Ocean wind wave and current combined load working condition sampling method

Abstract

The invention discloses an ocean wind wave and current combined load working condition sampling method, and belongs to the field of ocean engineering structures. According to the method, based on joint distribution of ocean wind wave flow, through distribution characteristic analysis of six actually-measured environmental parameters including wind speed, wind direction, wave height, period, flow velocity and flow direction, joint sampling is carried out, joint working conditions for engineering design and structure operation and maintenance are generated, reasonability of calculation results is verified, and extreme working conditions of extreme sea conditions are considered. According to the method, on the basis of statistical analysis of previous monitoring data, typical wind wave and flow joint distribution sea conditions used for simulation, verification and checking in the ocean engineering design process are obtained, and the workload of simulation calculation can be greatly reduced. Moreover, the method can fuse the actually measured environment load data accumulated after the service of the platform, and improves the accuracy of distribution fitting and the typicality of sea condition sampling. The method fits the field application, is suitable for monitoring the deepwater jacket platform, and is suitable for the ocean engineering fields of design, operation and maintenance of the platform and the like.

Description

Technical Field

[0001] The invention relates to a method for sampling ocean wind-wave-current combined load conditions, and belongs to the field of ocean engineering structures. Background Art

[0002] Offshore platforms generally operate at a fixed point in a certain sea area and are subjected to the effects of marine environmental loads for a long time. The design and operation and maintenance of marine engineering structures require calculations of the structure's response under different marine environmental conditions. This places higher demands on the stress, corrosion resistance, durability, and fatigue damage of the jacket structure. In order to ensure the long-term safe service of deep-water jacket platforms, the builders and operators of the platforms are eager to obtain more reasonable calculation results based on hydrodynamic simulation or model tests. Hydrodynamic simulation uses mathematical models and computational algorithms to convert the physical process of fluid flow into mathematical equations, and then numerically solves these equations to predict the behavior and characteristics of the fluid under different conditions. Hydrodynamic model tests construct models similar to actual projects and use experimental equipment and measuring instruments to observe and measure the characteristics of fluid flow.

[0003] To implement a method for analyzing distribution characteristics and selecting calculation conditions based on measured environmental load data on offshore platforms, it is necessary to calculate the response of the structure under different marine environmental conditions based on the measured data of the studied sea area. However, offshore platforms are generally made of thousands of thin-walled cylindrical rods welded together, with complex structures. In addition, some structures are located in deep water areas, making it difficult to deploy sensors for monitoring.

[0004] Therefore, in order to realize the digital technology of marine platforms, it is urgently necessary to have measured data of the studied sea area to calculate the response of the structure under different marine environmental conditions. Summary of the Invention

[0005] To address the above issues, the present invention provides a method for sampling ocean wind-wave-current combined load conditions. This method utilizes marine environmental monitoring data and analyzes its distribution characteristics to calculate and select load conditions, which are then displayed in real time on the relevant software for the marine platform.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for sampling ocean wind-wave-current combined load conditions, the hardware basis of which includes a monitoring terminal, a central controller, and a display terminal; wherein the central controller uses an industrial computer to electrically connect to a signal receiver; the signal receiver is electrically connected to an Internet of Things transmission node in the monitoring terminal; and the anemometer, wave measurement radar, ocean current, and fiber Bragg grating strain sensor in the monitoring terminal are electrically connected to the Internet of Things transmission node.

[0008] A method for sampling ocean wind-wave-current combined load conditions comprises the following steps:

[0009] A. Determine the platform's axis of symmetry and the direction of the main wind loads by combining the structural characteristics of the deepwater jacket platform with the nearby sea area monitoring data obtained from the aforementioned hardware foundation monitoring.

[0010] B. Based on a certain wind load direction in step A, statistical analysis of the wind load and wind speed in that direction is performed, wind speed distribution characteristics are simulated, and the wind speed is spatially extrapolated according to existing marine engineering design specifications;

[0011] C. Based on the wind load of a certain direction and wind speed in step B, simulate the joint distribution characteristics of wave height and period in this case;

[0012] D. Calculate the wind-wave joint distribution based on the wave height-period joint distribution characteristics in step C and the wind speed-direction distribution characteristics in step B;

[0013] E. Change the wind load direction in step A and repeat steps B, C, and D to obtain the joint distribution characteristics of wind speed, wind direction, wave height, and period.

