Marine ranch-oriented storm surge disaster numerical simulation optimization method

Abstract

The invention relates to the field of numerical simulation optimization, in particular to a marine ranch-oriented storm surge disaster numerical simulation optimization method. The method comprises the steps that the boundary and the initial still water level of a marine ranch are obtained, the geographic space of the marine ranch is discretized into a rectangular grid layout, meteorological grid point data of the marine ranch are obtained, the total water level increment is calculated through a wind pressure wind field physical driving water level increment calculation algorithm, and the updated water level is obtained; and based on the updated water level, introducing a similarity weight mechanism of storm path projection, performing local enhancement correction on a water level field in storm surge simulation to obtain a water level after weight correction, and outputting a water level spatial distribution map. The problem that most of traditional storm surge disaster numerical simulation optimization methods neglect the dynamic adjustment effect of wind speed changes on sea surface roughness and wind resistance is solved; the effect of'inverted barometer 'is neglected, and air pressure change cannot be dynamically responded; and the regulation and control effect of geographic space factors on the simulated water level field cannot be accurately reflected.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of numerical simulation optimization, in particular to a storm surge disaster numerical simulation optimization method for mariculture. BACKGROUND

[0002] Storm surge is an abnormal rise in sea level caused by strong winds and low pressure, often accompanied by extreme weather events such as typhoons, tropical cyclones, and monsoon pressure changes. The formation mechanism includes wind stress-driven horizontal accumulation of sea water, sea level rise caused by pressure drop, and the joint modulation of factors such as seabed topography and shoreline shape. Storm surge often leads to significant rise in coastal sea level, forming serious consequences such as seawater backflow, dike overtopping, and infrastructure immersion, and may also cause long-term ecological damage and economic losses. Therefore, storm surge numerical simulation is of great significance in mariculture risk management and disaster prevention decision-making.

[0003] Through physical modeling of the dynamic process of storm surge in mariculture and intelligent optimization of the numerical solution process, not only can the storm surge disaster be quickly predicted and accurately simulated, but also can provide scientific support for the layout optimization, disaster prevention engineering design and emergency response of mariculture.

[0004] However, the traditional storm surge disaster numerical simulation optimization method still has the problems of difficulty in fine modeling of mariculture, lack of physical precision and local adaptability in storm surge simulation, rough modeling of storm path influence, and lack of directionality and spatial heterogeneity expression. SUMMARY

[0005] The present application provides a storm surge disaster numerical simulation optimization method for mariculture to solve the technical problems that the traditional storm surge disaster numerical simulation optimization method mostly uses fixed wind resistance coefficient, ignores the dynamic adjustment effect of wind speed change on sea surface roughness and wind resistance, ignores the "inverted barometer" effect or uses constant correction, cannot dynamically respond to pressure changes, and cannot accurately reflect the regulatory effect of geographical spatial factors on the simulated water level field.

[0006] The present application provides a storm surge disaster numerical simulation optimization method for mariculture, which specifically includes the following technical solutions: A storm surge disaster numerical simulation optimization method for mariculture includes the following steps: S1. Obtain the boundary and initial static water level of the mariculture, discretize the mariculture geographical space into a rectangular grid layout, and obtain the mariculture meteorological grid data; based on the mariculture meteorological grid data, calculate the total water level increment by a wind pressure wind field physical driving water level increment calculation algorithm, and obtain the updated water level; S2. Based on the updated water level, a similarity weight mechanism of storm path projection is introduced to locally enhance and correct the water level field in the storm surge simulation, to obtain the weight corrected water level, and output the water level spatial distribution map.

[0007] Preferably, the S1 specifically comprises: Read the original meteorological grid point data from the external meteorological model, and map the original meteorological grid point data to the marine ranching grid domain to obtain marine ranching meteorological grid point data; the marine ranching meteorological grid point data includes wind speed vector and atmospheric pressure field data, and introduces standard atmospheric pressure, air density, seawater density and gravitational acceleration as constant parameters.

[0008] Preferably, the S1 specifically comprises: In the implementation process of the wind pressure wind field physical driving water level increment calculation algorithm, the wind stress dynamic pressure conversion mechanism and the atmospheric pressure counter pressure effect mechanism are introduced to calculate the independent driving contribution of the wind field and the atmospheric pressure field to the sea surface water level respectively, to obtain the water level increment driven by the wind stress and the water level increment driven by the atmospheric pressure.

