Deepwater riser fatigue design flow velocity profile analysis method, system, equipment and medium

Abstract

The invention relates to a deepwater riser fatigue design flow velocity profile analysis method, system, equipment and medium, and the method comprises the following steps: real-time flow velocity data is arranged into a flow velocity profile matrix, each row in the flow velocity profile matrix represents a flow velocity profile at a certain moment, and each column represents a flow velocity time history of a certain flow layer; performing singular value decomposition on the flow velocity profile matrix to obtain a time coefficient matrix, a saliency coefficient matrix and a spatial modal matrix; and analyzing the obtained time coefficient matrix, saliency coefficient matrix and spatial modal matrix, and obtaining the fatigue design flow velocity profile of the deepwater riser according to analysis results. The method can be widely applied to the technical field of offshore oil platforms.

Description

Technical Field

[0001] The invention relates to the technical field of offshore oil platforms, and in particular to a deepwater riser fatigue design velocity profile analysis method, system, equipment and medium based on measured velocity data. Background Art

[0002] The marine riser is the main connection between the offshore platform or drilling vessel and the seabed wellhead. During the period when the marine riser is in place, the marine environmental load it bears is mainly flow load. When the ocean current flows through the marine riser at a certain velocity, vortices can be formed alternately on both sides of the marine riser. The vortex shedding will produce a periodic variable force on the marine riser, causing the marine riser to undergo vortex-induced vibration (VIV). VIV is the main cause of dynamic damage to the marine riser structure, so it is necessary to analyze the VIV fatigue of the riser in the riser design. However, VIV is highly sensitive to the velocity profile, and the accuracy of the profile directly affects the numerical analysis results. It is necessary to consider the effects of different current profile shapes and their occurrence probabilities on the dynamic behavior of the marine riser.

[0003] When the measured velocity data is available, directly using the original data as the input load, tens of thousands of profiles will make the calculation amount unprecedentedly huge. Therefore, the general engineering practice is to extract limited characteristic velocity profiles (CPC, Current Profile Characterization) based on the measured velocity data, and use the characteristic velocity profiles as input loads for VIV fatigue analysis, which can reduce the calculation amount while ensuring the calculation accuracy.

[0004] In recent years, scholars have devoted themselves to constructing ocean current profile models by using two research methods: classification and dimensionality reduction. The classic CPC method divides the velocity of each layer into several intervals, and then arranges and combines these intervals. Each combination is regarded as a type of profile, and it is necessary to traverse all the time-course flow profiles to obtain the profile proportion; empirical orthogonal function (EOF), singular value decomposition (SVD) and proper orthogonal decomposition (POD) are three common methods for data analysis by obtaining low-dimensional approximations of high-dimensional data. All three methods can convert spatial data with multiple time points into a set of principal components with maximum distinguishability to achieve typical mode extraction.

[0005] However, the velocity interval division of the classical CPC method mostly relies on experience and is somewhat subjective. In order to avoid too many groups and reduce the spatial resolution, the classical CPC method is only applicable to limited depths and is no longer applicable to deepwater riser design. Although the dimensionality reduction analysis method represented by EOF is not limited by water depth, for deepwater complex flow environments, the first two or third order modes are usually not sufficient to dominate and characterize the spatial morphology and fluctuations of the velocity profile, and higher-order spatial modes need to be introduced. Therefore, a more effective method is needed to solve the CPC profile. Summary of the invention

[0006] In view of the above problems, the purpose of the present invention is to provide a velocity profile analysis method, system, equipment and medium for deepwater riser fatigue design based on measured velocity data, so as to solve the problem of multi-order modal characterization of velocity profile in complex flow environment.

[0007] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for analyzing a deepwater riser fatigue design velocity profile, comprising the following steps: 1) Arrange the real-time velocity data into a velocity profile matrix, wherein each row in the velocity profile matrix represents the velocity profile at a certain moment, and each column represents the velocity time course of a certain flow layer; 2) Perform singular value decomposition on the velocity profile matrix to obtain the time coefficient matrix, significance coefficient matrix and spatial mode matrix; 3) The obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix are analyzed respectively, and the deepwater riser fatigue design velocity profile is obtained based on the analysis results.

[0008] Furthermore, in step 2), when the velocity profile matrix is ​​subjected to singular value decomposition, the calculation expression is:

[0009] In the formula, is the velocity profile matrix, is the time coefficient matrix, is the significance coefficient matrix, is the spatial modal matrix.

