Calculation method for simulating blockage particle trajectory

Abstract

The invention relates to the technical field of marine environment simulation and risk assessment, in particular to a calculation method for simulating a blockage particle trajectory. The calculation method for simulating the particle trajectory of the blockages comprises the steps that a wave flow coupling model is constructed according to a grid file, and the grid file is constructed through a surface water simulation system; in a particle tracking module of the wave-flow coupling model, adjusting particle parameters of blockage particles according to the dynamic characteristics of the blockage; operating the particle tracking module to obtain simulated trajectory data of the blockage particles after particle parameter adjustment; and according to the simulated trajectory data, counting the number and probability of the blockage particles entering the predetermined range of the nuclear power station water intake so as to predict the blockage risk of the nuclear power station water intake. According to the calculation method, the drifting trajectory of the blockage object can be accurately simulated, the risk that the water intake is blocked is predicted, and a reliable quantitative basis is provided for water intake safety early warning and protection decision making of the nuclear power station.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of marine environment simulation and risk assessment, and particularly relates to a calculation method for simulating trajectories of particles of a blockage. BACKGROUND

[0002] At present, as an important clean energy, the safety of the water intake of a nuclear power plant is a key to normal operation of the nuclear power plant. Blockage of the water intake is one of the major risks threatening the safe operation of the nuclear power plant, and a large number of marine organisms such as kelp and jellyfish gathering and drifting with the flow is one of the main causes of blockage of the water intake of the nuclear power plant. Therefore, accurate prediction of the drift trajectory of kelp and other blockages in the marine environment is of great significance to risk assessment and early warning of the safe operation of the nuclear power plant.

[0003] There are mainly two kinds of prior art: one is to rely on satellite remote sensing, sonar detection and other technologies to observe the drift trajectory of the blockage. The other is to use two-dimensional water area simulation software (such as MIKE 21 software) to obtain the drift trajectory of the blockage by simulating the carrying effect of the flow to analyze the source of the blockage.

[0004] Satellite remote sensing, sonar detection and other technical means are easily affected by weather, hydrology and other factors, resulting in low accuracy. The method of using two-dimensional water area simulation software only involves source analysis of the initial spatial position of the single drift trajectory of the blockage, and cannot quantitatively predict the risk of blockage of the water intake. Therefore, there is an urgent need for a method that can accurately simulate the drift trajectory of the blockage and predict the risk of blockage of the water intake. SUMMARY

[0005] To overcome the problems in the related art, the present application provides a calculation method for simulating trajectories of particles of a blockage.

[0006] According to an embodiment of the present application, a calculation method for simulating trajectories of particles of a blockage is provided, and the calculation method comprises: constructing a wave-current coupling model according to a grid file, the grid file being constructed by a surface water simulation system; adjusting particle parameters of particles of the blockage in a particle tracking module of the wave-current coupling model according to dynamic characteristics of the blockage; running the particle tracking module to obtain simulation trajectory data of the particles of the blockage after adjustment of the particle parameters; statistically counting the number and probability of the particles of the blockage entering a predetermined range of a water intake of a nuclear power plant according to the simulation trajectory data to predict the risk of blockage of the water intake of the nuclear power plant.

[0007] In some embodiments of the present disclosure, the wave-current coupling model is constructed according to the grid file, which comprises: verify the hydrodynamic model according to the grid file, so that an error between a simulated water flow parameter output by the hydrodynamic model and an actual water flow parameter is within an error threshold range; construct the wave-current coupling model according to the grid file, the hydrodynamic model and a spectral wave model.

[0008] In some embodiments of the present disclosure, the verifying the hydrodynamic model according to the grid file comprises: inputting the grid file and a predicted boundary file into a hydrodynamic model, so that the hydrodynamic model outputs a simulated water flow parameter; adjusting a model parameter of the hydrodynamic model according to an error between an actual water flow parameter and the simulated water flow parameter, so that the error is within the error threshold range.

[0009] In some embodiments of the present disclosure, before the inputting the grid file and the predicted boundary file into the hydrodynamic model, the computing method further comprises: generating the predicted boundary file according to the grid file and a global tide model, so that the predicted boundary file obtains predicted boundary data varying with time and space.

[0010] In some embodiments of the present disclosure, the generating the predicted boundary file according to the grid file and the global tide model, so that the predicted boundary file obtains the predicted boundary data varying with time and space, comprises: extracting boundary node data in the grid file; performing data fusion processing on the boundary node data and tide prediction time series data in the global tide model, to generate the predicted boundary data varying with time and space, so as to obtain the predicted boundary file.

[0011] In some embodiments of the present disclosure, before the constructing the wave-current coupling model according to the grid file, the computing method further comprises: performing data fusion on coastline data and water depth data by the surface water simulation system, to generate an initial grid file; performing format conversion on the initial grid file, to generate the grid file required for constructing the wave-current coupling model.

[0012] In some embodiments of the present disclosure, the particle parameters of the blockage particles comprise at least one of position, weight, settling velocity and critical shear stress.

[0013] In some embodiments of the present disclosure, the counting the number and probability of the blockage particles entering a predetermined range of a water intake of a nuclear power plant according to the simulated trajectory data comprises: According to the simulated trajectory data, the number and probability of the blocking object particles entering a predetermined range of the water intake of the nuclear power plant when the ocean is in an extreme hydrological condition are obtained to predict the blocking risk of the water intake of the nuclear power plant when the ocean is in the extreme hydrological condition.

[0014] In some embodiments of the present disclosure, the number and probability of the blocking object particles entering a predetermined range of the water intake of the nuclear power plant are obtained according to the simulated trajectory data, including: According to the simulated trajectory data, the number and probability of the blocking object particles entering a predetermined range of the water intake of the nuclear power plant when the ocean is in an extreme hydrological condition are obtained to predict the blocking risk of the water intake of the nuclear power plant when the ocean is in the extreme hydrological condition.

