A harbor channel sudden siltation simulation method and system based on wind-wave-current-sand coupling

Abstract

This invention relates to the field of port and waterway engineering technology, and particularly to a method and system for simulating sudden siltation in port and waterway systems based on wind-wave-current-sand coupling. The method includes: acquiring driving data and measured data of the target area of ​​the port and waterway, and preprocessing the driving data; establishing a wind field model based on the WRF model, a wave model based on the SWAN model, a hydrodynamic model based on the FVCOM model, and a sediment model based on the FVCOM-SED model; coupling and integrating the above four models using an MCT coupler to obtain a wind-wave-current-sand coupled model; inputting initial wind field, water depth data, and bed elevation as initial conditions into the wind-wave-current-sand coupled model, and outputting the distribution and predicted trend of sudden siltation in the port and waterway. The multiphysics coupling model of this invention can more accurately simulate actual engineering scenarios, reduce errors and systematic biases caused by single models, and improve the accuracy of simulation results.

Description

Technical Field

[0001] This invention relates to the field of port and waterway engineering technology, and in particular to a method and system for simulating sudden siltation in port and waterway based on wind-wave-current-sand coupling. Background Technology

[0002] Siltation in ports and waterways is a significant issue affecting safe port operations and navigation efficiency. Sudden siltation is typically caused by the combined effects of strong winds, complex waves, and tidal circulation, characterized by its sudden onset and rapid siltation rate. In severe cases, it can drastically reduce channel depth in a short period, threatening navigation safety and increasing dredging and maintenance costs. The occurrence of sudden siltation in ports is closely related to atmospheric wind fields, wave dynamics, hydrodynamic flow fields, and sediment transport processes. These physical processes interact and constrain each other, making accurate quantitative simulation of them of significant engineering practical importance.

[0003] Currently, hydrodynamic and sediment numerical models such as Delft3D, ROMS, and MIKE are widely used in engineering fields. However, most of these models only consider the unidirectional or bidirectional interaction between the flow field and sediment, lacking a comprehensive consideration of the driving forces of atmospheric wind, waves, tidal currents, and sediment. The COAWST framework has achieved WRF-SWAN-ROMS coupling, but this framework is mainly geared towards regional-scale storm surge, typhoon, and coastal dynamics research, lacking specific applications for sudden siltation scenarios in ports and waterways. Furthermore, it fails to meet the needs of refined simulation of sudden siltation in ports and waterways in terms of data interaction strategies and mesh adaptation for model coupling.

[0004] Chinese patent CN121234828A discloses a method for simulating estuarine sediment transport considering the coupling of environmental factors. This method constructs a three-dimensional hydrodynamic-sediment coupled numerical model based on FVCOM, introducing the dynamic influence of temperature and salinity on sediment settling velocity into the sediment transport equation. Simultaneously, it considers the corrections to seawater density based on temperature, salinity, and sediment concentration in the momentum equation to improve the simulation accuracy under high-concentration sediment inflow scenarios. However, this method mainly addresses conventional sediment transport processes in estuaries and does not consider special scenarios of rapid sediment initiation and sudden siltation under extreme dynamic conditions such as strong storm surges, making it difficult to meet the refined simulation requirements of sudden siltation processes in ports and waterways. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for simulating sudden siltation in port channels based on wind-wave-current-sand coupling, in order to solve the problem in the prior art that there is a lack of systematic coupling of atmospheric wind field, wave, hydrodynamic and sediment transport multi-physics field, and it is difficult to simulate the sudden siltation process in port channels in a refined manner.

[0006] The technical solution of this invention is implemented as follows:

[0007] On the one hand, this invention provides a method for simulating sudden siltation in port channels based on wind-wave-current-sand coupling, comprising the following steps:

[0008] S1. Obtain driving data and measured data of the target area of ​​the port channel. The driving data includes bed elevation, water depth data and initial wind field data. The measured data includes measured wind field data, measured wave data, measured tide level and flow velocity and direction data, measured suspended sediment concentration and bed scouring and deposition data. The target area is divided into grids using an unstructured triangular mesh partitioning method, and the driving data is preprocessed.

[0009] S2. Establish a wind field model for the target area based on the WRF model. Run the wind field model with ERA5 global reanalysis data as the initial field and boundary field, and calculate the wind field parameters of the target area.

[0010] S3. Establish a wave model for the target area based on the SWAN model, use simulated wind field parameters as the driving force for the wave model operation, and calculate the wave parameters for the target area in combination with the offshore boundary conditions.

[0011] S4. Establish a hydrodynamic model of the target area based on the FVCOM model, run the model with wave parameters, and calculate the hydrodynamic parameters of the target area.

[0012] S5. Based on the FVCOM-SED model, establish a sediment model for the target area, run the model with hydrodynamic parameters, and calculate the sediment parameters of the target area by combining the measured suspended sediment concentration and bed sedimentation data.

[0013] S6. The wind field model, wave model, hydrodynamic model and sediment model are coupled and integrated using the MCT coupler, and the coupled model is verified based on measured data to obtain the wind-wave-current-sand coupled model.

[0014] S7. Input the initial wind field, water depth data and bed elevation as initial conditions into the wind-wave-current-sand coupling model, drive the wind-wave-current-sand coupling model to run, output the distribution of sudden siltation in the port channel, the distribution of suspended sediment concentration, the change in bed elevation and sediment flux, and predict the development trend of sudden siltation based on the above results.

[0015] Based on the above technical solutions, preferably, the calculation of wind field parameters in the target area in step S2 specifically includes:

[0016] ERA5 ground and upper-air variables were acquired and converted to GRIB format. In WPS, the simulation area grid was established, ERA5 data was decoded and horizontally interpolated to the WRF grid. Then, the initial field and boundary field were generated by vertical interpolation. Finally, WRF numerical integration was run to generate the wind speed at a height of 10m and the surface wind stress in the target area. The wind speed at a height of 10m was calculated based on the Monin-Obukhov similarity theory.

[0017] ;

[0018] in, At a height of 10m, For friction speed, For von Kármán coefficients, This represents the length of the surface roughness.

[0019] Based on the above technical solutions, preferably, the wave parameters of the target area calculated in step S3 specifically include:

[0020] Using wind field parameters as the driving force and combining them with offshore boundary conditions, the SWAN model is configured, and the wave parameters of the target region are calculated by solving the wave action balance equation. In Cartesian coordinates, the wave action balance equation is:

[0021] ;

[0022] in, For wave action spectral density, Relative angular frequency, For the direction of wave propagation, , For the horizontal propagation velocity component, For frequency space propagation speed, For the direction of spatial propagation speed, The total source and sink terms consist of the following:

[0023] ;

[0024] in, For wind energy input source terms, For the three-wave nonlinear interaction source term, For the four-wave nonlinear interaction source term, For the whitening and fragmentation dissipation term, This is the bottom friction dissipation term. The term represents the shallow water breaking and dissipation term. The effective wave height, dominant period, wave direction, radiation stress tensor, and bottom friction forcing of the target area are obtained by solving the equation.