[0010] Furthermore, in step 3), the obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix are analyzed respectively, and the deepwater riser fatigue design velocity profile is obtained according to the analysis results, including the following steps: 3.1) Calculate the modal variance contribution rate based on the significance coefficient matrix, and extract the significance coefficients corresponding to several order modes that meet the preset conditions; 3.2) Based on the modal order determined in step 3.1), the spatial modal vector of the corresponding order is extracted from the spatial modal matrix; 3.3) Based on the modal order determined in step 3.1), the time coefficient vector of the corresponding order is extracted from the time coefficient matrix, and the time coefficient vector is grouped and fully permuted and combined to determine the optimal time coefficient combination; 3.4) Based on the extracted significance coefficients, spatial modal vectors and the determined optimal time coefficient combination, the deepwater riser fatigue design velocity profile is synthesized.

[0011] Furthermore, in step 3.1), the modal variance contribution rate is calculated based on the significance coefficient matrix, and the significance coefficient is extracted, including: Based on the matrix diagonal elements in the significance coefficient matrix, the variance contribution rate of each mode is calculated; Based on the variance contribution rate and cumulative contribution rate of each order mode, several order modes that meet the preset conditions are taken as the dominant modes, and their corresponding significance coefficients are extracted.

[0012] Further, the Variance contribution of the first mode The calculation expression is:

[0013] In the formula, for The diagonal elements of a matrix of rank.

[0014] Furthermore, in step 3.3), extracting a time coefficient vector based on the time coefficient matrix, grouping and fully permuting and combining the time coefficient vectors, and determining a preferred time coefficient combination include the following steps: 3.3.1) Based on the modal order determined in step 3.1), extract the time coefficient vector of the corresponding order from the time coefficient matrix; 3.3.2) Group the extracted time coefficient vectors according to the sample value size and calculate the mean value of each group of samples; 3.3.3) Perform full permutation and combination of groups of different time coefficient vectors; 3.3.4) Merge all extracted time coefficient vectors into a matrix Q by column, and determine whether all time coefficients in each row of the matrix Q fall into a combination obtained in step 3.3.3). If so, add 1 to the count of the combination, and obtain the counts and proportions of all combinations; 3.3.5) According to the calculation of each combination and its proportion, the combination ranking is adjusted to obtain the first preset time coefficient combinations with the largest proportion as the preferred time coefficient combinations.

[0015] Furthermore, in step 3.3.2), the extracted time coefficient vectors of several modes are grouped according to the sample value size, and the mean value of each group of samples is calculated, including the following steps: ① Treat each element in a time coefficient vector as a sample and arrange them according to the sample value; ② Divide all samples into several groups according to the sample value size, and calculate the sample mean of each group; ③ Repeat steps ① to ② to obtain the grouping of all modal time coefficient vectors and the sample mean of each group.

[0016] In a second aspect, the present invention provides a deepwater riser fatigue design velocity profile analysis system, comprising: A matrix building module is used to organize the real-time velocity data into a velocity profile matrix, wherein each row in the velocity profile matrix represents a velocity profile at a certain moment, and each column represents a velocity time course of a certain flow layer; Matrix decomposition module, used to perform singular value decomposition on the velocity profile matrix to obtain the time coefficient matrix, significance coefficient matrix and spatial mode matrix; The profile synthesis module is used to analyze the obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix respectively, and obtain the deepwater riser fatigue design velocity profile according to the analysis results.

[0017] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any method.

[0018] In a fourth aspect, the present invention provides a computing device, comprising: one or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, and the one or more programs include instructions for executing any method.