[0015] In some embodiments of the present disclosure, before the running of the particle tracking module and the obtaining of the simulated trajectory data of the blocking object particles after the adjustment of the particle parameters, the calculation method further includes: The initial release position of the blocking object particles is set to be located in a potential source area of the blocking object to simulate the initial spatial position in the actual drift trajectory of the blocking object.

[0016] The technical solutions provided by the embodiments of the present disclosure can include the following beneficial effects: Based on the surface water simulation system, a grid file with an accurate calculation domain is constructed to provide a reliable spatial reference and terrain framework for the simulation of the trajectory of the blocking object particles. Then, based on the grid file, a wave-current coupling model is constructed to comprehensively consider the joint driving effect of the tide and the wave on the blocking object particles and to establish a complex dynamic field conforming to the real ocean environment. In the particle tracking module of the wave-current coupling model, the particle parameters of the blocking object particles are adjusted according to the dynamic characteristics of the blocking object, which can make the obtained simulated trajectory data more accurately reflect the actual drift trajectory of the blocking object in the ocean environment compared with the traditional method only considering the water flow carrying effect. According to the simulated trajectory data, the number and probability of the blocking object particles entering a predetermined range of the water intake of the nuclear power plant are obtained to quantitatively predict the blocking risk of the water intake of the nuclear power plant. In this way, the drift trajectory of the blocking object can be accurately simulated, and the blocking risk of the water intake can be predicted, thereby providing a reliable quantitative basis for the safety warning and protection decision of the water intake of the nuclear power plant.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure.

[0019] Figure 1is a schematic diagram of a calculation method for simulating trajectories of particles of a clogging material according to a first exemplary embodiment.

[0020] Figure 2 is a schematic diagram of a calculation method for simulating trajectories of particles of a clogging material according to a second exemplary embodiment.

[0021] Figure 3 is a schematic diagram of a calculation method for simulating trajectories of particles of a clogging material according to a third exemplary embodiment.

[0022] Figure 4 is a schematic diagram of a calculation method for simulating trajectories of particles of a clogging material according to a fourth exemplary embodiment.

[0023] Figure 5 is a schematic diagram of time series of simulated and actual tidal levels output by a hydrodynamic model according to an exemplary embodiment, where (a) is a time series diagram for a first monitoring location, (b) is a time series diagram for a second monitoring location, and (c) is a time series diagram for a third monitoring location.

[0024] Figure 6 is a schematic diagram of time series of simulated and actual flow velocities output by a hydrodynamic model according to an exemplary embodiment, where (a) is a time series diagram for a first monitoring location, (b) is a time series diagram for a second monitoring location, and (c) is a time series diagram for a third monitoring location.

[0025] Figure 7 is a block diagram of a computer device according to an exemplary embodiment.

[0026] In the drawings: 100 - computer device; 101 - calculation unit; 102 - ROM; 103 - RAM; 104 - bus; 105 - input / output interface; 106 - input unit; 107 - output unit; 108 - storage unit; 109 - communication unit. DETAILED DESCRIPTION

[0027] The exemplary embodiments will be described in detail below with reference to the drawings. In the following description, unless otherwise indicated, like numbers in the different drawings represent similar or identical elements. The following exemplary embodiments described in the detailed description are not meant to be limiting of the present application. Rather, they are example methods in which aspects of the present application can be practiced. It is also to be understood that the use of "and / or" means that any one of the listed items can be used, or any combination of the listed items can be used. It is further to be understood that the use of "or" means that any one of the listed items can be used, and that the use of "comprise" means "comprise, but not limited to".

[0028] At present, nuclear power as an important clean energy, the safety of the nuclear power plant water intake is the key to ensure the normal operation of nuclear power plant. Intake blockage is one of the major risks that threaten the safe operation of the nuclear power plant, and the large number of marine organisms such as kelp and jellyfish gathering and drifting with the flow is one of the main causes of the blockage of the nuclear power plant intake. Therefore, accurately predicting the drift trajectory of kelp and other blockages in the marine environment is of great significance to the risk assessment and early warning of the safe operation of nuclear power plants.

[0029] The prior art mainly has two kinds: one is to rely on satellite remote sensing, sonar detection and other technical means to observe the drift trajectory of the blockage. The other is to use two-dimensional water area simulation software (such as MIKE 21 software) to obtain the drift trajectory of the blockage by simulating the carrying effect of the flow to analyze the source of the blockage.

[0030] Satellite remote sensing, sonar detection and other technical means are easily affected by weather, hydrology and other factors, resulting in low accuracy. The method of using two-dimensional water area simulation software only involves the source analysis of the initial spatial position of the single drift trajectory of the blockage, and cannot quantitatively predict the risk of intake blockage. Therefore, there is an urgent need for a method that can accurately simulate the drift trajectory of the blockage and predict the risk of intake blockage.

[0031] In one example embodiment, as shown in Figure 1 a calculation method for simulating the trajectory of blockage particles is provided, and the calculation method comprises: S1, constructing a wave-current coupling model according to a grid file, and the grid file is constructed by a surface water simulation system.

[0032] S2, in the particle tracking module of the wave-current coupling model, adjusting the particle parameters of the blockage particles according to the dynamic characteristics of the blockage.

[0033] S3, running the particle tracking module to obtain the simulation trajectory data of the blockage particles after adjusting the particle parameters.

[0034] S4, according to the simulation trajectory data, statistics the number and probability of the blockage particles entering the predetermined range of the nuclear power plant intake to predict the blockage risk of the nuclear power plant intake.