[0025] Based on the above technical solutions, preferably, the hydrodynamic parameters of the target area calculated in step S4 specifically include:

[0026] Using wave parameters as wave drivers, combined with water depth data, and based on FVCOM... The model configuration is completed in coordinate system, and the continuity equation and three-dimensional momentum equation are solved to simulate tidal propagation, ocean current circulation, and wave-current interaction processes. The continuity equation is as follows:

[0027] ;

[0028] in, For time, For water level, Let be the total water depth, u be the velocity of the water flow in the x-direction, v be the velocity of the water flow in the y-direction, and ω be the velocity of the water flow in the vertical direction of the σ coordinate. The values ​​range from -1 at the seabed to 0 at the sea surface;

[0029] The momentum equation introduces the radiation stress tensor as a wave-driving forcing term, and uses the vertical turbulent viscosity coefficient as a means. The parameterized vertical mixing process outputs a three-dimensional velocity field, water level, and bottom shear stress.

[0030] Based on the above technical solutions, preferably, step S5, which calculates the sediment parameters of the target area, specifically includes:

[0031] Driven by hydrodynamic parameters and combined with bottom sediment parameters, the model was configured based on FVCOM-SED, and the transport of suspended loads was described based on concentration. The equation for the evolution of suspended sediment concentration is:

[0032] ;

[0033] in, Sediment component concentration, Components The settling velocity, For horizontal eddy current viscosity, Let w, u, and v be the vertical eddy viscosity, and w, u, and v be the velocity components in the x, y, and z spatial coordinate directions.

[0034] At the substrate boundary, sediment flux is determined by the difference between erosion and sedimentation, and the erosion rate... for:

[0035] ;

[0036] in, Components The erosion flux, The porosity at the bottom bottom sediment components The corresponding percentage For the bottom shear stress, Components The corresponding critical shear stress, For time intervals;

[0037] Vertical net sediment flux The calculation formula is:

[0038] ;

[0039] in, The erosion coefficient is... and These are the critical stresses for sediment resuspension and sediment deposition, respectively. The sediment settling velocity, The model outputs data on the sediment concentration at the bottom layer and the distribution of suspended sediment concentration and the change in bed elevation.

[0040] Based on the above technical solutions, preferably, in step S6, the process of coupling and integrating the wind field model, wave model, hydrodynamic model, and sediment model using the MCT coupler includes:

[0041] S61. The wind field parameters output by the wind field model are passed to the wave model and the hydrodynamic model respectively. The wave parameters output by the wave model are passed to the hydrodynamic model. The hydrodynamic parameters output by the hydrodynamic model are passed to the sediment model. At the same time, the sediment model feeds back the bottom boundary conditions to the hydrodynamic model.

[0042] S62. Add a threshold judgment submodule to the MCT coupler to monitor the key output variables of each model in real time. When any variable meets the threshold trigger condition, the data interaction step size between models is automatically shortened, and the normal step size is restored during the stable period.

[0043] S63. Design a bidirectional data interface in the MCT coupler, add a feedback data parsing module to each model, build a reverse transmission channel while retaining the forward transmission channel, and register independent routes for the feedback variables from the hydrodynamic model to the wave model and from the sediment model to the hydrodynamic model.

[0044] S64. To address the variable transfer between the structured grid used in the wind field model and the unstructured grid used in the hydrodynamic and wave models, the nearest point interpolation method is used to solve the interpolation weight coefficients, complete the interpolation transformation of variables between different grids, and construct the wind-wave-current-sand coupling model.

[0045] Based on the above technical solutions, preferably, in step S64, the threshold triggering conditions include wind field triggering conditions, wave triggering conditions, and sediment triggering conditions.

[0046] The wind field triggering condition is: the wind speed mutation rate at a height of 10m output by the wind field model reaches the level before the typhoon landslide or the wind stress increase is not less than 20%. After triggering, the wind field data interaction step between the wind field model and the wave model and hydrodynamic model will be shortened.

[0047] The wave triggering conditions are: the effective wave height calculated by the wave model is not lower than the critical wave height of sudden siltation in the port or the wave energy flux change rate is not lower than 30%. After triggering, the wave model is activated to transfer data to the hydrodynamic model, and the radiation stress and bottom shear stress are output synchronously.

[0048] The sediment triggering conditions are: the suspended sediment concentration simulated by the sediment model is not lower than the sudden siltation initiation concentration or the change in bed elevation exceeds the set threshold. After triggering, the frequency of flow field data transmission from the hydrodynamic model to the sediment model is increased, and the sediment model is simultaneously triggered to feed back to the bottom boundary of the hydrodynamic model.

[0049] Based on the above technical solutions, preferably, in step S65, the bidirectional coupling feedback includes flow-wave feedback and sand-flow feedback;

[0050] In the flow-wave feedback, the three-dimensional velocity field calculated by the hydrodynamic model is transmitted back to the wave model. When calculating the spatial propagation term of the wave action balance equation, the wave propagation speed is corrected from the wave group speed to the sum of the wave group speed and the ambient flow speed. The velocity correction is applied to the wave number vector through the Doppler frequency shift formula. During coupling, the near-surface flow speed is selected to form a two-dimensional velocity input, which is mapped to the wave model mesh through the coupler interpolation function.

[0051] In the sediment-flow feedback, the suspended sediment concentration distribution output by the sediment model is transmitted back to the hydrodynamic model to correct the water density field and simultaneously update the density field used in the pressure gradient force and buoyancy related terms in the momentum equation.

[0052] Based on the above technical solutions, preferably, step S6, which verifies the coupled model based on measured data, specifically includes:

[0053] The measured data include measured wind field data, measured wave data, measured tide level and flow velocity and direction data, measured suspended sediment concentration and bed sedimentation data;

[0054] The wind field parameters output by the wind field model are compared with the measured wind field data to complete the parameter calibration of the wind field model.

[0055] The wave parameters output by the wave model are compared with the measured wave data to complete the parameter calibration of the wave model.

[0056] The hydrodynamic parameters output by the hydrodynamic model are compared with the measured tidal level, flow velocity and direction data to complete the parameter calibration of the hydrodynamic model.

[0057] The sediment parameters output by the sediment model are compared with the measured suspended sediment concentration and bed sedimentation data to complete the parameter calibration of the sediment model.

[0058] This invention also provides a port channel siltation simulation system based on wind-wave-current-sand coupling, the system being used to implement the method described above, including:

[0059] The preprocessing module is used to acquire the driving data and measured data of the target area, and to perform grid division and data preprocessing on the target area;

[0060] The atmospheric module is used to build a wind field model of the target area and generate wind field parameters.

[0061] The wave module is used to receive wind speed and surface wind stress, build a wave model of the target area, and output wave parameters.

[0062] The hydrodynamic module is used to receive the radiation stress tensor, establish a hydrodynamic model of the target area, and output hydrodynamic parameters.

[0063] The sediment module is used to receive the three-dimensional flow velocity field and bottom shear stress, establish a sediment model of the target area, and output sediment parameters.

[0064] The coupling interaction module connects with the atmospheric module, wave module, hydrodynamic module and sediment module. Based on the MCT coupler, it is responsible for the positive transmission and bidirectional feedback of variables between modules, and has the functions of dynamic step size adjustment and heterogeneous mesh interpolation conversion.

