Coastal typhoon wave refined simulation method based on terrain adaptive grid and parameter dynamic correction

Abstract

The invention relates to the technical field of marine environment numerical simulation, in particular to a near-shore typhoon wave refined simulation method based on terrain adaptive grids and parameter dynamic correction, and the method comprises the steps: obtaining basic data and simulation parameters of a target sea area, constructing a computational domain and an initial grid, and constructing a target sea area; the initial grid is an unstructured grid fused with multiple physical constraint factors; performing optimization processing on the initial grid based on a numerical stability criterion to obtain a final calculation grid meeting a calculation convergence requirement; establishing a bidirectional coupling model, and applying the final computational grid to the bidirectional coupling model; carrying out localization iteration calibration on key physical parameters of the bidirectional coupling model to obtain an optimal parameter set; and carrying out typhoon wave process simulation calculation and constructing a typhoon wave feature database by utilizing the final calculation grid and the optimal parameter set, and realizing high-precision simulation and prediction of the typhoon wave process by constructing an adaptive optimization grid, a bidirectional coupling model and a parameter localization calibration mechanism.

Description

Technical Field

[0001] This invention belongs to the field of marine environmental numerical simulation technology, specifically relating to a refined simulation method for nearshore typhoon waves based on terrain-adaptive grids and dynamic parameter correction. Background Technology

[0002] With the deepening of the national strategy of building a maritime power, the scale of marine infrastructure construction, such as offshore wind power and cross-sea channels, continues to expand. The structural safety and life-cycle reliability of these facilities are highly dependent on accurate marine environmental design parameters. The hydrodynamic conditions in the southeastern coastal areas of my country and the Taiwan Strait are extremely complex. Affected by the Kuroshio Current, strong tides, monsoons, and frequent typhoons, strong wave-tide-current nonlinear coupling effects are formed, posing a huge challenge to typhoon wave simulation.

[0003] Existing numerical simulation techniques for typhoon waves suffer from two major shortcomings: First, the model coupling mechanism is imperfect. Most studies employ unidirectional or independent simulation frameworks driven by storm surge, wave, and tidal current models, neglecting the bidirectional feedback mechanism between waves, tides, and currents. This includes factors such as the modulation of storm surge levels by wave radiation stress and the influence of tidal currents on wave propagation. This leads to systematic biases in extreme load simulations, posing safety hazards or cost redundancy in engineering design. Second, the adaptability and stability of computational grids are insufficient. Traditional rectangular structured grids struggle to fit meandering coastlines and complex terrain, resulting in low simulation resolution for critical nearshore engineering areas and an inability to accurately characterize key physical processes such as wave deformation, refraction, and diffraction in shallow water. While there have been attempts at unstructured grid models, these are mostly limited to single-typhoon case inversions and lack adaptive optimization based on numerical stability. This makes it difficult to guarantee computational stability under complex terrain and large time steps, limiting their widespread application. Furthermore, existing methods lack localized dynamic correction mechanisms for physical parameters specific to particular sea areas. General parameters are not adaptable to different sea areas, resulting in insufficient simulation accuracy and failing to meet the high-precision design parameter requirements of marine engineering.

[0004] Therefore, there is an urgent need to develop a refined typhoon wave simulation method that integrates adaptive mesh, two-way coupled model and dynamic parameter correction mechanism to fundamentally solve the above-mentioned technical defects. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a refined simulation method for nearshore typhoon waves based on terrain-adaptive grids and dynamic parameter correction. By constructing an adaptive optimization grid, establishing a two-way coupled model, and implementing a parameter localization calibration mechanism, high-precision simulation and prediction of typhoon wave processes can be achieved.

[0006] The technical solution of the present invention is as follows: A refined simulation method for nearshore typhoon waves based on terrain-adaptive grids and dynamic parameter correction is proposed. The method is as follows: Step 1: Obtain basic data and simulation parameters for the target sea area; Step 2: Construct a computational domain and initial grid adapted to the topography and typhoon impact characteristics of the target sea area. The initial grid is an unstructured grid that incorporates multiple physical constraint factors. Step 3: Optimize the initial grid based on the numerical stability criterion to obtain the final computational grid that meets the computational convergence requirements; Step 4: Establish a two-way coupled model that includes wind field, storm surge, and ocean waves, and apply the final computational grid to the two-way coupled model; Step 5: Construct a wave-current coupled dynamic bottom friction mechanism, establish an error feedback closed loop using measured data from the target sea area, perform spatiotemporal local inversion of typhoon wind field parameters and seabed physical property parameters, and generate the optimal parameter set; Step 6: Using the final computational grid and optimal parameter set, conduct typhoon wave process simulation calculations. During the simulation, the typhoon wind field parameters and seabed physical property parameters are corrected in real time, and a typhoon wave characteristic database is constructed.