[0035] The simulation method for simulating the trajectory of the blockage particle provided in the embodiment is based on a surface water simulation system to construct a grid file with an accurate calculation domain, so as to provide a reliable spatial reference and terrain framework for the simulation of the trajectory of the blockage particle. Then, the wave-current coupling model is constructed based on the grid file, the combined driving effect of the tidal current and the wave on the blockage particle is comprehensively considered, and a complex dynamic field conforming to the real marine environment is established. In the particle tracking module of the wave-current coupling model, the particle parameters of the blockage particle are adjusted according to the dynamic characteristics of the blockage particle, compared with the traditional method of only considering the water flow carrying effect, the obtained simulation trajectory data can more accurately reflect the actual drift trajectory of the blockage particle in the marine environment. According to the simulation trajectory data, the number and probability of the blockage particles entering the predetermined range of the nuclear power plant water intake can be calculated, so as to realize the quantitative prediction of the blockage risk of the nuclear power plant water intake. In this way, the drift trajectory of the blockage particle can be accurately simulated, and the blockage risk of the water intake can be predicted, so as to provide a reliable quantitative basis for the safety warning and protection decision of the nuclear power plant water intake.

[0036] Exemplarily, the blockage particle can be plankton, including at least one of algae and cnidarians. The algae include at least one of kelp, enteromorpha, sargassum and ulva lactuca. The cnidarians include at least one of aequorea, rhopilema esculentum, ephyra and aglaspis xanthopterus.

[0037] In an embodiment, before the wave-current coupling model is constructed according to the grid file in step S1, the calculation method further comprises: The coastline data and the water depth data are fused by the surface water simulation system to generate an initial grid file.

[0038] The initial grid file is format-converted to generate a grid file conforming to the software required for constructing the wave-current coupling model.

[0039] In the embodiment, the coastline data and the water depth data are fused by the surface water simulation system to obtain an initial grid file, so that the real spatial terrain information can be more accurately simulated. Then, the initial grid file is format-converted, so that the grid file is more compatible with the software required for constructing the wave-current coupling model, thereby improving the adaptability of the grid file.

[0040] Exemplarily, the coastline data includes coastline coordinates.

[0041] The above step of fusing the coastline data and the water depth data by the surface water simulation system to generate an initial grid file comprises: The calculation grid is generated by the surface water simulation system according to the coastline data.

[0042] The water depth data is interpolated onto the nodes of the calculation grid by the surface water simulation system to generate an initial grid file.

[0043] In this embodiment, the shoreline data of the spatial range and the water depth data reflecting the terrain undulations are fused in a unified calculation grid framework to generate an initial grid file containing accurate spatial terrain information necessary for hydrodynamic simulation, providing a spatial calculation domain that conforms to the actual geographical features and meets the numerical calculation requirements for the subsequent wave-current coupling model, thus ensuring the accuracy and feasibility of the entire simulation process from the source.

[0044] Further, the step of generating the calculation grid according to the shoreline data comprises: The generated shoreline data is smoothed by the surface water simulation system to generate the calculation grid.

[0045] Further, the shoreline data comprises shoreline coordinate data and closed shoreline data. The generated shoreline data is smoothed by the surface water simulation system to generate the calculation grid, comprising: The shoreline coordinate data is input into the surface water simulation system by the surface water simulation system to form the closed shoreline data.

[0046] The closed shoreline data is smoothed by the surface water simulation system to gradually reduce the shoreline distance from the open sea to the target research area.

[0047] The calculation grid is generated by the surface water simulation system. The calculation grid is optimized by the surface water simulation system to ensure that the size of the calculation grid meets the research requirements.

[0048] The type of the closed shoreline data comprises marine and / or land.

[0049] It should be noted that the calculation grid comprises a large model calculation grid area and a small model calculation grid area. The large model calculation grid area and the small model calculation grid area can be obtained from a publicly available website (e.g. https: / / map.tianditu.gov.cn / ). For example, the large model calculation grid area comprises one or more calculation grids representing continental shelf sea areas. The small model calculation grid area comprises a calculation grid representing an area defined by longitude and latitude, which has a resolution of 0.5° in the center, north, east, and south directions, and the area range of one or more continental shelf sea areas is greater than the area range of the area defined by longitude and latitude. In actual engineering, the area represented by the large model calculation grid area and the small model calculation grid area can be adjusted and set according to requirements, which will not be described in detail here.

[0050] In this embodiment, the closed shoreline data generated by the surface water simulation system can distinguish between ocean and land boundary types, ensuring correct physical constraints. Smoothing eliminates minor irregularities in the shoreline, and the gradient design achieves a reasonable transition in computational grid density. Thus, a computational grid is generated that can accurately fit complex natural shorelines and possesses excellent numerical computational characteristics, thereby indirectly improving the accuracy of subsequent simulation processes.

[0051] Furthermore, the water depth data includes actual water depth data and Global Bathymetric Chart of the Oceans (GEBCO) data. The steps described above involve interpolating the water depth data to the nodes of the computational grid using a surface water simulation system to generate the initial grid file, including: The surface water simulation system interpolates actual water depth data to the nodes of the target study area of ​​the computational grid, and interpolates global seabed topography data to nodes outside the target study area of ​​the computational grid.

[0052] In this embodiment, spatial allocation is performed based on data accuracy characteristics and simulation requirements. High-precision actual water depth data is used for the target study area to ensure local realism, while comprehensive global seafloor topography data is used for the surrounding areas to ensure global integrity. This reduces simulation costs and provides a complete and accurate spatial benchmark and topographic framework for subsequent simulation processes.