[0009] Preferably, the S1 specifically comprises: In the implementation process of the wind stress dynamic pressure conversion mechanism, based on the wind speed vector, the wind resistance coefficient is introduced, and combined with the air density and the seawater density, the water level increment driven by the wind stress is obtained; the wind resistance coefficient is a segmented function according to the dynamic change of the wind speed.

[0010] Preferably, the S1 specifically comprises: In the implementation process of the atmospheric pressure counter pressure effect mechanism, based on the atmospheric pressure field data, combined with the seawater density, the gravitational acceleration and the standard atmospheric pressure, the water level increment driven by the atmospheric pressure is obtained.

[0011] Preferably, the S1 specifically comprises: Add the water level increment driven by the wind stress and the water level increment driven by the atmospheric pressure to each grid point to obtain the total water level increment; add the total water level increment to the current water level to obtain the updated water level.

[0012] Preferably, the S2 specifically comprises: In the implementation process of the similarity weight mechanism of storm path projection, the storm center coordinates are introduced, and the Euclidean distance between the grid point and the storm center is calculated, combined with the storm influence radius control parameter to construct an exponential decay function; by calculating the two-dimensional cross product sign of the radial vector from the storm center to the grid point and the storm moving direction vector, combined with the direction adjustment parameter, a direction enhancement factor is constructed; based on the distance decay function and the direction enhancement factor, the similarity weight of the storm path projection is obtained.

[0013] Preferably, the S2 specifically comprises: Based on the similarity weight of storm path projection, the updated water level is locally enhanced and corrected, the water level after weight correction is obtained, and the two-dimensional matrix sequence of water level evolution with time is finally output as the storm surge response diagram of the marine ranching, assisting the precise protection and emergency response of the marine ranching under the condition of storm surge.

[0014] The beneficial effects of the technical solutions of the present application are: 1. The meteorological grid data of the marine ranching is introduced, the wind stress dynamic pressure conversion mechanism and the air pressure counter pressure effect mechanism are adopted, the water level increment driven by the wind stress and the water level increment driven by the air pressure are calculated respectively, the rapid and physically reasonable simulation of the storm surge water level increase is realized, the water level sudden increase phenomenon caused by the wind pressure cooperation in the storm surge process is accurately captured, and the response speed and accuracy of the numerical model can be improved.

[0015] 2. The segmented change model of the wind resistance coefficient is set according to the wind speed intensity, so that the wind resistance coefficient can automatically adapt to the change of the wind speed range, the air-sea interaction law under low, medium and high wind speed is considered, the physical precision of the wind stress calculation is significantly improved, the simulation result caused by the out-of-control wind resistance coefficient under high wind speed can be avoided, and the real reliability of the wind field driving water level change can be ensured.

[0016] 3. The similarity weight mechanism of storm path projection is introduced to correct the local water level, improve the response ability of the water level simulation to the spatial movement of the storm and the asymmetric structure of the wind field, strengthen the local water level increase performance in the front or right side of the storm, and be more consistent with the observation data and the actual influence distribution. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flow chart of the storm surge disaster numerical simulation optimization method for the marine ranching is described. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0020] The specific scheme of the storm surge disaster numerical simulation optimization method for the marine ranching provided by the present application will be specifically described below in combination with the drawings.

