CN116227242A - Random irregular wave simulation method based on white noise filtering - Google Patents

Random irregular wave simulation method based on white noise filtering Download PDF

Info

Publication number
CN116227242A
CN116227242A CN202310510882.5A CN202310510882A CN116227242A CN 116227242 A CN116227242 A CN 116227242A CN 202310510882 A CN202310510882 A CN 202310510882A CN 116227242 A CN116227242 A CN 116227242A
Authority
CN
China
Prior art keywords
white noise
irregular wave
wave
frequency
filter
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310510882.5A
Other languages
Chinese (zh)
Other versions
CN116227242B (en
Inventor
孙金伟
李华军
刘世萱
邵萌
陈玉静
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ocean University of China
Original Assignee
Ocean University of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ocean University of China filed Critical Ocean University of China
Priority to CN202310510882.5A priority Critical patent/CN116227242B/en
Publication of CN116227242A publication Critical patent/CN116227242A/en
Application granted granted Critical
Publication of CN116227242B publication Critical patent/CN116227242B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Algebra (AREA)
  • Geometry (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention belongs to the technical field of ocean engineering, and relates to a random irregular wave simulation method based on white noise filtering. The method comprises the following steps: s1: constructing a continuous frequency response function of the filter based on the target wave spectrum density function, and performing frequency domain dispersion on the frequency response function; s2: based on the obtained filter discrete frequency response function, generating a Gaussian white noise sequence with standard normal distribution; s3: performing Fourier transform on the white noise sequence to obtain a white noise signal in a frequency domain; s4: calculating to obtain a random irregular wave frequency domain result based on the filter discrete frequency response function and the frequency domain white noise signal; s5: and carrying out inverse Fourier transform on the random irregular wave in the frequency domain to obtain a random irregular wave calendar. According to the invention, the continuous frequency response function of the filter is constructed, the response of the filter system under white noise excitation is solved in the frequency domain, the Fourier transform technology is applied, the random irregular wave calendar simulation is realized, and the method has the technical characteristics of simplicity and convenience in calculation and high efficiency.

