Ocean energy vertical axis water turbine wake flow velocity calculation method, system, equipment, medium and product

Abstract

The invention discloses an ocean energy vertical axis water turbine wake flow velocity calculation method, system and device, a medium and a product, and relates to the field of water turbine wake flow, the method comprises the following steps: fitting a deformation coefficient of a Gaussian curve, and determining an improved formula; establishing a mathematical relationship among the minimum wake flow velocity of the vertical-axis water turbine, the longitudinal distance along the downstream of the vertical-axis water turbine and the turbulence intensity; constructing a wake flow velocity calculation formula of the vertical-axis water turbine based on the improved formula and the mathematical relational expression; and calculating the wake flow velocity of the vertical-axis water turbine based on the wake flow velocity calculation formula of the vertical-axis water turbine. The method can improve the accuracy of wake flow velocity prediction of the ocean energy vertical axis water turbine.

Description

Technical Field

[0001] The present application relates to the field of turbine wake calculation, and in particular to a method, system, equipment, medium and product for calculating the wake velocity of an ocean energy vertical axis turbine. Background Art

[0002] With the increasing attention to energy conservation, emission reduction and climate change, renewable energy utilization technology has received more and more attention. Marine renewable energy, referred to as ocean energy, is a type of renewable energy, including tidal energy, tidal energy, wave energy, etc. Among them, tidal energy resources are abundant, the conversion and utilization technology is mature, and the development prospects are very promising. The tidal energy conversion and utilization devices are mainly in the form of horizontal axis turbines and vertical axis turbines. The actual tidal energy development is accompanied by large-scale turbine layout. The wake generated by the front row turbine has a severe impact on the input flow rate of the rear row turbine. Based on computational fluid dynamics technology (CFD), the use of numerical simulation methods to optimize the layout of turbine arrays requires massive computing resources and cannot quickly optimize the layout of large-scale turbine arrays. Therefore, the development of wake velocity calculation methods and models is crucial for large-scale tidal energy development and utilization.

[0003] In terms of the wake model of horizontal axis turbines, some scholars have studied array layout by referring to the Jensen wake model in wind turbines and achieved good results. However, this model can only infer the speed change with longitudinal distance and fails to consider the impact of changes in turbulence intensity on the wake velocity. In terms of vertical axis turbines, there is currently no wake model with good calculation results recognized by the academic community. By referring to the Gaussian curve and considering the characteristics of vertical axis turbines, Lam scholars proposed a wake model suitable for vertical axis turbines, but also failed to consider the impact of changes in turbulence intensity. In the actual marine water environment, there are always vortices of different sizes, the changes in turbulence intensity are very drastic, and the changes in turbulence intensity have a significant impact on the wake velocity of the turbine. The existing wake calculation methods have obvious limitations in this regard. At present, it is urgent to develop a wake velocity model that considers actual marine physical quantities such as turbulence intensity, improve the accuracy and scope of application of the wake model, and support the development and utilization of ocean energy. Summary of the invention

[0004] The purpose of this application is to provide a method, system, equipment, medium and product for calculating the wake velocity of an ocean energy vertical axis turbine, which can improve the accuracy of wake velocity prediction.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In the first aspect, the present application provides a method for calculating the wake velocity of an ocean energy vertical axis turbine, including: fitting the deformation coefficient of a Gaussian curve to determine an improved formula; the deformation coefficient includes a wake acceleration coefficient, a velocity valley recovery coefficient and a velocity valley width coefficient; establishing a mathematical relationship between the minimum wake velocity of the vertical axis turbine, the longitudinal distance downstream of the vertical axis turbine and the turbulence intensity; constructing a vertical axis turbine wake velocity calculation formula based on the improved formula and the mathematical relationship; and calculating the vertical axis turbine wake velocity based on the vertical axis turbine wake velocity calculation formula.