[0021] Refer to the drawings Figure 1 which shows a flow chart of a storm surge disaster numerical simulation optimization method for a mariculture farm according to an embodiment of the present application, the method comprising the following steps: S1, obtaining the mariculture farm boundary and the initial still water level, discretizing the mariculture farm geographical space into a rectangular grid layout, and obtaining the mariculture farm meteorological grid point data; based on the mariculture farm meteorological grid point data, calculating the total water level increment by a wind pressure wind field physical driving water level increment calculation algorithm, and obtaining the updated water level; Due to the fixed and floating facilities such as net cages, floating ropes, and baiting ships arranged in the mariculture farm, the spatial distribution is highly heterogeneous and extremely sensitive to water level fluctuations, so the mariculture farm boundary is taken as the core of the simulation domain to ensure accurate coverage of the dense area of the mariculture farm facilities and avoid interference from external irrelevant sea areas affecting the simulation accuracy; the mariculture farm boundary is obtained from the local fishery department, and the initial still water level is measured by the tide station, and WGS84 / UTM is used as the projection coordinate system; Subsequently, since the mariculture farm is in a shallow, near-shore environment, and is disturbed by artificial facilities such as net cages, platforms, and cofferdams, a higher grid resolution than the general ocean model, such as 10~50m, must be used to capture local small-scale hydrodynamic changes, therefore, a uniform grid resolution is set to discretize the continuous mariculture farm geographical space into a regular rectangular grid layout; Read the current time step's grid point wind speed vector and atmospheric pressure field data from an external meteorological model such as WRF as the original meteorological grid point data, map the original meteorological grid point data to the mariculture farm grid domain through bilinear interpolation to improve the matching of wind pressure and wind field input at the mariculture farm scale; at the same time, introduce standard atmospheric pressure, air density, seawater density, and gravitational acceleration as constant parameters; the above bilinear interpolation is a well-known technical means to those skilled in the art, and will not be described here; Based on the mariculture farm meteorological grid point data, through the wind pressure wind field physical driving water level increment calculation algorithm, the wind stress dynamic pressure conversion mechanism and the atmospheric pressure counter-pressure effect mechanism are introduced to calculate the independent driving contribution of the wind field and the atmospheric pressure field to the sea surface water level respectively, and finally combined to form the total water level increment, realizing the rapid physical simulation of the storm surge; The wind stress dynamic pressure conversion mechanism is the conversion of wind stress dynamic pressure to water level increment. First, the wind speed module length, i.e. the Euclidean norm of the wind speed vector, is calculated to reflect the overall intensity of the wind. Then, the wind resistance coefficient is introduced, which is not a fixed value but a piecewise function that changes dynamically with the wind speed. In the low wind speed area, i.e. when the wind speed module length is less than 10 meters per second, the sea surface is relatively smooth, the air-sea friction is mainly controlled by laminar flow, the roughness is low, and the momentum transfer efficiency is limited. Therefore, the wind resistance coefficient takes the low wind speed wind resistance coefficient constant value. As the wind speed enters the medium wind speed range, i.e. the wind speed module length ranges from 10 to 30 meters per second, the sea surface roughness increases significantly due to the increase in wind speed, which leads to the increase in sea surface wave breaking, white foam splashing, and vortex flow. Therefore, the wind resistance coefficient increases linearly. In the high wind speed area, i.e. when the wind speed module length exceeds 30 meters per second, the sea surface reaches a dynamic equilibrium state under hurricane-level wind, and excessive foam suppresses further drag growth. Therefore, the wind resistance coefficient reaches a saturated upper limit and no longer increases with wind speed. The wind resistance coefficient is determined based on field observations and wind tunnel experiments by ocean meteorologists. Then, the wind resistance coefficient is multiplied by the air density, and then multiplied by the square of the wind speed module length to obtain the wind stress dynamic pressure value, which represents the equivalent pressure of the horizontal force of the wind pushing the sea water accumulation on the sea surface. The wind stress dynamic pressure value is converted to the water level increment driven by the wind stress by dividing the wind stress dynamic pressure value by the product of the sea water density and the gravitational acceleration. The calculation formula of the water level increment driven by the wind stress is: , wherein, represents the water level increment driven by the wind stress at time and grid point ; represents the air density; represents the sea water density; represents the gravitational acceleration; represents the wind speed vector at time and grid point ; represents the wind speed module length at time and grid point ; represents the square of the wind speed module length at time and grid point ; represents the wind resistance coefficient, which can reflect the influence of sea surface roughness such as waves and foam, and is expressed by a piecewise function as follows: , wherein, represents the low wind speed wind resistance coefficient constant value; The wind resistance coefficient growth rate representing the moderate wind speed interval can reflect the wave breaking and enhanced spray caused by the increase of wind speed, so that the wind resistance coefficient increases linearly. The wind speed module length exceeding 10 m / s is represented as a variable of linear growth, which can quantify the gain effect of wind speed on sea surface roughness. The wind resistance coefficient upper limit at high wind speed prevents overestimating the wind stress; The air pressure counterpressure effect mechanism is due to the "inverted barometer" effect formed by the sea surface in the low pressure area being pushed by the surrounding high pressure, and the water level increment driven by air pressure is calculated accordingly. Specifically, first, calculate the air pressure difference between the current atmospheric pressure field data and the standard atmospheric pressure. The positive air pressure difference indicates that the air pressure decreases. Divide the air pressure difference by the product of the sea water density and the gravitational acceleration to obtain the water level increment driven by air pressure. The calculation formula of the water level increment driven by air pressure is: , Wherein, represents the water level increment driven by air pressure at time grid . represents the standard atmospheric pressure; represents the atmospheric pressure field data at time grid . represents the air pressure difference; Add the water level increment driven by wind stress and the water level increment driven by air pressure point by point to obtain the total water level increment, and then add the total water level increment to the current water level to obtain the updated water level. The formula is as follows: , Wherein, represents the water level at time grid , that is, the updated water level; represents the water level at time , that is, the initial still water level.

