Spectrum width-based short-term wave height distribution representation method

Abstract

The application discloses a kind of short-term wave height distribution characterization methods based on spectral width, it is related to marine engineering technical field, and the method comprises: based on the statistical parameter of given measured short-term sea state calculation spectral width factor;Wherein, statistical parameter includes spectral frequency, spectral peak frequency, energy density spectrum and energy density spectrum maximum;According to local measured data situation determines characteristic parameter;Wherein, local measured data includes the significant wave height under multiple groups of short-term sea state, three-one wave height, spectral frequency, spectral peak frequency, energy density spectrum and energy density spectrum maximum;Distribution parameter is calculated according to characteristic parameter;Based on spectral width factor and distribution parameter determine the exceeding probability of short-term wave height distribution, provide more accurate input parameter for the design and construction of related structure of nearshore engineering, ocean engineering, near island reef engineering, make preparation for the characterization of short-term wave height distribution and short-term wave height prediction.

Description

Technical Field

[0001] This invention relates to the field of marine engineering technology, and in particular to a method for characterizing short-term wave height distribution based on spectral width. Background Technology

[0002] Short-term wave height distribution refers to the wave height distribution under a single sea state (half an hour, one hour, or three hours). Short-term wave height distribution is an important input parameter for the design and construction of structures related to nearshore engineering, marine engineering, and near-island and reef engineering.

[0003] Currently, most methods assume that the wave spectrum under a single sea state is a narrow-spectrum process, and based on this, Rayleigh and Boccotti distributions are proposed to describe short-term wave height distributions. However, actual waves are often broad-spectrum processes. When waves from different sources, such as wind waves and swells, or when local wind-generated waves and distant waves act simultaneously on a single point, a multi-peak spectrum may even appear, with an even wider spectral width. In this case, the distributions proposed based on the narrow-band assumption are no longer applicable to describing the short-term wave height distribution of actual waves. Therefore, it is necessary to develop a wave height distribution description method related to spectral width to provide more accurate input parameters for the design and construction of structures related to nearshore engineering, marine engineering, and near-island and reef engineering. Summary of the Invention

[0004] To address the aforementioned problems and technical requirements, the inventors have proposed a method for characterizing short-term wave height distribution based on spectral width. The technical solution of this invention is as follows:

[0005] A method for characterizing short-term wave height distribution based on spectral width includes the following steps:

[0006] The spectral width factor is calculated based on the statistical parameters of the given measured short-term sea state; where the statistical parameters include spectral frequency, spectral peak frequency, energy density spectrum, and maximum energy density spectrum.

[0007] The characteristic parameters are determined based on local measured data; the local measured data includes significant wave height, tri-wave height, spectral frequency, spectral peak frequency, energy density spectrum, and maximum energy density spectrum value under multiple short-term sea states.

[0008] Calculate the distribution parameters based on the characteristic parameters;

[0009] The exceedance probability of short-term wave height distribution is determined based on the spectral width factor and distribution parameters.

[0010] Its further technical solution is to determine the characteristic parameters based on local measured data, including:

[0011] If sufficient local measured data is available, for each group of short-term sea states, calculate the ratio of the standard deviation of the three-wave height to the standard deviation of the significant wave height, denoted as ρ = H. 3m / σ mThe spectral width factor, denoted as ε, is calculated based on the spectral frequencies, peak frequencies, energy density spectra, and maximum energy density spectra from measured data. m ;

[0012] Fitting ρ and ε using the least squares method m The relationship is used to obtain the corresponding feature parameters;

[0013] Where ρ and ε m The fitting relationship is: ρ=aε m +b, where a and b are the calculated characteristic parameters; H 3m Indicates the height of the third wave, σ m This represents the standard deviation of the meaningful waveform height, and σ m =H sm / 4, H sm It indicates that the wave is high and meaningful.

[0014] Its further technical solution involves determining the characteristic parameters based on local measured data, and also includes:

[0015] If local measured data is scarce, then take a = -0.6407 and b = 3.726, where a and b are the calculated characteristic parameters.

[0016] A further technical solution involves calculating the spectral width factor based on given statistical parameters of measured short-term sea states. This method is similar to the method used to calculate the spectral width factor based on spectral frequencies, peak frequencies, energy density spectra, and maximum energy density spectra from measured data, including:

[0017] Calculate the dimensionless frequency based on the spectral frequency and spectral peak frequency, denoted as f. # =f / f p The dimensionless spectrum, denoted as S, is calculated based on the energy density spectrum and its maximum value. # (f # )=S(f) / S(f p );

[0018] Calculate the spectral width factor based on information entropy using dimensionless frequency and dimensionless spectrum;

[0019] Where f represents the spectral frequency, f p S(f) represents the peak frequency of the spectrum, and S(f) represents the energy density spectrum corresponding to the frequency. p ) represents the maximum value of the energy density spectrum.

