Sea surface wave model construction method and system based on spectral analysis

Abstract

The invention discloses a spectral analysis-based sea surface wave model construction method and system, and the method comprises the steps: carrying out the grid division of a sea surface, calculating parameters for each grid of the sea surface, and building a gravity wave model, a surge model and a shockwave model for the whole sea surface; obtaining a gravity waveform height amplitude according to the gravity wave model, obtaining a surge waveform height amplitude according to the surge model, and obtaining a shock wave height amplitude according to the established shock wave model; superposing the gravity waveform height amplitude, the surge waveform height amplitude and the shockwave height amplitude, and carrying out inverse fast Fourier transform on the superposed amplitudes to calculate a sea surface waveform height spectrum which changes along with time in an airspace within a certain range; and constructing a sea surface wave geometric model in an STL format according to the sea surface waveform height spectrum changing along with time in the airspace in a certain range. According to the invention, the authenticity and accuracy of simulation are improved.

Description

Technical Field

[0001] This application relates to the technical field of three-dimensional visual scene simulation in ocean scenarios, and particularly to a method and system for constructing a sea surface wave model based on spectral analysis. Background Art

[0002] The research on the electromagnetic scattering of ships on the sea surface mainly involves the following aspects: The electromagnetic scattering of ships on the sea surface is affected by the geometric structure of the sea surface, the dielectric constant of seawater, and the structure and motion state of the ships. And for ships, in the commonly used radar frequency bands, the observation positions are all in their near-field scattering regions. Therefore, in the modeling of the sea surface, geometric modeling is of utmost importance.

[0003] The sea spectrum is a spectrum that describes the distribution of internal energy of ocean waves with respect to frequency and direction. It is an important concept in the study of ocean waves, which can describe the distribution of wave energy with respect to each component wave and is an important statistical property of random ocean waves. The wave spectrum not only shows which component waves the ocean waves are composed of internally, but also gives the external characteristics of the ocean waves, such as characteristic wave height, average period, etc. Through the wave spectrum, the appearance characteristics of ocean waves can be calculated, which can be divided into gravity wave spectrum and capillary wave spectrum. The sea spectrum can be divided into gravity wave spectrum and capillary wave spectrum. So far, many scholars have proposed various forms of sea spectrum models, including:

[0004] The P-M spectrum is a relatively classical and widely used gravity wave spectrum;

[0005] Fung's semi-empirical sea spectrum is the earliest complete sea spectrum (including gravity waves and capillary waves), and the scattering results calculated according to this spectrum model are in good agreement with the measured values;

[0006] The D-B-J spectrum is a latest complete sea spectrum, and it effectively distinguishes the cases of downwind and upwind;

[0007] The JONSWAP spectrum is a non-steady sea spectrum and is considered as the international standard ocean spectrum.

[0008] In the existing FEKO 2021 version, as Figure 1 and Figure 2 shown, the automatic modeling function of the rough sea surface is extended based on the application program macro library, and the modeling process is as shown in the figure: Designers can complete the modeling of the rough sea surface by setting the wave height, wave period, sea surface boundary shape and size. At the same time, there are also relevant open-source software solutions, such as Figure 3 is a sea surface model constructed based on matlab.

[0009] Both of the above two existing software perform three-dimensional modeling of the sea surface based on the P-M spectrum and do not consider the wave direction distribution; and at the same time, the distribution characteristics of the sea surface shape are irregular, resulting in the inability of the existing sea spectrum models to accurately represent. Summary of the Invention

[0010] Based on this, in view of the above technical problems, a method and a system for constructing a sea surface wave model based on spectral analysis are provided to solve the problem that the sea spectrum model constructed by the prior art cannot accurately represent the sea surface shape distribution.

[0011] In a first aspect, a method for constructing a sea surface wave model based on spectral analysis, the method includes:

[0012] Divide the sea surface into grids, calculate parameters for each grid of the sea surface respectively, and establish a gravity wave model, a swell model and an oscillatory wave model for the entire sea surface;

[0013] Obtain the gravity wave height amplitude according to the gravity wave model, obtain the swell wave height amplitude according to the swell model, and obtain the oscillatory wave height amplitude according to the established oscillatory wave model;

[0014] Superimpose the gravity wave height amplitude, the swell wave height amplitude and the oscillatory wave height amplitude, and perform an inverse fast Fourier transform on the superimposed amplitude to calculate the sea surface wave height spectrum that changes with time in the spatial domain within a certain range;

[0015] Construct a sea surface wave geometric model in STL format according to the sea surface wave height spectrum that changes with time in the spatial domain within the certain range.