[0014] F. Independently count the flow characteristics and determine the main flow load direction;

[0015] G. Based on a certain flow load direction in step F, simulate the flow velocity distribution in that direction;

[0016] H. Change the wind load direction in step F and repeat step G to obtain the flow velocity-flow direction joint distribution characteristics.

[0017] I. Adjust the quantiles according to the actual situation, and perform sampling for the wind-wave joint distribution in step D and the flow velocity-flow direction distribution in step H to obtain wind-wave sampling conditions and flow velocity-flow direction sampling conditions;

[0018] J. Considering that wind-wave loads and flow loads are highly independent, all combinations of wind-wave sampling conditions and flow velocity-flow direction sampling conditions in step I are traversed to obtain the wind-wave-flow combined condition;

[0019] K. Change the quantile in step I and repeat step J to obtain the wind-wave-current verification condition;

[0020] L. Extrapolate the extreme working condition distribution based on the monitoring data, and change the main wind load direction and main flow load direction in steps A to J according to the characteristics of the extreme working conditions to obtain the wind-wave-current extreme working conditions.

[0021] The working conditions are six-dimensional data including wind direction, wind speed, significant wave height, spectrum peak period, flow direction, and surface flow velocity.

[0022] For the statistical analysis of wind speed described in step A, a two-parameter Weibull distribution is used to fit the average wind speed U according to the existing marine engineering design specifications. 10distribution and extrapolate it in space.

[0023] The joint distribution of wave height and period described in step C uses log-normal distribution to fit the distribution characteristics of significant wave height and zero-crossing period data.

[0024] The significant wave height distribution adopts a three-parameter Weibull distribution to describe the distribution characteristics of the significant wave height.

[0025] The velocity distribution in step J is statistically analyzed for ocean current loads, and the measured ocean current data is spatially extrapolated to the surface current commonly used in engineering according to existing ocean engineering design specifications.

[0026] Based on the velocity distribution of the first and bottom layers, the original data can be expressed as a linear superposition of several EOF modes based on EOF decomposition:

[0027]

[0028] Where V c is the ocean current profile, N is the total number of EOF modes, m is the number of selected EOF modes, i is the i-th order EOF mode.

[0029] In practical applications, since the time history of the surface velocity v0 is known, the time history of α1 can be obtained according to the above formula, and then the entire multi-layer flow velocity can be obtained based on the first-order EOF mode.

[0030] The main wind load direction and main flow load direction described in steps A and F are characterized by taking into account the symmetry of the deepwater jacket platform structure (for example, the symmetry axis of the current deepwater jacket is northwest-southeast, the wind direction interval can be 30°, and the flow direction interval can be 45°).

[0031] For the extreme working conditions described in step L, the main wind load direction and the main flow load direction can be selected in the direction with the highest frequency of extreme values, and the Pareto distribution is used to fit the significant wave height and the recurrence period. The spectral peak period corresponding to the given significant wave height is approximately obtained by regression, and the surface flow velocity is fitted using the Gumbel distribution.

[0032] The spectral peak period corresponding to a given significant wave height is approximately obtained by regression. The regression model is as follows:

[0033]

[0034] Among them, α, β, and γ are unknown parameters. The optimal parameters are obtained by fitting based on the extreme values ​​of the significant wave height and the spectral peak period.

[0035] The present invention utilizes measured data from the studied sea area to calculate the structural response under different marine environmental conditions. This includes combined operating conditions for engineering design and structural operation and maintenance, verification conditions to validate the calculation results, and extreme operating conditions to account for extreme sea conditions. This provides a more scientific and accurate basis for operation and maintenance decisions, helping to formulate reasonable maintenance plans in advance.

[0036] The present invention effectively solves the problem of calculating the response of the structure under different marine working conditions through the measured data of the studied sea area, solves the difficulties in the structural design and operation and maintenance of marine platforms under complex marine environment conditions, and is suitable for the digital and intelligent construction, design and operation and maintenance of marine platforms.