[0019] Due to the adoption of the above technical solution, the present invention has the following advantages: The CPC profile analysis method for deepwater riser fatigue design based on measured velocity data provided by the present invention is based on deepwater measured velocity data, flow field orthogonal decomposition, spatial mode selection, time coefficient arrangement and combination, etc., and is suitable for CPC profile solution of riser fatigue design under complex flow environment in the South China Sea. This method transforms the velocity profile classification problem into a multidimensional classification problem of time coefficient sample points through the innovative "time coefficient multidimensional distribution probability calculation", which can effectively solve the problem of multi-order modal characterization of velocity profile under complex flow environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings: Figure 1 A flow chart of a deepwater riser fatigue design velocity profile analysis method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a velocity profile matrix structure provided by an embodiment of the present invention; Figure 3 A schematic diagram of a significance coefficient matrix structure provided by an embodiment of the present invention; Figure 4 A schematic diagram of the first 10 modes provided by an embodiment of the present invention; Figure 5 The variance contribution rate and cumulative contribution rate of the first 10 modes provided in the embodiment of the present invention; Figure 6 A schematic diagram of a spatial modal matrix structure provided by an embodiment of the present invention; Figure 7 A schematic diagram of the structure of a time coefficient matrix provided by an embodiment of the present invention; Figure 8 A schematic diagram of a time coefficient vector grouping method provided by an embodiment of the present invention; Fig. 9 The first four modal time coefficient groups and the mean parameter diagram of each group of samples provided by the embodiment of the present invention; Fig.10 A schematic diagram of a time coefficient combination division and proportion statistics method provided by an embodiment of the present invention; Fig.11 A schematic diagram of a velocity profile synthesis method provided by an embodiment of the present invention; Fig.12 The top 10 synthetic flow velocity profiles with the largest proportion provided by the embodiments of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0022] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0023] In some embodiments of the present invention, a deepwater riser fatigue design velocity profile analysis method based on measured velocity data is provided. The method uses singular value decomposition (SVD) as an example to reduce the dimension of the velocity time series profile based on the measured velocity data in the South China Sea deepwater, selects typical spatial modes, calculates the multidimensional distribution probability of the time coefficient of the corresponding spatial mode, and finally obtains the characteristic profile CPC and its proportion. Among them, the calculation of the multidimensional distribution probability of the time coefficient can realize the introduction of the third-order or even higher-order modes into the reconstruction of the CPC, reduce the loss of high-order modes in the VIV fatigue analysis, and make the CPC profile more accurately represent the actual velocity profile.

[0024] Correspondingly, in some other embodiments of the present invention, a deepwater riser fatigue design velocity profile analysis system, equipment and medium based on measured velocity data are provided.

[0025] Example 1 like Figure 1 As shown, the present invention provides a deepwater riser fatigue design velocity profile analysis method, which includes the following steps: 1) Arrange the real-time velocity data into a velocity profile matrix, where each row in the velocity profile matrix represents the velocity profile at a certain moment, and each column represents the velocity time history of a certain flow layer; 2) Perform singular value decomposition on the velocity profile matrix to obtain the time coefficient matrix, significance coefficient matrix and spatial mode matrix; 3) The obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix are analyzed respectively, and the deepwater riser fatigue design velocity profile is obtained based on the analysis results.

[0026] Furthermore, in the above step 2), when the velocity profile matrix is ​​subjected to singular value decomposition, the calculation expression is:

[0027] In the formula, is the velocity profile matrix, is the time coefficient matrix, is the significance coefficient matrix, is the spatial modal matrix.

[0028] Furthermore, in the above step 3), the obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix are analyzed respectively, and the deepwater riser fatigue design velocity profile is obtained according to the analysis results, including the following steps: 3.1) Calculate the modal variance contribution rate based on the significance coefficient matrix, and extract the significance coefficients corresponding to several order modes that meet the preset conditions; 3.2) Based on the modal order determined in step 3.1), the spatial modal vector of the corresponding order is extracted from the spatial modal matrix; 3.3) Based on the modal order determined in step 3.1), the time coefficient vector of the corresponding order is extracted from the time coefficient matrix, and the time coefficient vector is grouped and fully permuted and combined to determine the optimal time coefficient combination; 3.4) Based on the extracted significance coefficients, spatial modal vectors and the determined optimal time coefficient combination, the deepwater riser fatigue design velocity profile is synthesized.

[0029] Furthermore, in the above step 3.1), the modal variance contribution rate is calculated based on the significance coefficient matrix, and the significance coefficient is extracted, including the following steps: 3.1.1) Based on the diagonal elements in the significance coefficient matrix, the variance contribution rate of each mode is calculated.

[0030] in, Variance contribution of the first mode The calculation expression is:

[0031] In the formula, for The diagonal elements of the matrix of order The significance coefficient of the first mode.

[0032] 3.1.2) Based on the variance contribution rate and cumulative contribution rate of each order mode, several order modes that meet the preset conditions are taken as the dominant modes, and their corresponding significance coefficients are extracted.

