Wind wave and flow coupling environment modeling method of FPSO system based on modelica

Abstract

The invention discloses a modeling method for a wind wave flow coupling environment of an FPSO system based on modelica, and relates to the technical field of model convergence, and the method comprises the steps: setting different time steps, carrying out simulation, recording the response of a key physical quantity, and calculating a time step sensitivity index; performing different grid division and simulation on the model, and calculating a grid independence index; and recording the dynamic response of the FPSO platform, and calculating the instability index of the simulation result. A time step sensitivity index, a grid independence index and a simulation result instability index are combined to calculate a convergence anomaly coefficient, the convergence anomaly coefficient is compared with a preset threshold value, whether the convergence of the model is qualified or not is judged, the standardized convergence of the FPSO system wind wave flow coupling model based on Modelica can be verified in the process, it is ensured that the convergence performance of the model is detected in time, and the reliability of the model is improved. Error accumulation is avoided, the accuracy and reliability of a simulation result are improved, and therefore potential influences on engineering decision and safety evaluation are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of model convergence, and in particular to a wind-wave-current coupled environment modeling method of an FPSO system based on Modelica. Background Art

[0002] Modelica-based wind-wave-current coupled environment modeling of FPSO systems refers to the use of Modelica, a multi-domain modeling language, to simulate and analyze the dynamic behavior of floating production storage and offloading units (FPSOs) under environmental conditions such as wind, waves and currents. The model fully simulates the motion response, structural response, and interaction between fluids and solids of FPSOs by coupling the dynamics of wind, wave and flow fields, and combines a variety of environmental factors (such as wave period, wind speed, current direction, etc.) to predict the stability, navigation performance and operational safety of FPSOs. This modeling method makes the simulation results more realistic and reliable by accurately describing the interactive relationships among fluid mechanics, structural mechanics and control systems in physical processes.

[0003] Among them, the wind-wave-current coupling model is the core part of the modeling method, which involves the complex physical interaction between the wind-wave-current environment and the FPSO platform. These interaction processes are highly nonlinear, and the coupling effect between the platform and the fluid makes the dynamic response of the system more complicated. For example, the different frequencies, amplitudes and directions of waves will directly affect the floating, tilting and swaying behavior of the FPSO platform, while changes in wind speed will have a significant impact on the lateral moment and additional load of the platform. At the same time, the impact of ocean currents on platform dynamics may lead to further stability problems. These factors work together to make the system response highly time-varying and sensitive.

[0004] Through such modeling, the impact of environmental conditions changes on the operational performance of FPSO can be accurately captured in the simulation, providing a scientific basis for design, operation and safety assessment. Specifically, in the design phase of the FPSO system, the wind-wave-current coupled environmental modeling based on Modelica can help engineers evaluate the response performance of FPSO under different environmental conditions and optimize the design scheme; in the operation phase, it can perform real-time simulation to predict the behavior of FPSO under different wind-wave-current conditions to ensure its safe operation; and in terms of fault detection and prediction, the model can provide support for maintenance decisions and timely detect possible abnormal conditions by monitoring the dynamic response of the platform; However, due to the lack of a standardized convergence verification method for the wind-wave-current coupling model of the FPSO system based on Modelica, the convergence of the model is often not effectively tested in a timely manner in practical applications. This lack of verification may cause the model to have problems of numerical instability or error accumulation, thereby affecting the accuracy and reliability of the simulation results, and further affecting the credibility of engineering decisions and safety assessments. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and provide a wind-wave-current coupled environment modeling method for an FPSO system based on Modelica.

[0006] The present invention proposes a wind-wave-current coupled environment modeling method for an FPSO system based on Modelica, the method comprising: Set multiple different time steps, run the simulation model for each time step, record the response of key physical quantities, and calculate the time step sensitivity index; Divide the fluid domain involved in the model into different grids, simulate the model separately, record the key physical quantities of the response, and calculate the grid independence index; Simulate the model and record the dynamic response of the FPSO platform, and calculate the instability index of the simulation results of the model; The convergence anomaly coefficient is calculated based on the time step sensitivity index, grid independence index and simulation result stability index, and the convergence of the model is judged to be qualified in combination with the preset convergence anomaly coefficient threshold.