[0007] In the second aspect, the present application provides a wake velocity calculation system for an ocean energy vertical axis turbine, comprising: a first formula construction module, which uses the deformation coefficient of the fitted Gaussian curve to determine an improved formula; the deformation coefficient includes a wake acceleration coefficient, a velocity valley recovery coefficient and a velocity valley width coefficient; a mathematical relationship construction module, which is used to establish a mathematical relationship between the minimum wake velocity of the vertical axis turbine, the longitudinal distance downstream of the vertical axis turbine and the turbulence intensity; a second formula construction module, which is used to construct a vertical axis turbine wake velocity calculation formula based on the improved formula and the mathematical relationship; a wake velocity calculation module, which is used to calculate the vertical axis turbine wake velocity based on the vertical axis turbine wake velocity calculation formula.

[0008] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for calculating the wake velocity of an ocean energy vertical axis turbine.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for calculating the wake velocity of an ocean energy vertical axis turbine.

[0010] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for calculating the wake velocity of an ocean energy vertical axis turbine.

[0011] According to the specific embodiments provided in this application, this application has the following technical effects.

[0012] (1) Improved prediction accuracy: This application greatly improves the prediction accuracy of the wake velocity by introducing the deformation coefficient of the non-variable Gaussian curve and considering the influence of physical factors (longitudinal distance and turbulence intensity downstream of the turbine along the vertical axis) on the lateral velocity of the wake, making it more closely matched with the simulation results in the high-speed area.

[0013] (2) Strong application flexibility: By establishing a mathematical relationship between the minimum wake velocity of a vertical-axis turbine, the longitudinal distance downstream of the vertical-axis turbine, and the turbulence intensity, the final vertical-axis turbine wake velocity calculation formula can adapt to different fluid conditions, providing a flexible and reliable calculation tool for turbine array layout and optimization.

[0014] (3) Convenient for practical application: Compared with the existing methods, this application does not need to directly extract the minimum speed through numerical simulation, but calculates through formulas, which simplifies the operation steps in practical applications and improves the convenience of engineering use. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 Schematic diagrams of horizontal-axis water turbines and vertical-axis water turbines, where (a) is a schematic diagram of a horizontal-axis water turbine and (b) is a schematic diagram of a vertical-axis water turbine.

[0017] Figure 2 Schematic diagram of the wake distribution downstream of a vertical-axis turbine.

[0018] Figure 3 Schematic diagram comparing the prediction effect of the formula established by Lam with CFD simulation data.

[0019] Figure 4 A schematic flow chart of a method for calculating the wake velocity of an ocean energy vertical axis turbine provided in one embodiment of the present application.

[0020] Figure 5 Schematic diagram of the control effect of the deformation coefficient of the Gaussian curve, where (a) is a schematic diagram of the control effect of the wake acceleration coefficient, (b) is a schematic diagram of the control effect of the velocity valley recovery coefficient, and (c) is a schematic diagram of the control effect of the velocity valley width coefficient.

[0021] Figure 6 Schematic diagram of the wake recovery curve along the center line of the vertical axis turbine under different turbulence intensities.

[0022] Figure 7 For different turbulence intensities, the longitudinal distance is 5D t (D t Schematic diagram of the lateral attenuation curve when ( is the vertical axis turbine diameter).

[0023] Figure 8Schematic diagram of the deformation coefficient obtained by fitting the wake lateral velocity data using the CFTOOL tool.

[0024] Fig. 9 is the minimum wake velocity of the vertical axis turbine and Schematic diagram of the relationship.

[0025] Fig.10 Schematic diagram of the comparison between the wake velocity of the vertical axis turbine of the ocean energy calculated by the calculation formula of the wake velocity of the vertical axis turbine and the simulated value in CFD, where (a) is when the turbulence intensity is 0.05 and the longitudinal distance is 6D t (b) is the comparison diagram when the turbulence intensity is 0.05 and the longitudinal distance is 7D t (c) is the comparison diagram when the turbulence intensity is 0.05 and the longitudinal distance is 8D t (d) is the comparison diagram when the turbulence intensity is 0.05 and the longitudinal distance is 9D t When the comparison chart.

