CN111141483B - Intelligent method for generating malformed waves in water pool based on neural network self-learning - Google Patents

Intelligent method for generating malformed waves in water pool based on neural network self-learning Download PDF

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CN111141483B
CN111141483B CN202010016991.8A CN202010016991A CN111141483B CN 111141483 B CN111141483 B CN 111141483B CN 202010016991 A CN202010016991 A CN 202010016991A CN 111141483 B CN111141483 B CN 111141483B
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CN111141483A (en
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李焱
谢芃
尹天畅
赵志娟
夏妮
祁宣博
魏高龙
刘晓毅
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Tianjin University
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Abstract

The invention discloses an intelligent method for generating malformation waves in a pool based on neural network self-learning, which comprises the following steps: generating a wave spectrum target spectrum and a wave making machine motion amplitude target spectrum; generating a numerical value malformed wave by adopting a numerical value wave generating module of the malformed wave, and obtaining a preset motion time history of the wave generator; the wave making machine generates an evolving malformed wave in a simulation mode according to a preset time history of the wave making machine; the error of the evolving malformed wave and the numerical malformed wave is detected by adopting a malformed wave detection module to obtain the evolving malformed wave meeting the experimental requirements and a preset time history of the wave making machine motion, and the actually measured malformed wave is made in the wave pool according to the time history; repeating the steps to record a plurality of groups of wave surface time histories of the numerical malformed waves and the actually measured malformed waves to form a training sample library; training and testing the intelligent neural network self-learning system based on the BP neural network algorithm, and using the intelligent neural network self-learning system for wave generation and prediction of malformed waves. The invention solves the limitation of abnormal wave generation of the wave generator in the current laboratory pool, and optimizes the wave generation effect by adopting the neural network autonomous learning technology.

Description

Intelligent method for generating malformed waves in water pool based on neural network self-learning
Technical Field
The invention relates to ship and ocean engineering experimental technology, in particular to an intelligent method for generating malformed waves in a water pool based on neural network self-learning.
Background
A freak wave is a wave in the ocean having an irregular wave form, also known as an inland wave, an extraordinary wave, usually having an ultra-high wave height, and can be said almost without rules. The malformed waves are often generated in areas with developed sea shipping or rich oil and gas storage, and are easy to cause great harm to offshore structures. For example, for most marine structures moored for long periods at operating sea, which are in varying marine environments at the time, there is also a risk of being affected by freak waves. It is therefore necessary to analyse the dynamic response of marine engineering structures under the action of teratogenic waves, the primary task being the simulation of teratogenic waves.
Compared with random waves, the malformed waves are formed by concentrating wave energy at a certain moment and a certain position at a certain moment, and if the generation method of the random waves is adopted, the generation efficiency of the malformed waves is very low, so that some special numerical methods are usually adopted to simulate the malformed waves. Commonly used methods include a phase modulation method, a focused wave method, and the like. However, these methods are generally used in numerical analysis and are relatively rarely used in laboratory basins. Compared with numerical simulation, experimental simulation is particularly irreplaceable in position and advantage, and is necessary for practical engineering.
The wave generator program commonly used in the current ship and ocean engineering water pool only comprises the wave generating function of regular waves and random waves. If the traditional function is adopted to make the malformed wave, the waveform time history of the malformed wave can be accidentally made only by carrying out a plurality of times of analog adjustment. Therefore, the wave generation process can be optimized by adopting a certain numerical algorithm, and the efficient simulation of the malformed waves is realized. However, in the actual wave-making process, it is found that the wave height does not reach the wave height value preset by the numerical algorithm, and therefore, the existing wave-making method for the abnormal wave experiment needs to be improved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides an intelligent method for generating abnormal waves in a water pool based on neural network self-learning, solves the limitation of the abnormal waves generated by a wave generator in a water pool in a current laboratory, optimizes the wave generating effect by adopting a neural network autonomous learning technology, and provides experimental basis for researching the dynamic response of a marine structure under the action of the abnormal waves.