[0007] Optionally, calculating a time step sensitivity index includes: Set multiple different time step values, namely: coarse time step ; Medium time step ; Fine time step ; For each time step, run the simulation and record the response of key physical quantities; the recorded physical quantities are: Physical quantity response under coarse time step ; Physical quantity response at medium time step ; Physical quantity response under fine time step ; Calculate the error at each time step, for example by calculating the relative error in the platform displacement or acceleration: ; ; ; In the formula, , , They are the error under coarse time step, the error under medium time step, and the error under fine time step respectively; is the preset reference solution; Calculate the error ratio of different time steps and combine the time step differences to get the time step sensitivity index: , where is the time step sensitivity index.

[0008] Optionally, computing a grid independence index includes: Select coarse and fine grids in the fluid domain, respectively: Coarse grid size , fine mesh size ; Simulate the coarse grid and fine grid separately, and record the key physical quantities of the response. The key physical quantities represent the displacement values ​​under the coarse grid and fine grid, respectively: Displacement response under the coarse grid , displacement response under fine mesh ; and ; Calculate the relative error between the coarse and fine grids based on their displacement responses , reflecting the influence of grid division on the results: the calculation formula is: ; Calculate the grid independence index, the calculation formula is: ; In the formula, is the grid independence index, is the mesh convergence index.

[0009] Optionally, the simulation result instability index of the calculation model includes: Simulate the model and record the dynamic response displacement of the FPSO platform , calculate the mean of the response and standard deviation ; The calculation formula is: , , where For in time The displacement at is the number of time steps; Calculate the instability index of the simulation results. The calculation formula is: ; In the formula, is the instability index of simulation results.

[0010] Optionally, the convergence anomaly coefficient is calculated including: ; In the formula, is the convergence anomaly coefficient, , , They are the time step sensitivity index, grid independence index and simulation result stability index, are the preset proportional coefficients of the time step sensitivity index, grid independence index and simulation result stability index, respectively, and Both are greater than 0.

[0011] Optionally, judging whether the convergence of the model is qualified in combination with a preset convergence abnormality coefficient threshold includes: The convergence anomaly coefficient is compared with the preset convergence anomaly coefficient threshold. If the convergence anomaly coefficient is less than the preset convergence anomaly coefficient threshold, it means that the convergence of the model is qualified. If the convergence anomaly coefficient is not less than the preset convergence anomaly coefficient threshold, it means that the convergence of the model is unqualified and the model needs to be optimized until the convergence anomaly coefficient is less than the preset convergence anomaly coefficient threshold.

[0012] Beneficial effects of the present invention: The invention proposes a modeling method for a wind-wave-current coupling environment of an FPSO system based on Modelica. The method includes setting a plurality of different time steps, and for each time step, running a simulation model and recording the response of key physical quantities, and calculating a time step sensitivity index; dividing the fluid domain involved in the model into different grids, simulating the model respectively, recording the key physical quantities of the response, and calculating a grid independence index; simulating the model and recording the dynamic response of the FPSO platform, and calculating an instability index of the simulation result of the model; calculating a convergence anomaly coefficient according to the time step sensitivity index, the grid independence index and the simulation result stability index, and judging whether the convergence of the model is qualified in combination with a preset convergence anomaly coefficient threshold, so as to verify the standardized convergence of a wind-wave-current coupling model of an FPSO system based on Modelica, so that in practical applications, the convergence performance of the model can be effectively detected in time; making the model numerically stable, and preventing the problem of error accumulation, ensuring the accuracy and reliability of the simulation result, and reducing the influence on the credibility of engineering decision-making and safety assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be further described below in conjunction with the accompanying drawings.

[0014] Figure 1 Flowchart of the wind-wave-current coupled environment modeling method for the FPSO system based on Modelica. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0017] The embodiment of the present invention provides a method for modeling the wind-wave-current coupling environment of an FPSO system based on Modelica. Figure 1 , Figure 1 A flow chart of a method for modeling a wind-wave-current coupled environment of an FPSO system based on Modelica provided in an embodiment of the present invention. The method comprises the following steps: Set multiple different time steps, run the simulation model for each time step, record the response of key physical quantities, and calculate the time step sensitivity index; Divide the fluid domain involved in the model into different grids, simulate the model separately, record the key physical quantities of the response, and calculate the grid independence index; Simulate the model and record the dynamic response of the FPSO platform, and calculate the instability index of the simulation results of the model; The convergence anomaly coefficient is calculated based on the time step sensitivity index, grid independence index and simulation result stability index, and the convergence of the model is judged to be qualified in combination with the preset convergence anomaly coefficient threshold.