Description

Random irregular wave simulation method based on white noise filtering
Technical Field
The invention relates to the technical field of ocean engineering, in particular to a random irregular wave simulation method based on white noise filtering.
Background
The wave is an important ocean environment design parameter of coast and ocean engineering, scientifically describes the sea wave condition of the sea area where the offshore structure is located, is a basis and a key for accurately evaluating the wave load born by the offshore structure, analyzing the dynamic response of the structure and forecasting the fatigue life of the offshore structure, and is of great importance for engineering design, optimization, operation and maintenance and the like.
The actual sea wave is a random irregular wave, and for fully grown waves, the wave can be generally described as a smooth random process, and is characterized by wave spectrums, such as a common P-M spectrum, jonswap spectrum, ISSC spectrum and the like. The wave spectrum gives out the frequency distribution characteristics of wave energy, and the structural dynamic response spectrum can be obtained by calculation by using a spectrum analysis method, but the response spectrum can only give out the frequency characteristics of structural response and cannot reflect the dynamic time-varying characteristics of the structural response. Therefore, based on the wave spectrum of the actual sea area, the irregular wave calendar is simulated and generated through numerical means, time-varying wave excitation input is provided for structural power analysis, and the method becomes an important research content in the fields of coastal and ocean engineering.
At present, the common random irregular wave simulation method comprises a harmonic superposition method and a white noise filtering method. The harmonic superposition method represents an irregular wave as a combination of multiple regular waves of different frequencies, different magnitudes, different phases. Wherein the amplitude of a single regular wave is determined by the spectral value corresponding to the wave frequency, and the phase is a random number uniformly distributed between 0 and 2 pi. The harmonic superposition method has definite physical meaning, but needs to generate a large number of regular wave components in the whole spectrum frequency range, can obtain a simulation result with high reliability, and is very time-consuming to calculate. The white noise filtering method constructs a filter system based on the target wave spectrum function, considers the irregular wave surface of the random wave as the output of the filter under the excitation of white noise, and solves the problem through a system dynamics method. The white noise filtering method provides a new thought for random wave simulation, but has the defects and defects of low efficiency, poor practicability and the like in practical application, and is specifically expressed in the following steps: (1) The design of the digital filter is complicated, so that the matching problem between the design of the filter and the target wave spectrum exists, and the order selection of the model of the filter and the solution of the parameters of the filter are sometimes a mathematical disease state problem; (2) The solution of the response of the filter system is mainly performed in the time domain, for example, the convolution of the impulse response function of the filter and white noise is calculated, and the problems that the calculation is time-consuming, the calculation accuracy is limited by time interval selection and the like exist.
Disclosure of Invention
The invention aims to solve the technical problems and provide a random irregular wave simulation method based on white noise filtering, which fully utilizes the white noise filtering technology and can quickly realize irregular wave simulation.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a random irregular wave simulation method based on white noise filtering comprises the following steps:
s1: based on a target wave spectral density function
Figure SMS_1
Constructing a continuous frequency response function of the filter>
Figure SMS_2
For->
Figure SMS_3
Performing dispersion to obtain a filter discrete frequency response function +.>