[0053] For example, the initial mesh file is converted to a format that meets the requirements for constructing a wave-current coupling model, including: The initial mesh file is in grd format. The surface water simulation system is used to change the format of the initial mesh file to mesh format, so that the mesh format conforms to the format required for constructing the wave-current coupling model.

[0054] In this embodiment, by converting the format of the initial mesh file, the mesh file can be made more compatible with the software required to build the wave-current coupling model, thereby improving the adaptability of the mesh file.

[0055] In one embodiment, such as Figure 2 As shown, step S1, which involves constructing a wave-current coupling model based on the mesh file, includes: S11. Based on the mesh file, verify the hydrodynamic model to ensure that the error between the simulated flow parameters and the actual flow parameters output by the hydrodynamic model is within the error threshold range.

[0056] S12, construct a wave-current coupling model based on the mesh file, hydrodynamic model, and spectral wave model.

[0057] In this embodiment, the water dynamic model is first verified by the grid file to make the simulated water flow parameters consistent with the actual water flow parameters, and to make the driving force field representing the tidal current more consistent with the real marine environment. On this basis, the spectrum wave model is introduced to introduce the driving force field representing the wave. By coupling the grid file, the spectrum wave model and the verified water dynamic model, a high-precision wave-current coupling model can be constructed to simulate the composite driving force field between the wave and the tidal current. This makes the wave-current coupling model accurately simulate the two-dimensional structural characteristics of the nearshore current under the action of the wave, significantly improves the prediction accuracy of the drift trajectory of the blocking object particles in the complex flow field area near the water intake, completely overcomes the serious distortion problem of the single tidal current model in simulating the particle motion in the nearshore area, and accurately simulates the drift trajectory of the blocking object.

[0058] It should be noted that the grid file is a data file which defines the calculation domain of the numerical model by discretizing (i.e. dividing) a complex, continuous natural geographical space (such as a sea area) into a large number of small, simple geometric units (such as triangles or quadrilaterals). The water dynamic model is a mathematical model for simulating the motion law of water body and related physical processes, which is constructed based on the basic principles of fluid mechanics and numerical discretization method. The water dynamic model reproduces the dynamics of the real water body (for example, simulates tides, wind-driven current, Coriolis force, outputs tidal level, flow rate, flow direction, etc.) by solving two-dimensional shallow water equations by computer. The water dynamic model can be set as the water dynamic model in the widely used MIKE 21 software in the world. The spectrum wave model is a numerical model which describes the spatio-temporal evolution law of the wave field by using the spectrum decomposition method based on the concept of wave spectrum and the principle of wave action conservation. The spectrum wave model simulates the generation, growth, propagation, attenuation and deformation of waves and other physical processes by solving the wave action balance equation. The spectrum wave model can be set as the spectrum wave model in the widely used MIKE 21 software in the world. The wave-current coupling model is a high-order numerical model which can simultaneously and bidirectionally simulate the interaction between waves and tidal currents. The wave-current coupling model includes a particle tracking module, which is a post-processing tool for simulating the drift trajectory of passive tracers based on pre-calculated flow field by solving the particle motion equation.

[0059] In an embodiment, as shown in FIG. 1, the step S11 of verifying the water dynamic model according to the grid file includes: Figure 3 S111, inputting the grid file and the prediction boundary file into the water dynamic model to make the water dynamic model output the simulated water flow parameters.

[0060] S112, adjusting the model parameters of the water dynamic model according to the error between the actual water flow parameters and the simulated water flow parameters to make the error within the error threshold range.

[0061] ​In the embodiment, the grid file with spatial terrain information and the prediction boundary file with time-space information are input into the hydrodynamic model, so that the output simulated water flow data has a time-space distribution. The model parameters of the hydrodynamic model are adjusted through the error between the real water flow data and the simulated water flow data, and the output simulated water flow data of the hydrodynamic model is more consistent with the real water flow data through closed-loop control, so that the output simulated water flow data of the hydrodynamic model is highly consistent with the actual water flow data of the real marine environment in time-space distribution, and the subsequent wave-current coupling model and the obtained drift trajectory of the blockage particle are more accurate.

[0062] In an embodiment, before the grid file and the prediction boundary file are input into the hydrodynamic model in step S111, the calculation method further includes: According to the grid file and the global tidal model, the prediction boundary file is generated, so that the prediction boundary file obtains prediction boundary data varying with time and space.

[0063] In the embodiment, the tidal prediction time series data provided by the global tidal model is fully utilized, so that the prediction boundary file can provide accurate prediction boundary data varying with time and space, thereby accurately simulating the dynamic process of the real tide to more accurately simulate the drift trajectory of the blockage.

[0064] In an embodiment, as shown in Figure 4 According to the grid file and the global tidal model, the prediction boundary file is generated, so that the prediction boundary file obtains prediction boundary data varying with time and space, including: S1111, extracting boundary node data in the grid file.

[0065] S1112, performing data fusion processing on the boundary node data and the tidal prediction time series data in the global tidal model to generate prediction boundary data varying with time and space, so as to obtain the prediction boundary file.

[0066] In the embodiment, the extraction of the boundary node data in the grid file can obtain the spatial terrain information of the local area represented by the boundary node data. Then, the data fusion processing of the boundary node data and the tidal prediction time series data can simultaneously obtain the prediction boundary data of the local area which is completely matched in space and continuously consistent in time, so that the prediction boundary data of the local area varies with time and space, thereby more accurately simulating the dynamic process of the real tide in the local area to more accurately simulate the drift trajectory of the blockage.

[0067] Exemplarily, the extraction of the boundary node data in the grid file in step S1111 includes: Opening the grid file in the surface water simulation system.

[0068] The boundary recognition function is called to automatically recognize and select all boundary nodes in the grid file.