[0018] Based on the wind-wave-current coupling environment modeling method of the FPSO system based on Modelica provided by the embodiment of the present invention, through the above steps, the standardized convergence of the wind-wave-current coupling model of the FPSO system based on Modelica can be verified, so that in practical applications, the convergence performance of the model can be effectively detected in time; the model is numerically stable and error accumulation problems will not occur, thereby ensuring the accuracy and reliability of the simulation results and reducing the impact on the credibility of engineering decisions and safety assessments.

[0019] In one embodiment, a plurality of different time steps are set, and for each time step, the simulation model is run and the response of key physical quantities is recorded. The time step sensitivity index is calculated including: Set multiple different time step values, namely: coarse time step (Large time step); Medium time step (moderate time step); fine time step (smaller time step); For each time step, run the simulation and record the response of key physical quantities (such as displacement and acceleration of the FPSO platform); the recorded physical quantities are: Physical quantity response under coarse time step (such as platform displacement, acceleration, etc.); physical quantity response at medium time steps ; Physical quantity response under fine time step ; Calculate the error at each time step, for example by calculating the relative error in the platform displacement or acceleration: ; ; ; In the formula, , , They are the error under coarse time step, the error under medium time step, and the error under fine time step respectively; is a preset reference solution (e.g. the result of the finest time step); Calculate the error ratio of different time steps and combine the time step differences to get the time step sensitivity index: , where is the time step sensitivity index.

[0020] It should be noted that the data acquisition method involved in the above calculations mainly depends on the operation results of the wind-wave-current coupling simulation model of the FPSO system based on Modelica. First, different time steps (coarse time step, medium time step and fine time step) are set in the model, and multiple simulations are performed respectively. In each simulation, the key physical quantities related to the dynamic response of the FPSO platform, such as the displacement, velocity, acceleration, etc. of the platform, are recorded. Then, by comparing the simulation results under coarse, medium and fine time steps, the relative errors of physical quantities such as platform displacement or acceleration are calculated, and the results of the finest time step are used as the reference solution. Finally, by calculating the error ratio under different time steps and combining the time step difference, the time step sensitivity index is obtained, thereby evaluating the impact of the time step on the simulation accuracy and stability, and providing a basis for further optimizing the model.

[0021] It should be noted that the time step sensitivity index is an indicator used to measure the sensitivity of the Modelica-based FPSO system wind-wave-current coupled simulation model to changes in the time step. Specifically, the larger the time step sensitivity index, the more significant the difference in the response of the model at different time steps, especially the larger the error gap between the coarse time step and the fine time step. This usually means that the numerical solution of the model is very sensitive to changes in the time step, and there may be large numerical instability or error accumulation, resulting in abnormal convergence. In other words, when the time step sensitivity index is large, it means that the dynamic response of the model is significantly affected by the time step setting, and it may not be able to accurately capture the true behavior of the system, thereby affecting the convergence of the model and the reliability of the simulation results. Therefore, in order to ensure the convergence of the model and obtain stable and accurate simulation results, it is necessary to optimize the time step setting so that the response of the model at different time scales is consistent and without significant errors, and to avoid numerical instability caused by too large or too small time steps.

[0022] In one implementation, the benefit of analyzing the time step sensitivity index for measuring whether the convergence degree of the wind-wave-current coupling simulation model of the FPSO system based on Modelica is abnormal is as follows: Analyzing the time step sensitivity index has important benefits for measuring whether the convergence degree of the wind-wave-current coupling simulation model of the FPSO system based on Modelica is abnormal. First, the time step sensitivity index can help us identify the consistency of the response of the model at different time steps, thereby revealing whether the model has numerical errors or instability caused by time discretization. When the time step sensitivity index is large, it indicates that the simulation results of the model are highly sensitive to the change of the time step, which may lead to poor convergence and even unstable numerical solutions. Through the analysis of this index, these potential problems can be discovered in time, and we can be guided to optimize the selection of the time step to ensure that the model can converge stably and accurately reflect the dynamic behavior of the system at multiple time scales. In addition, time step sensitivity analysis also helps to balance computational efficiency and accuracy in simulation, avoiding excessively fine or coarse time steps that waste computational resources or cause accuracy loss. Therefore, this analysis method not only improves the reliability and accuracy of the model, but also provides a scientific basis for model optimization, which helps to ensure that the wind-wave-current coupling simulation model of the FPSO system based on Modelica can accurately and stably simulate the real marine environment.