[0037] Based on the joint distribution of ocean wind, waves, and currents, the present invention conducts joint sampling by analyzing the distribution characteristics of six measured environmental parameters: wind speed, wind direction, wave height, period, flow velocity, and flow direction. This generates joint operating conditions for engineering design and structural operation and maintenance, verifies the rationality of the calculation results, and considers extreme operating conditions in extreme sea conditions. Based on statistical analysis of previous monitoring data, this method obtains typical joint distribution sea conditions of wind, waves, and currents for simulation, verification, and verification in the marine engineering design process, which can greatly reduce the workload of simulation calculations. Furthermore, this method can integrate the measured environmental load data accumulated after the platform is put into service, improving the accuracy of distribution fitting and the typicality of sea condition sampling. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the detection end of a sampling method for ocean wind, wave and current combined load conditions.

[0039] Figure 2 This is a schematic diagram of a conventional working condition combination for a sampling method of ocean wind, wave and current combined load conditions. DETAILED DESCRIPTION

[0040] The specific embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings. However, it should be understood that the drawings are provided only for a better understanding of the present invention and should not be construed as limiting the present invention.

[0041] like Figure 1 As shown, the hardware basis of the method includes a monitoring terminal 12, a central controller 11 and a display terminal 10; wherein, the central controller 11 adopts an industrial computer 9 to be electrically connected to a signal receiver 8; the signal receiver 8 is electrically connected to an Internet of Things transmission node 13 in the monitoring terminal 12; the anemometer 1, the wave measuring radar 2, the current meter 3, and the fiber Bragg grating strain sensor 4 in the monitoring terminal 12 are electrically connected to the Internet of Things transmission node 13; the industrial computer 13 has built-in measured environmental load data for distribution feature analysis and calculation of the working condition selection method algorithm.

[0042] Anemometer 1 is used to collect wind field data. Wave radar 2 is used to collect wave data. Current meter 3 is used to collect ocean current data. Fiber Bragg grating strain sensor 4 is used to collect stress and strain data at the measurement point. Signal conditioning module 5 is used to standardize the data collected by the sensors. Signal transmission module 6 is used to transmit the standardized monitoring data to signal receiver 8 in central controller 11. Signal storage module 7 is used to store and back up the standardized monitoring data.

[0043] The signal receiver 8 is used to receive the monitoring information transmitted by the signal transmission module 7 and transmit it to the industrial computer 9. The industrial computer 9 is used to analyze the distribution characteristics and calculate the algorithm for selecting the working condition based on the built-in measured environmental load data, and display it on the display terminal 10.

[0044] The working condition is a six-dimensional data including wind direction, wind speed, significant wave height, spectral peak period, flow direction, and surface velocity. The sampling steps of the ocean wind-wave-current combined load working condition of the present invention are as follows:

[0045] A. Determine the platform's axis of symmetry and the direction of the primary wind load based on the structural characteristics of the deepwater jacket platform and the directional characteristics of the monitoring data obtained from the hardware foundation. For example, if the axis of symmetry of an existing deepwater jacket is northwest-southeast, the wind direction interval can be 30°.

[0046] B. Based on a certain wind load direction in step A, statistical analysis of the wind speed in that direction is performed, and the wind speed distribution characteristics are fitted. The wind speed is spatially extrapolated according to the existing marine engineering design specifications. According to the existing marine engineering design specifications, a two-parameter Weibull distribution is used to fit the average wind speed U 10 distribution.

[0047] C. Based on the wind load of a certain direction and wind speed in step B, the joint distribution characteristics of wave height and period in this case are fitted; the distribution characteristics of significant wave height and zero-crossing period data are fitted using the lognormal distribution, and the distribution characteristics of significant wave height are described using the three-parameter Weibull distribution.

[0048] D. Calculate the wind-wave joint distribution based on the wave height-period joint distribution characteristics in step C and the wind speed-direction distribution characteristics in step B;

[0049] E. Change the wind load direction in step A and repeat steps B, C, and D to obtain the joint distribution characteristics of wind speed, wind direction, wave height, and period.