[0007] Furthermore, in step 1, the basic data includes topographic data, historical typhoon data, and measured marine environmental data, and the simulation parameters include the time integration step size; The topographic data was obtained by combining SRTM15 arcsecond water depth grid data with actual measurement data from coastal waters. Historical typhoon data were obtained using the CMA tropical cyclone best track dataset.

[0008] Furthermore, the specific method for constructing the computational domain in step 2 is as follows: a multi-level nested elliptical sector boundary structure is adopted, which includes at least an outer elliptical boundary and an inner elliptical boundary; the outer elliptical boundary covers the far-field influence range of the typhoon, and its semi-major axis direction is consistent with the historical dominant path of typhoons in the target sea area; the inner elliptical boundary focuses on the core nearshore area of ​​concern, and its center is located as the center of the key geographic unit of the target sea area; by adjusting the ellipse eccentricity, rotation angle and sector azimuth, invalid land areas and non-concerned deep-sea areas are eliminated.

[0009] Furthermore, the specific process of generating the initial mesh in step 2 is as follows: constructing a mesh scale control function H(x,y), which is composed of a shallow water wavelength constraint factor, a terrain slope response factor, and a mesh gradient smoothing factor. Among them, the shallow water wavelength constraint factor ensures that a preset number of mesh nodes are contained within a unit wavelength; the terrain slope response factor automatically densifies the mesh when the rate of change of the seabed terrain slope exceeds a preset threshold; and the mesh gradient smoothing factor controls the transition growth rate from the inner high-resolution mesh to the outer low-resolution mesh.

[0010] Furthermore, the mesh optimization process in step 3 specifically includes: Calculate the full-field Coulomb number (CFL) of the initial grid at a preset time integration step; Identify high-risk grid cells with a Courrand number greater than the stability threshold; Based on the stability threshold, the minimum characteristic scale of high-risk grid cells is derived in reverse. Local topology reconstruction or relaxation smoothing is then performed on the grid in this region until the Kronen number of the entire grid meets the stability requirements.

[0011] Furthermore, the grid optimization process in step 3 also includes terrain gradient correction: the slope of the water depth data after grid interpolation is detected, and when the terrain slope between adjacent nodes exceeds the critical value, the water depth data is smoothed and corrected.

[0012] Furthermore, the construction method of the coupled numerical model system in step 4 is as follows: the Holland typhoon model is used to generate the typhoon wind field, the ADCIRC model is used to calculate the storm surge water level and flow field, and the SWAN model is used to calculate the ocean wave field; the ADCIRC model and the SWAN model share the final calculation grid and exchange water level, flow velocity and wave radiation stress data in real time to realize the two-way coupling of storm surge, ocean wave and tidal current.

[0013] Furthermore, the spatiotemporal localization inversion and dynamic correction of typhoon wind field parameters and seabed physical property parameters described in steps 5 and 6 specifically includes: (1) Constructing a dynamic bottom friction function: Introducing the wave-current interaction (WCI) theory, a time-varying function of the bottom friction coefficient as the flow field state is constructed: ;

[0014] in For time-varying bottom friction, The density of seawater, Determined by the nonlinearity of the wave geometry parameters, The velocity of the wave's bottom trajectory. Background flow rate; (2) Determine the observation vector and control variables: Select the effective wave height, spectral peak period and measured tide level during the typhoon as the observation vector, and select the median grain size of seabed sediments and the shape coefficient of wind field pressure as the control variables to be inverted; (3) Error Feedback and Parameter Optimization: Within each time step or preset time window of the numerical simulation, a cost function is constructed. The residual between the simulated and measured values ​​is calculated using the adjoint assimilation algorithm or gradient descent optimization algorithm. The control variables are dynamically adjusted to obtain the optimal seafloor sediment grain size distribution field and instantaneous pressure shape coefficient. The cost function is: ;

[0015] in, For analog vectors, For the observation vector, Control parameter vector for wind field pressure shape coefficient These are the weighting coefficients. Let be the cost function.

[0016] (4) Dynamic correction: Based on the updated control variables, the bottom friction coefficient field and wind driving force of the entire field grid nodes are reconstructed in real time to realize the dynamic correction of physical parameters as the typhoon develops.

[0017] Furthermore, in step 6, the simulation calculation spans a period of no less than 50 years, and a long-term sea state dataset is generated by continuous hourly simulation. The typhoon wave characteristic database contains spatiotemporal distribution data of wind field, wave field, tidal field and tide level with different return periods. The data is obtained through analysis using a generalized extreme value distribution or Poisson distribution statistical model.