[0033] In this embodiment, the variance contribution rates of several order modes are accumulated to obtain the cumulative contribution rate. When the cumulative contribution rate exceeds a preset threshold, it can be used as the dominant mode and its corresponding significance coefficient can be extracted.

[0034] Furthermore, in the above step 3.2), since each row in the spatial modal matrix represents a mode, in this embodiment, based on the order extracted from the significance coefficient matrix, the first four modes in the spatial modal matrix are directly extracted for synthesis analysis.

[0035] Furthermore, in the above step 3.3), extracting a time coefficient vector based on the time coefficient matrix, grouping and fully permuting and combining the time coefficient vectors, and determining a preferred time coefficient combination include the following steps: 3.3.1) Based on the modal order determined in step 3.1), extract the time coefficient vector of the corresponding order from the time coefficient matrix; 3.3.2) Group the extracted time coefficient vectors according to the sample value size and calculate the mean value of each group of samples; 3.3.3) Perform full permutation and combination of groups of different time coefficient vectors; 3.3.4) Merge all extracted time coefficient vectors into a matrix Q by column, and determine whether all time coefficients in each row of the matrix Q fall into a combination obtained in step 3.3.3). If so, add 1 to the count of the combination, and obtain the counts and proportions of all combinations; 3.3.5) According to the calculation of each combination and its proportion, the combination ranking is adjusted to obtain the first preset time coefficient combinations with the largest proportion as the preferred time coefficient combinations.

[0036] Furthermore, in the above step 3.3.2), the time coefficient vectors of the extracted modes are grouped according to the sample values, and the mean of each group of samples is calculated, including the following steps: ① Treat each element in a time coefficient vector as a sample and arrange them according to the sample value; ② Divide all samples into several groups according to the sample value size, and calculate the sample mean of each group; ③ Repeat steps ① to ② to obtain the grouping of all modal time coefficient vectors and the sample mean of each group.

[0037] Example 2 This embodiment takes the full-time velocity profile data measured in the deep water of the South China Sea as an example to further introduce the deepwater riser fatigue design velocity profile analysis method provided by the present invention.

[0038] 1) Organize the velocity profile matrix The full-time velocity profile data of the South China Sea deepwater field are organized into a velocity profile matrix Each row of the matrix represents the velocity profile at a certain moment, totaling 133543 moments, and each column of the matrix represents the velocity time course of a certain flow layer, totaling 51 flow layers, see Figure 2 .

[0039] 2) Perform singular value decomposition on the velocity profile matrix.

[0040] Convection velocity matrix Perform singular value decomposition (SVD), the calculation expression is:

[0041] In the formula, is the velocity profile matrix, is the time coefficient matrix, is the significance coefficient matrix, is the spatial modal matrix.

[0042] 3) Decompose them respectively to get , , Matrix analysis.

[0043] Specifically, the steps include: 3.1) Analyze the significance coefficient matrix.

[0044] is the significance coefficient matrix, the diagonal elements of the matrix for k The significance coefficient of the first mode is shown in Figure 3 , the contribution rate of the mode can be calculated by the significance coefficient, and the calculation expression is:

[0045] See Figure 4 , for the first 10 modes, calculate the variance contribution rate and cumulative contribution rate of the first 10 modes, see Figure 5 ,Depend on Figure 5 It can be seen that for the velocity profile matrix , the cumulative contribution rate of the first four modes reaches more than 95%, which can be regarded as the dominant mode.

[0046] 3.2) Analyze the spatial modal matrix.

[0047] like Figure 6 As shown, is the spatial modal matrix, each row of the matrix represents a mode, and the matrix k Row represents k The first four modes are extracted, namely v 1. v 2. v 3. v 4.

[0048] 3.3) Analyze the time coefficient matrix.

[0049] 3.3.1) Extract the time coefficient vector.

[0050] is the time coefficient matrix. Each column of the matrix represents the time coefficient of a mode. k Column represents k The time coefficient corresponding to the first mode is shown in Figure 7, this embodiment extracts the time coefficients of the first four modes, namely u 1. u 2. u 3. u 4.

[0051] 3.3.2) Group the samples in the time coefficient vector and calculate the sample mean.

[0052] The time coefficient u 1. u 2. u 3. u 4 Group the samples according to their numerical values ​​and calculate the mean of each group of samples, which specifically includes the following steps: Time coefficient vector u 1 is a column vector containing 133543 elements. Each element can be regarded as a sample. A total of 133543 samples are obtained. The 133543 sample values ​​are arranged from small to large and grouped according to the value size. In this embodiment, they are divided into 5 groups, and the range of each group is A1, A2, A3, A4, and A5. Figure 8 , and then calculate the sample means a1, a2, a3, a4, and a5 of each group.

[0053] Time coefficient vector u 2. u 3. u 4 Repeat the above process to obtain the first four modal time coefficient groups and the sample mean of each group, see Fig. 9 .

[0054] 3.3.3) The groups of different spatial coefficient vectors are fully permuted and combined. In this embodiment, the time coefficients of the first four modes are divided into 5 groups, so there are 5×5×5×5=625 combinations in total.

[0055] 3.3.4) The time coefficient vector u 1. u 2. u 3. u 4 Merge columns into one matrix Q , the size of the matrix is ​​133543×4, each row element is regarded as a sample, a total of 133543 groups of samples, if the four time coefficient vectors in the sample fall within the four ranges of a combination in step 3.3.3), then the combination count is increased by 1, see Fig.10 , traverse the matrix Q After each row, we get the counts and proportions of 625 combinations.

[0056] 3.3.5) Adjust the combination order according to the descending proportion to obtain the top 10 time coefficient combinations with the largest proportion. Synthesize the velocity profile according to the time coefficient combination method. The combination coefficient takes the average of each time coefficient grouping. The velocity profile synthesis method is shown in Fig.11 The proportion of each combination is the proportion of the composite velocity profile. The top 10 composite velocity profiles with the largest proportions are shown in Fig.12 .

[0057] Example 3 The above-mentioned embodiment 1 provides a deepwater riser fatigue design velocity profile analysis method. Correspondingly, this embodiment provides a deepwater riser fatigue design velocity profile analysis system. The system provided in this embodiment can implement the deepwater riser fatigue design velocity profile analysis method of embodiment 1. The system can be implemented by software, hardware, or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of embodiment 1. Since the system of this embodiment is basically similar to the method embodiment, the process described in this embodiment is relatively simple. For relevant parts, please refer to the partial description of embodiment 1. The embodiment of the system provided in this embodiment is only illustrative.

[0058] The deepwater riser fatigue design velocity profile analysis system provided in this embodiment includes: A matrix building module is used to organize the real-time velocity data into a velocity profile matrix, wherein each row in the velocity profile matrix represents a velocity profile at a certain moment, and each column represents a velocity time course of a certain flow layer; Matrix decomposition module, used to perform singular value decomposition on the velocity profile matrix to obtain the time coefficient matrix, significance coefficient matrix and spatial mode matrix; The profile synthesis module is used to analyze the obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix respectively, and obtain the deepwater riser fatigue design velocity profile according to the analysis results.

[0059] Example 4 This embodiment provides a processing device corresponding to the deepwater riser fatigue design velocity profile analysis method provided in this embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0060] The processing device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The memory stores a computer program that can be run on the processor, and the processor executes the deepwater riser fatigue design velocity profile analysis method provided in this embodiment 1 when running the computer program.

[0061] Preferably, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0062] Preferably, the processor may be a central processing unit (CPU), a digital signal processor (DSP) or other general-purpose processors, which are not limited here.

[0063] Example 5 The deepwater riser fatigue design velocity profile analysis method of this embodiment 1 can be specifically implemented as a computer program product, which may include a computer-readable storage medium carrying computer-readable program instructions for executing the deepwater riser fatigue design velocity profile analysis method described in this embodiment 1.

[0064] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0065] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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. The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 One or more processes and / or boxes Figure 1 A function specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A deepwater riser fatigue design velocity profile analysis method, characterized in that: The following steps are involved: 1) Arrange the real-time velocity data into a velocity profile matrix, wherein each row in the velocity profile matrix represents the velocity profile at a certain moment, and each column represents the velocity time course of a certain flow layer; 2) Perform singular value decomposition on the velocity profile matrix to obtain the time coefficient matrix, significance coefficient matrix and spatial mode matrix; 3) The obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix are analyzed respectively, and the deepwater riser fatigue design velocity profile is obtained based on the analysis results.

2. A deepwater riser fatigue design velocity profile analysis method according to claim 1, characterized in that: In step 2), when the velocity profile matrix is ​​subjected to singular value decomposition, the calculation expression is: In the formula, is the velocity profile matrix, is the time coefficient matrix, is the significance coefficient matrix, is the spatial modal matrix.

3. A deepwater riser fatigue design velocity profile analysis method according to claim 1, characterized in that: In the step 3), the obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix are analyzed respectively, and the fatigue design velocity profile of the deepwater riser is obtained according to the analysis results, including the following steps: 3.1) Calculate the modal variance contribution rate based on the significance coefficient matrix, and extract the significance coefficients corresponding to several order modes that meet the preset conditions; 3.2) Based on the modal order determined in step 3.1), the spatial modal vector of the corresponding order is extracted from the spatial modal matrix; 3.3) Based on the modal order determined in step 3.1), the time coefficient vector of the corresponding order is extracted from the time coefficient matrix, and the time coefficient vector is grouped and fully permuted and combined to determine the optimal time coefficient combination; 3.4) Based on the extracted significance coefficients, spatial modal vectors and the determined optimal time coefficient combination, the deepwater riser fatigue design velocity profile is synthesized.

4. A deepwater riser fatigue design velocity profile analysis method as claimed in claim 3, characterized in that: In the step 3.1), the modal variance contribution rate is calculated based on the significance coefficient matrix, and the significance coefficient is extracted, including: Based on the matrix diagonal elements in the significance coefficient matrix, the variance contribution rate of each mode is calculated; Based on the variance contribution rate and cumulative contribution rate of each order mode, several order modes that meet the preset conditions are taken as the dominant modes, and their corresponding significance coefficients are extracted.

5. A deepwater riser fatigue design velocity profile analysis method as claimed in claim 4, characterized in that: Said Variance contribution of the first mode The calculation expression is: In the formula, for The diagonal elements of a matrix of rank.

6. A deepwater riser fatigue design velocity profile analysis method as claimed in claim 3, characterized in that: In the step 3.3), extracting a time coefficient vector based on the time coefficient matrix, grouping and fully permuting and combining the time coefficient vectors, and determining a preferred time coefficient combination include the following steps: 3.3.1) Based on the modal order determined in step 3.1), extract the time coefficient vector of the corresponding order from the time coefficient matrix; 3.3.2) Group the extracted time coefficient vectors according to the sample value size and calculate the mean value of each group of samples; 3.3.3) Perform full permutation and combination of groups of different time coefficient vectors; 3.3.4) Merge all extracted time coefficient vectors into a matrix Q by column, and determine whether all time coefficients in each row of the matrix Q fall into a combination obtained in step 3.3.3). If so, add 1 to the count of the combination, and obtain the counts and proportions of all combinations; 3.3.5) According to the calculation of each combination and its proportion, the combination ranking is adjusted to obtain the first preset time coefficient combinations with the largest proportion as the preferred time coefficient combinations.

7. A deepwater riser fatigue design velocity profile analysis method according to claim 6, characterized in that: In the step 3.3.2), the time coefficient vectors of the extracted modes of several orders are grouped according to the sample value size, and the mean value of each group of samples is calculated, including the following steps: ① Treat each element in a time coefficient vector as a sample and arrange them according to the sample value; ② Divide all samples into several groups according to the sample value size, and calculate the sample mean of each group; ③ Repeat steps ① to ② to obtain the grouping of all modal time coefficient vectors and the sample mean of each group.

8. A deepwater riser fatigue design velocity profile analysis system, characterized in that: include: A matrix building module is used to organize the real-time velocity data into a velocity profile matrix, wherein each row in the velocity profile matrix represents a velocity profile at a certain moment, and each column represents a velocity time course of a certain flow layer; Matrix decomposition module, used to perform singular value decomposition on the velocity profile matrix to obtain the time coefficient matrix, significance coefficient matrix and spatial mode matrix; The profile synthesis module is used to analyze the obtained time coefficient matrix, significance coefficient matrix and spatial modal matrix respectively, and obtain the deepwater riser fatigue design velocity profile according to the analysis results.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7.

10. A computing device, characterized in that include: One or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, wherein the one or more programs include instructions for executing any one of the methods described in claims 1 to 7.

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