[0069] The spatial coordinate data of the selected boundary nodes is extracted as boundary node data.

[0070] The number of boundary node data is associated with the resolution of the grid file.

[0071] In this embodiment, the boundary node data necessary for driving the global tide model is quickly and accurately generated, ensuring the accuracy of the predicted boundary data from the data source, and avoiding omissions or errors that may be caused by manual extraction.

[0072] Exemplarily, the boundary node data includes simulation time and boundary node coordinates. In step S1112, the boundary node data is data-fused with the tide prediction time series data in the global tide model to generate predicted boundary data that varies with time and space, including: The boundary node data is input into the global tide model.

[0073] The simulation time is used as the prediction time of the global tide model, so that the global tide model outputs the tidal level time series corresponding to the simulation time and the boundary node coordinates.

[0074] The tidal level time series corresponding to the simulation time and the boundary node coordinates is used as the predicted boundary data to form a predicted boundary file.

[0075] The format of the predicted boundary file can be set to DFS1 format.

[0076] In this embodiment, the predicted boundary data that combines the dynamics of tides in time and space is generated, thereby making the simulation process more accurate.

[0077] Exemplarily, in step S111, the grid file and the predicted boundary file are input into the hydrodynamic model to make the hydrodynamic model output simulated water flow parameters, including: The grid file is imported into the hydrodynamic model as the calculation domain of the hydrodynamic model.

[0078] The model parameters of the hydrodynamic model are initialized.

[0079] The predicted boundary file is imported into the hydrodynamic model to make the boundary condition of the hydrodynamic model be the predicted boundary data.

[0080] The hydrodynamic model is run to make the hydrodynamic model output simulated water flow data.

[0081] Exemplarily, the information of the input hydrodynamic model of the grid file in the above step includes boundary node coordinates, boundary types, simulation time. The information of the input hydrodynamic model of the predicted boundary file includes predicted boundary data corresponding to the simulation time and the boundary node coordinates. The predicted boundary data is a tidal level time series corresponding to the simulation time and the boundary node coordinates.

[0082] Exemplarily, the model parameters of the hydrodynamic model in the above step include at least one of a step interval of the simulation time, a critical convergence condition parameter, a bottom friction, a calculation grid, a boundary condition, and a water depth interpolation. In this embodiment, the accuracy of the hydrodynamic model simulation can be systematically improved through the synergistic effect of multiple model parameters.

[0083] Exemplarily, the model parameters of the initialized hydrodynamic model in the above step include: determining a shallow water equation, a transport equation, and a bottom friction parameter in the Cartesian coordinates of the hydrodynamic model.

[0084] wherein the formula of the shallow water equation is as follows:

[0085] wherein, is a horizontal coordinate, is a vertical coordinate, is a critical convergence condition parameter of the shallow water equation, is a total water depth, is is a velocity component in the x direction, is a velocity component in the y direction, is a velocity component in the z direction, is a gravitational acceleration, is is a characteristic length scale in the x direction, is is a characteristic length scale in the y direction, is a step of the simulation time, and is approximately calculated by the minimum side length of each calculation grid, , , is evaluated at the center of each calculation grid. The minimum value of is set to 0.01s, the maximum value is 120s, is 0.8. The grid file is input into the hydrodynamic model.

[0086] and the formula of the transport equation is as follows:

[0087] wherein, a critical convergence condition parameter of the transport equation, a characteristic length scale of the direction, a characteristic length scale of the direction, is a step length of the simulation time. The maximum value of is set to 0.01s, the minimum value of is 120s, is 0.8. The grid file is input into the hydrodynamic model.

[0088]

[0089] wherein, is the Manning number, is the bed roughness.

[0090] Exemplarily, the simulation flow parameters in the above step include at least one of a simulation flow velocity, a simulation flow direction, and a simulation tidal level. The actual flow parameters include at least one of an actual flow velocity, an actual flow direction, and an actual tidal level. In this embodiment, the dimension of the verification of the hydrodynamic model can be improved, so that the hydrodynamic model is more in line with the real marine environment.

[0091] Exemplarily, the step of importing the prediction boundary file into the hydrodynamic model to make the boundary condition of the hydrodynamic model the prediction boundary data in the above step includes: when the calculation grid in the hydrodynamic model is a large model calculation grid region, the boundary condition of the hydrodynamic model is the prediction boundary data in the prediction boundary file. Or, when the calculation grid in the hydrodynamic model is a small model calculation grid region, the prediction boundary data in the prediction boundary file is first input into the hydrodynamic model to obtain a simulation tidal level.

[0092] The prediction boundary data is updated using the simulation tidal level to obtain updated prediction boundary data.

[0093] The boundary condition of the hydrodynamic model is made the updated prediction boundary data.

[0094] In this embodiment, different prediction boundary data is used according to the type of the calculation grid, which can make the prediction boundary data more in line with the actual marine environment, and can improve the calculation efficiency of the hydrodynamic model.

[0095] Exemplarily, in step S112, the step of adjusting the model parameters of the hydrodynamic model according to the error between the actual flow parameters and the simulation flow parameters to make the error within the error threshold range in the above step includes: Calculate the error between simulated water flow parameters and actual water flow parameters.

[0096] Determine whether the error is within the error threshold range.

[0097] If the error is not within the error threshold range, adjust at least one model parameter and rerun the hydrodynamic model until the error is within the error threshold range.

[0098] In this embodiment, the error between simulated and actual flow parameters is used as an objective calibration index. Through iterative feedback loops, the model parameters in the hydrodynamic model are adjusted inversely, transforming the general hydrodynamic model into a customized high-precision model capable of accurately reproducing the local hydrological characteristics of a specific engineering sea area. This results in more accurate wave-current coupling models and drift trajectories of the acquired blockage particles.