Figure SMS_4
；
S2: based on the obtained filter discrete frequency response function, a Gaussian white noise sequence with standard normal distribution is generated
Figure SMS_5
；
S3: for Gaussian white noise sequences
Figure SMS_6
Performing Fourier transform to obtain white noise signal +.>
Figure SMS_7
；/>
S4: discrete frequency response function based on filter
Figure SMS_8
And frequency domain white noise signal->
Figure SMS_9
Calculating to obtain random irregular wave frequency domain signal +.>
Figure SMS_10
；
S5: for random irregular wave frequency domain signals
Figure SMS_11
Performing inverse Fourier transform to obtain random irregular wave calendar signal +.>
Figure SMS_12
。
In some embodiments of the invention, the filter is continuously frequency responsive to a function
Figure SMS_13
Performing dispersion to obtain a filter discrete frequency response function +.>
Figure SMS_14
The method comprises the following steps:
based on a target wave spectral density function
Figure SMS_15
Obtaining the continuous frequency response function of the filter>
Figure SMS_16
：
Figure SMS_17
；
wherein ,
Figure SMS_18
for frequency->
Figure SMS_19
Selecting frequency resolution
Figure SMS_20
Determining the equally spaced discrete frequencies +.>
Figure SMS_21
,/>
Figure SMS_23
Calculate the correspondence +.>
Figure SMS_24
Discrete frequency response function +.>
Figure SMS_25
；
wherein ,
Figure SMS_26
for frequency point number, +.>
Figure SMS_27
The number of the frequency points;
Figure SMS_28
the value is chosen to ensure->
Figure SMS_29
Equal to zero or close to zero in value.
In some embodiments of the invention, a standard normally distributed Gaussian white noise sequence is generated
Figure SMS_30
The method comprises the following steps:
computer-generated
Figure SMS_31
Random number meeting standard normal distribution +.>
Figure SMS_32
；
Cut-off frequency according to discrete frequency response functionValue of
Figure SMS_33
Determining standard deviation of Gaussian white noise signal spectrum:
Figure SMS_34
；
wherein
Figure SMS_35
As a function of the spectral density of white noise->
Figure SMS_36
；
According to standard deviation of white noise signal spectrum
Figure SMS_37
Obtaining a time domain Gaussian white noise sequence +.>
Figure SMS_38
：
Figure SMS_39
。
In some embodiments of the invention, a white noise signal in the frequency domain is obtained
Figure SMS_40
The method comprises the following steps:
white noise sequence
Figure SMS_41
Expressed as->
Figure SMS_42
，/>
Figure SMS_43
Performing Fourier transform to obtain white noise signal +.>
Figure SMS_44
：
Figure SMS_45
；
wherein ,
Figure SMS_46
is imaginary number and is->
Figure SMS_47
，/>
Figure SMS_48
Is the circumference ratio.
In some embodiments of the invention, a random irregular wave frequency domain signal is obtained
Figure SMS_49
The method comprises the following steps:
Figure SMS_50
。
in some embodiments of the invention, a random irregular wave calendar signal is obtained
Figure SMS_51
The method comprises the following steps:
irregular wave frequency domain signal
Figure SMS_52
Rear part (S)KThe value of 2-2 points is reassigned as: />
Figure SMS_53
conj/>
Figure SMS_54
；
wherein ,
Figure SMS_55
for the corresponding frequency point->
Figure SMS_56
Is used for the non-uniform wave values of (a),
Figure SMS_57
a series with a tolerance of 1 and a final value of K; />
Figure SMS_58
For the corresponding frequency point->
Figure SMS_59
Irregular wave value of +.>
Figure SMS_60
An arithmetic series with a tolerance of-1 and a final value of 2; conj represents a complex conjugate operation;
based on irregular wave frequency domain signals
Figure SMS_61
Calculating to obtain random irregular wave calendar signal +.>
Figure SMS_62
：
Figure SMS_63
；
wherein ：
Figure SMS_64
is irregular wave in frequency domain->
Figure SMS_65
Is indicated by->
Figure SMS_66
。
In some embodiments of the invention, irregular wave calendar
Figure SMS_67
Time interval +.>
Figure SMS_68
Cut-off frequency of discrete frequency response function of filter>
Figure SMS_69
And (3) determining:
Figure SMS_70
。
in some embodiments of the present invention,
Figure SMS_71
2 +.>
Figure SMS_72
To the power of (I)>
Figure SMS_73
Is a positive integer and is generally not less than 11.
