent tension of the mooring system.

[0044] Subsequently, based on the types of physical forces acting on each key dynamic response index, the corresponding dominant physical mechanisms were determined. Specifically, for each identified key dynamic response index, its physical causes were analyzed: If the response frequency is much lower than the wave frequency, it may be dominated by quasi-static wind, flow load gravity, and damping balance; if the response frequency is the same as the wave frequency, it is mainly dominated by wave inertial force and diffraction force (fluid inertial force); if the response frequency is very high, close to the structure's natural frequency, it is mainly dominated by the structure's elastic restoring force and hydrodynamic coupling (fluid elasticity); for periodic vibrations behind slender components (such as columns), it may be dominated by vortex-induced vibration effect.

[0045] Dimensionless analysis and working condition comparison: By changing the parameters of the simulation model (such as turning off the elastic module, ignoring viscous effects, etc.), comparative working conditions can be run to observe the changes in key dynamic response indicators. The type of physical force that significantly affects a certain response is its dominant physical mechanism.

[0046] The dominant physical mechanisms identified here correspond to at least the following: quasi-static wind / flow loads (balance between gravity and fluid drag), wave inertial forces and diffraction forces, the coupling effect of structural elastic restoring forces and hydrodynamic forces, and the nonlinear restoring force of the mooring system.

[0047] Step S130: Based on each key dynamic response index and the corresponding dominant physical mechanism, select the corresponding similarity criteria, construct the objective function with the comprehensive deviation between the calculated value of each similarity criterion and the corresponding ideal value, and construct an optimization decision model with multiple sets of candidate scaling ratio combination schemes as decision variables.

[0048] First, based on the type of the dominant physical mechanism, corresponding similarity criteria are matched for each key dynamic response index. Specifically, based on the determined dominant physical mechanism, classical dimensionless similarity criteria are selected: for the translational and rotational responses of rigid bodies with gravity as the dominant physical mechanism, the Froude similarity criterion is selected; for the component deformation, vibration, and dynamic stress response at connection points with the structural elastic restoring force as the dominant physical mechanism, the Cauchy similarity criterion is selected; for the vibration response of risers or slender components with fluid periodic vortex-induced action as the dominant physical mechanism, the Struhal similarity criterion is selected; for low-frequency coupled responses that are significantly affected by both gravity and elastic force, both the Froude criterion and the Cauchy criterion are associated.

[0049] Subsequently, for each group of candidate scaling ratio combinations, based on the geometric scaling ratio, material property scaling ratio, and load-time scaling ratio included, the predicted values ​​of the similarity criteria corresponding to each key dynamic response index are calculated; the deviation between the predicted values ​​of each similarity criterion and the corresponding ideal values ​​is calculated; after assigning weight coefficients to each deviation, a weighted summation or weighted square sum algorithm is used to obtain a comprehensive scalar value, which is then used as the comprehensive similarity evaluation value of the candidate scheme, and minimizing the comprehensive similarity evaluation value is set as the objective function.

[0050] Specifically, the optimization goal is to find the optimal combination of scaling ratios. A set of basic scaling ratio parameters can be defined as the decision vector λ, including: geometric scaling ratio (model size / prototype size), material property scaling ratio (such as material elastic modulus scaling ratio and material density scaling ratio), and load-time scaling ratio, etc.

[0051] Furthermore, the scheme must be engineering feasible, requiring the ultimate capability parameters of the test facility to be transformed into constraints on λ. These constraints reflect that the physical limitations of the model test are insurmountable hard boundaries. The test facility capability parameters, as constraints, constitute the feasible region Ω, including: Geometric constraints: The total dimensions of the model made according to λ_L (geometric scaling ratio) must be smaller than the effective length, width, and depth of the test pool.

[0052] Load simulation capability constraints: The maximum wave height and flow velocity required for the model test, calculated based on λ_L (geometric scaling ratio) and λ_t (time scaling ratio), must be within the performance range of the wave generator and flow generation system.

[0053] Measurement accuracy constraints: The model size cannot be too small, that is, λ_L (geometric scaling ratio) cannot be too large, so that its motion and force are lower than the minimum measurable threshold of displacement sensors, tension meters, etc.

[0054] Within the feasible region Ω, K candidate scaling ratio combinations are generated through systematic sampling (such as Latin hypercube sampling).

[0055] For constructing the objective function: Under multiple constraints, it is impossible to find a solution that simultaneously and perfectly satisfies all similarity criteria (i.e., the ratio of the number of all criteria equals 1). Therefore, the goal of optimization is to find a solution that minimizes the overall deviation of all criteria.