[0065] The model validation module is used to compare and validate the output results of each model module and calibrate the parameters based on measured data to obtain the wind-wave-current-sand coupling model.

[0066] The present invention has the following advantages over the prior art:

[0067] This invention integrates the WRF wind field model, SWAN wave model, FVCOM hydrodynamic model, and FVCOM-SED sediment model via an MCT coupler to construct a multiphysics coupling framework covering wind field, waves, hydrodynamics, and sediment transport, enabling full-chain numerical simulation of the sudden siltation process in port channels. Compared to traditional methods that only consider the unidirectional interaction between flow field and sediment, this invention can simultaneously describe the interactions between atmospheric drive, wave propagation, tidal circulation, and sediment transport. Under extreme dynamic conditions such as strong storm surges, the interaction between various physical fields has a more significant impact on the sudden siltation process. Multiphysics coupling can reduce the systematic bias introduced by neglecting cross-field coupling effects, thereby improving the reliability of simulation results for sudden siltation processes in port channels.

[0068] This invention adds a threshold judgment submodule to the MCT coupler. By monitoring key indicators such as wind speed change rate, significant wave height, suspended sediment concentration, and bed elevation change in real time, it automatically shortens the data interaction step size between models when trigger conditions are met, and restores the normal step size during stable periods. This mechanism enables the coupling data interaction frequency to adaptively adjust with the intensity of dynamic disturbances, solving the problem of data transmission lag in the critical stage of rapid sedimentation under a fixed coupling step size. Without significantly increasing the overall computational cost, it improves the coupling timeliness and computational stability during strong dynamic stages.

[0069] This invention designs a bidirectional data interface and a feedback data parsing module within the MCT coupling framework. Through flow-wave feedback, the three-dimensional velocity field of the hydrodynamic model is fed back to the wave model to correct the wave propagation speed. Through sediment-flow feedback, the suspended sediment concentration distribution is fed back to the hydrodynamic model to update the water density field. Thus, the wave-current interaction and the feedback mechanism of high-concentration sediment on hydrodynamics are restored during the coupled calculation process, improving the physical consistency of the simulation of the sudden siltation process. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a flowchart of the port channel siltation simulation method based on wind-wave-current-sand coupling of the present invention;

[0072] Figure 2 This is a schematic diagram of the wind-wave-current-sand coupling process of the present invention;

[0073] Figure 3 This is a schematic diagram of the threshold determination submodule of the present invention;

[0074] Figure 4 This is a schematic diagram illustrating the wind-wave-current-sand coupling optimization effect of the present invention;

[0075] Figure 5 This is a schematic diagram of the port / channel siltation simulation system of the present invention. Detailed Implementation

[0076] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0077] like Figure 1 As shown, this embodiment provides a simulation method for sudden siltation in port channels based on wind-wave-current-sand coupling, including the following steps:

[0078] S1. Obtain driving data and measured data of the target area of ​​the port channel. The driving data includes bed elevation, water depth and initial wind field data. The measured data includes measured wind field data, measured wave data, measured tide level and flow velocity and direction data, measured suspended sediment concentration and bed scouring and deposition data. The target area is divided into grids using an unstructured triangular mesh partitioning method, and the driving data is preprocessed.

[0079] S2. Establish a wind field model for the target area based on the WRF model. Run the wind field model with ERA5 global reanalysis data as the initial field and boundary field, and calculate the wind field parameters of the target area.

[0080] S3. Establish a wave model for the target area based on the SWAN model, use simulated wind field parameters as the driving force for the wave model operation, and calculate the wave parameters for the target area in combination with the offshore boundary conditions.

[0081] S4. Establish a hydrodynamic model of the target area based on the FVCOM model, run the model to simulate wave parameters, and calculate the hydrodynamic parameters of the target area.

[0082] S5. Based on the FVCOM-SED model, establish a sediment model for the target area, run the model with hydrodynamic parameters, and calculate the sediment parameters of the target area by combining the measured suspended sediment concentration and bed sedimentation data.

[0083] S6. The wind field model, wave model, hydrodynamic model and sediment model are coupled and integrated using the MCT coupler, and the coupled model is verified based on measured data to obtain the wind-wave-current-sand coupled model.

[0084] S7. Input the initial wind field, water depth data and bed elevation as initial conditions into the wind-wave-current-sand coupling model, drive the wind-wave-current-sand coupling model to run, and output the distribution and prediction trend of sudden siltation in the port channel, suspended sediment concentration field, bed elevation change and sediment flux.

[0085] In a specific embodiment of the present invention, step S1 specifically includes: collecting initial bed elevation and shape, high-precision water depth sounding data, and initial wind field data for the target area of ​​the port channel; simultaneously collecting measured data for model calibration and verification, including measured tide levels, flow velocity and direction, waves, suspended sediment concentration, and bed scouring and deposition data. Using mesh generation software (such as SMS), an unstructured triangular mesh file required by FVCOM is created. This file contains node coordinates, triangular element topology, and boundary markers. Node water depth interpolation is performed using measured sounding data to generate a complete mesh file with water depth information. The obtained driving data is preprocessed, including coordinate system unification, format conversion, spatiotemporal resolution matching, and missing measurement value interpolation, to ensure the integrity and consistency of subsequent model input data.

[0086] In one embodiment of the present invention, step S2 specifically includes: downloading ERA5 ground and upper-air variables and converting them to GRIB format; establishing a simulation area grid in WPS, decoding ERA5 data and horizontally interpolating it to the WRF grid; generating the initial field and boundary field through vertical interpolation; and finally running WRF numerical integration to generate the wind speed and surface wind stress covering the target area as wind field parameters.

[0087] Specifically, firstly, ERA5 global reanalysis data, including surface and multi-level upper-air variables, is downloaded from the ECMWF data server and converted to GRIB format. Then, in the WRF preprocessing system WPS, geogrid.exe is used to establish the simulation area grid and perform geographic static data interpolation, ungrib.exe decodes the ERA5 GRIB format data, and metgrid.exe horizontally interpolates the ERA5 meteorological variables into the WRF simulation grid. Next, real.exe performs vertical interpolation, generating the initial field file wrfinput and the boundary field file wrfbdy required for WRF operation. Finally, wrf.exe is run for numerical integration, using multi-nested grids to refine the wind field progressively from the regional scale to the nearshore scale, generating a high-resolution wind field covering the target area. The momentum equation solved in the WRF numerical integration stage is:

[0088] ;

[0089] in, It is a three-dimensional wind speed vector. air density, For pressure gradient, Coriolis force, For friction or turbulent diffusion, t represents time. This is the wind speed at a height of 10m output by the WRF. Based on the Monin-Obukhov similarity theory, the following was obtained:

[0090] ;

[0091] in, At a height of 10m, For friction speed, Here is the von Kármán coefficient, with a value of approximately 0.4. The length represents the surface roughness. The 10m height wind speed component and surface wind stress output by WRF are spatially interpolated and then input into the SWAN wave model to provide wind field driving conditions for the wave model.