[0099] For example, such as Figure 5 and Figure 6 As shown, the errors in the above steps include tidal level correlation rate and / or current velocity correlation rate. The error threshold range corresponding to the tidal level correlation rate is the tidal current error threshold range, and the error threshold range corresponding to the current velocity correlation rate is the current velocity error threshold range. Errors within the error threshold range include, for example: Figure 5 The tidal level correlation rate shown is within the tidal error threshold range, and / or, as Figure 6 The velocity correlation rates shown are within the velocity error threshold range. The tidal level correlation rate is the error between the simulated and actual tidal levels, and the velocity correlation rate is the error between the simulated and actual flow velocities. Nine monitoring locations can be set. Figure 5 Only the simulated and actual tide levels at three monitoring locations are shown, where (a) is the simulated and actual tide level at the first monitoring location, (b) is the simulated and actual tide level at the second monitoring location, and (c) is the simulated and actual tide level at the third monitoring location. Figure 6 Only simulated and actual flow velocities at three monitoring locations are shown. (a) shows the simulated and actual flow velocities at the first monitoring location, (b) shows the simulated and actual flow velocities at the second monitoring location, and (c) shows the simulated and actual flow velocities at the third monitoring location. The tidal level correlation rate ranges from [98%, 100%], and can be 98%, 99%, or 100%. The flow velocity correlation rate ranges from [86%, 100%], and can be 86%, 90%, 95%, or 100%.

[0100] For example, in step S12, a wave-current coupling model is constructed based on the mesh file, the hydrodynamic model, and the spectral wave model, including: The hydrodynamic model and the spectral wave model are coupled to generate an initial wave-current coupled model.

[0101] inputting the mesh file into the initial wave-current coupling model, so that the initial wave-current coupling model adopts the calculation domain in the mesh file.

[0102] adjusting the hydrodynamic model control equation and the spectral wave model control equation in the initial wave-current coupling model, so that the initial wave-current coupling model generates a wave-current coupling model.

[0103] The wave-current coupling model comprises a particle tracking module.

[0104] Exemplarily, the hydrodynamic model control equation in the above step adopts a two-dimensional shallow water equation set along a deep integral of a Navier-Stokes equation, and turbulence action is parameterized in the form of a eddy viscosity coefficient, and the hydrodynamic model control equation is as follows:

[0105] wherein, is a still water depth; is a free water surface elevation; is time; , denote a horizontal coordinate and a vertical coordinate of a calculation mesh respectively; and are vertical average flow velocities along directions of and respectively; and are vertical average flow velocities along directions of and respectively; is a gravitational acceleration; is a water body density; is a Coriolis force coefficient; and are horizontal eddy viscosity coefficients along directions of and respectively; , are components of a surface wind stress along directions of and respectively; , are components of a bed shear stress along directions of and respectively; , are components of a bed shear stress along directions of and respectively; , are components of a bed shear stress along directions of and respectively; , are components of a bed shear stress along directions of and respectively; , , and are directional components of wave radiation stress, which are calculated by using a Longuet-Higgins (1964) wave radiation stress formula.

[0106] The spectral wave model control equation in the above step is as follows:

[0107] wherein, is a wave action quantity, is a source-sink term, is a propagation speed, is a current speed, is a water depth, 、 is a wave number and = λ / 2π, λ is a wavelength, , respectively represent the horizontal and vertical coordinates of the calculation grid, is a wave frequency, is a wave direction, is a simulation time, , respectively represent the displacement of the wave in the , direction, is a variation of the wave frequency, is a variation of the wave direction, , is a wave group speed, is a gradient of the water depth in the direction, is a coordinate perpendicular to the direction.

[0108] In this embodiment, the hydrodynamic model describing large-scale tidal current motion is dynamically coupled with the spectral wave model describing the fine process of the wave, and the physical energy transfer and mutual modulation between the wave and the tidal current are realized. The complex nonlinear interaction between the wave and the tidal current in the nearshore area can be accurately reproduced, and the authenticity and accuracy of the drift trajectory of the blocking particles are significantly improved.

[0109] Exemplarily, the spectral wave model in the above steps can be set as the spectral wave model in the widely used MIKE 21 software in the world.

[0110] In an embodiment, the particle parameters of the blocking particles in step S2 include at least one of the position, the weight, the settling velocity, and the critical shear stress. It should be noted that in actual engineering, the particle parameters must include the position and the weight, and the settling velocity and the critical shear stress can be adjusted and set according to the type or characteristics of the blocking particles, that is, the particle parameters can not include the settling velocity or the critical shear stress, or can include the settling velocity or the critical shear stress.

[0111] It should be noted that the settling velocity refers to the settling velocity of the blocking particles in the static water settling experiment, which can reflect the settling law of the blocking particles in the vertical direction. The critical shear stress refers to the threshold of the hydrodynamic condition required by the particle to start drifting in the water tank experiment.

[0112] Exemplarily, the dynamic characteristics of the blockage in step S2 include at least one of position, weight, settling velocity, critical shear stress, and correspond to one of the particle parameters.

[0113] When the blockage type is kelp, the weight of the blockage particle is set to be less than or equal to 0.01 kg, the settling velocity is set to be 0.019 m / s, and the critical shear stress is set to be the default value in the wave-current coupling model implementation software. The settling velocity in the static water settling experiment is obtained by performing a static water settling experiment on kelp fragments cut into strips in a barrel with a water depth of 50 cm, a barrel bottom diameter of 45 cm, and a barrel top diameter of 56 cm.

[0114] In this embodiment, the position is associated with the spatial distribution of the blockage particles in the potential source area, the weight is associated with the inertia characteristics and motion response sensitivity of the blockage particles, the settling velocity is associated with the depth in the drift trajectory of the blockage particles (especially when the blockage is set to kelp, which has a unique settling-suspending dynamic balance behavior), and the critical shear stress is associated with the threshold of the hydrodynamic condition required to start drifting. The two-dimensional motion process of different types and different physical states of blockages in a complex flow field can be accurately simulated. The problem of distorted drift trajectory caused by improper parameter setting of the general particle model is solved, and the accuracy of the wave-current coupling model in predicting the drift trajectory of specific blockages is greatly improved.

[0115] Exemplarily, the format of the simulated trajectory data in the above steps can be xml format and dfsu format, and the file in dfsu format is used as the carrier of the file in xml format to facilitate subsequent programmed statistics of the simulated trajectory data.

[0116] In an embodiment, the number and probability of the blockage particles entering the predetermined range of the nuclear power plant intake according to the simulated trajectory data in step S4 include: According to the simulated trajectory data, the number and probability of the blockage particles entering the predetermined range of the nuclear power plant intake under extreme hydrological conditions are obtained to predict the blockage risk of the nuclear power plant intake under extreme hydrological conditions.

[0117] Exemplarily, the extreme hydrological conditions include a high tide level that occurs once in a hundred years. The high tide level is a value range of the tide level height [1.72m, +∞], and the value can be 1.72m, 1.73m, 1.8m. The predetermined range of the nuclear power plant intake includes that the blockage appears within 1 kilometer from the nuclear power plant intake as the center within one day, or the blockage appears within 500 meters from the nuclear power plant intake as the center within one day.

[0118] In this embodiment, the maximum blockage risk of the nuclear power plant water intake under extreme weather events can be quantitatively predicted, and the number and probability of the blockage material invading the water intake under specific extreme hydrological conditions can be accurately calculated, thereby providing reliable quantitative basis for formulating emergency plans to deal with extreme weather, optimizing the design standards of water intake protection engineering, and evaluating the effectiveness of existing protection measures, and greatly improving the resilience and disaster prevention and reduction capacity of nuclear power plants in response to climate change.

[0119] In one embodiment, the number and probability of the blockage material particles entering the predetermined range of the nuclear power plant water intake according to the simulated trajectory data in step S4 include: According to the simulated trajectory data, the number and probability of the blockage material particles entering the predetermined range of the nuclear power plant water intake under different operating conditions of the nuclear power plant are obtained to predict the blockage risk of the nuclear power plant water intake under different operating conditions of the nuclear power plant.

[0120] For example, the different operating conditions of the nuclear power plant include at least one of all water intakes operating simultaneously, water intakes operating alternately, water intakes operating in different zones, and water intakes being repaired.

[0121] In this embodiment, the correlation between the operation strategy of the nuclear power plant and the blockage risk of the water intake can be quantitatively evaluated, the high-risk operating conditions can be identified, and the trend of the blockage probability of the water intake under different power generation plans can be predicted, thereby providing scientific basis for the power plant operator to formulate differentiated operation warning thresholds and prevention and control measures, realizing the leap from static risk evaluation to dynamic risk control, and improving the safe and economic operation level of the nuclear power plant.

[0122] For example, the number and probability of the blockage material particles entering the predetermined range of the nuclear power plant water intake according to the simulated trajectory data in step S4 can be counted by a programming language. The programming language includes but is not limited to python, C language, C++, Java, and assembly language. In this embodiment, the intelligence and efficiency of risk prediction can be improved.

[0123] In one embodiment, before the particle tracking module is run in step S3 to obtain the simulated trajectory data of the blockage material particles after the particle parameters are adjusted, the method further includes: The initial release position of the blockage material particles is set to be located in the potential source area of the blockage material, so as to simulate the initial spatial position in the actual drift trajectory of the blockage material.

[0124] In this embodiment, the comprehensive risk contribution of all potential source areas in the entire target research area to the water intake can be evaluated without omission, the key high-risk source area and the main drift path can be accurately identified, and the spatial probability distribution of the blockage material invading the water intake in the global range can be calculated, thereby providing complete scientific basis for implementing precise source control measures, optimizing monitoring and early warning deployment, and formulating regional risk management strategies.

[0125] When the type of the blockage is kelp, the initial release position of the blockage particles is set to the potential source area of the target study area, the extreme hydrological condition is set to the high tide level of 100-year return period, i.e. the tide level height is 1.72 m, and the wave-current coupling model is run 15 days in advance to make the hydrodynamic field in the wave-current coupling model reach a steady state. After the wave-current coupling model reaches the steady state, the release interval of the blockage particles is set to 1000 particles per hour within 24 hours per day, and the total number of the released blockage particles is 24000, and the blockage particles are released. In the 2-hour movement trajectory of the blockage particles, it can be seen that the blockage particles have gathered in the predetermined range of the nuclear power plant water intake. It can be calculated that the number and probability of the blockage appearing in the 500-meter radius around the nuclear power plant water intake within one day are 2327 and 0.78% respectively. In the calculation, the repeatability of the blockage particles is not ignored considering the tidal dynamics. The blockage particles that have entered the predetermined range of the nuclear power plant water intake may be taken away from the predetermined range again at the rising slack, falling slack, rising rapid and falling rapid, and may be repeated to return to the predetermined range of the nuclear power plant water intake next time.

[0126] In an example embodiment, a computer device is provided, comprising a processor and a memory, the memory storing a computer program, the processor implementing the steps of the above-mentioned any simulation of the trajectory of the blockage particles when executing the computer program.

[0127] In an example embodiment, a computer readable storage medium is provided, storing a computer program, the computer program implementing the steps of the above-mentioned any simulation of the trajectory of the blockage particles when executed by a processor. The computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0128] In an example embodiment, a computer program product is provided, comprising a computer program, the computer program implementing the steps of the above-mentioned any simulation of the trajectory of the blockage particles when executed by a processor.

[0129] Reference Figure 7 will now be described that can be a structural block diagram of the computer device of the present application, the computer device 100 comprises a computing unit 101, which can perform various appropriate actions and processes according to the computer program stored in the ROM 102 or the computer program loaded from the storage unit 108 to the RAM 103. In the RAM 103, various programs and data required for the operation of the computer device 100 can also be stored. The computing unit 101, the ROM 102 and the RAM 103 are connected to each other through a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0130] A plurality of components in the computer device 100 are connected to the I / O interface 105, including: an input unit 106, an output unit 107, a storage unit 108, and a communication unit 109. The input unit 106 can be any type of device that can input information to the computer device 100, receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the computer device 100, and can include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 107 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 108 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 109 allows the computer device 100 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0131] The computing unit 101 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 101 performs various methods and processes described above, such as a computing method of simulating trajectories of particles of a blockage. For example, in some embodiments, a computing method of simulating trajectories of particles of a blockage can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed onto the computer device 100 via the ROM 102 and / or the communication unit 109. When the computer program is loaded onto the RAM 103 and executed by the computing unit 101, one or more steps of a computing method of simulating trajectories of particles of a blockage described above can be performed. Alternatively, in other embodiments, the computing unit 101 can be configured to perform a computing method of simulating trajectories of particles of a blockage by any other appropriate means, such as by means of firmware.

[0132] The computer device 100 can be implemented with one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components, for performing the above-mentioned method of simulating a trajectory of a particle of a blockage.

[0133] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0134] It is to be understood that the application is not limited to the precise structures hereinabove described and shown in the drawings, for purposes of illustration and education only, and that various modifications and changes can be made therein without departing from the scope thereof. The scope of the application is indicated by the claims appended hereto.

Claims

1. A method for calculating the trajectory of particles simulating blockages, characterized in that, The calculation method includes: A wave-current coupling model is constructed based on a mesh file, which is generated using a surface water simulation system. In the particle tracking module of the wave-current coupling model, the particle parameters of the blockage particles are adjusted according to the dynamic characteristics of the blockage. Run the particle tracking module to obtain simulated trajectory data of the blockage particles after the particle parameters have been adjusted; Based on the simulated trajectory data, the number and probability of the blockage particles entering the predetermined range of the nuclear power plant's water intake are statistically analyzed to predict the blockage risk of the nuclear power plant's water intake.

2. The calculation method according to claim 1, characterized in that, The step of constructing the wave-current coupling model based on the mesh file includes: Based on the mesh file, the hydrodynamic model is verified so that the error between the simulated flow parameters and the actual flow parameters output by the hydrodynamic model is within the error threshold range. The wave-current coupling model is constructed based on the mesh file, the hydrodynamic model, and the spectral wave model.

3. The calculation method according to claim 2, characterized in that, The step of verifying the hydrodynamic model based on the mesh file includes: The mesh file and the predicted boundary file are input into the hydrodynamic model so that the hydrodynamic model outputs simulated flow parameters. Based on the error between the actual water flow parameters and the simulated water flow parameters, the model parameters of the hydrodynamic model are adjusted so that the error is within the error threshold range.

4. The calculation method according to claim 3, characterized in that, Before inputting the mesh file and predicted boundary file into the hydrodynamic model, the calculation method further includes: The prediction boundary file is generated based on the grid file and the global tidal model, so that the prediction boundary file obtains prediction boundary data that varies with time and space.

5. The calculation method according to claim 4, characterized in that, The step of generating a prediction boundary file based on the grid file and the global tidal model includes: Extract the boundary node data from the mesh file; The boundary node data is fused with the tidal prediction time series data in the global tidal model to generate the prediction boundary data that varies with time and space, thus obtaining the prediction boundary file.

6. The calculation method according to any one of claims 1 to 5, characterized in that, Before constructing the wave-current coupling model based on the mesh file, the calculation method further includes: The surface water simulation system fuses shoreline data with water depth data to generate an initial grid file. The initial mesh file is converted to a format that meets the requirements for constructing the wave-current coupling model.

7. The calculation method according to any one of claims 1 to 5, characterized in that, The particle parameters of the blockage particles include at least one of the following: position, weight, settling velocity, and critical shear stress.

8. The calculation method according to any one of claims 1 to 5, characterized in that, The step of calculating the number and probability of the blockage particles entering the predetermined range of the nuclear power plant's water intake based on the simulated trajectory data includes: Based on the simulated trajectory data, the number and probability of the blockage particles entering the predetermined range of the nuclear power plant intake under extreme ocean hydrological conditions are obtained, so as to predict the blockage risk of the nuclear power plant intake under extreme ocean hydrological conditions.

9. The calculation method according to any one of claims 1 to 5, characterized in that, The step of calculating the number and probability of the blockage particles entering the predetermined range of the nuclear power plant's water intake based on the simulated trajectory data includes: Based on the simulated trajectory data, the number and probability of the blockage particles entering the predetermined range of the nuclear power plant intake under different operating conditions are obtained, so as to predict the blockage risk of the nuclear power plant intake under different operating conditions.

10. The calculation method according to any one of claims 1 to 5, characterized in that, Before running the particle tracking module to obtain the simulated trajectory data of the blockage particles after the particle parameters have been adjusted, the calculation method further includes: The initial release position of the blockage particles is set to be located in the potential source region of the blockage, in order to simulate the initial spatial position in the actual drift trajectory of the blockage.

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