[0050] F. Independently calculate the flow characteristics to determine the main flow load direction; the symmetry axis of the current deepwater jacket is northwest-southeast, so the flow direction interval is taken as 45°.

[0051] G. Based on a certain flow load direction in step F, simulate the flow velocity distribution in that direction;

[0052] Statistical analysis of ocean current loads is conducted, and based on existing marine engineering design specifications, the measured ocean current data are spatially extrapolated to the surface current commonly used in engineering.

[0053] Based on EOF decomposition, the original data can be expressed as a linear superposition of several EOF modes:

[0054]

[0055] Where V c is the ocean current profile, N is the total number of EOF modes, m is the number of selected EOF modes, i is the i-th order EOF mode.

[0056] In practical applications, since the time history of the surface velocity v0 is known, the time history of α1 can be obtained using the above equation, and thus the velocity of all 26 layers can be obtained based on the first-order EOF mode. For water depths less than 36 m and greater than 236 m, extrapolation can be performed using the profile model provided by Det Norske Veritas. For the range of 36 m to 236 m, the velocity can be simply obtained by linear interpolation of the velocity of two adjacent layers.

[0057] H. Change the wind load direction in step F and repeat step G to obtain the flow velocity-flow direction joint distribution characteristics.

[0058] I. Adjust the quantiles according to the actual situation, and perform sampling for the wind-wave joint distribution in step D and the flow velocity-flow direction distribution in step H to obtain wind-wave sampling conditions and flow velocity-flow direction sampling conditions;

[0059] J. Considering that wind-wave loads and flow loads are highly independent, all combinations of wind-wave sampling conditions and flow velocity-flow direction sampling conditions in step I are traversed to obtain the wind-wave-flow combined condition;

[0060] K. Change the quantile in step I and repeat step J to obtain the wind-wave-current verification condition;

[0061] L. Extrapolate the extreme working condition distribution based on the monitoring data, and change the main wind load direction and main flow load direction in steps A to G according to the characteristics of the extreme working conditions to obtain the wind-wave-current extreme working conditions.

[0062] The main wind load direction and the main flow load direction can be selected from the direction with the highest frequency of extreme values, and the Pareto distribution is used to fit the significant wave height and recurrence period. The regression method is used to approximately obtain the spectral peak period corresponding to the given significant wave height, and the Gumbel distribution is used to fit the surface flow velocity.

[0063] The spectral peak period corresponding to a given significant wave height is approximately obtained by regression. The regression model is as follows:

[0064]

[0065] Among them, α, β, and γ are unknown parameters. The optimal parameters are obtained by fitting based on the extreme values ​​of the significant wave height and the spectral peak period.

[0066] In summary, if Figure 2 As shown in Table 1, the sampled joint operating conditions include 4 wind speeds, 6 wind directions, 3 flow speeds, and 5 flow directions. Each wind speed corresponds to 4 wave heights, and each wave height corresponds to 4 periods, for a total of 5760 operating conditions. The extreme operating conditions are shown in Table 1.

[0067] Table 1 Wind speed, flow velocity, wave height and period information under extreme conditions

[0068]

[0069] The above embodiments are only used to illustrate the present invention, wherein the structure and environmental load of the deepwater jacket are subject to change. Any equivalent transformations and improvements based on the technical solution of the present invention should not be excluded from the scope of protection of the present invention.