The random irregular wave simulation method based on white noise filtering has the beneficial effects that:
according to the invention, the filter analysis frequency response function is constructed, and the response output of the filter system under white noise excitation is solved in the frequency domain. The method fully utilizes the calculation advantages of a Fast Fourier Transform (FFT) algorithm, realizes the calendar simulation of random irregular waves, has the technical characteristics of simple calculation and high calculation efficiency compared with the traditional simulation method, provides a new reliable, practical and efficient random irregular wave simulation technology for actual ocean engineering, and assists the field study of the ocean engineering.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an efficient simulation method of random irregular waves based on white noise filtering;
FIG. 2 is a graph of P-M wave spectrum and filter analysis frequency response functions disclosed in an embodiment of the present invention;
FIG. 3 is a graph of the results of a random irregular wave calendar simulation obtained by the method of the present invention corresponding to the P-M wave spectrum;
FIG. 4 is a graph comparing a wave spectrum obtained from the random irregular wave simulation result of the method of the present invention with a target P-M wave spectrum.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides a random irregular wave simulation method based on white noise filtering, which can efficiently simulate an offshore irregular wave calendar and comprises the following steps.
S1: based on a target wave spectral density function
Figure SMS_74
Constructing a continuous frequency response function of the filter>
Figure SMS_75
Continuous frequency response function for filter>
Figure SMS_76
Performing dispersion to obtain a filter discrete frequency response function +.>
Figure SMS_77
。
Continuous frequency response function for filter
Figure SMS_78
Performing dispersion to obtain a filter discrete frequency response function +.>
Figure SMS_79
The method comprises the following steps:
based on a target wave spectral density function
Figure SMS_80
Obtaining the continuous frequency response function of the filter>
Figure SMS_81
：
Figure SMS_82
；
wherein ,
Figure SMS_83
for frequency->
Figure SMS_84
Selecting frequency resolution
Figure SMS_85
Determining the equally spaced discrete frequencies +.>
Figure SMS_86
,/>
Figure SMS_88
The corresponding +.>
Figure SMS_89
Discrete frequency response function +.>
Figure SMS_90
；
wherein ,
Figure SMS_91
for frequency point number, +.>
Figure SMS_92
The number of the frequency points;
Figure SMS_93
the value is chosen to ensure->
Figure SMS_94
Equal to zero or close to zero in value.
In some embodiments of the invention, to enable the fast fourier transform algorithm to improve efficiency in the computation,
Figure SMS_95
2 +.>
Figure SMS_96
To the power of (I)>
Figure SMS_97
Is a positive integer and is generally not less than 11.
S2: based on the obtained filter discrete frequency response function, a Gaussian white noise sequence with standard normal distribution is generated
Figure SMS_98
。
In some embodiments of the invention, a standard normally distributed Gaussian white noise sequence is generated
Figure SMS_99
The method comprises the following steps:
s21: computer-generated
Figure SMS_100
Random number meeting standard normal distribution +.>
Figure SMS_101
；
S22: cut-off frequency values according to discrete frequency response functions
Figure SMS_102
Determining standard deviation of Gaussian white noise signal spectrum:
Figure SMS_103
；
wherein
Figure SMS_104
As a function of the spectral density of white noise->
Figure SMS_105
。
S23: standard deviation based on white noise signal spectrum
Figure SMS_106
Obtaining a time domain Gaussian white noise sequence +.>
Figure SMS_107
：
Figure SMS_108
=/>
Figure SMS_109
。
S3: for Gaussian white noise sequences
Figure SMS_110
Performing Fourier transform to obtain white noise signal +.>
Figure SMS_111
。
In some embodiments of the invention, a white noise signal in the frequency domain is obtained
Figure SMS_112
The method comprises the following steps:
white noise sequence
Figure SMS_113
Expressed as->
Figure SMS_114
，/>
Figure SMS_115
Performing Fourier transform to obtain white noise signal +.>