[0020] A further technical solution involves calculating the distribution parameters based on the characteristic parameters, expressed as:

[0021] c1 = (1.4157 / b) 2 c2 = -a / b;

[0022] Where a and b are the calculated characteristic parameters, and c1 and c2 are the distribution parameters.

[0023] A further technical solution involves determining the exceedance probability of the short-term wave height distribution based on the spectral width factor and distribution parameters, expressed as:

[0024] P(H / σ>h)=exp[-c1h 2 (1-c2ε) -2 ];

[0025] Where H is the short-term wave height, σ is the corresponding standard deviation of the short-term wave height, and h is the dimensionless wave height, all of which are given values; c1 and c2 are the calculated distribution parameters, and ε is the calculated spectral width factor of the measured short-term sea state.

[0026] A further technical solution involves calculating the spectral width factor based on information entropy using dimensionless frequencies and dimensionless spectra, expressed as:

[0027]

[0028] A further technical solution is that the method also includes:

[0029] Using the ratio of short-term wave height to the corresponding standard deviation as the x-axis and the exceedance probability of the short-term wave height distribution as the y-axis, a measured short-term wave height distribution map is plotted to characterize the short-term wave height distribution of actual waves.

[0030] The beneficial technical effects of this invention are:

[0031] This application calculates the spectral width factor based on statistical parameters as a control parameter for the exceedance probability of wave height distribution. It further determines characteristic parameters based on local measured data to calculate the distribution parameters of exceedance probability. Finally, based on the spectral width factor and distribution parameters, the exceedance probability of short-term wave height distribution is determined, and the maximum wave height and its probability of occurrence are given by the wave height distribution. This method is applicable to the characterization and forecasting of short-term wave height distribution in nearshore engineering, marine engineering, near-island and reef engineering, and marine science. Attached Figure Description

[0032] Figure 1 This is a flowchart of the short-term wave height distribution characterization method based on spectral width provided in this application.

[0033] Figure 2 This application provides the results of measured short-term wave height distribution within a lagoon in the South China Sea. Detailed Implementation

[0034] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0035] like Figure 1As shown, a method for characterizing short-term wave height distribution based on spectral width includes the following steps:

[0036] Step 1: Calculate the spectral width factor based on the given statistical parameters of measured short-term sea states, which includes the following sub-steps:

[0037] Step 11: Given the statistical parameters of the required short-term sea state, including the (wave) spectral frequency f and the spectral peak frequency f p Energy density spectrum S(f) and maximum energy density spectrum S(f) p ).

[0038] Step 12: Based on spectral frequency f and spectral peak frequency f p Calculate the dimensionless frequency, denoted as f. # =f / f p Based on the energy density spectrum S(f) and the maximum value of the energy density spectrum S(f) p Calculate the dimensionless spectrum, denoted as S. # (f # )=S(f) / S(f p ).

[0039] Step 13: Based on the dimensionless frequency f # and dimensionless spectrum S # (f # The spectral width factor based on information entropy is calculated using the following expression:

[0040]

[0041] Step 2: Determine the characteristic parameters based on local measured data.

[0042] The local measured data includes multiple sets of meaningful wave heights (H) under short-term sea states. sm 31 wave high H 3m Spectral frequency f, spectral peak frequency f p Energy density spectrum S(f) and maximum energy density spectrum S(f) p ).

[0043] This step specifically includes the following sub-steps:

[0044] Step 21: If sufficient local measured data is available, calculate the third wave height H for each short-term sea state group. 3m The standard deviation σ corresponding to the meaningful wave height m The ratio of ρ to H is denoted as ρ = H. 3m / σ m , and σ m =H sm / 4. And based on measured data, the spectral frequency f and the spectral peak frequency f p Energy density spectrum S(f) and maximum energy density spectrum S(f)p Calculate the spectral width factor, denoted as ε. m Calculate ε m The method is the same as that in steps 12 and 13, and will not be repeated here.

[0045] Fitting ρ and ε using the least squares method m The relationship between ρ and ε is used to obtain the corresponding characteristic parameters a and b. Here, ρ and ε... m The fitting relationship is: ρ=aε m +b.

[0046] Step 22: If local measured data is scarce, then take a = -0.6407 and b = 3.726.

[0047] Step 3: Calculate the distribution parameter based on the characteristic parameters. The expression is: c1 = (1.4157 / b) 2 c2 = -a / b.

[0048] Step 4: Determine the exceedance probability of the short-term wave height distribution based on the spectral width factor ε and distribution parameters c1 and c2, expressed as:

[0049] P(H / σ>h)=exp[-c1h 2 (1-c2ε) -2 (2)

[0050] Where H is the short-term wave height, σ is the corresponding standard deviation of the short-term wave height, and h is the dimensionless wave height, all of which are given values.