[0026] Fig.11 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] At present, the conversion and utilization of tidal energy is mainly based on horizontal axis turbines and vertical axis turbines, such as Figure 1 As shown. The existing turbine wake formula is based on the jet similarity theory, that is, it is mostly based on the Gaussian function to represent the velocity change relationship of the transverse section behind the turbine. In addition, the horizontal axis turbine often uses the Jensen wake model commonly used in the wind energy field, and there are relatively few studies on the vertical axis turbine wake model.

[0029] Lam et al. established a prediction formula (1) for the wake lateral velocity of a vertical axis turbine using a Gaussian function. Figure 2 is the wake distribution downstream of the vertical axis turbine, where the r direction is the horizontal direction and the x direction is the vertical direction. The center axis of the Gaussian wake behind the vertical axis may deviate from the center axis of the turbine. As it moves away from the turbine, the center axis of the wake Gaussian line will gradually approach the center axis of the turbine until they coincide.

[0030] (1) In the formula, represents the wake velocity at any position behind the vertical axis turbine, and is a function of the longitudinal distance and transverse distance r (dimensionless) downstream of the vertical axis turbine; is the incoming flow velocity, is the velocity at the Gaussian vertex, which has the smallest velocity; is the Gaussian velocity valley shift coefficient; is the Gaussian transverse velocity distribution offset coefficient; is the diameter of the vertical axis turbine.

[0031] It can be seen from the formula that Lam's prediction formula only reveals the relationship between wake velocity and longitudinal distance and lateral distance. There are many physical factors that can affect wake velocity, such as turbulence intensity under different environments. Figure 3 It can be seen that the high-speed areas on both sides of the velocity valley have a relatively general degree of agreement. This shows that Lam's prediction formula still has room for improvement in prediction accuracy and physical relationship, and there is no turbulence parameter in the formula, which cannot reflect the changes in turbulence intensity parameters in the real environment.

[0032] The purpose of this application is to introduce physical factors that can affect the wake velocity to adjust the parameters in the formula to improve the accuracy of wake velocity prediction.

[0033] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] In an exemplary embodiment, Figure 4 As shown, a method for calculating the wake velocity of an ocean energy vertical axis turbine is provided. The method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In an embodiment of the present application, the method is applied to a server as an example for explanation, including the following steps S1 to S4.

[0035] S1: Fit the deformation coefficient of the Gaussian curve to determine the improved formula; the deformation coefficient includes the wake acceleration coefficient, the velocity valley recovery coefficient and the velocity valley width coefficient. Specifically, a simulation experiment is performed using computational fluid dynamics software to obtain the wake velocity data of the vertical axis turbine under different turbulence intensities; the deformation coefficient is fitted based on the wake velocity data to determine the improved formula.

[0036] First of all, computational fluid dynamics (CFD) software is used to conduct simulation experiments to obtain the velocity distribution of the vertical-axis turbine wake field under different turbulence intensities. The turbulence intensity should include the range of 0.05~0.25, that is, 5%~25%.

[0037] Turbulence intensity will seriously affect the wake velocity change behind the vertical axis turbine. In the ocean, the turbulence intensity varies greatly. To illustrate the effect of turbulence intensity on the wake velocity of the vertical axis turbine, the turbulence intensity ( ) as an example in the vertical axis turbine wake prediction formula, we need to know how turbulence intensity affects the change in the shape of the Gaussian curve of the vertical axis turbine wake velocity.

[0038] Lam used the Gaussian probability distribution curve to approximate the relationship between the lateral velocity of the wake of a vertical axis turbine and the lateral distance r, and established formula (1).