The technical scheme adopted by the invention is as follows: an intelligent method for generating malformed waves in a pool based on neural network self-learning, which adopts a device comprising a wave generator and a wave height meter, and comprises the following steps:
step 1, selecting the height H of a sense wave according to an experimental scheme and the built-in random wave spectrum and the form of a malformed wave of a wave making machinesPeriod T, generating a wave spectrum target spectrum S1(f) Target spectrum S of motion amplitude of sum wave generator2(f)；
Step 2, according to the wave spectrum target spectrum S1(f) Generating numerical value abnormal waves by adopting a numerical value wave generating module of the abnormal waves based on randomly selected wave elements and randomly generated phase angles, counting relevant parameters of the numerical value abnormal waves, and obtaining a wave surface time duration eta (t) of the numerical value abnormal waves;
step 3, adopting a numerical wave generating module of the malformed waves to generate a target spectrum S of the motion amplitude of the wave generator2(f) Inverting to preset time history a (t) for the motion of the wave making machine;
step 4, inputting the preset motion time history a (t) of the wave making machine into a built-in control module of the wave making machine, simulating and generating the evolutionary malformed wave by adopting a built-in evolution module of the wave making machine, and obtaining the wave surface time history eta of the evolutionary malformed waves(t)；
Step 5, a malformed wave inspection module is adopted to inspect errors of the evolving malformed waves and the numerical malformed waves, and the evolving malformed waves meeting the experimental requirements and the preset motion time history of the wave making machine are obtained;
step 6, inputting the preset motion time history of the wave making machine meeting the experimental requirements into a built-in control module of the wave making machine, controlling the wave making machine to make an actually measured malformed wave in a wave water pool, and recording the wave surface time history eta of the actually measured malformed wave in real time*(t) counting relevant parameters of the actually measured malformation waves;
7, reselecting wave elements and regenerating phase angles, repeating the steps 2 to 6, recording a plurality of groups of wave surface time histories of the numerical value abnormal waves and the actually measured abnormal waves, and forming a learning training sample library;
step 8, training an intelligent neural network self-learning system based on a BP neural network algorithm;
step 9, testing the trained intelligent neural network self-learning system;
and step 10, using the tested intelligent neural network self-learning system for wave generation and prediction of the malformed waves.
Further, in step 2, the numerical wave generating module of the abnormal wave is adopted to generate the numerical abnormal wave, and the relevant parameters of the numerical abnormal wave are counted, including:
step 2-1, target spectrum S of wave spectrum1(f) Dispersing the frequency of (a) into M parts to form a dispersed frequency set F;
step 2-2, focusing time t of the malformed waves according to a preset numerical valuecFocal position xcRandomly selecting a part of wave elements corresponding to the frequency in the frequency set F, and setting an initial phase angle of the wave elements corresponding to the selected frequency by adopting a random frequency phase angle modulation method
Figure GDA0002709673980000021
The phase angle of the wave element corresponding to the other frequencies is from 0,2 pi]Randomly selecting and uniformly distributing the intervals to form a phase angle set phi which corresponds to the frequency elements in the frequency set F one by one;
step 2-3, based on the Longuet-Higgins wave model, adopting a Fourier series superposition method to target the wave spectrum S1(f) And (3) carrying out inversion to generate a numerical value malformation wave, and counting relevant parameters of the numerical value malformation wave according to the formula (1) to the formula (4):
a1＝Hmax/Hs (1)
a2＝Hmax/H1 (2)
a3＝Hmax/H2 (3)
a4＝ηmax/Hmax (4)
in the formula, a1、a2、a3And a4Four judgment parameters of the numerical value malformation wave; hmaxIs the instantaneous maximum wave height; h1And H2The wave height of two waves adjacent to the peak of the numerical malformed wave, wherein H1Before the numerical malformation wave, H2After the numerical freak wave; etamaxIs the height of the numerical value of the peak of the malformed wave.
Further, in step 5, the step of checking the error of the evolving freak wave and the numerical freak wave by using the freak wave checking module includes:
step 5-1, calculating the wave surface time history eta of the evolutionary malformed waves(t) a peak deviation from a wavefront of the numerical malformed wave of η (t);
step 5-2, comparing the peak deviation of the two waves with a preset threshold error of 1%, if the peak deviation of the two waves exceeds an acceptable threshold range, repeating the step 2 to the step 4 to regenerate the numerical value freak wave and the evolution freak wave, and calculating the wave surface time history eta of the regenerated evolution freak wave according to the step 5-1s(t) comparing the peak deviation of the regenerated numerical malformed wave with a preset threshold error of 1%; and if the peak deviation of the two waves is within the acceptable threshold range, the obtained evolving malformed waves and the preset movement time duration of the wave maker are considered to meet the experimental requirements.