[0018] Furthermore, in step 4, the wind field is driven by a combined wind field. The combined wind field is constructed by fusing the Holland typhoon model wind field and the ERA5 wind field using a dynamic weighting formula. The weighting formula is as follows: ;

[0019] in, For combined wind fields; The wind field calculated using the Holland typhoon model; Reanalyze the wind field for ERA5; These are the weighting coefficients.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The adaptability and stability of the mesh are significantly improved: invalid regions are eliminated by multi-level nested elliptical computational domains, and an adaptive unstructured mesh is generated by combining multi-physics factor coupling algorithms to accurately fit complex shorelines and terrains; the inverse optimization mechanism based on CFL conditions and terrain gradient correction solve the problem of easy divergence of traditional meshes from the bottom up, ensuring computational stability under large time steps.

[0021] The simulation accuracy has been greatly improved: a two-way coupled model of storm surge-wave-tidal current has been established to fully characterize the nonlinear feedback mechanism of wave-tidal current; an innovative hybrid wind field driving mode has been developed to make up for the limitations of a single wind field model; and by iteratively calibrating localized parameters, the problem of poor universality of general parameters and inability to adapt to different sea areas has been overcome, thus achieving high-precision simulation of typhoon wave processes.

[0022] Highly practical for engineering applications: It generates a long-term, continuous, and highly accurate spatiotemporal database of typhoon wave characteristics, containing extreme environmental parameters with different return periods. This database can directly serve the design and safety assessment of marine infrastructure such as offshore wind power and cross-sea channels, while also providing a scientific basis for disaster prevention, mitigation, and forecasting. It has significant engineering application value and socio-economic benefits. Attached Figure Description

[0023] Figure 1 To construct dynamic mesh parameter correction and mesh stability; Figure 2 A terrain-adaptive grid for a strait; Figure 3 Typhoon wind field model and reanalysis wind field; Figure 4 For collecting observational data; Figure 5 Calibrate key parameters for wind input; Figure 6 This is the result of post-processing the dataset; Figure 7 This is a flowchart of the method of the present invention. Detailed Implementation

[0024] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0025] See Figure 7 A refined simulation method for nearshore typhoon waves based on terrain-adaptive grids and dynamic parameter correction, the method comprising the following steps: Step 1: Obtain basic data and simulation parameters for the target sea area; Step 2: Construct a computational domain and initial grid adapted to the topography and typhoon impact characteristics of the target sea area. The initial grid is an unstructured grid that incorporates multiple physical constraint factors. Step 3: Optimize the initial grid based on the numerical stability criterion to obtain the final computational grid that meets the computational convergence requirements; Step 4: Establish a two-way coupled model that includes wind field, storm surge, and ocean waves, and apply the final computational grid to the two-way coupled model; Step 5: Construct an error feedback mechanism based on measured data, perform localized iterative calibration of key physical parameters of the bidirectional coupling model, and obtain the optimal parameter set; Step 6: Using the final computational grid and optimal parameter set, conduct typhoon wave process simulation calculations and construct a typhoon wave characteristic database.

[0026] The basic data includes topographic data, historical typhoon data, and measured marine environmental data, and the simulation parameters include the time integration step. Topographic data: We acquired 15 arcsecond resolution water depth grid data from the Space Shuttle Radar Topography (SRTM) mission. This data, processed and revised over many years, employed interpolation algorithms to fill gaps in the original data, achieving a horizontal accuracy of ±20 meters and an elevation error of less than 16 meters (with a 90% confidence level). It is widely used in topographic analysis, disaster prediction, and GIS fields. Simultaneously, we collected 50m resolution field survey data from coastal waters to correct the SRTM data, ensuring the accuracy of the topographic data. Typhoon data: The CMA tropical cyclone optimal track dataset from the China Meteorological Administration's Tropical Cyclone Data Center is used. Since 2017, for typhoons making landfall, the optimal track time frequency has been increased to once every 3 hours in the 24 hours before landfall; since 2018, this frequency has been extended to the 24 hours before landfall and during the typhoon's activity over land, which can accurately extract key information such as typhoon track, central pressure, and maximum wind speed affecting the target sea area. Measured data: Long-term observation data from ocean buoys and tide gauge stations in the target sea area were selected, including key indicators such as significant wave height, wave period, wave direction, and tide level, as the basis for model validation and parameter calibration; Simulation parameters: Set the time integration step size for numerical simulation, clarify the simulation time span, and provide basic parameters for subsequent grid stability optimization and model operation.