[0023] In one embodiment, the fluid domain involved in the model is divided into different grids, the models are simulated respectively, key physical quantities of the response are recorded, and the grid independence index is calculated, including: Select coarse and fine grids in the fluid domain, respectively: Coarse grid size , fine mesh size ; Simulate the coarse grid and fine grid separately, and record the key physical quantities of the response, which are: Displacement response under coarse grid , displacement response under fine mesh ; and Represent the displacement values ​​under coarse grid and fine grid respectively, which are the specific values ​​of physical response; Calculate the relative error between the coarse and fine grids based on their displacement responses , reflecting the influence of grid division on the results: the calculation formula is: ; Calculate the grid independence index, the calculation formula is: ; In the formula, is the grid independence index, is the grid convergence index, which is usually obtained through experience or numerical convergence theory; It should be noted that mesh independence analysis is to compare the simulation results of the model under different mesh divisions, calculate the relative error, and combine the convergence index to evaluate the accuracy of the model and the influence of mesh division. Data acquisition mainly depends on numerical simulation after fine mesh division, and records the physical response under different meshes to calculate the error, so as to determine whether the mesh has achieved sufficient accuracy.

[0024] It should be noted that the grid independence index is an indicator used to evaluate the impact of grid division accuracy on simulation results. It reflects the difference in physical response between coarse grid and fine grid, and indicates the convergence of model solution as the grid is gradually refined. If the grid independence index is larger, it means that the error difference between coarse grid and fine grid is larger, indicating that as the grid is refined, the simulation results have not converged significantly, and there may be numerical errors or model design problems. In the wind-wave-current coupled simulation model of FPSO system based on Modelica, if the grid independence index is large, it may mean that the physical response of the system (such as wind and wave loads, fluid mechanics response, etc.) is very sensitive to grid division and fails to meet sufficient accuracy requirements, which usually implies that the numerical solution of the model may be unstable and the convergence of the simulation results is abnormal. Therefore, the larger the grid independence index, it may indicate that the grid division is not fine enough during numerical simulation, or there are other problems, such as improper solver settings, incomplete physical model, etc., which lead to the failure of the system to achieve good convergence. This requires further optimization of grid division or adjustment of simulation model to ensure the reliability and accuracy of the results.

[0025] In one implementation method, the benefits of analyzing the grid independence index for measuring whether the convergence degree of the wind-wave-current coupling simulation model of the FPSO system based on Modelica is abnormal are: The grid independence index (GCI) plays an important role in measuring the convergence degree of the wind-wave-current coupled simulation model of the FPSO system based on Modelica, especially in ensuring the accuracy and reliability of the simulation results. By calculating the GCI, the influence of mesh refinement on the physical response can be quantified, so as to judge whether the model solution tends to be stable and whether the expected accuracy requirements are met. If the GCI value is small, it means that with the refinement of the mesh, the change of the simulation results gradually decreases, indicating that the model has reached convergence, and the influence of mesh division on the results can be ignored. At this time, the reliability of the simulation results can be ensured. When the GCI value is large, it may indicate that the solution of the model has not yet fully converged, the accuracy of the mesh division is insufficient, or there are problems in the numerical method, which requires further adjustment and optimization. Therefore, GCI provides an effective evaluation criterion for the model, helping researchers to discover possible convergence anomalies or numerical instability, and guiding model optimization, thereby improving the accuracy of the simulation results and providing a more accurate basis for the analysis of wind-wave-current coupled effects.

[0026] In one embodiment, simulating the model and recording the dynamic response of the FPSO platform, and calculating the instability index of the simulation result of the model includes: Simulate the model and record the dynamic response displacement of the FPSO platform , calculate the mean of the response and standard deviation ; The calculation formula is: , , where For in time The displacement at is the number of time steps; Calculate the instability index of the simulation results. The calculation formula is: ; In the formula, is the instability index of simulation results.