Further, in step 6, the real-time recording of the wave surface time history η of the actually measured malformed wave*(t) comprises: at the focus position x of the measured malformed wavecArranging a wave height instrument, and recording the wave surface time history eta of the actually measured abnormal wave in real time by adopting the wave height instrument*(t)；
The statistics of the relevant parameters of the actually measured malformed waves are as follows:
Figure GDA0002709673980000031
Figure GDA0002709673980000032
Figure GDA0002709673980000041
Figure GDA0002709673980000042
in the formula (I), the compound is shown in the specification,
Figure GDA0002709673980000043
and
Figure GDA0002709673980000044
four judgment parameters of the actually measured malformed waves;
Figure GDA0002709673980000045
wave surface time calendar eta of actually measured malformed wave recorded in real time for wave height instrument*(t) the corresponding wave height;
Figure GDA0002709673980000046
the wave surface time history eta of the actually measured malformed wave recorded in real time by the wave height instrument (5)*(t) corresponding sense wave height;
Figure GDA0002709673980000047
and
Figure GDA0002709673980000048
the wave heights of two waves adjacent to the wave crest of the actually measured malformed wave are shown, wherein,
Figure GDA0002709673980000049
prior to the actual measurement of the anomalous wave,
Figure GDA00027096739800000410
after the measured malformed wave;
Figure GDA00027096739800000411
the height of the actually measured deformed wave crest is obtained.
Further, in step 8, the training of the intelligent neural network self-learning system based on the BP neural network algorithm includes:
the wave surface time history eta of the n numerical value abnormal waves in the training sample library obtained in the step 7iN wave spectrum target spectrums S corresponding to the wave surface of 1,2, …, n, n numerical value malformed waves1i(f) I-1, 2, …, n, n frequency sets FiI-1, 2, …, n and n sets of phase angles ΦiPreset time history a of movement of 1,2, …, n, n wave-making machinesi(t), i is 1,2, …, n is used as the estimated control parameter, n wave surface time courses of n measured malformed waves corresponding to the preset time courses of n wave making machine motions are used as the estimated control parameter, n is used as the estimated control parameter, and n is used as the estimated control parameteri *And (t), i is 1,2, …, n is used as a label and is input into the intelligent neural network self-learning system, the least square result of the pre-estimated control parameters and the label is used as a loss function, and the intelligent neural network self-learning system is trained through a gradient descent method.
Further, in step 9, the testing of the trained intelligent neural network self-learning system includes:
step 9-1, the wave surface time of the numerical malformed wave used for inspection is subjected to etac(t) and the wavefront time period η of the numerical freak wavec(t) corresponding wave spectrum target spectrum S for inspection1c(f) Frequency set F for testingcPhase set for testing phicThe numerical wave generating module input into the malformed wave obtains the preset motion time history a of the wave generator for inspectionc(t)；
Step 9-2, target spectrum S of wave spectrum for inspection1c(f) Wave surface time history eta of numerical malformed wave for inspectionc(t) frequency set F for testingcPhase set for testing phicWave making machine movement presetting time calendar a for inspectionc(t) inputting the trained intelligent neural network self-learning system, and obtaining the wave surface time history eta of the predicted malformed wave through the nonlinear mapping of the neural networkαc *(t)；
Step 9-3, presetting a time history a of the wave maker movement for inspectionc(t) inputting the wave generator to generate wave, and recording the measured abnormal wave duration eta for inspection by using wave height instrumentc *(t)；
Step 9-4, calculating and predicting the wave surface time history eta of the malformed waveαc *(t) and measured malformed wave time η for inspectionc *And (t) comparing the peak deviation with a preset threshold value in the malformed wave inspection module, repeating the step 8 to continue training the intelligent neural network self-learning system if the peak deviation of the two peak deviations exceeds an acceptable threshold value range, and considering that the intelligent neural network self-learning system passes the test at the moment if the peak deviation of the two peak deviations is within the acceptable threshold value range.
Further, in step 10, the method for using the tested intelligent neural network self-learning system for wave generation and prediction of malformed waves includes:
after the wave height, the period and the appearance time and the position of the abnormal wave are set at the beginning of an experiment, a wave spectrum target spectrum is selected, a numerical wave generating module of the abnormal wave is adopted to generate a plurality of groups of frequency sets, phase sets and numerical abnormal wave surfaces, the generated frequency sets, phase sets and numerical abnormal wave surfaces are input into an intelligent neural network self-learning system passing through the test, the wave generating result is predicted, abnormal wave surface time histories meeting the experimental conditions of a physical model are screened, and subsequent wave generation and experiments are carried out.
The invention has the beneficial effects that:
1. the intelligent method for generating the malformed waves in the water pool based on the neural network self-learning is simple, efficient, intelligent and feasible.
2. The intelligent method for generating the malformed waves in the water pool based on the neural network self-learning realizes the prediction of the wave surface through the neural network self-learning technology, has high intelligent degree, is simple and easy to operate, is convenient to operate, reduces the difficulty of experimental operation, and improves the accuracy of the experiment.