[0027] In one embodiment of the present invention, the computational domain and initial grid are constructed as follows: The computational domain overcomes the limitations of traditional rectangular computational domains, constructing computational boundaries and an initial grid adapted to the characteristics of the target sea area, specifically including: Computational domain boundary construction: The core technology involves constructing a double-layered elliptical sector boundary, comprising at least an outer and an inner layer. The outer elliptical boundary covers the far-field influence domain of the typhoon, with its semi-major axis aligned with the historical dominant typhoon path direction. The inner elliptical boundary covers the near-shore core area of ​​interest, with its center located at the center of the target strait or island / reef area. By setting the eccentricity, rotation angle, and sector azimuth of the ellipse, invalid land and non-interesting deep-sea areas are effectively eliminated, significantly improving computational efficiency. In this invention, the initial mesh generation is based on a multi-physics factor coupling algorithm to generate an unstructured initial terrain mesh. A mesh scale control function H(x,y) is constructed, which is determined by a weighted average of the shallow water wavelength constraint factor, the terrain slope response factor, and the mesh gradient smoothing factor. Shallow water wavelength constraint factor: used to ensure that a preset number of grid nodes are included within a unit wavelength, ensuring accurate depiction of the wave propagation process; Terrain slope response factor: used to automatically refine the grid in areas where the rate of change of seabed topographic slope is higher than a preset threshold, such as a resolution of 500 meters to 1 kilometer, to accurately capture complex nearshore topographic features. The grid gradient smoothing factor is used to control the growth rate of the transition from the inner high-resolution grid to the outer low-resolution grid, avoiding computational instability caused by abrupt changes in grid scale.

[0028] See Figure 1 Mesh optimization addresses potential stability issues with the initial mesh by performing inverse optimization based on numerical criteria to ensure the model does not diverge over long-term operation. Specifically, this includes: Courant number (CFL) calculation and optimization: Calculate the Courant number (CFL) of all grid cells at a preset time integration step. Identify local high-risk grid cells where the Courant number exceeds the stability threshold; Based on the stability threshold, the minimum feature scale required for high-risk grid cells is calculated in reverse, and the grid in that region is locally reconstructed or relaxed and smoothed until the entire grid meets the stability requirements. Terrain gradient correction: Slope detection is performed on the water depth data after grid interpolation. When the terrain slope between adjacent nodes exceeds the critical value (0.08), the water depth data is smoothed to prevent false accumulation of wave energy and divergence in numerical calculation. The final result is an optimized topological mesh that satisfies numerical stability.

[0029] In one embodiment of the present invention, the construction of the two-way coupling model includes establishing a two-way coupling system of wind field, storm surge, and ocean waves, which is key to simulating the core physical processes of typhoon waves, specifically including: An ADCIRC storm surge model is established to simulate tidal levels and flow fields. Using standard quadratic parameterization of the base stress, a simplified eddy viscosity model is used to approximate the combination of turbulent diffusion and momentum dispersion effects. Ignoring baroclinic terms, a set of primitive, non-conservative conservation expressions in Cartesian coordinates is obtained. ; ; ;

[0030] in, : Free surface elevation relative to the geoid; , : Average speed at depth; Total water level; : The water depth relative to the geoid; Coriolis parameters This is the Earth's rotational angular velocity. Latitude; Atmospheric pressure on a free surface; : Gravitational acceleration; Newton set the tides; The effective elasticity factor of the Earth is, notably, typically taken as 0.690 for all tidal components. However, its value has been shown to be component-dependent. Seawater density; , External free surface stress, including wind stress and wave-induced radiation stress; : Bottom friction coefficient.

[0031] The ADCIRC storm surge model employs the generalized wave continuity equation (GWCE). GWCE combines the time differential form of the original continuity equation with the space differential form of the original momentum conservation equation, reformulating the convection term into a non-conservative form by multiplying the original form of the continuity equation by time and space constants. And rearrange the eddy viscosity terms. The GWCE equation in Cartesian coordinates is expressed as: ;

[0032] By combining the original form of the momentum equation, we can solve the original continuity equation and substitute it into the momentum equation to obtain the generalized wave continuity equation GWCE.

[0033] A SWAN wave model was established to simulate ocean wave fields. It is an extension of the third-generation deep-water wave model based on the equilibrium equations of active and passive wave action, integrating the deep-water process formulas of the WAM model and supplementing the dissipation formulas for bottom friction, third-order wave-wave interaction, and depth-induced breakup. A two-dimensional action density spectrum was employed. (Instead of the energy density spectrum) describes waves (density conservation under flow field conditions), which is different from the energy density spectrum. The relationship is: ;

[0034] In the formula, the interaction density spectrum Equal to energy density spectrum Divide by relative frequency ,in The direction of propagation is given. Therefore, the conservation of interaction density can be written as: ;

[0035] In the Cartesian coordinate system: ;

[0036] in and This represents the propagation of wave energy in two-dimensional geographic space xy; and This indicates the effect of frequency shift and refraction on wave energy caused by changes in water depth and average current. Non-conservative source and sink terms represent all physical processes that generate, dissipate, or redistribute wave energy.