Figure SMS_116
：
Figure SMS_117
；
wherein ,
Figure SMS_118
is imaginary number and is->
Figure SMS_119
，/>
Figure SMS_120
Is the circumference ratio.
In the above calculation process, symbols are introduced
Figure SMS_121
To facilitate the conversion of signals in different domains.
Meanwhile, in order to improve the calculation efficiency, the above formula is solved by adopting a fast Fourier transform method.
S4: discrete frequency response function based on filter
Figure SMS_122
And frequency domain white noise signal->
Figure SMS_123
Calculating to obtain random irregular wave frequency domain signal +.>
Figure SMS_124
。
Specifically, a random irregular wave frequency domain signal is obtained by calculation of the following formula
Figure SMS_125
：
Figure SMS_126
=/>
Figure SMS_127
。
S5: for random irregular wave frequency domain signals
Figure SMS_128
Performing inverse Fourier transform to obtain random irregular wave calendar signal +.>
Figure SMS_129
。
In some embodiments of the invention, a random irregular wave calendar signal is obtained
Figure SMS_130
The method comprises the following steps:
s51: irregular wave frequency domain signal
Figure SMS_131
Back->
Figure SMS_132
The value of 2-2 points is reassigned as:
Figure SMS_133
=/>
Figure SMS_134
；
wherein ,
Figure SMS_135
for the corresponding frequency point->
Figure SMS_136
Is used for the non-uniform wave values of (a),
Figure SMS_137
a series with a tolerance of 1 and a final value of K; />
Figure SMS_138
For the corresponding frequency point->
Figure SMS_139
Irregular wave value of +.>
Figure SMS_140
An arithmetic series with a tolerance of-1 and a final value of 2; conj represents a complex conjugate operation;
based on irregular wave frequency domain signals
Figure SMS_141
Calculating to obtain random irregular wave calendar signal +.>
Figure SMS_142
：
Figure SMS_143
；
wherein ：
Figure SMS_144
is irregular wave in frequency domain->
Figure SMS_145
Is indicated by->
Figure SMS_146
。
In some embodiments of the invention, irregular wave calendar signals
Figure SMS_147
Time interval +.>
Figure SMS_148
Cut-off frequency of discrete frequency response function of filter>
Figure SMS_149
And (3) determining:
Figure SMS_150
。
the following will describe the implementation of the method provided by the present invention using a P-M wave spectrum as the target spectrum.
The following unilateral P-M wave spectrum is selected as a target spectrum:
Figure SMS_151
wherein
Figure SMS_152
，/>
Figure SMS_153
Is the gravitational acceleration constant, spectral peak period +.>
Figure SMS_154
= 0.4218 rad/s. FIG. 2 shows the target wave spectrum +.>
Figure SMS_155
The filter system analysis frequency response function obtained from the root number of the filter system analysis frequency response function>
Figure SMS_156
As a result.
In practice, the frequency resolution is selected
Figure SMS_157
rad/s, frequency points->
Figure SMS_158
Cut-off frequency
Figure SMS_159
rad/s, the time interval is simulated by applying the method of the invention>
Figure SMS_160
s, random irregular wave calendar with total duration 819.15 s, as shown in fig. 3.
In order to verify the effectiveness of the invention, the method is repeatedly executed for 100 times, irregular wave calendar samples corresponding to 100 times of different random white noise excitation are obtained through simulation, the WELCH method is used for calculating the energy spectrum of each irregular wave calendar sample, the 100 energy spectrums are averaged, the wave spectrum result considering the randomness of the actual sea condition is obtained, and compared with the theoretical value of the target P-M spectrum, as shown in fig. 4, the random wave simulation result obtained by the invention is better matched with the theoretical value of the P-M spectrum, and the effectiveness and the reliability of the invention are fully verified.
The frequency response function of the filter system is directly obtained by the root number of the wave spectrum function, so that the filter system is suitable for any wave spectrum. In addition, the invention uses FFT algorithm to carry out numerical solution, and even under the condition of more data points, the invention still has higher calculation efficiency.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.