3. In the past, wave making waves in a laboratory need to be debugged on a wave making machine so as to make a preset wave surface, and the time and economic cost are higher. According to the intelligent method for generating the abnormal waves in the water pool based on the neural network self-learning, the trained neural network prediction is adopted, the wave adjusting process can be performed on any computer in a preposed mode, the operation efficiency is greatly improved, and the economic cost is reduced.
4. According to the intelligent method for generating the abnormal waves in the water pool based on the neural network self-learning, the result of each wave generation can be used as a training sample used in a subsequent experiment, the neural network trained based on a large data volume sample can continuously improve the prediction precision of the neural network through a self-learning technology, the experiment efficiency is improved, and the experiment error is reduced.
5. According to the intelligent method for generating the abnormal wave in the water pool based on the neural network self-learning, disclosed by the invention, the database formed by the training samples can store a large number of abnormal wave samples for direct calling in subsequent experiments, and the intelligence is high.
Drawings
FIG. 1: the invention relates to a schematic diagram of an intelligent method for generating malformation waves in a pool based on neural network self-learning;
FIG. 2 a: target spectrum S of wave spectrum target1(f) A schematic diagram;
FIG. 2 b: wave making machine motion amplitude spectrum S2(f) A schematic diagram;
FIG. 3 a: a schematic diagram of the wave surface time eta (t) of the numerical malformation wave;
FIG. 3 b: a related malformed wave definition parameter schematic diagram;
FIG. 4: a schematic diagram of a (t) of the wave making machine during preset movement time;
FIG. 5: wave surface time calendar eta of evolvement malformed waves(t) schematic drawing;
FIG. 6: wave surface time history eta of actually measured malformed wave generated by wave making machine*(t) schematic drawing;
the attached drawings are marked as follows: 1 is a wave making machine; 2 is a numerical wave generating module of the malformed waves; 3, abnormal wave; 4, a built-in control module of the wave making machine; 5 is a wave height instrument; 6 is a wave pool; 7 is an intelligent neural network self-learning system; and 8, a malformed wave inspection module.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings:
the invention provides an intelligent method for generating abnormal waves in a water pool based on neural network self-learning based on ocean engineering hydrodynamics, which realizes the generation of abnormal waves in a laboratory water pool, expands the functions of a laboratory wave generator and has important reference significance for ensuring dynamic response experimental tests of ocean structures under the action of abnormal waves developed in a laboratory. And a neural network self-learning technology is adopted, and control parameters are optimized, so that the wave generation meets the requirements.
As shown in fig. 1, the intelligent method for generating the malformed waves in the pool based on the neural network self-learning is used for generating the malformed waves 3 in a wave pool 6, and the adopted devices comprise a wave generator 1 and a wave height gauge 5, wherein the wave generator 1 can adopt a wave generator manufactured by 702 institute or university of technology, and is provided with a built-in wave generator control module 4; the method comprises the following steps:
step 1, selecting proper sense wave height H according to an experimental scheme and the built-in random wave spectrum and the form of the malformed wave 3 of the wave making machine 1sPeriod T, generating a wave spectrum target spectrum S1(f) Target spectrum S of motion amplitude of sum wave generator2(f) FIG. 2a shows the target spectrum S of the generated wave spectrum1(f) FIG. 2b shows the produced wave generator motion amplitude spectrum S2(f) And the abscissa f in the graph represents the frequency.
Step 2, according to the selected wave spectrum target spectrum S1(f) The numerical wave generating module 2 of the abnormal wave is adopted to generate the numerical abnormal wave based on the randomly selected wave elements and the randomly generated phase angle, and the relevant parameters of the numerical abnormal wave are counted to obtain the wave surface time eta (t) of the numerical abnormal wave.
The working steps of the numerical wave generating module 2 for the malformed waves are as follows: random frequency phase angle modulation method (random frequency phase angle modulation method refers to the influence of second-order Wave load on tension leg platform Dynamic response under abnormal Wave action) and Dynamic Analysis of Turret-Moored FPSO System in free Wave) For example, the wave spectrum is targeted to the spectrum S1(f) Dispersing the frequency of (a) into M parts to form a dispersed frequency set F; focusing time t of abnormal wave according to preset numerical valuecFocal position xcRandomly selecting a part of wave elements corresponding to the frequency in the frequency set F, and setting an initial phase angle of the wave elements corresponding to the selected frequency by adopting a random frequency phase angle modulation method
Figure GDA0002709673980000071
The phase angle of the wave element corresponding to the other frequencies is from 0,2 pi]Randomly selecting and uniformly distributing the intervals, thus forming a phase angle set phi which corresponds to the frequency elements in the frequency set F one by one; based on the Longuet-Higgins wave model, a Fourier series superposition method is adopted to obtain a wave spectrum target spectrum S1(f) Inversion is performed to generate numerical malformed waves, and fig. 3a is a surface time η (t) curve of the numerical malformed waves; and (3) counting relevant parameters of the abnormal wave according to the formulas (1) to (4):
a1＝Hmax/Hs (1)
a2＝Hmax/H1 (2)
a3＝Hmax/H2 (3)
a4＝ηmax/Hmax (4)
in the formula, a1、a2、a3And a4Four judgment parameters of the numerical value malformation wave; as shown in FIG. 3b, HmaxIs the instantaneous maximum wave height, H1And H2The wave height of two waves adjacent to the peak of the numerical malformed wave, wherein H1Before the numerical malformation wave, H2After a numerical malformation wave, ηmaxIs the height of the numerical value of the peak of the malformed wave.