[0037] Establishing the Holland typhoon model and mixed wind field: The Holland model is a circular symmetrical typhoon model used to generate the typhoon core wind field. Its pressure and wind speed calculation formulas are as follows: ; ; ; ; ; in, The pressure is located at a distance r from the center of the typhoon. The central pressure of the typhoon; The ambient air pressure (one standard atmosphere); These are the scale parameters for the typhoon model. The radius of maximum wind speed; Coriolis parameters; Let r be the latitude and longitude components of the wind speed at radius r; The angle representing the direction of movement of the typhoon center; , This represents the velocity component of the typhoon center's movement.

[0038] The Holland typhoon model performs well in simulating areas near the typhoon center, but its performance is relatively poor in areas far from the center. To improve its overall accuracy, this invention uses the ERA5 wind field as the background wind field and performs a weighted calculation with the typhoon model wind field to construct a combined wind field. The calculation formula is as follows: ; in, For combined wind fields; The wind field calculated using the Holland typhoon model; Reanalyze the wind field for ERA5. As weighting coefficients, in this invention use To calculate, , This refers to the distance from the center of the typhoon. .

[0039] Two-way coupling setting: The ADCIRC model and the SWAN model share the final computational grid and exchange water level, flow velocity and wave radiation stress data in real time to realize two-way feedback of storm surge-wave-tidal current and fully characterize the nonlinear coupling effect of wave-tidal current.

[0040] In step 5 of this invention, the spatiotemporal localization inversion and dynamic correction of typhoon wind field parameters and seabed physical property parameters described in steps 5 and 6 specifically includes: (1) Constructing a dynamic bottom friction function: Introducing the wave-current interaction (WCI) theory, a time-varying function of the bottom friction coefficient as the flow field state is constructed: ; in For time-varying bottom friction, The density of seawater, Determined by the nonlinearity of the wave geometry parameters, The velocity of the wave's bottom trajectory. Background flow rate; (2) Determine the observation vector and control variables: Abandoning the traditional manual trial and error parameter tuning, this invention establishes an error feedback closed-loop inversion mechanism, selects the effective wave height, spectral peak period and tide level measured values ​​of representative buoy stations during typhoons as the observation vector, and selects the median grain size of seabed sediments and the shape coefficient of wind field pressure as the control variables to be inverted; (3) Error feedback and parameter optimization: Based on the observation vector, construct a control parameter vector containing the wind field pressure shape coefficient, and define the cost function between the simulated value and the observed value; Within each time step or preset time window of the numerical simulation, a cost function is constructed. The residuals between the simulated and measured values ​​are calculated using an adjoint assimilation algorithm or gradient descent optimization algorithm. The control variables are then dynamically adjusted to obtain the optimal seafloor sediment grain size distribution field and instantaneous pressure shape coefficient. The cost function is: ; in, For analog vectors, For the observation vector, This is a vector of control parameters for the shape coefficient of the wind field pressure. These are the weighting coefficients. The cost function; (4) Dynamic correction: Based on the updated control variables, the bottom friction coefficient field and wind driving force of the entire field grid nodes are reconstructed in real time to realize the dynamic correction of physical parameters as the typhoon develops.

[0041] Based on this, regional source function sensitivity control is implemented to address the multi-scale characteristics of the sea area: In nearshore shallow waters, where physical processes are controlled by the seabed, the focus is on activating and calibrating the bottom friction dissipation term, the third-order wave-wave nonlinear interaction, and the depth-induced breaking term to accurately simulate the nonlinear deformation and energy attenuation of waves in shallow waters; while in offshore deep waters, the emphasis is on refining the wind input source function, the white crown dissipation term, and the fourth-order wave-wave nonlinear interaction to ensure the energy balance of wave growth in deep waters.

[0042] In addition, the accuracy of the parameter scheme is evaluated using coefficients such as correlation coefficient (CC), root mean square error (RMSD), relative deviation (RB), mean error (MAE), and maximum absolute error (AE) in the embodiments. The calculation formula is as follows: ; ; ; ; in, and These are measured data and model simulation results, respectively. and They are respectively and The average value; and They are respectively and The standard deviation.

[0043] Iterative optimization: Adjust parameters using gradient descent or least squares method. If the error at the representative point exceeds a preset threshold (e.g., RMSD>0.3m), systematically modify the parameters and rerun the model until the error converges to the allowable range (e.g., RMSD≤0.3m, CC≥0.85), and output a localized optimal parameter set suitable for the target sea area.