[0056] The objective function is used to quantitatively evaluate the merits of any candidate scaling ratio combination scheme. Its core is to calculate the degree to which each similarity criterion is satisfied under the scheme.

[0057] Calculating Predicted Values: For this candidate scaling ratio combination scheme, the scaling relationships of all physical quantities in the model can be derived based on its included scaling ratio parameters. Using these relationships, the predicted values ​​of the similarity criteria corresponding to the key dynamic response indicators in the floating photovoltaic model under this candidate scaling ratio combination scheme can be calculated. For a given candidate scheme λ, for the i-th similarity criterion: Based on the specific scaling ratio of the candidate scheme λ, the prediction ratio of the model and the prototype for the i-th similarity criterion is calculated. Since, under perfect ideal similarity, the corresponding criteria of the model and the prototype should be strictly equal, the ratio is 1. Therefore, the deviation of the similarity criterion is calculated, where the square of the deviation can be used to amplify significant deviations, such as... .in, Let λ be the ratio of the model to the prototype similarity criterion, calculated under a given candidate scaling ratio combination scheme λ, representing the i-th key dynamic response index.

[0058] Weighted synthesis: Due to the varying importance of different key dynamic response indicators (e.g., mooring fracture is more critical than local vibration), weight coefficients are assigned to their corresponding deviations. These weight coefficients can be determined by expert scoring using the Analytic Hierarchy Process (AHP), reflecting the degree of importance of the key dynamic response indicator to the system's safety or functionality.

[0059] The objective function F(λ) is defined as the weighted sum of squares of all deviations: ,in, λ is the weighting coefficient of the i-th key dynamic response index, and n is the total number of key dynamic response indices considered. The smaller the function value F(λ), the better the candidate scheme takes into account multiple similarity criteria as a whole, that is, the higher the comprehensive similarity.

[0060] Using multiple candidate scaling ratio combinations as decision variables, the objective function minF(λ), and constraints constitute a complete constrained nonlinear optimization problem, i.e., an optimization decision model. The solution of this optimization decision model is the optimal scaling ratio combination that minimizes the objective function value (highest overall similarity) and satisfies all experimental constraints.

[0061] Step S140: Solve the optimization decision model under the constraint of the test facility capacity parameters to obtain a set of target scaling ratio combinations.

[0062] In practice, intelligent optimization algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), or mathematical programming algorithms, are used to automatically solve the established optimization decision model on a computer.

[0063] Initialization: Population size N, maximum number of generations G_max, crossover probability pc, mutation probability pm. Within the feasible region Ω defined by the constraints, N individuals are randomly generated as the initial population Λ(0). Each individual represents a set of candidate scaling ratio combinations, encoded as a decision vector λ.

[0064] Iterative optimization: The algorithm includes the following core operations in each generation: (1) Calculate the fitness value Fit(λ) of each individual λ in the h-th generation population Λ(h), as the evaluation criterion for their quality. The fitness function is defined as: ,in, As a penalty item, , A sufficiently large penalty coefficient ensures that the search is biased towards the feasible region; Denotes the j-th inequality constraint. This indicates that when the constraint is satisfied ( The penalty is 0 when the constraint is violated. The penalty is a positive value; m is the total number of constraints.

[0065] (2) Selection: Based on the fitness value of individuals, roulette wheel selection or tournament selection is used to select superior individuals from the current population to enter the mating pool. Individuals with higher fitness Fit(λ) have a greater probability of being selected to participate in reproduction.

[0066] (3) Crossover: Simulated binary crossover (SBX) or arithmetic crossover is performed on the parent individuals in the mating pool with probability pc to exchange some genes (scaling ratio parameter) and generate new offspring individuals. This helps to perform global exploration in the solution space.

[0067] (4) Mutation: Multinomial mutation and other operations are performed on certain genes of offspring individuals with probability pm to introduce small random perturbations in their neighborhood. This helps maintain population diversity and avoid premature convergence.

[0068] (5) New generation population formation: The new individuals generated through selection, crossover and mutation are merged with some elite individuals of the previous generation (the individuals with the highest fitness directly retained) to form a new generation population Λ(g+1).

[0069] Convergence and Output: After each iteration, check if the convergence criterion is met. The convergence criterion is usually set as follows: reaching the maximum number of generations h = G_max, or the improvement in the optimal fitness of the population is less than a preset threshold for multiple consecutive generations (e.g., 10 generations). .

[0070] The algorithm terminates when any convergence criterion is met. At this point, the individual with the highest fitness (i.e., the largest Fit(λ), which is equivalent to the smallest F(λ) + P(λ)) and which fully satisfies the constraint (i.e., P(λ) = 0) is selected from the final generation population Λ(final).

[0071] The decision vector represented by this individual is the solution to the optimization problem, which is the target scaling combination scheme.

[0072] This application establishes a mapping relationship between key dynamic responses and physical mechanisms, and constructs a multi-criteria collaborative optimization model to automatically solve the optimal scaling ratio combination under the constraints of experimental facilities. This systematically solves the problem of multi-physics coupling similarity distortion in floating photovoltaic model tests, and significantly improves the model's prediction fidelity and experimental feasibility for complex dynamic behaviors of prototypes.

[0073] Corresponding to the above method, this application also provides a scale determination device for floating photovoltaic model experiments, such as... Figure 2 As shown, the device includes: The acquisition unit 210 is used to acquire the multi-dimensional characteristic parameters of the prototype floating photovoltaic system and the load spectrum of the design environment. Unit 220 is used to determine several key dynamic response indicators and corresponding dominant physical mechanisms of the prototype floating photovoltaic system based on the design environmental load spectrum and multi-dimensional characteristic parameters. Selection unit 230 is used to select the corresponding similarity criteria based on each key dynamic response index and the corresponding dominant physical mechanism; The construction unit 240 is used to construct an objective function based on the comprehensive deviation between the calculated values ​​of each similarity criterion and the corresponding ideal values, and to construct an optimization decision model using multiple sets of candidate scaling ratio combination schemes as decision variables. Solver 250 is used to solve the optimization decision model under the constraint of the test facility capability parameters, and obtain a set of target scaling ratio combinations, which include geometric scaling ratio, material property scaling ratio and load-time scaling ratio.

[0074] The functions of each functional unit in the scale ratio determination device for floating photovoltaic model testing provided in the above embodiments of this application can be implemented through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the scale ratio determination device for floating photovoltaic model testing provided in the embodiments of this application will not be repeated here.

[0075] This application also provides an electronic device, such as... Figure 3 As shown, it includes a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340.

[0076] Memory 330 is used to store computer programs; When the processor 310 executes the program stored in the memory 330, it performs the following steps: Obtain multi-dimensional characteristic parameters and the load spectrum of the design environment of the prototype floating photovoltaic system; Based on the design environmental load spectrum and multi-dimensional characteristic parameters, several key dynamic response indicators and corresponding dominant physical mechanisms of the prototype floating photovoltaic system were determined. Based on the key dynamic response indicators and the corresponding dominant physical mechanisms, the corresponding similarity criteria are selected, and the objective function is constructed by the comprehensive deviation between the calculated values ​​of each similarity criterion and the corresponding ideal values. An optimization decision model is constructed by using multiple sets of candidate scaling ratio combination schemes as decision variables. Solving the optimization decision model under the constraint of the test facility capacity parameters yields a set of target scaling ratio combinations, which include geometric scaling ratio, material property scaling ratio, and load-time scaling ratio.

[0077] 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.

[0078] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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 the scaling method for floating photovoltaic model testing as described in any of the above embodiments.

[0083] 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 the scaling method for floating photovoltaic model testing as described in any of the above embodiments.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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 the scale ratio in floating photovoltaic model experiments, characterized in that, The method includes: Obtain multi-dimensional characteristic parameters and the load spectrum of the design environment of the prototype floating photovoltaic system; Based on the design environmental load spectrum and multi-dimensional characteristic parameters, several key dynamic response indicators and corresponding dominant physical mechanisms of the prototype floating photovoltaic system were determined. Based on the key dynamic response indicators and the corresponding dominant physical mechanisms, the corresponding similarity criteria are selected, and the objective function is constructed by the comprehensive deviation between the calculated values ​​of each similarity criterion and the corresponding ideal values. An optimization decision model is constructed by using multiple sets of candidate scaling ratio combination schemes as decision variables. Solving the optimization decision model under the constraint of the test facility capacity parameters yields a set of target scaling ratio combinations, which include geometric scaling ratio, material property scaling ratio, and load-time scaling ratio.

2. The method as described in claim 1, characterized in that, The multi-dimensional characteristic parameters include the geometric and mass parameters, stiffness parameters, connection characteristic parameters, and mooring system characteristics of the prototype floating photovoltaic system; The design environmental load spectrum includes extreme or typical sea state parameters of the target sea area where the prototype floating photovoltaic system is located.