[0016] In the above solution, optionally, the establishment of the gravity wave model includes: for each grid area, calculate the position vector of each grid point;

[0017] Calculate the wave number and the wave number vector according to the relationship between the wave number and the wavelength based on the wavelength of each grid area;

[0018] Calculate the frequency of the gravity wave according to the calculated wave number based on the dispersion relationship of the gravity wave;

[0019] Generate a random phase through a Gaussian random number generator;

[0020] Calculate the gravity wave height amplitude by using the calculated wave number, the gravity wave frequency and the random data with the waveform height power spectral density function;

[0021] Construct a gravity wave model according to the gravity wave height amplitude and the position vector of each grid area.

[0022] In the above solution, optionally, the establishment of the swell model includes:

[0023] Calculate the wave number vector of the wave crest in the Fourier space according to the swell wave crest direction distribution function and the wave number;

[0024] Calculate the mean square deviation of the swell wave crest according to the wave number;

[0025] Calculate the height spectrum of the swell waveform by performing an inverse Fourier transform on the Gaussian power spectral density function based on the wave number vector of the Fourier space peak and the mean square deviation of the swell peak.

[0026] Calculate the swell model based on the height spectrum of the swell waveform for each grid area.

[0027] In the above solution, further optionally, the shock wave model includes:

[0028] According to the gravity wave model, while keeping the height of the grid points after displacement unchanged, translate the horizontal grid point positions according to the magnitude of the proportionality coefficient to calculate the height amplitude of the shock waveform after displacement.

[0029] In the above solution, optionally, generating a random phase through a Gaussian random number generator includes: adding a Gaussian random number generator to the direction function to generate a random phase.

[0030] In the above solution, optionally, the direction function conforms to the following formula:

[0031] Or

[0032] where G(k, θ) is the wave number of the sea wave, G(f, Θ) is the generation rate, and θ is the wind direction angle.

[0033] In the above solution, optionally, dividing the sea surface into grids includes: dividing the sea surface into a sea surface of 500m × 500m, and the resolution of the grid is 1m.

[0034] In a second aspect, a sea surface wave model construction system based on spectral analysis, the system includes:

[0035] Divide the sea surface into grids, calculate parameters for each grid sea surface respectively, and establish a gravity wave model, a swell model, and a shock wave model for the entire sea surface;

[0036] Obtain the height amplitude of the gravity waveform according to the gravity wave model, obtain the height amplitude of the swell waveform according to the swell model, and obtain the height amplitude of the shock wave according to the established shock wave model;

[0037] Superimpose the height amplitude of the gravity waveform, the height amplitude of the swell waveform, and the height amplitude of the shock wave, and perform an inverse fast Fourier transform on the superimposed amplitude to calculate the sea surface waveform height spectrum that changes with time in a certain range in the spatial domain;

[0038] Construct a sea surface wave geometric model in STL format according to the sea surface waveform height spectrum that changes with time in a certain range in the spatial domain.

[0039] In a third aspect, a computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method for constructing a sea surface wave model based on spectral analysis described in the first aspect above are implemented.

[0040] In a fourth aspect, a computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the method for constructing a sea surface wave model based on spectral analysis described in the first aspect above are implemented.

[0041] This application has at least the following beneficial effects:

[0042] By superimposing the height amplitude of the gravity waveform, the height amplitude of the swell waveform, and the height amplitude of the oscillatory wave, this application more comprehensively considers the physical characteristics of sea surface waves, improving the authenticity and accuracy of the simulation. Combining with the FFT method, the solution can simulate the random sea surface wave patterns that change over time, which is of great significance for the simulation and analysis of the dynamic ocean environment. At the same time, grid division is performed, enabling the simulation of a larger sea surface area with limited computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is an interface for constructing a sea surface model using feko in the prior art;

[0044] Figure 2 It is a sea surface model constructed using feko in the prior art;

[0045] Figure 3 It is a sea surface modeling constructed using matlab in the prior art;

[0046] Figure 4 It is a schematic flowchart of a method for constructing a sea surface wave model based on spectral analysis provided in an embodiment of the application;

[0047] Figure 5 It is a detailed schematic flowchart of a method for constructing a sea surface wave model based on spectral analysis provided in an embodiment of the application;

[0048] Figure 6 It is a sea surface wave model constructed using the method for constructing a sea surface wave model based on spectral analysis provided in an embodiment of the application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the objectives, technical solutions, and advantages of this application clearer, the following further elaborates on this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used to explain this application and are not used to limit this application.