[0022] S2, based on the updated water level, introduce the similarity weight mechanism of storm path projection to locally enhance and correct the water level field in storm surge simulation, obtain the weight corrected water level, and output the water level spatial distribution map.

[0023] The specific topography and the form of the breeding structure along the coast of the mariculture farm lead to the uneven and asymmetric spatial distribution characteristics of storm surge influence. The traditional uniform adjustment is difficult to accurately simulate the actual water level increase. Based on the updated water level, through the similarity weight mechanism of storm path projection, a spatial weight function can be constructed according to the actual storm center position and moving direction to assign different weights to the water level influence of different areas in the mariculture farm, and the local storm surge effect of the mariculture farm can be accurately described. The storm center coordinates are obtained from the weather forecast, and the Euclidean distance of each grid point to the storm center is calculated, which is used as the basis for weight attenuation, reflecting the radial propagation characteristics of storm influence. The square of the above Euclidean distance is used to construct the exponential term of the exponential decay function to achieve rapid decay. The exponential term is close to 1 when the distance is short, and it quickly approaches 0 when the distance is far, thus forming a bell-shaped weight distribution with the storm center as the peak in space. The storm influence radius control parameter is introduced in the denominator to determine the size of the influence range. Affected by the Coriolis force and friction effect, the wind speed on the right side of the relative moving direction of the typhoon in the northern hemisphere is significantly higher than that on the left side. This asymmetry directly leads to more serious water level increase on the right side of the coastal area. Therefore, on the basis of distance attenuation, a directional enhancement factor is introduced to capture the asymmetric wind field structure brought by storm movement. Specifically, by calculating the two-dimensional cross product sign of the radial vector from the storm center to the grid point and the storm moving direction vector, the left and right direction sensitivity modeling is realized. The cross product is positive, indicating that the grid point is located on the right side of the storm center, and negative, indicating that the grid point is located on the left side of the storm center, directly quantifying the phenomenon that the right side water level increase is more serious due to the asymmetric wind field structure. The directional enhancement factor is particularly important for mariculture farms because the breeding facilities are located on a specific coastline. When the storm path approaches from a specific direction, the right side or front of the mariculture farm will suffer from stronger wind wave superposition effect. The directional correction can reflect the actual observation law of "water level increase bias in the mariculture farm area". The spatial weight function is constructed by multiplying the exponential decay function and the directional enhancement factor point by point to obtain the storm path projection similarity weight, which is expressed as follows: , wherein, represents the storm path projection similarity weight at time and grid point ; represents the exponential decay function, which can achieve the rapid decay characteristic of "the farther the distance, the smaller the influence"; represents the plane coordinate vector at grid point ; represents the storm center position vector at time ; represents the storm influence radius control parameter, which determines the effective range of storm influence, and is obtained based on the fitting of the maximum wind speed radius of historical typhoons. represents a direction adjustment parameter, which is used to quantitatively characterize the degree of asymmetry of the wind field caused by the movement of the storm, that is, the intensity of the right strong and left weak deviation of the typhoon in the northern hemisphere (relative to the direction of advance), and is obtained by least square fitting, with a value range of The least square method is a technical means familiar to those skilled in the art, and will not be described here; represents a radial vector from the storm center to the grid point; represents a two-dimensional cross product symbol; represents a direction enhancement factor; represents a two-dimensional cross product; represents a sign function, which returns (right side), (left side), (front and rear axes); The updated water level is multiplied by the storm path projection similarity weight, and the water level field in the storm surge simulation is locally enhanced and corrected to obtain the water level after weight correction, which is expressed as follows: , wherein, represents the water level after weight correction at the grid point at time ; Through the above water level updating process of each grid point and each time step, the water level two-dimensional matrix sequence evolving with time is finally output as the marine ranching storm surge response diagram, which not only reflects the spatiotemporal evolution characteristics of the storm surge, but also directly serves the safety management, ecological regulation and economic loss assessment of the marine ranching. The water level change distribution at each time can be dynamically displayed in the digital twin monitoring platform to assist the fishery department in implementing disaster prevention scheduling, facility reinforcement and personnel evacuation decisions, and to realize precise protection and emergency response of the marine ranching under the condition of storm surge.