Claims (8)

1. The random irregular wave simulation method based on white noise filtering is characterized by comprising the following steps of:
s1: based on a target wave spectral density function
Figure QLYQS_1
Constructing a continuous frequency response function of the filter>
Figure QLYQS_2
For->
Figure QLYQS_3
Performing dispersion to obtain a filter discrete frequency response function +.>
Figure QLYQS_4
；
S2: based on the obtained filter discrete frequency response function, a Gaussian white noise sequence with standard normal distribution is generated
Figure QLYQS_5
；
S3: for Gaussian white noise sequences
Figure QLYQS_6
Performing Fourier transform to obtain white noise signal +.>
Figure QLYQS_7
；
S4: discrete frequency response function based on filter
Figure QLYQS_8
Sum frequency domain white noise signalNumber->
Figure QLYQS_9
Calculating to obtain random irregular wave frequency domain signal +.>
Figure QLYQS_10
；
S5: for random irregular wave frequency domain signals
Figure QLYQS_11
Performing inverse Fourier transform to obtain random irregular wave calendar signal +.>
Figure QLYQS_12
。
2. The random irregular wave simulation method based on white noise filtering according to claim 1, wherein the filter is continuously frequency responsive to a function
Figure QLYQS_13
Performing dispersion to obtain a filter discrete frequency response function +.>
Figure QLYQS_14
The method comprises the following steps:
based on a target wave spectral density function
Figure QLYQS_15
Obtaining the continuous frequency response function of the filter>
Figure QLYQS_16
：
Figure QLYQS_17
；
wherein ,
Figure QLYQS_18
for frequency->
Figure QLYQS_19
Selecting frequency resolution
Figure QLYQS_20
Determining the equally spaced discrete frequencies +.>
Figure QLYQS_21
,/>
Figure QLYQS_23
Calculate the correspondence +.>
Figure QLYQS_24
Discrete frequency response function +.>
Figure QLYQS_25
；
wherein ,
Figure QLYQS_26
for frequency point number, +.>
Figure QLYQS_27
The number of the frequency points;
Figure QLYQS_28
the value is chosen to ensure->
Figure QLYQS_29
Equal to zero or close to zero in value.
3. The random irregular wave simulation method based on white noise filtering according to claim 2, wherein a standard normally distributed gaussian white noise sequence is generated
Figure QLYQS_30
The method comprises the following steps:
computer-generated
Figure QLYQS_31
Random number meeting standard normal distribution +.>
Figure QLYQS_32
；
Cut-off frequency values according to discrete frequency response functions
Figure QLYQS_33
Determining standard deviation of Gaussian white noise signal spectrum:
Figure QLYQS_34
；
wherein
Figure QLYQS_35
As a function of the spectral density of white noise->
Figure QLYQS_36
；
According to standard deviation of white noise signal spectrum
Figure QLYQS_37
Obtaining a time domain Gaussian white noise sequence +.>
Figure QLYQS_38
：
Figure QLYQS_39
。
4. A random irregular wave simulation method based on white noise filtering according to claim 3, wherein white noise signals in the frequency domain are obtained
Figure QLYQS_40
The method comprises the following steps:
white noise sequence
Figure QLYQS_41
Expressed as->
Figure QLYQS_42
，/>
Figure QLYQS_43
Calculating to obtain white noise signal in frequency domain
Figure QLYQS_44
：/>
Figure QLYQS_45
；
wherein ,
Figure QLYQS_46
is imaginary number and is->
Figure QLYQS_47
，/>
Figure QLYQS_48
Is the circumference ratio.
5. The method for random irregular wave simulation based on white noise filtering according to claim 4, wherein a random irregular wave frequency domain signal is obtained
Figure QLYQS_49
The method comprises the following steps:
Figure QLYQS_50
。
6. the random irregular wave simulation method based on white noise filtering according to claim 5, wherein a random wave is obtainedIrregular wave calendar signal
Figure QLYQS_51
The method comprises the following steps:
irregular wave frequency domain signal
Figure QLYQS_52
Rear part (S)KThe value of 2-2 points is reassigned as:
Figure QLYQS_53
=/>
Figure QLYQS_54
；
wherein ,
Figure QLYQS_55
for the corresponding frequency point->
Figure QLYQS_56
Is used for the non-uniform wave values of (a),
Figure QLYQS_57
a series with a tolerance of 1 and a final value of K; />
Figure QLYQS_58
For the corresponding frequency point->
Figure QLYQS_59
Irregular wave value of +.>
Figure QLYQS_60
An arithmetic series with a tolerance of-1 and a final value of 2; conj represents a complex conjugate operation;
based on irregular wave frequency domain signals
Figure QLYQS_61
Calculating to obtain random irregular wave calendar signal +.>
Figure QLYQS_62
：
Figure QLYQS_63
；
wherein ：
Figure QLYQS_64
is irregular wave in frequency domain->
Figure QLYQS_65
Is indicated by->
Figure QLYQS_66
。
7. The random irregular wave simulation method based on white noise filtering according to claim 6, wherein the irregular wave calendar
Figure QLYQS_67
Time interval +.>
Figure QLYQS_68
Cut-off frequency of discrete frequency response function of filter>
Figure QLYQS_69
And (3) determining:
Figure QLYQS_70
。
8. a random irregular wave simulation method based on white noise filtering according to claim 2, wherein,
Figure QLYQS_71
2 +.>
Figure QLYQS_72
To the power of (I)>
Figure QLYQS_73
Is a positive integer and is generally not less than 11./>
CN202310510882.5A 2023-05-09 2023-05-09 Random irregular wave simulation method based on white noise filtering Active CN116227242B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310510882.5A CN116227242B (en) 2023-05-09 2023-05-09 Random irregular wave simulation method based on white noise filtering