[0050] Since most of the methods for wave spectrum modeling only consider the linear sea surface composed of wind waves and swells and do not involve non-linear problems, the authenticity of the sea surface remains to be further studied.

[0051] Based on the FFT method, the undulation characteristics of a large-sized sea surface under different wind speeds and wind directions can be quickly simulated, which has been widely used in ocean dynamics research.

[0052] The FFT method has good periodic characteristics and can generate a small piece of sea surface. Therefore, the sea surface can be extended accordingly according to actual needs. As long as the repeatability is not very obvious, these small pieces can be spliced to form a larger sea surface.

[0053] In one embodiment, as Figure 4 shown, a method for constructing a sea surface wave model based on spectral analysis is provided. The method includes:

[0054] Step S1: Divide the sea surface into grids, calculate parameters for each grid of the sea surface respectively, and establish a gravity wave model, a swell model, and an oscillatory wave model for the entire sea surface.

[0055] Since the actual sea surface is relatively infinite, it is very difficult to simulate an infinite sea surface model. The selection of the sea surface size in the sea surface modeling process will affect the accuracy and correctness of the modeling, and also affect the calculation speed. Therefore, better real-time performance can be obtained by dividing the sea surface into grids. The present invention can generate a sea surface of 500m×500m, and the resolution of the grid is 1m.

[0056] Step S2: Obtain the gravity wave height amplitude according to the gravity wave model, obtain the swell wave height amplitude according to the swell model, and obtain the oscillatory wave height amplitude according to the established oscillatory wave model;

[0057] Step S3: Superimpose the gravity wave height amplitude, the swell wave height amplitude, and the oscillatory wave height amplitude, and perform an inverse fast Fourier transform on the superimposed amplitude to calculate the sea surface wave height spectrum that changes with time in a certain range in the spatial domain;

[0058] Step S4: Construct an STL format sea surface wave geometric model according to the sea surface wave height spectrum that changes with time in a certain range in the spatial domain.

[0059] In the above method for constructing a sea surface wave model based on spectral analysis, by superimposing the gravity wave height amplitude, the swell wave height amplitude, and the oscillatory wave height amplitude, the physical characteristics of the sea surface waves are more comprehensively considered, improving the authenticity and accuracy of the simulation. Combined with the FFT method, the solution can simulate the random sea surface wave patterns that change over time, which is of great significance for the simulation and analysis of dynamic ocean environments. At the same time, grid division is performed, enabling the simulation of a relatively large sea surface area with limited computing resources.

[0060] In one embodiment, establishing the gravity wave model includes: for each grid region, calculating the position vector of each grid point; calculating the wave number and wave number vector based on the relationship between the wave number and the wavelength according to the wavelength of each grid region; calculating the frequency of the gravity wave based on the dispersion relationship of the gravity wave according to the calculated wave number; generating a random phase through a Gaussian random number generator; calculating the gravity wave height amplitude using the waveform height power spectral density function with the calculated wave number, gravity wave frequency, and random data; and constructing the gravity wave model based on the gravity wave height amplitude and position vector of each grid region.

[0061] Specifically, a gravity wave model is established. A relatively appropriate sea surface area is selected according to actual needs, and wave surface grid division is performed on this area. It is like placing a regular grid on the sea surface, and the position vectors of the horizontal grid points are calculated. The wave number and wave number vector are derived according to the relationship between the wave number and the wavelength, and then the frequency is calculated according to the dispersion relationship of the gravity wave. The random phase determines the random characteristics of the wind-generated waves and is generated by a Gaussian random number generator with a mean of 0 and a variance of 1. Finally, the waveform height amplitude is calculated through the effective waveform height power spectral density function. When a certain wind speed is given, by combining the FFT method, the random sea surface wave patterns that change over time can be simulated.

[0062] In one embodiment, establishing the swell model includes: calculating the wave number vector of the wave crest in the Fourier space according to the swell wave crest direction distribution function and the wave number; calculating the mean square deviation of the swell wave crest according to the wave number; performing an inverse Fourier transform using the Gaussian power spectral density function with the wave number vector of the wave crest in the Fourier space and the mean square deviation of the swell wave crest to calculate the swell waveform height spectrum; and calculating the swell model according to the swell waveform height spectrum of each grid region.