[0044] In step 6 of this invention, a long-term high-precision dataset is generated and transformed into an engineering-usable result, specifically including: Long-term simulation: Based on the optimized grid and the optimal parameter set, the bidirectional coupled model is driven to perform hourly continuous simulation (e.g., 50 years) to generate a long-term sea state dataset covering the target sea area; Independent verification: Collect various independent observation data such as satellite altimeter wave data and sea level height data, and conduct extensive comparison and verification of the simulated wave elements, tidal currents and tide levels. If the deviation exceeds the preset tolerance, return to steps 2-5 to readjust the grid and parameters to ensure the reliability of the model. Post-processing of results: Using extreme value analysis methods (such as generalized extreme value distribution or Poisson distribution statistical models), wind, wave, current and tide level parameters with different return periods of 1-100 years are extracted from long-term datasets. Database construction: Generate a spatiotemporal distribution atlas, which displays in detail the spatial distribution and temporal evolution characteristics of wind field, wave field, tidal field and tide level in grid form (such as wave height distribution under different seasons and extreme events), and build a typhoon wave characteristic database to provide data support for engineering design, disaster prevention and mitigation, etc.

[0045] The present invention will be further described below with reference to a specific embodiment: A refined simulation method for nearshore typhoon waves based on terrain-adaptive grids and dynamic parameter correction is implemented according to the following steps: This embodiment simulates a target sea area (111.8°E-130°E, 20.5°N-30.5°N) to verify the effectiveness and practicality of the method.

[0046] 1. Basic Data Preparation Topographic data: SRTM15 arcsecond water depth grid data were acquired, and interpolation algorithms were used to fill data gaps. The data was then corrected by combining it with 50m resolution survey data along the coast of the target sea area to ensure a horizontal accuracy of ±20 meters and an elevation error of less than 16 meters. Typhoon data: Obtain the best track dataset of CMA tropical cyclones from 1975 to 2024, and extract information such as typhoon track, central pressure, and maximum wind speed affecting the target sea area; Measured data: Observational data from 6 marine buoys and surrounding tide gauge stations in the target sea area were selected, including indicators such as significant wave height, spectral peak period, and tide level; Simulation parameters: The time integration step is set to 5 seconds, and the simulation time span is 50 years (1975-2024).

[0047] 2. Computational Domain and Initial Mesh Construction Computational domain boundary: A double-layered elliptical sector boundary is constructed. The outer ellipse has a major semi-axis of 1300 km, and its major axis direction is consistent with the dominant typhoon path in the Northwest Pacific (azimuth 120° to 300°). The inner ellipse has a major semi-axis of 500 km, and its center coordinates are used to locate the target sea area (119°E, 25°N). The computational domain covers an area of ​​111.8°E-130°E and 20.5°N-30.5°N. See the final shape for reference. Figure 2 .

[0048] Initial mesh generation: Construct a mesh scale control function H(x,y), set the shallow water wavelength constraint factor to include 8 mesh nodes per unit wavelength, set the terrain slope response factor to a slope threshold of 0.05, set the mesh gradient smoothing factor to a transition growth rate of 1.2, and generate an unstructured triangular initial mesh.

[0049] 3. Mesh optimization processing Courant number optimization: Calculate the global Courant number (CFL) of the initial grid at a time integration step of 5s, identify local high-risk grid cells whose Courant number exceeds the stability threshold (CFL>0.25), and reverse-engineer the minimum feature scale required for the high-risk cells based on the stability threshold. Perform local topology reconstruction or relaxation smoothing on the grid in this region until the global grid meets the stability requirements.

[0050] Terrain gradient correction: Slope detection is performed on the water depth data after grid interpolation. When the terrain slope between adjacent nodes exceeds the critical value of 0.08, the water depth data is smoothed to prevent false wave energy accumulation and numerical calculation divergence.

[0051] Final computational mesh shape: The final computational mesh generated after optimization contains 23,124 nodes and 42,504 triangular elements, forming an optimized topology that satisfies numerical stability.

[0052] 4. Construction of the bidirectional coupling model Hybrid wind field generation: The core wind field of the typhoon is generated based on the Holland typhoon model and combined with the ERA5 reanalysis wind field. The wind field is then fused through a dynamic weighting formula. The Holland typhoon model dominates near the eyewall of the typhoon, while the weight of the ERA5 wind field is increased in the far field. Two-way coupling settings: The ADCIRC model and the SWAN model share the final computational grid and exchange water level, flow velocity and wave radiation stress data in real time. The coupling time step is set to 60s to ensure that the data is updated synchronously.