[0051] Step 5: Plot the measured short-term wave height distribution using the ratio of short-term wave height H to the corresponding standard deviation σ as the x-axis and the exceedance probability P of the short-term wave height distribution as the y-axis. This plot represents the actual short-term wave height distribution. For example... Figure 2 As shown, the short-term wave height distribution curve obtained by the above method is closer to the measured short-term wave height distribution curve than the wave height distribution curve calculated by Rayleigh distribution.

[0052] This application calculates the spectral width factor based on statistical parameters as a control parameter for the exceedance probability of the wave height distribution. It further determines characteristic parameters based on local measured data to calculate the distribution parameters of the exceedance probability. Finally, based on the spectral width factor and distribution parameters, the exceedance probability of the short-term wave height distribution is determined, and the maximum wave height and its probability of occurrence are given from the wave height distribution. This method is applicable to the characterization and forecasting of short-term wave height distributions in nearshore engineering, marine engineering, near-island and reef engineering, and marine science.

[0053] The above descriptions are merely preferred embodiments of this application, and the present invention is not limited to the above embodiments. It is understood that other improvements and variations directly derived or conceived by those skilled in the art without departing from the spirit and concept of the present invention should be considered to be included within the protection scope of the present invention.

Claims

1. A method for characterizing short-term wave height distribution based on spectral width, characterized in that, The method includes: The spectral width factor is calculated based on the statistical parameters of the given measured short-term sea state; wherein the statistical parameters include spectral frequency, spectral peak frequency, energy density spectrum, and maximum energy density spectrum value. The characteristic parameters are determined based on local measured data; the local measured data includes significant wave height, tri-wave height, spectral frequency, spectral peak frequency, energy density spectrum, and maximum energy density spectrum value under multiple short-term sea states. Calculate the distribution parameters based on the characteristic parameters; The exceedance probability of the short-term wave height distribution is determined based on the spectral width factor and the distribution parameters; Using the ratio of short-term wave height to the corresponding standard deviation as the horizontal axis and the exceedance probability of the short-term wave height distribution as the vertical axis, a measured short-term wave height distribution map is plotted to characterize the short-term wave height distribution of actual waves. The determination of characteristic parameters based on local measured data includes: If sufficient local measured data is available, for each group of short-term sea states, calculate the ratio of the standard deviation of the triad wave height to the corresponding standard deviation of the significant wave height, denoted as . And the spectral width factor is calculated based on the spectral frequency, spectral peak frequency, energy density spectrum, and maximum energy density spectrum value from the measured data, denoted as ; Fitting using the least squares method and The relationship is used to obtain the corresponding feature parameters; in, and The fitting relationship is as follows: , a , b These are the calculated characteristic parameters; H 3m This indicates the height of the three-wavelength wave. The standard deviation of the meaningful wave height is given, and , H sm Indicates the meaningful wave height; The distribution parameters are calculated based on the feature parameters, and the expression is: c 1=(1.4157 / b ) 2 ， c 2=－ a / b ； in, c 1. c 2 represents the calculated distribution parameters; The transcendence probability of the short-term wave height distribution, determined based on the spectral width factor and the distribution parameters, is expressed as follows: ； in, H This is a short-term high. This represents the standard deviation of the short-term volatility. h For dimensionless wave height, all three are given values; This is the spectral width factor for the measured short-term sea state obtained through calculation.

2. The method for characterizing short-term wave height distribution based on spectral width according to claim 1, characterized in that, The determination of characteristic parameters based on local measured data also includes: If local measured data is scarce, then take a =-0.6407, b =3.

726.

3. The method for characterizing short-term wave height distribution based on spectral width according to claim 1, characterized in that, The method for calculating the spectral width factor based on given statistical parameters of measured short-term sea states is the same as the method for calculating the spectral width factor based on spectral frequencies, peak frequencies, energy density spectra, and maximum energy density spectra from measured data, including: Dimensionless frequencies are calculated based on spectral frequencies and spectral peak frequencies, denoted as . The dimensionless spectrum is calculated based on the energy density spectrum and the maximum value of the energy density spectrum, denoted as . ; Calculate the spectral width factor based on information entropy based on the dimensionless frequency and the dimensionless spectrum; in, f Indicates the spectral frequency. f p This indicates the frequency of the spectral peak. S ( f () represents the energy density spectrum corresponding to the spectral frequency. S ( f p ) represents the maximum value of the energy density spectrum.

4. The method for characterizing short-term wave height distribution based on spectral width according to claim 3, characterized in that, The spectral width factor based on information entropy, calculated according to the dimensionless frequency and the dimensionless spectrum, is expressed as follows: 。

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