[0063] Specifically, establishing the swell model includes: after calculating the specific wave number of the swell according to the given wavelength, calculating the wave number vector of the wave crest in the Fourier space according to the swell wave crest direction distribution function and the specific wave number; then calculating the mean square deviation of the swell wave crest from the swell wave number, and performing an inverse Fourier transform on the swell Gaussian power spectral density to obtain the waveform height spectrum; finally, superimposing the swell waveform height spectrum onto the gravity wave model to simulate the sea surface wave patterns with both gravity waves and swells.

[0064] In one embodiment, the shock wave model includes: according to the gravity wave model, while keeping the height of the grid points unchanged after displacement, translating the horizontal grid point positions according to the magnitude of the proportionality coefficient, and calculating the height amplitude of the shock wave form after displacement.

[0065] Specifically, a shock wave model is established. This model is based on the gravity wave model. While keeping the height of the grid points unchanged after displacement, the horizontal grid point positions are translated according to the magnitude of the proportionality coefficient, and the height amplitude of the shock wave form after displacement is calculated.

[0066] In one embodiment, generating a random phase through a Gaussian random number generator includes: adding a Gaussian random number generator to the direction function to generate a random phase.

[0067] To reflect the anisotropy of the sea spectrum caused by the wind direction, the present invention introduces the concept of a direction function in the process of generating the random phase of the sea wave direction, which is usually also called the angular spreading function, the directional spectrum. After the sea waves enter shallow water, a series of changes occur under the influence of the terrain, such as refraction, diffraction, reflection, etc. These complex phenomena need to be analyzed for the component waves in different directions, so it is necessary to rely on the directional spectrum. However, the number of direction functions proposed so far is much less than that of the power spectrum. The main reason is that the observation method and data processing are more difficult than obtaining the frequency spectrum. The observation of the directional spectrum can be divided into two categories: direct measurement and remote sensing. In the direct measurement method, the commonly used ones are the use of instrument arrays and the so-called "free floats". The former is suitable for regular observation and analysis work, while the latter is suitable for nearshore waters or the open sea. In the remote sensing method, sea wave data is obtained through photography or radio waves. Photography can be carried out from an airplane, a ship, a shore observation tower or a satellite. In recent years, a remote sensing method using radio backscattering technology has been developed, which is a promising means.

[0068] In one embodiment, the direction function is generally a function of the sea wave number or the production rate and the wind direction angle, expressed as G(k, θ) or G(f, θ), and must satisfy the following formula

[0069] or

[0070] where k is the sea wave number, f is the production rate, and θ is the wind direction angle.

[0071] After generating the sea surface geometric data, the present invention can directly generate a sea surface geometric model in STL format, and this model can be directly imported into CAE software for electromagnetic simulation calculation.

[0072] The advantages of this application are as follows:

[0073] 1. A three-dimensional irregular short-peak wave stochastic sea wave model based on spectral analysis is used to implement sea surface modeling, and the modeling includes the randomness of the directional distribution of sea waves.

[0074] 2. It can generate a three-dimensional sea surface model of any size.

[0075] 3. The STL grid data for generating the three-dimensional sea surface model can be directly used for CAE simulation.

[0076] In one embodiment, a sea surface wave model construction system based on spectral analysis, the system includes:

[0077] Gravity wave model, swell model and oscillatory wave model construction units: used to divide the sea surface into grids, calculate parameters for each grid of the sea surface respectively, so as to establish a gravity wave model, a swell model and an oscillatory wave model for the entire sea surface;

[0078] Data acquisition unit: used to obtain the gravity wave height amplitude according to the gravity wave model, the swell wave height amplitude according to the swell model, and the oscillatory wave height amplitude according to the established oscillatory wave model;

[0079] Sea surface waveform height spectrum calculation unit: used to superimpose the gravity wave height amplitude, the swell wave height amplitude and the oscillatory wave height amplitude, and perform an inverse fast Fourier transform on the superimposed amplitude to calculate the sea surface waveform height spectrum that changes with time in the spatial domain within a certain range;

[0080] Sea surface wave model construction unit: used to construct a sea surface wave geometric model in STL format according to the sea surface waveform height spectrum that changes with time in the spatial domain within a certain range.

[0081] The sea spectrum model constructed by the present invention optimizes the construction of the sea surface geometric model, and on the premise of verifying the accuracy of the geometric model, generates a three-dimensional sea surface geometric model that can be used for simulation calculations to obtain the sea surface backscattering coefficients under multiple sea conditions, multiple frequency bands and multiple incident angles.