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310510882.5A CN116227242B (en) 2023-05-09 2023-05-09 Random irregular wave simulation method based on white noise filtering

Publications (2)

Publication Number Publication Date
CN116227242A true CN116227242A (en) 2023-06-06
CN116227242B CN116227242B (en) 2023-07-07

Family

ID=86571600

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310510882.5A Active CN116227242B (en) 2023-05-09 2023-05-09 Random irregular wave simulation method based on white noise filtering

Country Status (1)

Country Link
CN (1) CN116227242B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116805028A (en) * 2023-08-17 2023-09-26 中国海洋大学 Wave surface inversion method and system based on floating body motion response
CN117909665A (en) * 2024-03-18 2024-04-19 青岛哈尔滨工程大学创新发展中心 Ship motion envelope forecast data processing method and system based on Fourier filtering

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2568178A1 (en) * 2004-07-02 2006-02-16 Vibration Research Corporation System and method for simultaneously controlling spectrum and kurtosis of a random vibration
CN107490790A (en) * 2017-10-10 2017-12-19 北京航空航天大学 A kind of emulation mode of continuous multiple-pulse coherent sea clutter
CN111738593A (en) * 2020-06-22 2020-10-02 中国海洋大学 Automatic wave scatter diagram block partitioning method based on cluster analysis
CN113505470A (en) * 2021-06-11 2021-10-15 中国海洋大学 Novel non-Gaussian random wave simulation method
CN113702924A (en) * 2021-08-31 2021-11-26 安徽隼波科技有限公司 Method for realizing radar target interference simulator based on digital noise modulation
CN114880619A (en) * 2022-05-10 2022-08-09 中国海洋大学 Floating ocean structure random dynamic response analytic calculation method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2568178A1 (en) * 2004-07-02 2006-02-16 Vibration Research Corporation System and method for simultaneously controlling spectrum and kurtosis of a random vibration
CN107490790A (en) * 2017-10-10 2017-12-19 北京航空航天大学 A kind of emulation mode of continuous multiple-pulse coherent sea clutter
CN111738593A (en) * 2020-06-22 2020-10-02 中国海洋大学 Automatic wave scatter diagram block partitioning method based on cluster analysis
CN113505470A (en) * 2021-06-11 2021-10-15 中国海洋大学 Novel non-Gaussian random wave simulation method
CN113702924A (en) * 2021-08-31 2021-11-26 安徽隼波科技有限公司 Method for realizing radar target interference simulator based on digital noise modulation
CN114880619A (en) * 2022-05-10 2022-08-09 中国海洋大学 Floating ocean structure random dynamic response analytic calculation method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ZHENDONG LIU等: "Dynamic visual simulation of marine vector field based on LIC- a case study of surface wave field in typhoon condition", 《JOURNAL OF OCEANOLOGY AND LIMNOLOGY》 *
孙金伟: "深水Spar平台水动力性能和运动响应数值研究", 《万方》 *
张梦等: "基于条件模拟理论的波浪数值模拟", 《万方》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116805028A (en) * 2023-08-17 2023-09-26 中国海洋大学 Wave surface inversion method and system based on floating body motion response
CN116805028B (en) * 2023-08-17 2023-12-08 中国海洋大学 Wave surface inversion method and system based on floating body motion response
CN117909665A (en) * 2024-03-18 2024-04-19 青岛哈尔滨工程大学创新发展中心 Ship motion envelope forecast data processing method and system based on Fourier filtering

Also Published As

Publication number Publication date
CN116227242B (en) 2023-07-07

Similar Documents

Publication Publication Date Title
CN116227242B (en) Random irregular wave simulation method based on white noise filtering
CN103323821B (en) Water surface target radiation noise simulation method
CN105353408B (en) A kind of Wigner higher-order spectrum seismic signal spectral factorization methods based on match tracing
CN107844849A (en) A kind of new energy output short term prediction method returned based on experience wavelet transformation with improving Gaussian process
Javors’ kyj et al. Methods of periodically correlated random processes and their generalizations
CN107729289B (en) Polynomial phase signal self-adaptive time-frequency transformation method based on genetic optimization
CN114578417B (en) Pulse type earthquake motion fitting method based on wavelet adjustment
Sanabria-Villamizar et al. Characterization of household-consumption load profiles in the time and frequency domain
CN107480391A (en) Nearly tomography Nonstationary MDP analogy method based on data-driven
CN103383413A (en) Real-time harmonic detection method based on direct weight determination method
Kavrakov et al. Comparison metrics for time-histories: Application to bridge aerodynamics
Reddy et al. Hybrid approach for short term wind power forecasting
An et al. Application of adaptive local iterative filtering and approximate entropy to vibration signal denoising of hydropower unit
Wang et al. Feature extraction techniques of non-stationary signals for fault diagnosis in machinery systems
Draycott et al. The numerical re-creation of experimentally generated nonlinear irregular wave fields using a time-reversal approach
Zhang et al. System identification and generalisation of elastic mooring line forces on a multi-float wave energy converter platform in steep irregular waves
Wang et al. Fault detection of water hydraulic motor by demodulated vibration signal analysis with the hilbert transform and genetic algorithm
Ren et al. Research on Noise Reduction Method of Pressure Pulsation Signal of Draft Tube of Hydropower Unit Based on ALIF‐SVD
Sun et al. An Efficient Noise Elimination Method for Non‐stationary and Non‐linear Signals by Averaging Decomposed Components
Chistiakova et al. Nonlinear system identification of the dissolved oxygen to effluent ammonia dynamics in an activated sludge process
CN107622035B (en) Polynomial phase signal self-adaptive time-frequency transformation method based on simulated annealing
Price et al. Frequency domain techniques for numerical and experimental modelling of wave energy converters
CN110275157A (en) Vector acoustic bearing method based on genetic algorithm self-adapting random resonant
Zhou et al. Morphological Filter‐Assisted Ensemble Empirical Mode Decomposition
Wang et al. Persistence of Peregrine Breather in Random Sea States

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant