Design wave joint parameter analysis method and system for floating type ocean structure

Abstract

The invention belongs to the technical field of floating type ocean structures, and provides a design wave joint parameter analysis method and system of a floating type ocean structure.The method comprises the steps that firstly, a joint probability model about wave height and period joint probability distribution is established; then, by constructing an expected log-likelihood function of sample data and hidden variables and solving a partial derivative, the maximum value of the expected log-likelihood function is determined, an iterative value of an unknown parameter is obtained, and an optimal probability model is obtained; and finally, according to the optimal probability model, obtaining a floating type ocean structure design wave joint parameter value. According to the method, the correlation between the wave height and the periodic data is considered and is close to the actual wave condition, the floating type ocean structure is designed and analyzed on the basis, the failure of the floating type ocean structure under the extreme ocean environment condition can be avoided, and the service life is prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of floating ocean structures, and particularly relates to a method and system for analyzing combined parameters of design waves of a floating ocean structure. Background Art

[0002] When designing a floating ocean structure, it is necessary to analyze dynamic responses and the like based on ocean environmental wave parameters to avoid the failure of the floating ocean structure under extreme ocean environmental conditions.

[0003] The failure of floating ocean structures such as floating offshore wind turbines and photovoltaics under extreme ocean environmental conditions is closely related to wave parameters such as wave height and period. The traditional single-variable extreme value theory only considers the probability characteristics of a single parameter and does not consider the correlation between wave height and period data, resulting in a large deviation from the actual wave conditions and being unable to provide an effective basis for the design and analysis of floating ocean structures. Summary of the Invention

[0004] To solve the above problems, the present invention proposes a method and system for analyzing combined parameters of design waves of a floating ocean structure. By establishing and solving a joint probability model for the joint probability distribution of wave height and period, the present invention obtains the combined parameter values of the design waves of the floating ocean structure; considering the correlation between wave height and period data, it is close to the actual wave conditions, and based on this, the design and analysis of the floating ocean structure are carried out, which can avoid the failure of the floating ocean structure under extreme ocean environmental conditions and improve the service life.

[0005] To achieve the above object, the present invention is realized by the following technical solutions:

[0006] In a first aspect, the present invention provides a method for analyzing combined parameters of design waves of a floating ocean structure, including:

[0007] Obtaining wave height data and period data;

[0008] According to the obtained wave height data and period data, establishing a joint probability model for the joint probability distribution of wave height and period;

[0009] Determining the parameters of the joint probability model to obtain an optimal probability model; specifically, constructing an expected log-likelihood function of sample data and latent variables, and determining the maximum value of the expected log-likelihood function by taking partial derivatives to obtain the iterative values of the unknown parameters. When the iteration accuracy meets the preset requirements, the iteration is stopped to obtain the parameters of the joint probability model;

[0010] According to the optimal probability model, obtaining the combined parameter values of the design waves of the floating ocean structure.

[0011] Further, the joint probability model is:

[0012]

[0013] where g(x) is the joint probability distribution of wave height and period; π k is the weight coefficient of the k-th Gaussian distribution; φ is the Gaussian distribution model; x = (H s , T p ) is the sample value of wave parameters, where Hs and Tp represent wave height and period respectively; K is the number of components of the Gaussian distribution; μ k and Σ k represent the mean vector and the standard deviation matrix in the k-th Gaussian distribution respectively.

[0014] Furthermore, the iteration termination rule is:

[0015]

[0016] |L[ξ (r+1) -L[ξ (r) | < ε;

[0017] where L(ξ) is the log-likelihood function of the probability model; ε is the precision value; r is the number of iterations.

[0018] Furthermore, the number of components of the Gaussian distribution in the optimal probability model is determined based on the Bayesian information criterion.

[0019] Furthermore, the design variables of the parameters in the standard normal space are determined based on the inverse reliability theory.

[0020] Furthermore, the joint parameter values of the design waves of the floating ocean structure are obtained based on the Rosenblatt transformation.

[0021] In a second aspect, the present invention also provides a system for analyzing the joint parameters of the design waves of a floating ocean structure, including:

[0022] A data acquisition module, configured to: acquire wave height data and period data;

[0023] A joint probability model establishment module, configured to: establish a joint probability model regarding the joint probability distribution of wave height and period according to the acquired wave height data and period data;

[0024] A model parameter determination module, configured to: determine the parameters of the joint probability model to obtain an optimal probability model; specifically, construct the expected log-likelihood function of the sample data and the latent variables, and determine the maximum value of the expected log-likelihood function by taking partial derivatives to obtain the iterative values of the unknown parameters, and stop the iteration when the iteration precision meets the preset requirements to obtain the parameters of the joint probability model;

[0025] An analysis module, configured to: obtain the combined parameter value of the design wave of the floating ocean structure according to the optimal probability model.

[0026] In a third aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method for analyzing the combined parameters of the design wave of the floating ocean structure described in the first aspect are implemented.

[0027] In a fourth aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and when the processor executes the program, the steps of the method for analyzing the combined parameters of the design wave of the floating ocean structure described in the first aspect are implemented.

[0028] In a fifth aspect, the present invention further provides a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method for analyzing the combined parameters of the design wave of the floating ocean structure described in the first aspect are implemented.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] In the present invention, first, a joint probability model regarding the joint probability distribution of wave height and period is established; then, by constructing the expected log-likelihood function of the sample data and the latent variable, and obtaining the maximum value of the expected log-likelihood function by taking partial derivatives to obtain the iterative value of the unknown parameter, the optimal probability model is obtained; finally, according to the optimal probability model, the combined parameter value of the design wave of the floating ocean structure is obtained; the correlation between wave height and period data is considered, which is close to the actual wave condition, and based on this, the design and analysis of the floating ocean structure are carried out, which can avoid the failure of the floating ocean structure under extreme ocean environmental conditions and improve the service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The schematic diagrams of the accompanying drawings forming a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions thereof of this embodiment are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0032] Figure 1 It is a flowchart of the method of Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0034] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0035] A floating ocean structure can refer to a building structure floating on the sea surface, which can be an offshore wind power generation device, such as a floating wind turbine; it can also be an offshore oil and gas exploration device, an offshore tourism device, etc.

[0036] Wave height is the vertical distance from the wave crest to the wave trough of a wave; it can be divided into significant wave height, maximum wave height, and average wave height.

[0037] The Rosenblatt transformation is a method for dealing with the distribution of multi-dimensional random variables, mainly used for solving the predictive distribution in Bayesian statistics.

[0038] Example 1:

[0039] As recorded in the background art, the failure of floating offshore wind turbines and photovoltaic and other ocean structures under extreme ocean environmental conditions is closely related to wave parameters such as wave height and period. The traditional single-variable extreme value theory only considers the probability characteristics of a single parameter, which has a large deviation from the actual wave conditions; to address this problem, as Figure 1 shown, this embodiment provides a method for analyzing the combined parameters of design waves for a floating ocean structure. First, a joint probability model regarding the joint probability distribution of wave height and period is established; then, by constructing the expected log-likelihood function of the sample data and latent variables, and determining the maximum value of the expected log-likelihood function by taking partial derivatives to obtain the iterative values of the unknown parameters, an optimal probability model is obtained; finally, according to the optimal probability model, the combined parameter values of the design waves for the floating ocean structure are obtained; the correlation between wave height and period data is considered, which is close to the actual wave conditions. Based on this, the design and analysis of the floating ocean structure can be carried out, which can avoid the failure of the floating ocean structure under extreme ocean environmental conditions and improve the service life; specifically:

[0040] S1. Establish a long-term wave database for the sea area where the floating ocean structure is in service.

[0041] Optionally, n-year hourly data of wave height and corresponding period can be obtained through numerical hindcasting, with a data interval of 1 hour or 3 hours. Usually, it is required that n≥20. The specific detection of wave height can be achieved through devices such as wave gauges.

[0042] S2. Establish a joint probability model of wave parameters.

[0043] Optionally, assume x=(H s ,T p) is the sample value of long-term wave parameters. If Hs and Tp represent the significant wave height and wave period respectively, then the joint probability distribution g(x) of wave height and period can be expressed as a linear combination of multiple Gaussian distributions:

[0044]

[0045] where φ is the Gaussian distribution model; π k is the weight coefficient of the k-th Gaussian distribution, and 0 ≤ π k ≤ 1, K is the number of components of the Gaussian distribution; μ k and Σ k represent the mean vector and the standard deviation matrix in the k-th Gaussian distribution respectively.

[0046] S3. Estimate the parameters of the joint probability model.

[0047] Construct the expected log-likelihood function Q of the sample data and the latent variable:

[0048]

[0049] where ξ represents the unknown parameters in the joint probability model, ξ = (π k , μ k , Σ k ); r represents the number of iterations; z k is the introduced latent variable, z k = 1 indicates that the data sample comes from the k-th Gaussian distribution model.

[0050] S4. Select the initial values of the parameters and update the estimated values of the joint probability model parameters.

[0051] Determine the iterative values of the unknown parameters by finding the maximum value of the expected log-likelihood function Q through partial derivatives:

[0052]

[0053] S5. Stop the iteration when the iteration accuracy meets the requirements, determine the final model parameters, determine the optimal probability model according to the final model parameters, and obtain the wave height-period joint probability model. The iteration termination rule is:

[0054]

[0055] |L[ξ (r+1) -L[ξ (r) | < ε

[0056] where L(ξ) is the log-likelihood function of the probability model; ε is the accuracy value.

[0057] S6. Determine the number of components of the Gaussian distribution.

[0058] Optionally, determine the number of components K of the Gaussian distribution in the optimal probability model based on the Bayesian information criterion:

[0059] BIC = -2L(ξ) + n ξ log N

[0060] where n ξ is the number of unknown parameters in the probability model; N is the number of environmental data; the smallest BIC value corresponds to the optimal number of Gaussian distribution models K.

[0061] S7. Determine the design variables of the wave parameters in the standard normal space based on the inverse reliability theory:

[0062]

[0063] where N represents the design return period; T r represents the time interval of the sample data;

[0064] S8. Obtain the joint parameter values h s and t p of the design wave of the floating ocean structure based on the Rosenblatt transformation as:

[0065]

[0066] where and respectively represent the inverse function of the wave height cumulative distribution and the inverse distribution of the period cumulative distribution under the given wave height condition; Φ represents the standard normal cumulative distribution function.

[0067] S9. Based on the wave parameters obtained by the Rosenblatt transformation, perform the dynamic response analysis of the floating ocean structure, and determine the wave parameters corresponding to the maximum structural response value as the final design environmental parameters; the process of the dynamic response analysis of the floating ocean structure can be realized by conventional techniques and will not be elaborated here.

[0068] Example 2:

[0069] This example provides a design wave joint parameter analysis system for a floating ocean structure, including:

[0070] A data acquisition module, configured to: obtain wave height data and period data;

[0071] A joint probability model establishment module, configured to: establish a joint probability model of the joint probability distribution of the wave height and the period according to the obtained wave height data and period data;

[0072] A model parameter determination module, configured to: determine the parameters of the joint probability model to obtain an optimal probability model; specifically, construct an expected log-likelihood function of sample data and latent variables, determine the maximum value of the expected log-likelihood function by taking partial derivatives to obtain iterative values of unknown parameters, and stop the iteration when the iteration accuracy meets the preset requirements to obtain the parameters of the joint probability model.

[0073] An analysis module, configured to: obtain the joint parameter values of the design waves of the floating ocean structure according to the optimal probability model.

[0074] The working method of the system is the same as that of the design wave joint parameter analysis method of the floating ocean structure in Embodiment 1, which will not be elaborated here.

[0075] Embodiment 3:

[0076] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the design wave joint parameter analysis method of the floating ocean structure described in Embodiment 1 are implemented.

[0077] Embodiment 4:

[0078] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the program, the steps of the design wave joint parameter analysis method of the floating ocean structure described in Embodiment 1 are implemented.

[0079] Embodiment 5:

[0080] This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the design wave joint parameter analysis method of the floating ocean structure described in Embodiment 1 are implemented.

[0081] The above are only the preferred embodiments of this embodiment and are not used to limit this embodiment. For those skilled in the art, this embodiment can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this embodiment shall be included within the protection scope of this embodiment.

Claims

1. A method for analyzing the design wave parameters of floating marine structures, characterized in that: include: Get wave height data and period data; According to the acquired wave height data and period data, a joint probability model of the joint probability distribution of wave height and period is established; Determine the parameters of the joint probability model to obtain the optimal probability model; specifically, construct the expected log-likelihood function of the sample data and the latent variable, determine the maximum value of the expected log-likelihood function by taking partial derivatives to obtain the iterative value of the unknown parameter, stop iterating when the iterative accuracy meets the preset requirements, and obtain the parameters of the joint probability model; According to the optimal probability model, the wave joint parameter values ​​for the design of floating marine structures are obtained.

2. The design wave joint parameter analysis method for floating marine structures according to claim 1, characterized in that: The joint probability model is: Where g(x) is the joint probability distribution of wave height and period; π k is the weight coefficient of the kth Gaussian distribution; φ is the Gaussian distribution model; x=(H s ,T p ) is the wave parameter sample value, H s and T p Represent wave height and period respectively; K is the number of components of Gaussian distribution; μ k and Σ k They represent the mean vector and standard deviation matrix in the kth Gaussian distribution respectively.

3. The design wave joint parameter analysis method for floating marine structures according to claim 2, characterized in that: The iteration termination rule is: |L[ξ (r+1) ]-L[ξ (r) ]|<e; Among them, L(ξ) is the log-likelihood function of the probability model; ε is the precision value; r is the number of iterations.

4. The design wave joint parameter analysis method for floating marine structures according to claim 1, characterized in that: The number of components of the Gaussian distribution in the optimal probability model is determined based on the Bayesian information criterion.

5. The design wave joint parameter analysis method for floating marine structures according to claim 1, characterized in that: The design variables of parameters in the standard normal space are determined based on the inverse reliability theory.

6. The design wave joint parameter analysis method for floating marine structures according to claim 1, characterized in that: The wave joint parameter values ​​for floating marine structure design are obtained based on Rosenblatt transformation.

7. Design wave joint parameter analysis system for floating marine structures, characterized in that: include: The data acquisition module is configured to: obtain wave height data and period data; The joint probability model building module is configured to: build a joint probability model about the joint probability distribution of wave height and period according to the acquired wave height data and period data; The model parameter determination module is configured to: determine the parameters of the joint probability model to obtain the optimal probability model; specifically, construct the expected log-likelihood function of the sample data and the latent variable, determine the maximum value of the expected log-likelihood function by taking partial derivatives to obtain the iterative value of the unknown parameter, stop iterating when the iterative accuracy meets the preset requirements, and obtain the parameters of the joint probability model; The analysis module is configured to obtain the wave joint parameter value of the floating marine structure design according to the optimal probability model.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for analyzing the design wave joint parameters of a floating marine structure as described in any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the program, the steps of the design wave joint parameter analysis method for floating marine structures as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for analyzing the design wave joint parameters of a floating marine structure according to any one of claims 1 to 6 are implemented.

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