Step 3, adopting a numerical wave generating module 2 of the abnormal waves to generate a target spectrum S of the motion amplitude of the wave generator according to the discrete frequency set F and the phase angle set phi2(f) Inverting to preset time history a (t) for the motion of the wave making machine; fig. 4 shows the wave generator movement preset time period a (t).
Step 4, the obtained wave generatorInputting the preset motion time history a (t) into a built-in control module 4 of the wave maker, simulating and generating an evolution malformed wave by adopting an evolution module built in the wave maker 1, and obtaining the wave surface time history eta of the evolution malformed waves(t) as shown in FIG. 5.
And 5, adopting a malformed wave inspection module 8 to inspect errors of the evolving malformed waves and the numerical malformed waves to obtain the evolving malformed waves meeting the experimental requirements and the preset time history of the wave making machine motion.
Step 5-1, calculating the wave surface time history eta of the evolutionary malformed waves(t) a peak deviation from a wavefront of the numerical malformed wave of η (t);
step 5-2, comparing the peak deviation of the two waves with a preset threshold error of 1%, if the peak deviation of the two waves exceeds an acceptable threshold range, repeating the step 2 to the step 4 to regenerate the numerical value freak wave and the evolution freak wave, and calculating the wave surface time history eta of the regenerated evolution freak wave according to the step 5-1s(t) comparing the peak deviation of the regenerated numerical malformed wave with a preset threshold error of 1%; and if the peak deviation of the two waves is within the acceptable threshold range, the obtained evolving malformed waves and the preset time course a (t) of the wave maker motion meet the experimental requirements.
Step 6, inputting the preset time a (t) of the wave maker motion meeting the experimental requirements into a built-in control module 4 of the wave maker to control the wave maker 1 to make actually measured malformed waves in a wave water pool 6; at the focus position x of the measured malformed wavecArranging a wave height instrument 5, recording the wave surface time history eta of the actually measured abnormal wave in real time*(t), as shown in FIG. 6; and according to formulas (5) to (8), counting relevant parameters of the measured malformation waves:
Figure GDA0002709673980000081
Figure GDA0002709673980000082
Figure GDA0002709673980000083
Figure GDA0002709673980000084
in the formula (I), the compound is shown in the specification,
Figure GDA0002709673980000085
and
Figure GDA0002709673980000086
four judgment parameters of the actually measured malformed waves;
Figure GDA0002709673980000087
wave surface time calendar eta of actually measured malformed wave recorded in real time for wave height instrument 5*(t) the corresponding wave height;
Figure GDA0002709673980000088
the wave surface time history eta of the actually measured malformed wave recorded in real time by the wave height instrument (5)*(t) corresponding sense wave height;
Figure GDA0002709673980000089
and
Figure GDA00027096739800000810
the wave heights of two waves adjacent to the wave crest of the actually measured malformed wave are shown, wherein,
Figure GDA00027096739800000811
prior to the actual measurement of the anomalous wave,
Figure GDA00027096739800000812
after the measured malformed wave;
Figure GDA00027096739800000813
the height of the actually measured deformed wave crest is obtained.
Step 7, because the frequencies in the frequency set F in the step 2 are randomly selected, the frequencies selected each time are different, and the wave element and the phase angle are regenerated each time; and (6) repeating the steps 2 to 6, and recording a plurality of groups of wave surface time histories of the numerical malformed waves and the actually measured malformed waves to form a learning training sample library.
And 8, training the intelligent neural network self-learning system 7 based on a BP neural network algorithm, wherein the intelligent neural network self-learning system 7 is the existing algorithm. The wave surface time history eta of the n numerical value abnormal waves in the training sample library obtained in the step 7iN wave spectrum target spectrums S corresponding to the wave surface of 1,2, …, n, n numerical value malformed waves1i(f) I-1, 2, …, n, n frequency sets FiI-1, 2, …, n and n sets of phase angles ΦiPreset time history a of movement of 1,2, …, n, n wave-making machinesi(t), i is 1,2, …, n is used as the estimated control parameter, n wave surface time courses of n measured malformed waves corresponding to the preset time courses of n wave making machine motions are used as the estimated control parameter, n is used as the estimated control parameter, and n is used as the estimated control parameteri *And (t), i is 1,2, …, n is used as a label and is input into the intelligent neural network self-learning system 7, the least square result of the pre-estimated control parameters and the label is used as a loss function, and the intelligent neural network self-learning system 7 is trained through a gradient descent method. Therefore, the intelligent neural network self-learning system 7 is trained by adopting the method, and the wave-making structure with the given parameter can be predicted.
On the basis, the frequency set F and the phase set phi can be used as training labels, the actually measured wave surface profile is used as an estimated control parameter, the frequency set F and the phase set phi capable of creating the actually measured wave surface of the target abnormal wave can be predicted, the parameters can be given by the intelligent neural network self-learning system 7, and the required abnormal wave 3 can be directly created.
In addition, the result of each wave generation is stored in a training sample library to be used as a training sample used in a subsequent experiment, so that the neural network can be more fully learned and trained.
And 9, testing the trained intelligent neural network self-learning system 7, and checking the pre-estimation accuracy.
Step 9-1, numerical malformation wave to be used for inspectionWave surface time calendar etac(t) and the wavefront time period η of the numerical freak wavec(t) corresponding wave spectrum target spectrum S for inspection1c(f) Frequency set F for testingcPhase set for testing phicThe numerical value wave generating module 2 input into the malformed wave obtains the preset motion time a of the wave generator for inspectionc(t)；
Step 9-2, target spectrum S of wave spectrum for inspection1c(f) Wave surface time history eta of numerical malformed wave for inspectionc(t) frequency set F for testingcPhase set for testing phicWave making machine movement presetting time calendar a for inspectionc(t) inputting the trained intelligent neural network self-learning system 7, and obtaining the wave surface time history eta of the predicted malformed wave through the nonlinear mapping of the neural networkαc *(t)；
Step 9-3, presetting a time history a of the wave maker movement for inspectionc(t) inputting the wave generator 1 to generate wave, and recording the measured abnormal wave duration eta for inspection by the wave height instrument 5c *(t)；
Step 9-4, calculating and predicting the wave surface time history eta of the malformed waveαc *(t) and measured malformed wave time η for inspectionc *And (t) comparing the peak deviation with a preset threshold value in the malformed wave inspection module 8, if the peak deviation of the two peak deviations exceeds an acceptable threshold value range, repeating the step 8 to continue training the intelligent neural network self-learning system 7, and if the peak deviation of the two peak deviations is within the acceptable threshold value range, considering that the intelligent neural network self-learning system 7 passes the test.
And step 10, after the intelligent neural network self-learning system 7 passes the test, the wave generation and prediction of the malformed wave 3 can be realized. After the wave height, the period and the appearance time and the position of the abnormal wave 3 are set at the beginning of an experiment, a wave spectrum target spectrum is selected, a numerical wave generation module 2 of the abnormal wave is adopted to generate a plurality of groups of frequency sets, phase sets and numerical abnormal wave surfaces, the generated frequency sets, phase sets and numerical abnormal wave surfaces are input into an intelligent neural network self-learning system 7 which passes a test, the wave generation result is predicted, abnormal wave surface time histories meeting the experimental conditions of a physical model (namely, a structural object model for the experiment, such as a scale model of a ship and an ocean platform) are screened, and subsequent wave generation and experiments are carried out.
Although the preferred embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and those skilled in the art can make many modifications without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims (7)

1. An intelligent method for generating malformed waves in a pool based on neural network self-learning is characterized in that the intelligent method comprises the following steps:
step 1, selecting the height H of a sense wave according to an experimental scheme and the forms of a random wave spectrum and a malformed wave (3) built in a wave making machine (1)sPeriod T, generating a wave spectrum target spectrum S1(f) Target spectrum S of motion amplitude of sum wave generator2(f)；
Step 2, according to the wave spectrum target spectrum S1(f) A numerical wave generation module (2) of the abnormal waves is adopted to generate numerical abnormal waves based on randomly selected wave elements and randomly generated phase angles, relevant parameters of the numerical abnormal waves are counted, and meanwhile, the wave surface time duration eta (t) of the numerical abnormal waves is obtained;
step 3, adopting a numerical wave generating module (2) of the abnormal waves to generate a target spectrum S of the motion amplitude of the wave generator2(f) Inverting to preset time history a (t) for the motion of the wave making machine;
step 4, inputting the preset motion time history a (t) of the wave maker into a built-in control module (4) of the wave maker, simulating and generating the evolutionary malformed wave by adopting a built-in evolution module of the wave maker (1), and obtaining the wave surface time history eta of the evolutionary malformed waves(t)；
Step 5, a malformed wave inspection module (8) is adopted to inspect errors of the evolving malformed waves and the numerical malformed waves, so that the evolving malformed waves meeting the experimental requirements and the preset motion time history of the wave making machine are obtained;
step 6, inputting the wave making machine motion preset time history meeting the experimental requirements into a wave making machine built-in control module (4), controlling the wave making machine (1) to make an actually measured malformed wave in a wave water pool (6), and recording the wave surface time history eta of the actually measured malformed wave in real time*(t) counting relevant parameters of the actually measured malformation waves;
7, reselecting wave elements and regenerating phase angles, repeating the steps 2 to 6, recording a plurality of groups of wave surface time histories of the numerical value abnormal waves and the actually measured abnormal waves, and forming a learning training sample library;
step 8, training an intelligent neural network self-learning system (7) based on a BP neural network algorithm;
step 9, testing the trained intelligent neural network self-learning system (7);
and step 10, using the tested intelligent neural network self-learning system (7) for wave generation and prediction of the malformed waves (3).
2. The intelligent method for generating malformed waves in the pool based on the neural network self-learning as claimed in claim 1, wherein in the step 2, the numerical wave generating module (2) of the malformed waves is adopted to generate numerical malformed waves, and relevant parameters of the numerical malformed waves are counted, and the method comprises the following steps:
step 2-1, target spectrum S of wave spectrum1(f) Dispersing the frequency of (a) into M parts to form a dispersed frequency set F;
step 2-2, focusing time t of the malformed waves according to a preset numerical valuecFocal position xcRandomly selecting a part of wave elements corresponding to the frequency in the frequency set F, and setting an initial phase angle of the wave elements corresponding to the selected frequency by adopting a random frequency phase angle modulation method
Figure FDA0002709673970000021
The phase angle of the wave element corresponding to the other frequencies is from 0,2 pi]Randomly selecting and uniformly distributing the intervals to form a phase angle set phi which corresponds to the frequency elements in the frequency set F one by one;
step 2-3, based on the Longuet-Higgins wave model, adopting a Fourier series superposition method to target the wave spectrum S1(f) And (3) carrying out inversion to generate a numerical value malformation wave, and counting relevant parameters of the numerical value malformation wave according to the formula (1) to the formula (4):
a1＝Hmax/Hs (1)
a2＝Hmax/H1 (2)
a3＝Hmax/H2 (3)
a4＝ηmax/Hmax (4)
in the formula, a1、a2、a3And a4Four judgment parameters of the numerical value malformation wave; hmaxIs the instantaneous maximum wave height; h1And H2The wave height of two waves adjacent to the peak of the numerical malformed wave, wherein H1Before the numerical malformation wave, H2After the numerical freak wave; etamaxIs the height of the numerical value of the peak of the malformed wave.
3. The intelligent pool malformation wave generation method based on neural network self-learning as claimed in claim 1, wherein in step 5, the error of the evolving malformation wave and the numerical malformation wave is verified by using the malformation wave verifying module (8), which comprises:
step 5-1, calculating the wave surface time history eta of the evolutionary malformed waves(t) a peak deviation from a wavefront of the numerical malformed wave of η (t);
step 5-2, comparing the peak deviation of the two waves with a preset threshold error of 1%, if the peak deviation of the two waves exceeds an acceptable threshold range, repeating the step 2 to the step 4 to regenerate the numerical value freak wave and the evolution freak wave, and calculating the wave surface time history eta of the regenerated evolution freak wave according to the step 5-1s(t) comparing the peak deviation of the regenerated numerical malformed wave with a preset threshold error of 1%; if the peak deviation of the two waves is within the acceptable threshold range, the evolving malformed waves and the wave generator obtained at the moment are consideredThe preset time duration of the movement meets the experimental requirements.
4. The intelligent neural network self-learning-based pool malformation wave generation method as claimed in claim 1, wherein in step 6, the time-course η of the measured malformation wave is recorded in real time*(t) comprises: at the focus position x of the measured malformed wavecArranging a wave height instrument (5), and recording the wave surface time-course eta of the actually measured malformed wave in real time by adopting the wave height instrument (5)*(t)；
The statistics of the relevant parameters of the actually measured malformed waves are as follows:
Figure FDA0002709673970000031
Figure FDA0002709673970000032
Figure FDA0002709673970000033
Figure FDA0002709673970000034
in the formula (I), the compound is shown in the specification,
Figure FDA0002709673970000035
and
Figure FDA0002709673970000036
four judgment parameters of the actually measured malformed waves;
Figure FDA0002709673970000037
the wave surface time history eta of the actually measured malformed wave recorded in real time by the wave height instrument (5)*(t) the corresponding wave height;
Figure FDA0002709673970000038
the wave surface time history eta of the actually measured malformed wave recorded in real time by the wave height instrument (5)*(t) corresponding sense wave height;
Figure FDA0002709673970000039
and
Figure FDA00027096739700000310
the wave heights of two waves adjacent to the wave crest of the actually measured malformed wave are shown, wherein,
Figure FDA00027096739700000311
prior to the actual measurement of the anomalous wave,
Figure FDA00027096739700000312
after the measured malformed wave;
Figure FDA00027096739700000313
the height of the actually measured deformed wave crest is obtained.
5. The intelligent pool malformation wave generation method based on neural network self-learning as claimed in claim 1, wherein in step 8, the intelligent neural network self-learning system (7) is trained based on BP neural network algorithm, comprising:
the wave surface time history eta of the n numerical value abnormal waves in the training sample library obtained in the step 7iN wave spectrum target spectrums S corresponding to the wave surface of 1,2, …, n, n numerical value malformed waves1i(f) I-1, 2, …, n, n frequency sets FiI-1, 2, …, n and n sets of phase angles ΦiPreset time history a of movement of 1,2, …, n, n wave-making machinesi(t), i is 1,2, …, n is used as the estimated control parameter, n wave surface time courses of n measured malformed waves corresponding to the preset time courses of n wave making machine motions are used as the estimated control parameter, n is used as the estimated control parameter, and n is used as the estimated control parameteri *(t), i is 1,2, …, n is used as label, input into intelligent neural network self-learning system (7), and estimation control is carried outAnd (3) taking the least square result of the parameter and the label as a loss function, and training the intelligent neural network self-learning system (7) by a gradient descent method.
6. The intelligent pool malformation wave generation method based on neural network self-learning as claimed in claim 1, wherein in step 9, the testing of the trained intelligent neural network self-learning system (7) comprises:
step 9-1, the wave surface time of the numerical malformed wave used for inspection is subjected to etac(t) and the wavefront time period η of the numerical freak wavec(t) corresponding wave spectrum target spectrum S for inspection1c(f) Frequency set F for testingcPhase set for testing phicThe numerical value wave generating module (2) input into the malformed wave obtains the preset motion time a of the wave generator for inspectionc(t)；
Step 9-2, target spectrum S of wave spectrum for inspection1c(f) Wave surface time history eta of numerical malformed wave for inspectionc(t) frequency set F for testingcPhase set for testing phicWave making machine movement presetting time calendar a for inspectionc(t) inputting the trained intelligent neural network self-learning system (7), and obtaining the wave surface time history eta of the predicted malformed wave through the nonlinear mapping of the neural networkαc *(t)；
Step 9-3, presetting a time history a of the wave maker movement for inspectionc(t) inputting the wave generator (1) to generate waves, and recording the measured abnormal wave duration eta for inspection by using a wave height instrument (5)c *(t)；
Step 9-4, calculating and predicting the wave surface time history eta of the malformed waveαc *(t) and measured malformed wave time η for inspectionc *(t) comparing the peak deviation with a preset threshold value in the malformed wave inspection module (8), repeating the step 8 to train the intelligent neural network self-learning system (7) if the peak deviation of the two peak deviations exceeds an acceptable threshold value range, and considering that the peak deviation of the two peak deviations is within the acceptable threshold value range at the momentThe intelligent neural network self-learning system (7) passes the test.
7. The intelligent neural network self-learning based pool malformation wave generation method as claimed in claim 1, wherein the step 10 of applying the tested intelligent neural network self-learning system (7) to the wave generation and prediction of the malformed waves (3) comprises:
after the wave height, the period and the appearance time and the position of the abnormal wave (3) are set at the beginning of an experiment, a wave spectrum target spectrum is selected, a numerical wave generation module (2) of the abnormal wave is adopted to generate a plurality of groups of frequency sets, phase sets and numerical abnormal wave surfaces, the generated frequency sets, phase sets and numerical abnormal wave surfaces are input into an intelligent neural network self-learning system (7) passing the test, the wave generation result is predicted, abnormal wave surface histories meeting the experiment conditions of a physical model are screened, and subsequent wave generation and experiments are carried out.
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