[0027] It should be noted that the data involved in the above calculations are mainly obtained by simulating the FPSO system based on Modelica. During the simulation process, the system will perform time step calculations based on the input conditions of the wind-wave-current coupling model (such as wind speed, wave height, flow velocity, etc.), and record the dynamic response displacement data of the FPSO platform at each time step. Through these dynamic response data, the displacement value of each time step can be calculated, and the mean and standard deviation of the response can be further calculated; the acquisition of these data usually relies on high-precision numerical simulation tools, such as the numerical integration and solution process performed by Modelica, to ensure the accuracy of the response data and the effectiveness of the simulation results.

[0028] It should be noted that the instability index of the simulation results is an indicator used to measure the volatility and instability of the dynamic response in the wind-wave-current coupling simulation model of the FPSO system based on Modelica. It evaluates the dynamic characteristics of the system within a certain period of time by calculating the relationship between the mean and standard deviation of the platform response. If the instability index is large, it means that the displacement response of the platform has a large volatility, which may indicate that the model cannot converge stably in some cases, or that the model has a large sensitivity within a certain parameter range, resulting in increased uncertainty in the prediction results. This instability may be caused by factors such as the complexity of the internal coupling of the system, the drastic changes in external excitation, and the convergence problems of the numerical calculation method. In other words, the larger the instability index of the simulation results, it often means that the system has failed to reach a convergence state during the simulation process, and the model may need to be more finely adjusted or optimized to ensure its stability and the reliability of the prediction results.

[0029] In one implementation method, the benefits of analyzing the instability index of the simulation results for measuring whether the convergence degree of the wind-wave-current coupling simulation model of the FPSO system based on Modelica is abnormal are: Analyzing the instability index of the simulation results has important guiding significance for measuring whether the convergence degree of the wind-wave-current coupling simulation model of the FPSO system based on Modelica is abnormal; by monitoring the instability index, the numerical instability that may occur in the model during the simulation process can be identified in real time, helping engineers to determine whether there are convergence problems or accumulation of numerical errors. If the instability index is too high, it may indicate that the model cannot converge effectively under certain specific conditions, or that there is excessive oscillation in the dynamic response of the system, which is usually caused by improper selection of simulation parameters, insufficient calculation accuracy or unreasonable model assumptions. By analyzing the instability index, these potential problems can be discovered in time, so that the model can be adjusted and optimized as necessary to ensure the accuracy and stability of the simulation results. Therefore, the instability index is not only a quantitative indicator to measure the fluctuation of system response, but also an effective tool to determine whether the simulation model is reliable.

[0030] In one embodiment, the convergence anomaly coefficient calculated according to the time step sensitivity index, the grid independence index and the simulation result stability index includes: ; In the formula, is the convergence anomaly coefficient, , , They are the time step sensitivity index, grid independence index and simulation result stability index, are the preset proportional coefficients of the time step sensitivity index, grid independence index and simulation result stability index, respectively, and Both are greater than 0.

[0031] It should be noted that the time step sensitivity index, grid independence index and simulation result stability index need to be normalized before calculating the convergence anomaly coefficient. Commonly used normalization methods include Min-Max normalization and Z-Score normalization. Set it according to the actual situation. For example, adopt the expert empowerment method, that is, invite experts in relevant fields to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations; In one embodiment, judging whether the convergence of the model is qualified in combination with a preset convergence abnormality coefficient threshold includes: The convergence anomaly coefficient is compared with the preset convergence anomaly coefficient threshold. If the convergence anomaly coefficient is less than the preset convergence anomaly coefficient threshold, it means that the convergence of the model is qualified. If the convergence anomaly coefficient is not less than the preset convergence anomaly coefficient threshold, it means that the convergence of the model is unqualified and the model needs to be optimized until the convergence anomaly coefficient is less than the preset convergence anomaly coefficient threshold.