[0092] Through multi-nested grid configuration, the wind field model can gradually downscale ERA5's global-scale meteorological information to the nearshore area of ​​the port, effectively improving the spatial resolution of the nearshore wind field. This allows the generated wind speed and wind stress fields to better reflect the fine structure of strong dynamic processes such as typhoons, providing more accurate driving input for downstream wave models.

[0093] In one embodiment of the present invention, step S3 includes: using the wind speed and surface wind stress generated in step S2 as the wind field driver, and configuring the SWAN model in conjunction with the offshore boundary conditions, which are given through spectral parameterization. The SWAN model calculates the wave energy spectrum by solving the wave action balance equation, considering physical processes such as bottom friction dissipation, three-wave and four-wave interaction, and shallow water wave breaking, thereby obtaining wave parameters such as effective wave height, dominant period, and wave direction. In the Cartesian coordinate system, the wave action balance equation is:

[0094] ;

[0095] in, For wave action spectral density, Relative angular frequency, For the direction of wave propagation, , For the horizontal propagation velocity component, For frequency space propagation speed, For the direction of spatial propagation speed, This represents the total source and sink terms. This equation describes the transport and variation of the spectrum in time, space, frequency, and direction, and is the core governing equation of the SWAN model. Total source and sink terms Its composition is:

[0096] ;

[0097] in, For wind energy input source terms, For the three-wave nonlinear interaction source term, For the four-wave nonlinear interaction source term, For the whitening and fragmentation dissipation term, This is the bottom friction dissipation term. This is the shallow water breakage dissipation term.

[0098] in, The wind energy input term represents the energy source of the waves. Surface winds are an important energy source for wave generation and growth. The kinetic energy transfer caused by the surface wind field can be described by resonance and feedback mechanisms. Based on these two wind and wave growth mechanisms, the SWAN model expresses the wind energy input term as a sum of linear and exponential growth:

[0099] ;

[0100] In the formula: A corresponds to the resonance mechanism, which describes the energy input in the early stage of wave generation and has a linear relationship with time; B is a coefficient related to the wind field and wave state, which corresponds to the feedback mechanism, describing the energy input in the middle and late stages of wave growth and has an exponential relationship with time. The energy spectrum density is a two-dimensional wave.

[0101] in, The whitening and fragmentation dissipation term represents the white cap dissipation, which dominates the saturation in the high-frequency part of the spectrum in deep water conditions. In the SWAN mode, it can be calculated by the Hasselmann model based on the pulse principle, which is controlled by wave steepness.

[0102] ;

[0103] In the formula: Wave number; and These are the average frequency and the average wavenumber, respectively. Depends on the mean steepness coefficient The expression is:

[0104] ;

[0105] For the Pierson-Moskowitz spectrum , , and The white hat dissipation is a parameter that depends on the selected wind input method.

[0106] in, This represents the bottom friction dissipation term. In medium and shallow water conditions, bottom friction is important during wave propagation.

[0107] ;

[0108] In the formula: d is the water depth; The coefficient of friction at the bottom generally depends on the root mean square velocity of the wave trajectory at the bottom. The expression is:

[0109] ;

[0110] Of the above six source / dissipation terms, , , The main physical processes of deep-sea waves, and , , This refers to the physical process of ocean waves propagating into shallow water.

[0111] The effective wave height, dominant period, wave direction, radiation stress tensor, and bottom friction forcing of the target area are obtained by solving and used as wave parameters. The radiation stress tensor is transmitted to the hydrodynamic model through the coupling interface to correct the wave driving term in the momentum equation.

[0112] In one embodiment of the present invention, step S4 includes: generating an unstructured triangular mesh based on collected topographic data, shoreline data, wind field data, tidal data, and current velocity data; combining the radiation stress tensor and bottom friction forcing obtained in step S3; and based on FVCOM... Configure the hydrodynamic model in coordinate system, setting the model's open boundary, land boundary, and meteorological driving conditions, and providing initial conditions for the tidal and flow fields. FVCOM in The governing equations in coordinate system include the continuity equation and the three-dimensional momentum equation, where the continuity equation is:

[0113] ;

[0114] in, For time, For water level, Let be the total water depth, u be the velocity of the water flow in the x-direction, v be the velocity of the water flow in the y-direction, and ω be the velocity of the water flow in the vertical direction of the σ coordinate. The value ranges from -1 at the seabed to 0 at the sea surface. direction and The three-dimensional momentum equations for the directions are as follows:

[0115] ;

[0116] ;

[0117] in, It is the acceleration due to gravity. The density of seawater, For reference density of water, Coriolis parameters, The vertical turbulent viscosity coefficient, For the momentum diffusion term in the x-direction, For the momentum diffusion term in the y-direction, .

[0118] FVCOM The temperature equation, salinity equation, and state equation are also solved in the coordinate system to describe the influence of seawater temperature and salinity fields on density. The temperature equation is as follows:

[0119] ;

[0120] The salinity equation is:

[0121] ;

[0122] The state equation is:

[0123] ;

[0124] in, For seawater temperature, For seawater salinity, The vertical thermal diffusivity is... Solar radiation absorbed by water bodies , These are the horizontal diffusion terms for temperature and salinity, respectively. For the sake of temperature With salinity The density of seawater is jointly determined. The model outputs three-dimensional velocity field, water level, and bottom shear stress as hydrodynamic parameters through integration.

[0125] In one embodiment of the present invention, step S5 includes: preparing various data for the target area, including but not limited to topographic parameters, wind field parameters, and wave parameters, wherein the wind field parameters and wave parameters can be calculated by the wind field model and the wave model; and defining the open boundary, land boundary, and river boundary of the model according to the hydrodynamic characteristics of the study area, obtaining measured suspended sediment concentration and bed sedimentation data as measured sediment parameters, including physical parameters such as particle composition, porosity, density, and critical shear stress of the bed sediments, to provide initial conditions for the sediment model, and completing the sediment model configuration based on FVCOM-SED. The sediment model uses a concentration-based method to describe suspended load transport, and each sediment component... The equation for the evolution of suspended sediment concentration is:

[0126] ;

[0127] in, Sediment component concentration, Components The settling velocity is specified by the user individually for each sediment component in the input parameter file. For horizontal eddy current viscosity, Let w represent the vertical eddy viscosity, and w, u, and v represent the velocity components along the x, y, and z spatial coordinates. At the sea surface, sediment concentration is calculated using flux-free boundary conditions.

[0128] ;

[0129] At the substrate boundary, sediment flux is determined by the difference between erosion and deposition:

[0130] ;

[0131] in, Components The erosion rate, Components The deposition rate, The water is still and deep. Erosion rate. The formula for calculation is:

[0132] ;

[0133] in, Components The erosion flux, The porosity at the bottom bottom sediment components The corresponding percentage For bottom shear force, Components The corresponding critical shear stress, The time interval. According to the research of Ariathurai & Krone, the net vertical sediment flux on the seabed caused by deposition and erosion processes. The calculation formula is:

[0134] ;

[0135] in, The erosion coefficient is... and These are the critical stresses for sediment resuspension and sediment deposition, respectively. The sediment settling velocity, This represents the sediment content of the bottom layer of the model. The model outputs sediment parameters such as suspended sediment concentration, sediment thickness, erosion rate, and bed elevation variation.