[0039] Figure 3 The diagram of the comparison between the prediction effect of the wake model established for Lam for the vertical axis turbine and the CFD simulation data shows that the theoretical prediction value is higher than the simulation result in the high-speed area on both sides, and there is a large deviation on the right side of the valley. In order to better match the theoretical results of the formula calculation with the simulation experimental results, the valley width and valley height of the Gaussian curve shape represented by formula (2) and the difference between the theory and the simulation in the high-speed area on both sides are adjusted with the help of MATLAB's CFTOOL tool. In formula (2), the exponential part, as well as The adjustment corresponds to the change in valley width, valley height and high-speed area difference.

[0040] Therefore, in formula (1), the deformation coefficients of three Gaussian curves are introduced: , , , the formula becomes as follows.

[0041] (2) To avoid the influence of dimension, formula (2) is dimensionless and becomes the following formula.

[0042] (3) Formula (3) is the improved formula. is the dimensionless lateral distance.

[0043] is the wake acceleration coefficient, such as Figure 3 As shown, the lateral distance on both sides of the curve valley point is 1D t To 3D tWithin the distance, the wake velocity is greater than the initial velocity in front of the turbine, V / V∞ exceeds 1.0, and the wake has an acceleration phenomenon. It is the factor that adjusts the wake lateral velocity acceleration zone.

[0044] It is the velocity valley recovery coefficient, which is used to indicate how fast the lateral velocity valley recovers to the initial value. The larger the value, the slower the recovery.

[0045] It is the speed valley width coefficient, which is used to indicate the width between the critical points of the speed acceleration zone on both sides of the speed valley. The larger the value, the smaller the width.

[0046] The control effect of the deformation coefficient of the Gaussian curve is as follows: Figure 5 As shown, adjust The effect is as Figure 5 As shown in (a), by adjusting , can be adjusted to the acceleration amplitude in the wake area. The effect is as Figure 5 As shown in (b), the adjusted Size, you can adjust the amplitude of the valley value. The effect is as Figure 5 As shown in (c), adjust The size of can adjust the width of the curve, that is, the sharpness of the curve, without changing the amplitude of the valley value and the acceleration area.

[0047] Based on the existing velocity data of each transverse section of the vertical axis turbine wake field, the deformation coefficient of the Gaussian curve with the highest correlation in each transverse section of the wake under different turbulence intensities was calculated using the CFTOOL tool for fitting.

[0048] The theoretical model specifically provides a theoretical basis for the subsequent optimization design of vertical axis turbine arrays in actual use scenarios. Figure 6 and Figure 7 As shown in the experimental results, the 4D t When the velocity valley point is offset from the downstream centerline, the velocity valley point is very small. t When the vertical axis turbine is actually arranged in the ocean, the distance between the front and rear rows should be greater than 5D for safe operation and maintenance. t Therefore, in formula (3), the Gaussian velocity valley offset coefficient B is taken as 0, and the Gaussian lateral velocity distribution offset coefficient C is taken as 0.4 according to Lam's original formula.

[0049] Introduction coefficient , , Finally, the CFTOOL tool was used to fit the highest correlation of each cross section of the wake under different turbulence intensities. , , The value of Figure 8 As shown in the figure, the black scattered points represent the CFD simulation data, and the blue solid line is the fitting curve. According to this process, we can find the value of the deformation coefficient corresponding to each transverse section under each turbulence intensity condition.

[0050] The above conclusion , , With different sections and specific values ​​of turbulence intensity, although the purpose of adjusting the wake curve has been achieved, this brings inconvenience to the subsequent prediction formula. , , It is expressed as a function of longitudinal distance and turbulence intensity. A polynomial is used to fit the relationship between turbulence intensity, longitudinal distance and deformation coefficient. The final expression is: (4) (5) (6) in, is the longitudinal distance, [5,10]. In this way, all unknown variables have established a fitting mathematical relationship with the known variables.

[0051] S2: Establish a mathematical relationship between the minimum wake velocity of a vertical axis turbine, the longitudinal distance downstream of the vertical axis turbine, and the turbulence intensity.

[0052] The above mathematical relationship is not enough to predict the wake velocity of the vertical axis turbine. It is also necessary to know the minimum value of the lateral velocity at each section downstream. (i.e. the minimum wake velocity of a vertical axis turbine). Lam is The processing method is extracted from the CFD numerical simulation experiment results, which is not convenient in practical applications. Figure 6 Shows the minimum wake velocity for different turbulence intensities , we can see that the minimum wake velocity With x and There is a one-to-one correspondence. is the dependent variable, x and Establish a binary functional relationship for the independent variables, such as Fig. 9 shown.

[0053] By using the analysis of the two-variable polynomial fitting function in CFTOOL, the input is , x is the independent variable, As the dependent variable, we established and , The binary function of is a relationship intuitively recorded in matrix form: (7) Here the coefficient matrix and the variable matrix , as follows: (8) (9) (10) The coefficient values ​​are shown in Table 1: Table 1

[0054] S3: Construct a vertical axis turbine wake velocity calculation formula based on the improved formula and the mathematical relationship.

[0055] The combination of formula (3) and formula (7) is the improved formula for predicting the wake velocity of vertical axis tidal current energy, that is, the vertical axis turbine wake velocity calculation formula constructed in this application.

[0056] (11) S4: Calculate the vertical axis turbine wake velocity based on the vertical axis turbine wake velocity calculation formula.

[0057] Applying the above formula, we can make a quick and accurate prediction of the wake velocity at different longitudinal distances and lateral distances under different turbulence intensities. By inputting a specific turbulence intensity TI and longitudinal distance x, we can predict the wake field velocity downstream of the turbine.

[0058] Prediction effect Fig.10 As shown in (a)-(d), Fig.10 The solid blue line represents the predicted value of the formula established in this application, and the red dots represent the simulated values ​​in CFD.

[0059] This application introduces Gaussian curve deformation coefficients α, β, and γ to control the acceleration zone, velocity valley recovery rate, and velocity valley width of the wake velocity, respectively, so as to improve the matching degree between theoretical prediction and simulation results. In addition, by fitting the simulation data using MATLAB's CFTOOL tool, the change of wake velocity under different physical factors is calculated. To go a step further, this application establishes the functional relationship between physical influencing factors and deformation coefficients α, β, and γ through fitting, and With x, The relationship between The binary function expression of the model has higher application value and accuracy. The model is particularly suitable for the optimization design and performance prediction of turbine arrays.

[0060] Based on the same inventive concept, the embodiment of the present application also provides a system for implementing the above-mentioned method for calculating the wake velocity of a vertical axis water turbine for ocean energy. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the wake velocity calculation system for ocean energy vertical axis water turbine provided below can refer to the limitations of the wake velocity calculation method for ocean energy vertical axis water turbine above, and will not be repeated here.

[0061] In an exemplary embodiment, a system for calculating wake velocity of an ocean energy vertical axis turbine is provided, comprising: The deformation coefficient calculation module is used to calculate the deformation coefficient of the Gaussian curve; the deformation coefficient includes the wake acceleration coefficient, the velocity valley value recovery coefficient and the velocity valley width coefficient.

[0062] The first formula building module uses the deformation coefficient of the fitted Gaussian curve to determine the improved formula; the deformation coefficient includes the wake acceleration coefficient, the velocity valley value recovery coefficient and the velocity valley width coefficient.

[0063] The second formula building module is used to build a vertical axis turbine wake velocity calculation formula based on the improved formula and the mathematical relationship.

[0064] The wake velocity calculation module is used to calculate the wake velocity of the vertical axis turbine based on the vertical axis turbine wake velocity calculation formula.

[0065] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program. The computer device may be a server or a terminal, and its internal structure diagram may be as shown in FIG. Fig.11As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for calculating the wake velocity of an ocean energy vertical axis turbine is implemented.