[0032] It should be noted that comparing the convergence anomaly coefficient with the preset convergence anomaly coefficient threshold is a key step to ensure the stability and accuracy of the simulation model. When the calculated convergence anomaly coefficient is less than the preset threshold, it means that the simulation results of the model show good convergence under the given time step and mesh division, and its error is within an acceptable range, which means that there is no obvious instability or excessive fluctuation in the simulation process, and the solution of the model tends to be accurate and stable, so the simulation result can be considered qualified. On the contrary, if the convergence anomaly coefficient is not less than the preset threshold, it means that there is a large error or instability in the simulation process of the model, which may be caused by inappropriate mesh division, numerical inaccuracy, improper boundary condition setting or unreasonable physical model assumptions. At this time, the model needs to be optimized by adjusting parameters, refining the mesh, and improving the accuracy of numerical solutions until the convergence anomaly coefficient meets the preset threshold requirements, ensuring that the model can converge stably and provide more reliable and accurate simulation results. Therefore, as an important indicator of model verification, the convergence anomaly coefficient plays a vital role in model optimization and improving simulation accuracy.

[0033] It should be noted that when optimizing the model, there are many ways to improve the accuracy and stability of the simulation results. For example: 1. Adjust model parameters: Adjusting model parameters is an important means to optimize simulation results. For the wind-wave-current coupled simulation model of the FPSO system based on Modelica, the following operations can be considered: Adjust physical parameters: such as fluid density, viscosity, wave frequency and other parameters, which will directly affect the dynamic response and stability of the model. Determine the reasonable range of physical parameters through experiments or theoretical analysis, and make appropriate adjustments to avoid the model from calculating in the unstable area. Optimize numerical parameters: such as time step, maximum number of iterations, etc. Too large a time step may lead to the accumulation of numerical errors, while too small a time step may increase the computational burden and cause numerical instability. By gradually adjusting the time step and finding the most suitable numerical solution, the convergence of the model can be improved.

[0034] 2. Refine the grid division: The grid division has a significant impact on the accuracy and stability of the simulation results. For the wind-wave-current coupled simulation of the FPSO system, the refinement of the grid can improve the calculation accuracy and convergence; Locally refine the grid: For areas with complex flows in fluid dynamics simulations, such as places where the flow velocity changes drastically or where turbulence effects exist, the calculation accuracy can be improved by locally refining the grid. For such areas, a higher resolution grid can be used to capture more detailed physical phenomena and reduce calculation errors.

[0035] Global mesh refinement: Appropriately reducing the mesh size throughout the model, especially in areas where the calculation results are sensitive, can effectively reduce numerical errors. However, over-refining the mesh may significantly increase the amount of calculation, so it is necessary to balance accuracy and computational efficiency according to the specific problem.

[0036] 3. Improve the accuracy of numerical solutions: During the simulation process, the accuracy of the numerical solution directly affects the convergence speed and stability of the model. The accuracy of the numerical solution can be improved by the following means: Choose the appropriate solver: Different numerical solvers are suitable for different types of equations. For most physical models, choosing a solver suitable for nonlinear dynamic systems, such as an implicit solver, can improve the stability and convergence of the model. Through experimentation and experience, the most suitable numerical solution method can be selected.

[0037] Optimize the solution algorithm: During the model optimization process, you may need to adjust the convergence conditions of the solution algorithm or increase the number of iterations to ensure that the solver can work stably within the convergence error range. For example, increase the number of iterations, adjust the convergence criteria of the solver, or apply methods to accelerate convergence.

[0038] Add precision control mechanism: During the simulation process, an error control mechanism can be added to monitor various errors (such as relative error, absolute error) during the simulation process and adjust the solution accuracy in real time. When the error reaches the set threshold, the adaptive adjustment strategy can be triggered to improve the numerical stability and accuracy of the model.

[0039] 4. Check physical model assumptions: Simulation models are often simplified based on some assumptions, such as ignoring certain physical phenomena or simplifying the fluid dynamics model. In order to improve the convergence of the model, it is sometimes necessary to re-examine these assumptions: Confirm the applicability of the model: Make sure that the physical assumptions in the model are applicable to the current engineering problem, for example, whether certain higher-order effects can be ignored or whether more fluid mechanics details need to be introduced.

[0040] Verify the rationality of boundary conditions: During the modeling process, ensure that the boundary conditions (such as inflow and outflow conditions, fixed or free boundary conditions) are reasonable and consistent with the actual engineering situation. Incorrect or unreasonable boundary conditions will lead to unstable simulation results, thus affecting the convergence of the model.