[0136] In one embodiment of the present invention, such as Figure 2 As shown, step S6 specifically includes:

[0137] S61. The wind field parameters output by the wind field model are passed to the wave model and the hydrodynamic model respectively. The wave parameters output by the wave model are passed to the hydrodynamic model. The hydrodynamic parameters output by the hydrodynamic model are passed to the sediment model. At the same time, the sediment model feeds back the bottom boundary conditions to the hydrodynamic model.

[0138] Specifically, a forward data transfer link is established between the various models: the wind field parameters output by the wind field model (including the 10m height wind speed component and surface wind stress) are used as surface forcing and input into the wave model and hydrodynamic model respectively; the radiation stress tensor and bottom friction forcing quantity output by the wave model are input into the hydrodynamic model to correct the wave driving term in the momentum equation; the three-dimensional velocity field and bottom shear stress output by the hydrodynamic model are input into the sediment model to provide hydrodynamic boundary conditions for the calculation of sediment initiation and transport; the sediment model feeds back the bottom boundary conditions to the hydrodynamic model, realizing bidirectional coupling between sediment and flow. The above data transfer link is uniformly managed through the registration mechanism of the MCT coupler. Each model submits its output data (such as 10m wind speed, significant wave height, bottom shear stress, suspended sediment concentration, bed elevation change, etc.) to the MCT at each coupling moment. After receiving the data, the MCT temporarily stores the variables in the field buffer of the coupler for downstream models to read at the next interaction moment.

[0139] S62. Add a threshold judgment submodule to the MCT coupler to monitor the key output variables of each model in real time. When any variable meets the threshold trigger condition, the data interaction step size between models is automatically shortened, and the normal step size is restored during the stable period.

[0140] Specifically, such as Figure 3 As shown, a threshold judgment submodule is added to the MCT coupler. This submodule is integrated into the MCT coupler loop as a plug-in module, running as a monitoring and scheduling link during each coupling exchange. During each coupling exchange, the submodule directly reads the output data of each model from the previous time step from the MCT's field buffer, calculates the rate of change of key indicators, or determines whether the threshold is exceeded. When any variable meets the threshold trigger condition, the submodule automatically shortens the data interaction step size between models to increase the exchange frequency. During the stable period, it restores the normal step size to reduce computational costs, thereby realizing dynamic step size interaction. This makes the data interaction frequency match the sudden characteristics of port siltation, improving the coupling accuracy and stability during strong disturbance phases.

[0141] Threshold triggering conditions include wind field triggering conditions, wave triggering conditions, and sediment triggering conditions:

[0142] The wind field triggering condition is: the wind speed change rate at a height of 10m output by the wind field model reaches the level before the typhoon landslide or the wind stress increase is not less than 20%. After triggering, the wind field data interaction step between the wind field model and the wave model and hydrodynamic model will be shortened (for example, from 1 hour to 10 minutes).

[0143] The wave triggering conditions are: the effective wave height calculated by the wave model is not lower than the critical wave height for sudden siltation in the port or the wave energy flux change rate is not lower than 30%. After triggering, the wave model is activated to transmit high-frequency data to the hydrodynamic model (e.g., every 5 minutes), and the radiation stress and bottom shear stress are output synchronously.

[0144] The sediment triggering conditions are: the suspended sediment concentration simulated by the sediment model is not lower than the sudden siltation initiation concentration or the change in bed elevation exceeds the set threshold. After triggering, the frequency of flow field data transmission from the hydrodynamic model to the sediment model is increased, and the sediment model is simultaneously triggered to feed back to the bottom boundary of the hydrodynamic model.

[0145] The dynamic step size threshold judgment submodule solves the problem of untimely transmission of key data in sudden siltation under fixed step size, enabling the frequency of coupled data interaction to be adaptively adjusted according to the dynamic intensity, thereby improving the coupling accuracy and calculation stability in the strong disturbance stage.

[0146] S63. Design a bidirectional data interface in the MCT coupler, add a feedback data parsing module to each model, build a reverse transmission channel while retaining the forward transmission channel, and register independent routes for the feedback variables from the hydrodynamic model to the wave model and from the sediment model to the hydrodynamic model.

[0147] Specifically, such as Figure 4 As shown, a bidirectional data interface is designed in the MCT coupler, and a feedback data parsing module is added to each model, constructing a reverse transmission channel while retaining the forward transmission channel. Under the MCT framework, the reverse data flow is achieved through the newly added reverse channel: independent Router routes are registered for feedback variables from the hydrodynamic model to the wave model and from the sediment model to the hydrodynamic model, respectively, and corresponding Rearrangers are configured to complete parallel partition rearrangement and cross-grid interpolation. The Rearranger uses GlobalSegMap for region decomposition, representing rearrangement or interpolation as sparse matrix multiplication and storing non-zero mapping relationships using SparseMatrix. The priority of feedback data is defined by a priority identifier, with smaller values ​​indicating higher priority. Delay control employs a buffer queue and timestamp comparison mechanism to achieve precise control of feedback timing under complex coupling environments. The newly added feedback data parsing module in each model is inserted into the existing boundary data receiving entry point of the model as an adaptation layer. By extracting feedback quantities from the MCT receiving buffer according to variable identifiers, completing timing verification and dimension / unit conversion, and then backfilling into the original data structure of the model, bidirectional feedback integration is achieved.

[0148] The bidirectional coupling feedback includes flow-wave feedback and sand-flow feedback: In flow-wave feedback, the three-dimensional velocity field calculated by the hydrodynamic model is transmitted back to the wave model. When calculating the spatial propagation term of the wave action balance equation, the wave propagation speed is corrected from the wave group speed to the sum of the wave group speed and the ambient flow speed. The velocity correction is applied to the wave number vector through the Doppler frequency shift formula. At the same time, since the hydrodynamic model outputs a three-dimensional velocity field while the wave model calculates a two-dimensional wave field, a discrete mapping format based on finite volume conservation remapping is adopted. During coupling, the near-surface flow velocity is selected to form a two-dimensional velocity input, which is mapped to the wave model mesh through the coupler interpolation function to achieve stable transmission under the condition that the two model meshes are inconsistent.

[0149] In the sediment-flow feedback, the suspended sediment concentration distribution output by the sediment model is transmitted back to the hydrodynamic model to correct the water density field and simultaneously update the density field used in the pressure gradient force and buoyancy related terms in the momentum equation. When the suspended sediment concentration is within the normal range, a mixed density is used to correct the pressure gradient and buoyancy terms in the momentum equation. When the suspended sediment concentration reaches the high concentration condition of slurry formation, its non-Newtonian fluid characteristics need to be further considered to improve the stability and accuracy of the dynamic calculation. Specifically, after density correction, in addition to updating the density calculation in the equation of state, the momentum equation also simultaneously updates the density field used in the pressure gradient force and buoyancy related terms in the momentum equation. The density correction does not directly affect the continuity equation, but it will cause changes in the velocity field through changes in the pressure gradient and buoyancy terms, thereby indirectly changing the solution result of the continuity equation. At the same time, when the suspended sediment concentration is within the normal range, a mixed density is sufficient to correct the pressure gradient and buoyancy terms in the momentum equation. When the suspended sediment concentration reaches the high concentration condition of slurry formation, its non-Newtonian fluid characteristics need to be considered to improve the stability and accuracy of the dynamic calculation.