3. The method as described in claim 1, characterized in that, Based on the design environmental load spectrum and multi-dimensional characteristic parameters, several key dynamic response indicators and corresponding dominant physical mechanisms of the prototype floating photovoltaic system were determined, including: The design environmental load spectrum and multi-dimensional characteristic parameters are input into a numerical simulation model that includes fluid dynamics, structural dynamics and mooring coupling algorithms to obtain the dynamic response data of the prototype floating photovoltaic system under extreme or typical conditions calculated by the numerical simulation model. Based on the analysis of the spectral characteristics, spatial distribution and correlation with load of the obtained dynamic response data, the dynamic response data that plays a controlling role in the safety and performance of the prototype floating photovoltaic system are identified as key dynamic response indicators. Based on the physical force types of each key dynamic response index, the corresponding dominant physical mechanism is determined.

4. The method as described in claim 3, characterized in that, The key dynamic response indicators include at least: the low-frequency drift amplitude of the array as a whole, the motion response amplitude at wave frequency, the high-order elastic vibration stress of the flexible photovoltaic panel, and the maximum dynamic tension of the mooring system. The dominant physical mechanisms include at least the following: quasi-static wind and flow loads, wave inertial force and diffraction force, the coupling effect of structural elastic restoring force and hydrodynamics, and the nonlinear restoring force of the mooring system.

5. The method as described in claim 1, characterized in that, Based on the key dynamic response indicators and corresponding dominant physical mechanisms, appropriate similarity criteria are selected, and an objective function is constructed using the comprehensive deviation between the calculated values ​​and the corresponding ideal values ​​of each similarity criterion. An optimization decision model is then built using multiple candidate scaling ratio combinations as decision variables, including: Based on the type of the dominant physical mechanism, match the corresponding similarity criteria for each key dynamic response index; For each group of candidate scaling ratio combinations, based on the included geometric scaling ratio, material property scaling ratio, and load-time scaling ratio, the predicted values ​​of the similarity criteria corresponding to each key dynamic response index are calculated; the deviation between the predicted values ​​of each similarity criterion and the corresponding ideal values ​​is calculated; after assigning weight coefficients to each deviation, a weighted summation or weighted square sum algorithm is used to obtain a comprehensive scalar value, which is then used as the comprehensive similarity evaluation value of the candidate scheme, and minimizing the comprehensive similarity evaluation value is set as the objective function; The multiple candidate scaling ratio combinations are defined as decision variables, the test facility capability parameters are used as constraints, and the objective function is used to form an optimization decision model. The solution of the optimization decision model is the optimal scaling ratio combination that minimizes the objective function value.

6. The method as described in claim 5, characterized in that, Based on the type of the corresponding dominant physical mechanism, similarity criteria are matched for each key dynamic response index, including: For the translational and rotational responses of rigid bodies with gravity as the dominant physical mechanism, the Froude similarity criterion is selected. To evaluate the deformation, vibration, and dynamic stress response at connection points of components where structural elastic restoring force is the dominant physical mechanism, the Cauchy similarity criterion is selected. For the vibration response of risers or slender components with fluid periodic vortex-induced action as the dominant physical mechanism, the Struhal similarity criterion is selected. For a low-frequency coupled response that is significantly affected by both gravity and elastic force, the Froude criterion and the Cauchy criterion are simultaneously correlated.

7. The method as described in claim 5, characterized in that, The test facility capability parameters, as constraints, include: The maximum geometric dimensions to be simulated in the experiment are constrained by the size of the test pool; the maximum wave height and flow velocity to be simulated in the experiment are constrained by the capacity of the wave generator and flow system; and the minimum force and motion measurement requirements to be simulated in the experiment are constrained by the accuracy of the sensors.

8. A scale ratio determination device for floating photovoltaic model experiments, characterized in that, The device includes: The acquisition unit is used to acquire multi-dimensional characteristic parameters of the prototype floating photovoltaic system and the load spectrum of the design environment. The determination unit is used to determine several key dynamic response indicators and corresponding dominant physical mechanisms of the prototype floating photovoltaic system based on the design environmental load spectrum and multi-dimensional characteristic parameters. The selection unit is used to select the corresponding similarity criteria based on each key dynamic response index and the corresponding dominant physical mechanism; The building unit is used to construct the objective function by the comprehensive deviation between the calculated values ​​of each similarity criterion and the corresponding ideal values, and to construct an optimization decision model by using multiple sets of candidate scaling ratio combinations as decision variables; The solver unit is used to solve the optimization decision model under the constraint of the test facility capability parameters, and obtain a set of target scaling ratio combinations, which include geometric scaling ratio, material property scaling ratio and load-time scaling ratio.

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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