[0024] In summary, an optimization method for numerical simulation of storm surge disaster for marine ranching is completed.

[0025] The order of the embodiments is merely for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or can be advantageous.

[0026] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0027] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for numerical simulation optimization of storm surge disaster for ocean ranching, characterized in that, The method comprises the following steps: S1. Obtain the marine ranching boundary and the initial still water level, discretize the marine ranching geospatial into a rectangular grid layout, and obtain marine ranching meteorological grid point data; based on the marine ranching meteorological grid point data, calculate the total water level increment by a wind pressure wind field physical driving water level increment calculation algorithm, and obtain the updated water level; S2. Based on the updated water level, introduce a similarity weight mechanism of storm path projection to locally enhance and correct the water level field in the storm surge simulation, obtain the water level after weight correction, and output the water level spatial distribution map.

2. The method according to claim 1, wherein, The S1 specifically comprises: Read the original meteorological grid point data from an external meteorological model, and map the original meteorological grid point data to the marine ranching grid domain to obtain marine ranching meteorological grid point data; the marine ranching meteorological grid point data includes wind speed vector and atmospheric pressure field data, and standard atmospheric pressure, air density, seawater density and gravitational acceleration are introduced as constant parameters.

3. The method of claim 2, wherein, The S1 specifically comprises: In the implementation process of the wind pressure wind field physical driving water level increment calculation algorithm, the wind stress dynamic pressure conversion mechanism and the atmospheric pressure counter pressure effect mechanism are introduced to calculate the independent driving contribution of the wind field and the atmospheric pressure field to the sea surface water level respectively, and the water level increment driven by the wind stress and the water level increment driven by the atmospheric pressure are obtained.

4. The method according to claim 3, wherein, The S1 specifically comprises: In the implementation process of the wind stress dynamic pressure conversion mechanism, based on the wind speed vector, the wind resistance coefficient is introduced, and the air density and the seawater density are combined to obtain the water level increment driven by the wind stress; the wind resistance coefficient is a segmented function according to the dynamic change of the wind speed.

5. The method of claim 3, wherein, The S1 specifically comprises: In the implementation process of the atmospheric pressure counter pressure effect mechanism, based on the atmospheric pressure field data, the seawater density, the gravitational acceleration and the standard atmospheric pressure are combined to obtain the water level increment driven by the atmospheric pressure.

6. The method of claim 3, wherein, The S1 specifically comprises: The water level increment driven by the wind stress and the water level increment driven by the atmospheric pressure are added to each grid point to obtain the total water level increment; the total water level increment is added to the current water level to obtain the updated water level.

7. The method of claim 1, wherein, The S2 specifically comprises: In the implementation process of the similarity weight mechanism of storm path projection, the storm center coordinates are introduced, and the Euclidean distance between the grid point and the storm center is calculated, combined with the storm influence radius control parameter to construct an exponential decay function; the two-dimensional cross product sign of the radial vector from the storm center to the grid point and the storm moving direction vector is calculated, combined with the direction adjustment parameter to construct a direction enhancement factor; based on the distance decay function and the direction enhancement factor, the similarity weight of the storm path projection is obtained.

8. The numerical simulation optimization method for storm surge disaster of a mariculture-oriented sea ranching according to claim 7, characterized in that, The S2 specifically comprises: Based on the similarity weight of the storm path projection, the updated water level is locally enhanced and corrected to obtain the water level after weight correction, and finally a two-dimensional matrix sequence of water level evolution over time is output as a marine ranching storm surge response map to assist the precise protection and emergency response of the marine ranching under the storm surge condition.

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