[0150] S64. To address the variable transfer between the structured grid used in the wind field model and the unstructured grid used in the hydrodynamic and wave models, the nearest point interpolation method is used to solve the interpolation weight coefficients, complete the interpolation transformation of variables between different grids, and construct the wind-wave-current-sand coupling model.

[0151] Specifically, regarding the variable transfer between the structured grid used in the wind field model and the unstructured grid used in the hydrodynamic and wave models, since the grid point positions are different, it is necessary to interpolate the variables of different models. Therefore, the interpolation function provided by the MCT coupler is used to realize the interpolation of variables between different grids.

[0152] Since the MCT coupler currently does not support solving for interpolation weight coefficients, the nearest-point interpolation method is used to solve for the interpolation weight coefficients to ensure successful bidirectional interpolation of structured and unstructured meshes. The matrix formed by the interpolation weight coefficients has most elements as 0, and is called a sparse matrix. The essence of mesh interpolation transformation is multiplying the sparse matrix by the attribute vector. The attribute vector is essentially a linearized one-dimensional vector obtained by decomposing the horizontal mesh using GlobalSegMap.

[0153] The interpolation expression is ,in It is a linearized one-dimensional row vector of the global cell numbers of the target mesh. It is a linearized one-dimensional row vector of the original mesh global cell number. It is a sparse matrix.

[0154] The MCT coupler uses the SparseMatrix data type to perform one-dimensional compressed storage of non-zero elements in the sparse matrix, recording only the row number, column number, and value corresponding to the non-zero elements. Taking the interpolation conversion from a structured rectangular mesh of a wind field model to a triangular mesh of a wave model as an example, assuming the target mesh (wave model) contains... The linear length is (number of unit nodes). , The number of rows in the sparse matrix M is determined; the original mesh (wind field model) contains A cell grid with a linear length of [number] cells. , The number of columns in the sparse matrix M is determined, therefore the number of elements contained in the sparse matrix M is ( · ).

[0155] First, the non-zero interpolation weights need to be calculated. And its row and column indices in the sparse matrix M. column number Corresponding to the numbers of the three nearest interpolation points in the original grid, row number ; This corresponds to the interpolation point number in the target mesh. Then, the non-zero elements are... , , The values ​​are linearly placed into a one-dimensional array, resulting in a one-dimensional weight array, row number array, and column number array for each non-zero element. The lengths of these three arrays must be equal. Finally, the following function is called to create a SparseMatrix data type. The output sMat sparse matrix is ​​a matrix that records global interpolation information.

[0156] ;

[0157] In the formula: This represents the element in the k-th row and first column of the sparse matrix; This represents the element in the first row and the l-th column; This represents the element in the k-th row and l-th column; This represents the element in the l-th row and first column; This represents the element in the k-th row and l-th column.

[0158] Then, the interpolation transformation of variables between different grids is completed, and a wind-wave-current-sand coupling model is constructed.

[0159] In this invention, the dynamic step size threshold judgment submodule solves the problem of untimely transmission of key data in sudden siltation under fixed step size; the bidirectional data interface and feedback data parsing module restore the interaction mechanism between various physical fields such as wind, waves, current and sand during sudden siltation, improving the physical consistency of the simulation; the nearest point interpolation and sparse matrix scheme effectively solve the variable transmission problem of mismatch between structured and unstructured multi-grids. The three together ensure the computational accuracy and stability of the coupled model in the scenario of strong dynamic sudden siltation.

[0160] The process of validating the coupled model based on measured data includes: measured wind field data, measured wave data, measured tidal level and velocity / direction data, measured suspended sediment concentration and bed scouring / deposition data; comparing the wind speed output by the wind field model with the measured wind field data to complete the parameter calibration of the wind field model; comparing the significant wave height, dominant period, and wave direction output by the wave model with the measured wave data to complete the parameter calibration of the wave model; comparing the tidal level, velocity, and direction output by the hydrodynamic model with the measured tidal level, velocity, and direction data to complete the parameter calibration of the hydrodynamic model; and comparing the suspended sediment concentration and bed scouring / deposition data output by the sediment model with the measured suspended sediment concentration and bed scouring / deposition data to complete the parameter calibration of the sediment model. Only after the parameter calibration of each model reaches an acceptable error range can the coupled model-driven operation phase begin.

[0161] In one embodiment of the present invention, step S7 specifically includes: determining the initial conditions of the wind-wave-current-sand coupling model, including the initial wind field, the initial tidal level and velocity of the initial hydrodynamic field, the initial suspended sediment concentration field, the initial bed composition and bed surface roughness, and further correcting the initial conditions and parameters of the coupling model by combining observation data assimilation methods, fusing suspended sediment concentration data retrieved from tide gauges, ADCP, depth sounders or remote sensing, to improve the accuracy of the model's initial state. The initial wind field, water depth data, and bed surface elevation are input as initial conditions into the wind-wave-current-sand coupling model to drive its operation, outputting the distribution and predicted trend of sudden siltation in port channels, the suspended sediment concentration field, the change in bed elevation, and sediment flux, providing quantitative basis for port channel dredging planning and navigation safety management.

[0162] like Figure 5As shown in the figure, this embodiment provides a port channel siltation simulation system based on wind-wave-current-sand coupling, including a preprocessing module, an atmospheric module, a wave module, a hydrodynamic module, a sediment module, a coupling interaction module, and a model verification module.

[0163] The preprocessing module is used to acquire the driving data and measured data of the target area, divide the target area into unstructured triangular meshes, and perform preprocessing operations such as coordinate unification, format conversion, spatiotemporal resolution matching and missing measurement value interpolation on the driving data to generate a mesh file with water depth information.

[0164] The atmospheric module is connected to the preprocessing module. Based on the WRF model, a wind field model of the target area is established. ERA5 global reanalysis data is used as the initial field and boundary field. The wind field is gradually refined through multiple nested grids to generate high-resolution wind speed and surface wind stress covering the target area.

[0165] The wave module is connected to the atmosphere module and receives the wind speed and surface wind stress output by the atmosphere module as the wind field driver. Based on the SWAN model, a wave model of the target area is established. The wave energy spectrum is calculated by solving the wave action balance equation and the effective wave height, dominant period, wave direction and radiation stress tensor are output.

[0166] The hydrodynamic module is connected to both the atmospheric and wave modules. It receives wind speed and surface wind stress from the atmospheric module as surface forcing, and receives the radiation stress tensor from the wave module as wave forcing input. A hydrodynamic model of the target region is established based on the FVCOM model. Solve the continuity equation, three-dimensional momentum equation, temperature equation, salinity equation and state equation in coordinate system, and output the three-dimensional velocity field, water level and bottom shear stress.

[0167] The sediment module is connected to the hydrodynamic module and receives the three-dimensional velocity field and bottom shear stress output by the hydrodynamic module as hydrodynamic drive. Based on the FVCOM-SED model, a sediment model of the target area is established, the suspended sediment concentration evolution equation and the seabed net sediment flux equation are solved, and the suspended sediment concentration distribution and seabed elevation change data are output.

[0168] The coupling interaction module is connected to the atmospheric module, wave module, hydrodynamic module and sediment module respectively, and the above models are coupled and integrated based on the MCT coupler. The coupling interaction module includes a forward transmission unit, a threshold judgment unit, a bidirectional feedback unit, and a grid interpolation unit. The forward transmission unit establishes a forward data transmission link between models through the registration mechanism of the MCT coupler, and manages the variable transmission between models in a unified manner. The threshold judgment unit is integrated into the MCT coupler loop as a plug-in module, which monitors key variables such as wind speed change rate, significant wave height, suspended sediment concentration, and bed elevation change in real time. When any variable meets the wind field triggering condition, wave triggering condition, or sediment triggering condition, it automatically shortens the corresponding data interaction step size, and restores the normal step size during the stable period, realizing dynamic step size interaction. The bidirectional feedback unit registers independent router routes for each feedback variable under the MCT framework, configures the Rearranger, and adds a feedback data parsing module to build a reverse transmission channel in addition to the forward transmission channel, realizing flow-wave feedback and sand-flow feedback. The grid interpolation unit uses the nearest point interpolation method to pre-calculate the interpolation weight coefficients between structured grids and unstructured grids, stores the sparse matrix in the SparseMatrix data type, and completes the variable interpolation conversion between different grids.

[0169] The model validation module is connected to the coupling interaction module. The output results of the wind field model, wave model, hydrodynamic model and sediment model are compared with the corresponding measured data to complete the parameter calibration of each model. After the validation is passed, the validated wind-wave-current-sand coupling model is output for subsequent prediction of port channel siltation distribution and dredging decision-making.

[0170] In summary, this application employs a multiphysics coupling model to simulate the sudden siltation phenomenon, using WRF, SWAN, FVCOM, and FVCOM-SED to simulate the coupling of wind, waves, current, and sand. On the one hand, the use of a multiphysics coupling model can more accurately simulate actual engineering scenarios, greatly reducing the errors and systematic biases caused by using a single model. The consideration of more comprehensive influencing factors significantly improves the accuracy of the simulation results. On the other hand, when the coupled models interact with each other, a threshold judgment submodule is added to the MCT coupler to monitor the output of each model in real time. When any threshold condition is met, the interaction step size is automatically adjusted. During the stable period, the normal step size is restored to reduce the computational cost. This solves the problem of untimely transmission of key data in sudden siltation under fixed step size, and makes the data interaction accurately match the sudden characteristics of port siltation, thus improving the simulation timeliness. A bidirectional data interface is designed in the MCT coupler to define the priority and delay control of feedback data. At the same time, a feedback data parsing module is added to each model to realize reverse data processing, restore the interaction mechanism of wind-wave-current-sand in sudden siltation, and improve the physical consistency of the simulation. The wind field model uses a structured grid, while the hydrodynamic model and wave model use the same unstructured grid. When transferring variables between the hydrodynamic model, wave model and wind field model, the variables of different models need to be interpolated due to the different grid point positions. The interpolation function provided by the MCT coupler is used to realize the interpolation of variables between different grids, thereby solving the problem of multi-grid mismatch.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A simulation method for sudden siltation in port channels based on wind-wave-current-sand coupling, characterized in that, Includes the following steps: S1. Obtain driving data and measured data of the target area of ​​the port channel. The driving data includes bed elevation, water depth data and initial wind field data. The measured data includes measured wind field data, measured wave data, measured tide level and flow velocity and direction data, measured suspended sediment concentration and bed scouring and deposition data. The target area is divided into grids using an unstructured triangular mesh partitioning method, and the driving data is preprocessed. S2. Establish a wind field model for the target area based on the WRF model. Run the wind field model with ERA5 global reanalysis data as the initial field and boundary field, and calculate the wind field parameters of the target area. S3. Establish a wave model for the target area based on the SWAN model, use simulated wind field parameters as the driving force for the wave model operation, and calculate the wave parameters for the target area in combination with the offshore boundary conditions. S4. Establish a hydrodynamic model of the target area based on the FVCOM model, run the model with wave parameters, and calculate the hydrodynamic parameters of the target area. S5. Based on the FVCOM-SED model, establish a sediment model for the target area, run the model with hydrodynamic parameters, and calculate the sediment parameters of the target area by combining the measured suspended sediment concentration and bed sedimentation data. S6. The wind field model, wave model, hydrodynamic model and sediment model are coupled and integrated using the MCT coupler, and the coupled model is verified based on measured data to obtain the wind-wave-current-sand coupled model. S7. Input the initial wind field, water depth data and bed elevation as initial conditions into the wind-wave-current-sand coupling model, drive the wind-wave-current-sand coupling model to run, output the distribution of sudden siltation in the port channel, the distribution of suspended sediment concentration, the change in bed elevation and sediment flux, and predict the development trend of sudden siltation based on the above results.

2. The port channel siltation simulation method based on wind-wave-current-sand coupling as described in claim 1, characterized in that, Step S2, which calculates the wind field parameters for the target area, specifically includes: ERA5 ground and upper-air variables were acquired and converted to GRIB format. In WPS, the simulation area mesh was established, ERA5 data was decoded, and horizontal interpolation was performed to the WRF mesh. Then, vertical interpolation was used to generate the initial and boundary fields. Finally, WRF numerical integration was run to generate the 10m height wind speed and surface wind stress in the target area. The 10m height wind speed was calculated based on the Monin-Obukhov similarity theory. ； in, At a height of 10m, For friction speed, For von Kármán coefficients, This represents the length of the surface roughness.

3. The port channel siltation simulation method based on wind-wave-current-sand coupling as described in claim 2, characterized in that, The wave parameters for the target area calculated in step S3 specifically include: Using wind field parameters as the driving force and combining them with offshore boundary conditions, the SWAN model is configured, and the wave parameters of the target region are calculated by solving the wave action balance equation. In Cartesian coordinates, the wave action balance equation is: ； in, For wave action spectral density, Relative angular frequency, For the direction of wave propagation, , For the horizontal propagation velocity component, For frequency space propagation speed, For the direction of spatial propagation speed, The total source and sink terms consist of the following: ； in, For wind energy input source terms, For the three-wave nonlinear interaction source term, For the four-wave nonlinear interaction source term, For the whitening and fragmentation dissipation term, This is the bottom friction dissipation term. The term represents the shallow water breaking and dissipation term. The effective wave height, dominant period, wave direction, radiation stress tensor, and bottom friction forcing of the target area are obtained by solving the equation.