Claims

1. A method for sampling ocean wind-wave-current combined load conditions, characterized by: A signal mediation module (5), a signal transmission module (6) and a signal storage module (7) are provided in an Internet of Things transmission node (13); the method comprises the following steps: S1. Determine the platform's symmetry axis and wind load and direction distribution characteristics based on the structural characteristics of the deepwater jacket platform and the monitoring data obtained from hardware foundation monitoring. S2. Based on the wind load direction in step S1, statistically analyze the wind speed in that direction, approximate the wind speed-direction distribution characteristics, and spatially extrapolate the wind speed according to existing marine engineering design specifications; S3, based on the wind speed in a certain direction in step S2, fitting the joint distribution characteristics of wave height and period in this case; S4. Calculating a wind-wave joint distribution feature based on the wave height-period joint distribution feature and the wind speed-wind direction distribution feature; S5. Adjust the wind load direction and repeat steps S2-S4 to obtain wind-wave joint distribution characteristics of wind speed, wind direction, wave height and period; S6. Independently count the flow characteristics and determine the flow load direction; S7, based on the flow load direction in step S6, fitting the flow velocity distribution in this direction; S8, changing the flow load direction in step S6, repeating step S7, and obtaining the flow velocity-flow direction joint distribution characteristics; S9, adjusting the quantiles, sampling the wind-wave joint distribution characteristics in step S4 and the flow velocity-flow direction distribution characteristics in step S8, respectively, to obtain wind-wave sampling conditions and flow velocity-flow direction sampling conditions; S10, traversing all combinations of wind-wave sampling conditions and flow velocity-flow direction sampling conditions in step S9 to obtain a wind-wave-current combined condition; S11, changing the quantile in step S9, repeating step S10, and obtaining the wind-wave-current verification condition; S12. Extrapolate the extreme working condition distribution based on the monitoring data, the wind load direction and the flow load direction in the above steps, and obtain the wind-wave-flow extreme working condition.

2. The method for sampling ocean wind-wave-current combined load conditions according to claim 1, characterized in that: The hardware basis of the method includes a monitoring terminal (12), a central controller (11) and a display terminal (10); wherein the central controller (11) is electrically connected to a signal receiver (8) using an industrial computer (9); the signal receiver (8) is electrically connected to an Internet of Things transmission node (13) in the monitoring terminal (12); and the anemometer (1), a wave measuring radar (2), a current meter (3), and a fiber grating strain sensor (4) in the monitoring terminal (12) are electrically connected to the Internet of Things transmission node (13).

3. The method for sampling ocean wind-wave-current combined load conditions according to claim 1, characterized in that: The working conditions are six-dimensional data including wind direction, wind speed, significant wave height, spectrum peak period, flow direction, and surface flow velocity.

4. The method for sampling ocean wind-wave-current combined load conditions according to claim 1, characterized in that: In step S2, the average wind speed U is fitted using a two-parameter Weibull distribution according to existing marine engineering design specifications. 10 distribution and extrapolate it in space.

5. The method for sampling ocean wind-wave-current combined load conditions according to claim 1, characterized in that: In step S3, the joint distribution characteristics of wave height and period are fitted by log-normal distribution to fit the distribution characteristics of significant wave height and zero-crossing period data.

6. The method for sampling ocean wind-wave-current combined load conditions according to claim 5, characterized in that: The significant wave height distribution adopts three-parameter Weibull distribution to describe the distribution characteristics of the significant wave height.

7. The method for sampling ocean wind-wave-current combined load conditions according to claim 1, characterized in that: The velocity distribution in step S7 is statistically analyzed for ocean current loads; Based on EOF decomposition, the original data can be expressed as a linear superposition of several EOF modes: ； Where V c is the ocean current profile, N is the total number of EOF modes, m is the number of selected EOF modes, i is the i-th order EOF mode; If the time history of the surface velocity v0 is known, the time history of α1 can be obtained according to the above formula, and then the full laminar velocity can be obtained based on the first-order EOF mode.

8. The method for sampling ocean wind-wave-current combined load conditions according to claim 1, characterized in that: The wind load direction is adjusted to an interval of 30°, and the flow load direction is changed to an interval of 45°.

9. The method for sampling ocean wind-wave-current combined load conditions according to claim 1, characterized in that: For the extreme working conditions, the wind load direction and the main flow load direction are selected in the direction with the highest frequency of extreme values, and the significant wave height and return period are fitted by Pareto distribution. The spectral peak period corresponding to the given significant wave height is approximately obtained by regression, and the surface flow velocity is fitted by Gumbel distribution. The spectral peak period corresponding to a given significant wave height is approximately obtained by regression; the regression model is as follows: ； Among them, α, β, and γ are unknown parameters; the optimal parameters are obtained by fitting based on the extreme values ​​of the significant wave height and the spectral peak period.

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