[0066] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0067] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0068] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0070] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0071] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0072] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for calculating the wake velocity of an ocean energy vertical axis turbine, characterized in that: include: Fit the deformation coefficient of the Gaussian curve and determine the improved formula; The deformation coefficients include wake acceleration coefficient, velocity valley recovery coefficient and velocity valley width coefficient; Establish a mathematical relationship between the minimum wake velocity of a vertical axis turbine, the longitudinal distance downstream of the vertical axis turbine, and the turbulence intensity; Constructing a vertical axis turbine wake velocity calculation formula based on the improved formula and the mathematical relationship; The vertical axis turbine wake velocity is calculated based on the vertical axis turbine wake velocity calculation formula.

2. The method for calculating the wake velocity of a vertical axis turbine of ocean energy according to claim 1, characterized in that: Fit the deformation coefficient of the Gaussian curve and determine the improved formula, which includes: The simulation experiment was carried out using computational fluid dynamics software to obtain the wake velocity data of the vertical axis turbine under different turbulence intensities; The deformation coefficient is obtained based on the wake velocity data fitting, and an improved formula is determined.

3. The method for calculating the wake velocity of a vertical axis turbine of ocean energy according to claim 1, characterized in that: After fitting the deformation coefficient of the Gaussian curve and determining the improved formula, the method further includes: determining a relationship between turbulence intensity, longitudinal distance and the deformation coefficient; The relationship is as follows: ； in, is the wake acceleration coefficient, is the velocity valley recovery coefficient, is the velocity valley width coefficient, is the turbulence intensity and x is the longitudinal distance.

4. The method for calculating the wake velocity of a vertical axis turbine of ocean energy according to claim 1, characterized in that: The improved formula is: ； in, is the wake velocity at any position behind the vertical axis turbine, is the wake acceleration coefficient, is the velocity valley recovery coefficient, is the velocity valley width coefficient, is the incoming flow velocity, is the minimum wake velocity of the vertical axis turbine, B is the Gaussian velocity valley offset coefficient, C is the Gaussian lateral velocity distribution offset coefficient, is the vertical axis turbine diameter, is the dimensionless lateral distance.

5. The method for calculating the wake velocity of a vertical axis turbine of ocean energy according to claim 1, characterized in that: The mathematical relationship is: ； in, is the incoming flow velocity, is the minimum wake velocity of the vertical axis turbine, is the turbulence intensity and longitudinal distance The binary function of is the coefficient matrix, and are all variable matrices, and T is the matrix transpose.

6. The method for calculating the wake velocity of a vertical axis turbine of ocean energy according to claim 1, characterized in that: The vertical axis turbine wake velocity calculation formula is: ； in, is the wake velocity at any position behind the vertical axis turbine, is the incoming flow velocity, is the wake acceleration coefficient, is the velocity valley recovery coefficient, is the velocity valley width coefficient, is the coefficient matrix, and are all variable matrices, T is the matrix transpose, B is the Gaussian velocity valley offset coefficient, C is the Gaussian lateral velocity distribution offset coefficient, is the vertical axis turbine diameter, is the dimensionless lateral distance.

7. A wake velocity calculation system for a vertical axis turbine of ocean energy, characterized in that: include: The first formula building module uses the deformation coefficient of the fitted Gaussian curve to determine the improved formula; The deformation coefficients include wake acceleration coefficient, velocity valley recovery coefficient and velocity valley width coefficient; A mathematical relationship building module for establishing a mathematical relationship between the minimum wake velocity of a vertical axis turbine, the longitudinal distance downstream of the vertical axis turbine, and the turbulence intensity; A second formula building module is used to build a vertical axis turbine wake velocity calculation formula based on the improved formula and the mathematical relationship; The wake velocity calculation module is used to calculate the wake velocity of the vertical axis turbine based on the vertical axis turbine wake velocity calculation formula.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for calculating the wake velocity of an ocean energy vertical axis turbine according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for calculating the wake velocity of an ocean energy vertical axis turbine according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for calculating the wake velocity of an ocean energy vertical axis turbine according to any one of claims 1 to 6 is implemented.

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