[0082] For the specific limitations on a sea surface wave model construction system based on spectral analysis, reference can be made to the limitations on a sea surface wave model construction method based on spectral analysis in the above text, which will not be elaborated here. Each module in the above sea surface wave model construction system based on spectral analysis can be implemented in whole or in part through software, hardware and their combinations. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0083] In one embodiment, a computer device is provided, and the computer device may be a server. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements the above-mentioned method for constructing a sea surface wave model based on spectral analysis.

[0084] In one embodiment, a computer program product is further provided, including a computer program / instructions, and when the computer program / instructions are executed by the processor, they involve all or part of the processes in the method of the above embodiment.

[0085] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application may include at least one of non-volatile and volatile memories. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0086] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0087] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for constructing a sea surface wave model based on spectral analysis, characterized in that: The method comprises: Divide the sea surface into grids, calculate parameters for each grid sea surface, and establish a gravity wave model, a surge wave model, and a shock wave model for the entire sea surface; Obtaining the height amplitude of the gravity waveform according to the gravity wave model, obtaining the height amplitude of the surge wave waveform according to the surge wave model, and obtaining the height amplitude of the shock wave according to the established shock wave model; The gravity waveform height amplitude, the surge waveform height amplitude and the shock wave height amplitude are superimposed, and the superimposed amplitude is subjected to inverse fast Fourier transform to calculate the sea surface waveform height spectrum that changes with time in the airspace within a certain range; A sea surface wave geometry model in STL format is constructed according to the sea surface waveform height spectrum that changes with time in the airspace within the certain range.

2. The method for constructing a sea surface wave model based on spectral analysis according to claim 1, characterized in that: The establishing of the gravity wave model comprises: For each grid area, calculate the position vector of each grid point; Calculate the wave number and wave number vector based on the relationship between wave number and wavelength according to the wavelength of each grid area; Calculate the frequency of gravity waves based on the calculated wave number based on the diffusion relation of gravity waves; Generate random phases through a Gaussian random number generator; The gravity waveform height amplitude is calculated using the waveform height power spectral density function from the calculated wave number, gravity wave frequency and random data; A gravity wave model is constructed based on the gravity waveform height amplitude and position vector of each grid area.

3. The method for constructing a sea surface wave model based on spectral analysis according to claim 2, characterized in that: The establishment of the surge model comprises: Calculate the wave number vector of the Fourier space crest according to the surge crest direction distribution function and the wave number; Calculating the mean square error of surge peaks based on the wave number; According to the wave number vector of the Fourier space crest and the mean square error of the surge wave crest, the Gaussian power spectrum density function is used to perform Fourier inverse transform to calculate the surge waveform height spectrum; The surge model is calculated based on the surge waveform height spectrum of each grid area.

4. The method for constructing a sea surface wave model based on spectral analysis according to claim 2, characterized in that: The shock wave model includes: According to the gravity wave model, while keeping the height of the grid point unchanged after displacement, the horizontal grid point position is translated according to the size of the proportional coefficient, and the height amplitude of the oscillation waveform after displacement is calculated.

5. The method for constructing a sea surface wave model based on spectral analysis according to claim 2, characterized in that: The generating of the random phase by using a Gaussian random number generator includes: generating the random phase by adding a Gaussian random number generator to a direction function.

6. The method for constructing a sea surface wave model based on spectral analysis according to claim 5, characterized in that: The direction function conforms to the following formula: Among them, G(k, θ) is the wave number, G(f, θ) is the generation rate, and θ is the wind direction angle.

7. The method for constructing a sea surface wave model based on spectral analysis according to claim 1, characterized in that: The grid division of the sea surface includes: dividing the sea surface into 500m×500m sea surfaces, and the resolution of the grid is 1m.

8. A sea surface wave model construction system based on spectral analysis, characterized in that: The system comprises: Gravity wave model, surge wave model and shock wave model building unit: used to divide the sea surface into grids, calculate parameters for each grid sea surface respectively, so as to establish a gravity wave model, surge wave model and shock wave model for the entire sea surface; Data acquisition unit: used to acquire the height amplitude of gravity waveform according to the gravity wave model, acquire the height amplitude of surge wave waveform according to the surge wave model, and acquire the height amplitude of shock wave according to the established shock wave model; Sea surface waveform height spectrum calculation unit: used to superimpose the gravity waveform height amplitude, the surge waveform height amplitude and the shock wave height amplitude, and perform inverse fast Fourier transform on the superimposed amplitude to calculate the sea surface waveform height spectrum that changes with time in the airspace within a certain range; The sea surface wave model construction unit is used to construct a sea surface wave geometric model in STL format according to the sea surface waveform height spectrum that changes with time in the airspace within the certain range.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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