[0041] 5. Perform sensitivity analysis: Sensitivity analysis of the model is an effective means to discover and solve convergence problems. By systematically changing the model input parameters and observing the changes in the output response, we can identify which parameters have a greater impact on the stability of the model. Focus on the key parameters that affect the stability and accuracy of the system and optimize them in detail.

[0042] By adjusting model parameters, refining grid division, improving numerical solution accuracy, verifying physical model assumptions and conducting sensitivity analysis, the wind-wave-current coupled simulation model of the FPSO system based on Modelica can be effectively optimized to improve its convergence and stability. These measures complement each other and can not only ensure the accuracy of the simulation results, but also reduce the waste of computing resources, and ultimately improve the reliability and practicality of the model in engineering applications.

[0043] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be used to artificially limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A wind-wave-current coupled environment modeling method for FPSO system based on Modelica, characterized in that: The following steps are involved: Set multiple different time steps, run the simulation model for each time step, record the response of key physical quantities, and calculate the time step sensitivity index; Divide the fluid domain involved in the model into different grids, simulate the model separately, record the key physical quantities of the response, and calculate the grid independence index; Simulate the model and record the dynamic response of the FPSO platform, and calculate the instability index of the simulation results of the model; The convergence anomaly coefficient is calculated based on the time step sensitivity index, grid independence index and simulation result stability index, and the convergence of the model is judged to be qualified in combination with the preset convergence anomaly coefficient threshold.

2. The wind-wave-current coupled environment modeling method of the FPSO system based on Modelica according to claim 1 is characterized in that: Calculating the time step sensitivity index involves: Set multiple different time step values, namely: coarse time step ; Medium time step ; Fine time step ; For each time step, run the simulation and record the response of key physical quantities; the recorded physical quantities are: Physical quantity response under coarse time step ; Physical quantity response at medium time step ; Physical quantity response under fine time step ; Calculate the error at each time step, for example by calculating the relative error in the platform displacement or acceleration: ; ; ; In the formula, , , They are the error under coarse time step, the error under medium time step, and the error under fine time step respectively; is the preset reference solution; Calculate the error ratio of different time steps and combine the time step differences to get the time step sensitivity index: , where is the time step sensitivity index.

3. The wind-wave-current coupled environment modeling method of the FPSO system based on Modelica according to claim 1 is characterized in that: Computational grid independence indices include: Select coarse and fine grids in the fluid domain, respectively: Coarse grid size , fine mesh size ; Simulate the coarse grid and fine grid separately, and record the key physical quantities of the response. The key physical quantities represent the displacement values ​​under the coarse grid and the fine grid, respectively: Displacement response under the coarse grid , displacement response under fine mesh ; and ; Calculate the relative error between the coarse and fine grids based on their displacement responses , reflecting the influence of grid division on the results: the calculation formula is: ; Calculate the grid independence index, the calculation formula is: ; In the formula, is the grid independence index, is the mesh convergence index.

4. The wind-wave-current coupled environment modeling method of the FPSO system based on Modelica according to claim 1 is characterized in that: The simulation result instability index of the calculation model includes: Simulate the model and record the dynamic response displacement of the FPSO platform , calculate the mean of the response and standard deviation ; The calculation formula is: , , where For in time The displacement at is the number of time steps; Calculate the instability index of the simulation results. The calculation formula is: ; In the formula, is the instability index of simulation results.

5. The wind-wave-current coupled environment modeling method of the FPSO system based on Modelica according to claim 1, characterized in that: The calculated convergence anomaly coefficients include: ； In the formula, is the convergence anomaly coefficient, , , They are the time step sensitivity index, grid independence index and simulation result stability index, are the preset proportional coefficients of the time step sensitivity index, grid independence index and simulation result stability index, respectively, and Both are greater than 0.

6. The wind-wave-current coupled environment modeling method of the FPSO system based on Modelica according to claim 1, characterized in that: And combined with the preset convergence abnormal coefficient threshold, the convergence of the model is judged to be qualified, including: The convergence anomaly coefficient is compared with the preset convergence anomaly coefficient threshold. If the convergence anomaly coefficient is less than the preset convergence anomaly coefficient threshold, it means that the convergence of the model is qualified. If the convergence anomaly coefficient is not less than the preset convergence anomaly coefficient threshold, it means that the convergence of the model is unqualified and the model needs to be optimized until the convergence anomaly coefficient is less than the preset convergence anomaly coefficient threshold.

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