[0053] like Figure 3 As shown: Optimal typhoon path data was obtained from the China Meteorological Administration, and the core vortex wind field V was generated using the Holland typhoon model. tc ; Obtain contemporaneous ERA5 reanalysis data to generate large-scale background wind field V era5 The fusion is performed using the distance-weighted formula designed in this invention. Figure 3 The study demonstrates the dynamic changes in wind speed with distance from the typhoon center: near the eyewall of the typhoon, the Holland model dominates to accurately characterize extreme wind speeds; in regions far from the typhoon center, the weight of the ERA5 background field gradually increases, effectively correcting the bias of the Holland model in the far field and ensuring that the mixed wind field conforms to the actual structure of the typhoon while maintaining coordination with the large-scale circulation field.

[0054] 5. Parameter localization rate setting Measured data from the typical typhoon event "Doksuri" were selected as the validation dataset. Parameter space settings: such as Figure 4 As shown in the observation data collection site, measured wave (significant wave height, period) and tide data from six ocean buoys and their surrounding areas in the target sea area were selected as the target set. The parameter space was used to lock key physical parameters sensitive to typhoon waves, including the dissipation coefficient, bottom friction coefficient, and wind field pressure shape coefficient in the Janssen scheme of the wind input term.

[0055] Iterative optimization: Run the initial model, extract and Figure 4 The simulated data corresponding to the shown sites are used to calculate evaluation indicators such as the root mean square error (RMSD) between the simulated and measured values. Judgment and adjustment: (e.g.) Figure 5 As shown, the algorithm automatically adjusts parameters based on the direction of the error gradient. For example, if the nearshore wave height is too high, the bottom friction coefficient is increased; if the offshore wave height is too low, the wind input term is corrected. After multiple rounds of iterative calculations, until all evaluation indicators converge to the preset standard, a localized optimal parameter set suitable for the target sea area is output.

[0056] 6. Simulation calculation and database construction Long-sequence simulation: Drive the bidirectional coupled model to perform hourly continuous simulations from 1975 to 2024, generating wind field, wave field, tidal field and tidal level data; Independent Validation: Wide-area validation was conducted using satellite altimeter wave data. Multi-source satellite altimeter observation data along the orbit were acquired during the simulation period. After quality control and near-shore outlier removal of the raw data, a spatiotemporal matching mechanism between satellite observation points and numerical model grid points was established. By comparing and analyzing the simulated wave height and satellite observed wave height at the matching points, a wide-area verification of the wave simulation results in open sea areas was achieved, effectively supplementing the limitations of insufficient spatial coverage of conventional fixed stations. Database construction: By using generalized extreme value distribution analysis of different return period parameters, a spatiotemporal distribution atlas of wind, waves, currents and tides with a return period of 1 to 100 years is generated, and a "high-precision feature database of typhoon waves in the target sea area" is constructed.

[0057] This embodiment, through the above steps, achieves a refined simulation of typhoon waves in a certain strait. The simulation results can be directly applied to engineering practices such as offshore wind power foundation design, submarine cable route planning, and typhoon disaster prevention in this region, verifying the effectiveness and practicality of the invention. Figure 6 It can directly serve the design of offshore wind power foundations and disaster prevention and mitigation forecasting services in the Taiwan Strait.

[0058] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for refined simulation of nearshore typhoon waves based on terrain-adaptive grids and dynamic parameter correction, characterized in that, The method is as follows: Step 1: Obtain basic data and simulation parameters for the target sea area; Step 2: Construct a computational domain and initial grid adapted to the topography and typhoon impact characteristics of the target sea area. The initial grid is an unstructured grid that incorporates multiple physical constraint factors. Step 3: Optimize the initial grid based on the numerical stability criterion to obtain the final computational grid that meets the computational convergence requirements; Step 4: Establish a two-way coupled model that includes wind field, storm surge, and ocean waves, and apply the final computational grid to the two-way coupled model; Step 5: Construct a wave-current coupled dynamic bottom friction mechanism, establish an error feedback closed loop using measured data from the target sea area, perform spatiotemporal local inversion of typhoon wind field parameters and seabed physical property parameters, and generate the optimal parameter set; Step 6: Using the final computational grid and optimal parameter set, conduct typhoon wave process simulation calculations. During the simulation, the typhoon wind field parameters and seabed physical property parameters are corrected in real time, and a typhoon wave characteristic database is constructed.

2. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, In step 1, the basic data includes topographic data, historical typhoon data, and measured marine environmental data, and the simulation parameters include the time integration step size; The topographic data was obtained by combining SRTM15 arcsecond water depth grid data with actual measurement data from coastal waters. Historical typhoon data were obtained using the CMA tropical cyclone best track dataset.

3. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, The specific method for constructing the computational domain in step 2 is as follows: a multi-level nested elliptical sector boundary structure is adopted, which includes at least an outer elliptical boundary and an inner elliptical boundary; the outer elliptical boundary covers the far-field influence range of the typhoon, and its semi-major axis direction is consistent with the dominant path of historical typhoons in the target sea area; the inner elliptical boundary focuses on the core nearshore area of ​​concern, and its center is located as the center of the key geographic unit of the target sea area; by adjusting the ellipse eccentricity, rotation angle and sector azimuth, invalid land areas and non-concerned deep-sea areas are eliminated.

4. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, The specific process of generating the initial mesh in step 2 is as follows: Construct a mesh scale control function H(x,y), which is composed of a shallow water wavelength constraint factor, a terrain slope response factor, and a mesh gradient smoothing factor. The shallow water wavelength constraint factor ensures that a preset number of mesh nodes are contained within a unit wavelength. The terrain slope response factor automatically densifies the mesh when the rate of change of the seabed terrain slope exceeds a preset threshold. The mesh gradient smoothing factor controls the transition growth rate from the inner high-resolution mesh to the outer low-resolution mesh.

5. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, The specific mesh optimization process in step 3 includes: Calculate the full-field Coulomb number (CFL) of the initial grid at a preset time integration step; Identify high-risk grid cells with a Courrand number greater than the stability threshold; Based on the stability threshold, the minimum characteristic scale of high-risk grid cells is derived in reverse. Local topology reconstruction or relaxation smoothing is then performed on the grid in this region until the Kronen number of the entire grid meets the stability requirements.

6. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, Step 3, the grid optimization process also includes terrain gradient correction: the slope of the water depth data after grid interpolation is detected, and when the terrain slope between adjacent nodes exceeds the critical value, the water depth data is smoothed and corrected.

7. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, The construction method of the coupled numerical model system in step 4 is as follows: the Holland typhoon model is used to generate the typhoon wind field, the ADCIRC model is used to calculate the storm surge water level and flow field, and the SWAN model is used to calculate the ocean wave field; the ADCIRC model and the SWAN model share the final calculation grid and exchange water level, flow velocity and wave radiation stress data in real time to realize the two-way coupling of storm surge, ocean wave and tidal current.

8. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, Steps 5 and 6, which involve spatiotemporal localization inversion and dynamic correction of typhoon wind field parameters and seabed physical property parameters, specifically include: (1) Constructing the dynamic bottom friction function: Introducing the wave-current interaction (WCI) theory, a time-varying function of bottom friction as the flow field state evolves is constructed: ； in Let m be the time-varying bottom friction force. The density of seawater, Determined by the nonlinearity of the wave geometry parameters, The velocity of the wave's bottom trajectory. Background flow rate; (2) Determine the observation vector and control variables: Select the effective wave height, spectral peak period and measured tide level during the typhoon as the observation vector, and select the median grain size of seabed sediments and the wind field pressure shape coefficient as the control variables to be inverted; (3) Error Feedback and Parameter Optimization: Within each time step or preset time window of the numerical simulation, a cost function is constructed. The residual between the simulated and measured values ​​is calculated using the adjoint assimilation algorithm or gradient descent optimization algorithm. The control variables are dynamically adjusted to obtain the optimal seafloor sediment grain size distribution field and instantaneous pressure shape coefficient. The cost function is: ； in, For analog vectors, For the observation vector, This is a vector of control parameters for the shape coefficient of the wind field pressure. These are the weighting coefficients. The cost function; (4) Dynamic correction: Based on the updated control variables, the bottom friction coefficient field and wind driving force of the entire field grid nodes are reconstructed in real time to realize the dynamic correction of physical parameters as the typhoon develops.

9. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, In step 6, the simulation calculation spans a period of no less than 50 years, and a long-term sea state dataset is generated by continuous hourly simulation. The typhoon wave characteristic database contains spatiotemporal distribution data of wind field, wave field, tidal field and tide level at different return periods. The data is obtained by analysis using a generalized extreme value distribution or Poisson distribution statistical model.

10. The method for refined simulation of nearshore typhoon waves based on terrain-adaptive grid and dynamic parameter correction according to claim 1, characterized in that, In step 4, the wind field is driven by a combined wind field. The combined wind field is constructed by fusing the Holland typhoon model wind field and the ERA5 wind field through a dynamic weighting formula. The weighting formula is as follows: ； in, For combined wind fields; The wind field calculated using the Holland typhoon model; Reanalyze the wind field for ERA5; These are the weighting coefficients.

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