4. The port channel siltation simulation method based on wind-wave-current-sand coupling as described in claim 3, characterized in that, The hydrodynamic parameters of the target area calculated in step S4 specifically include: Using wave parameters as wave drivers, combined with water depth data, and based on FVCOM... The model configuration is completed in coordinate system, and the continuity equation and three-dimensional momentum equation are solved to simulate tidal propagation, ocean current circulation, and wave-current interaction processes. The continuity equation is as follows: ； in, For time, For water level, Let be the total water depth, u be the velocity of the water flow in the x-direction, v be the velocity of the water flow in the y-direction, and ω be the velocity of the water flow in the vertical direction of the σ coordinate. The values ​​range from -1 at the seabed to 0 at the sea surface; The momentum equation introduces the radiation stress tensor as a wave-driving forcing term, and uses the vertical turbulent viscosity coefficient as a means. The parameterized vertical mixing process outputs a three-dimensional velocity field, water level, and bottom shear stress.

5. The port channel siltation simulation method based on wind-wave-current-sand coupling as described in claim 4, characterized in that, Step S5 calculates the sediment parameters for the target area, specifically including: Driven by hydrodynamic parameters and combined with bottom sediment parameters, the model was configured based on FVCOM-SED, and the transport of suspended loads was described based on concentration. The equation for the evolution of suspended sediment concentration is: ； in, Sediment component concentration, Components The settling velocity, For horizontal eddy current viscosity, Let w, u, and v be the vertical eddy viscosity, and w, u, and v be the velocity components in the x, y, and z spatial coordinate directions. At the substrate boundary, sediment flux is determined by the difference between erosion and sedimentation, and the erosion rate... for: ； in, Components The erosion flux, The porosity at the bottom bottom sediment components The corresponding percentage For the bottom shear stress, Components The corresponding critical shear stress, For time intervals; Vertical net sediment flux The calculation formula is: ； in, The erosion coefficient is... and These are the critical stresses for sediment resuspension and sediment deposition, respectively. The sediment settling velocity, The model outputs data on the sediment concentration at the bottom layer and the distribution of suspended sediment concentration and the change in bed elevation.

6. The port channel siltation simulation method based on wind-wave-current-sand coupling as described in claim 1, characterized in that, In step S6, the process of coupling and integrating the wind field model, wave model, hydrodynamic model, and sediment model using the MCT coupler includes: S61. The wind field parameters output by the wind field model are passed to the wave model and the hydrodynamic model respectively. The wave parameters output by the wave model are passed to the hydrodynamic model. The hydrodynamic parameters output by the hydrodynamic model are passed to the sediment model. At the same time, the sediment model feeds back the bottom boundary conditions to the hydrodynamic model. S62. Add a threshold judgment submodule to the MCT coupler to monitor the key output variables of each model in real time. When any variable meets the threshold trigger condition, the data interaction step size between models is automatically shortened, and the normal step size is restored during the stable period. S63. Design a bidirectional data interface in the MCT coupler, add a feedback data parsing module to each model, build a reverse transmission channel while retaining the forward transmission channel, and register independent routes for the feedback variables from the hydrodynamic model to the wave model and from the sediment model to the hydrodynamic model. S64. To address the variable transfer between the structured grid used in the wind field model and the unstructured grid used in the hydrodynamic and wave models, the nearest point interpolation method is used to solve the interpolation weight coefficients, complete the interpolation transformation of variables between different grids, and construct the wind-wave-current-sand coupling model.

7. The port channel siltation simulation method based on wind-wave-current-sand coupling as described in claim 6, characterized in that, In step S64, the threshold triggering conditions include wind field triggering conditions, wave triggering conditions, and sediment triggering conditions; The wind field triggering condition is: the wind speed mutation rate at a height of 10m output by the wind field model reaches the level before the typhoon landslide or the wind stress increase is not less than 20%. After triggering, the wind field data interaction step between the wind field model and the wave model and hydrodynamic model will be shortened. The wave triggering conditions are: the effective wave height calculated by the wave model is not lower than the critical wave height of sudden siltation in the port or the wave energy flux change rate is not lower than 30%. After triggering, the wave model is activated to transfer data to the hydrodynamic model, and the radiation stress and bottom shear stress are output synchronously. The sediment triggering conditions are: the suspended sediment concentration simulated by the sediment model is not lower than the sudden siltation initiation concentration or the change in bed elevation exceeds the set threshold. After triggering, the frequency of flow field data transmission from the hydrodynamic model to the sediment model is increased, and the sediment model is simultaneously triggered to feed back to the bottom boundary of the hydrodynamic model.

8. The port channel siltation simulation method based on wind-wave-current-sand coupling as described in claim 6, characterized in that, In step S65, the bidirectional coupling feedback includes flow-wave feedback and sand-flow feedback; In the flow-wave feedback, the three-dimensional velocity field calculated by the hydrodynamic model is transmitted back to the wave model. When calculating the spatial propagation term of the wave action balance equation, the wave propagation speed is corrected from the wave group speed to the sum of the wave group speed and the ambient flow speed. The velocity correction is applied to the wave number vector through the Doppler frequency shift formula. During coupling, the near-surface flow speed is selected to form a two-dimensional velocity input, which is mapped to the wave model mesh through the coupler interpolation function. In the sediment-flow feedback, the suspended sediment concentration distribution output by the sediment model is transmitted back to the hydrodynamic model to correct the water density field and simultaneously update the density field used in the pressure gradient force and buoyancy related terms in the momentum equation.

9. The port channel siltation simulation method based on wind-wave-current-sand coupling as described in claim 1, characterized in that, Step S6, which verifies the coupled model based on measured data, specifically includes: The measured data include measured wind field data, measured wave data, measured tide level and flow velocity and direction data, measured suspended sediment concentration and bed sedimentation data; The wind field parameters output by the wind field model are compared with the measured wind field data to complete the parameter calibration of the wind field model. The wave parameters output by the wave model are compared with the measured wave data to complete the parameter calibration of the wave model. The hydrodynamic parameters output by the hydrodynamic model are compared with the measured tidal level, flow velocity and direction data to complete the parameter calibration of the hydrodynamic model. The sediment parameters output by the sediment model are compared with the measured suspended sediment concentration and bed sedimentation data to complete the parameter calibration of the sediment model.

10. A port channel siltation simulation system based on wind-wave-current-sand coupling, characterized in that, The system is used to implement the method as described in any one of claims 1 to 9, comprising: The preprocessing module is used to acquire the driving data and measured data of the target area, and to perform grid division and data preprocessing on the target area; The atmospheric module is used to build a wind field model of the target area and generate wind field parameters. The wave module is used to receive wind speed and surface wind stress, build a wave model of the target area, and output wave parameters. The hydrodynamic module is used to receive the radiation stress tensor, establish a hydrodynamic model of the target area, and output hydrodynamic parameters. The sediment module is used to receive the three-dimensional flow velocity field and bottom shear stress, establish a sediment model of the target area, and output sediment parameters. The coupling interaction module connects with the atmospheric module, wave module, hydrodynamic module and sediment module. Based on the MCT coupler, it is responsible for the positive transmission and bidirectional feedback of variables between modules, and has the functions of dynamic step size adjustment and heterogeneous mesh interpolation conversion. The model validation module is used to compare and validate the output results of each model module and calibrate the parameters based on measured data to obtain the wind-wave-current-sand coupling model.

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