CN106021933A - Wave and turbulence nonlinear transactional analysis (TA) method - Google Patents
Wave and turbulence nonlinear transactional analysis (TA) method Download PDFInfo
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Abstract
A wave and turbulence nonlinear transactional analysis (TA) method characterized by using wave and turbulence interactional speed time sequence, obtained by offshore real observation or laboratory tests, to carry out wave and turbulence nonlinear transactional analysis (TA) according to an EMD method; the wave and turbulence nonlinear transactional analysis (TA) method comprises the following steps: determining a turbulence power spectrum frequency range; obtaining Eigenmode component of the frequency in the inertia area; analyzing wave and turbulence nonlinear interaction contained by the turbulence component for Eigenmode component of each turbulence; carrying out EMD resolving for the time sequence At so as to obtain an IMF component group; repeating step 3 and 4 for all turbulence Eigenmode components, and carrying out corresponding power spectrum analysis so as to obtain the frequency-frequency energy spectrum. The advantages are that the novel method can simultaneously capture frequency modulation and amplitude modulation information from the signals, and can quantized the nonlinear interaction between different scale signals in the data. The method can help people to spot the hidden nonlinear interaction between different scales in the data, thus providing very wide application field and application prospect.
Description
Technical field
The present invention relates to a kind of analysis method of quantitative analysis wave and turbulent flow interaction strength, except being used for analyzing ripple
Wave and the observation data analysis of turbulent flow nonlinear interaction, it may also be used for other multiple dimensioned dynamic process phases of air and ocean
The analysis of interaction and geophysical day long data analysis etc..The method can help to we have found that the chi being hidden in data
Nonlinear interaction between degree.
Background technology
Exist in marine superstructure, wave and turbulent flow simultaneously, have an effect each other, this for research wave and turbulent flow
Interaction process, the first step needs to do wave motion and separates with turbulent motion.Wave is the regular motion of two dimension, and turbulent flow
Being three-dimensional random motion, and the fluctuation velocity of the orbital velocity of wave and turbulent flow is not in a magnitude, it is rapid that this is by ripple
First difficult point of mask work；Wave is with under turbulent flow concurrent conditions, and wave not only modulates turbulent flow in frequency, on amplitude also
Having modulation, this is another difficult point of this work.Thus from the velocity series measured, isolate velocity of wave translation and turbulent velocity
It it is a problem not the most being fully solved.Many researchers proposes different ripple rapids separation methods, mainly has three kinds: the
A kind of can obtain turbulence pulsation time series through separation, including: position phase average method, moving average method, non-linear
A young waiter in a wineshop or an inn takes advantage of decomposition method and non-linear three-component method (TDM) etc.；The second is the ripple rapids separation method of spectrum；The third method is
Linear filter technology, first obtains turbulence power spectrum, then tries to achieve turbulence pulsation time series.
Mostly contain certain a priori assumption due to said method, be linear phase as wave track moves with surface undulation
Close, and wave orbital velocity and turbulent flow are incoherent etc., therefore, there is limitation in ripple rapids Signal separator.Experience mode division
Solve (Empirical Mode Decomposition, EMD) method be developed in the last few years a kind of time-frequency analysis method.Should
Method is based on the Temporal-spatial scale characteristic of data self to carry out signal decomposition, it is not necessary to any a priori assumption, is especially suitable for non-thread
Property, Non-stationary Signal Analysis.EMD method has just obtained the most effective application at different engineering fields once proposition, such as
It is used in ocean, air, astronomical observation data and earthquake record analysis, mechanical fault diagnosis and the mould of large scale civil engineering structure
State parameter identification aspect.The key technology of EMD is narrow spectrum signal (i.e. eigen mode letter that signal decomposition is one group of nearly stable state
Number, Intrinsic Mode Functions, IMF) sum, what different IMF components was corresponding is then the different space-times of original signal
The feature of yardstick.The data signal interacted for ripple-rapids, first we carry out EMD to the Velocity Time sequence of observation and divide
Solve；It is narrow band signal due to IMF, therefore can pass through its instantaneous frequency of Hilbert change calculations, extract height therein
Frequently turbulent flow information, it is achieved wave separates with turbulent flow.According to theory of dynamic system, the dramatically different process of yardstick can be approximately considered
Its power mechanism is separate.It is to say, the wave motion of above-mentioned steps extraction and turbulent flow information are not enough to ripple rapids is described
Between exist interact.For proving to there is nonlinear interaction between ripple rapids, it would be desirable to carry from the rapid information of high frequency
Take out the information of wave.
In science and engineering research, spectral analysis method is the strong a kind of hands disclosing random data statistical nature
Section, is a kind of conversion that the time series of random length is converted on finite frequency territory.At present by vast researcher institute
The spectral analysis method used mainly has Fourier spectral analysis method and Hilbert spectral analysis method, but they have the office of oneself
Sex-limited.
Summary of the invention
It is desirable to provide a kind of analysis method that wave motion interacts with turbulent flow, it is to develop on the basis of EMD
A kind of higher-dimension time-frequency spectrum analysis method, with solve prior art exist the problems referred to above.
The technical scheme is that a kind of analysis method that wave interacts with turbulent flow, it is characterised in that utilize sea
The Velocity Time sequence that ripple rapids measured by upper observation or laboratory experiment interacts, carries out ripple based on EMD method rapid mutually
Function analysis, comprises the steps:
(1) determination of turbulence power spectrum frequency range.Primordial time series data Xt is carried out power spectrumanalysis, and finding out slope is-5/3
Frequency range w1 ~ w2, i.e. inertia district；
(2) frequency eigen mode component in this inertia district is obtained；
(3) the eigen mode component IMFi of each turbulent flow component is taken absolute value, it is thus achieved that corresponding time series Yt, find out each Yt
All maximum, obtained time series At of coenvelope line by matching, for analyzing the wave pair comprised in turbulent flow component
The nonlinear interaction of turbulent flow；
(4) time series At is carried out EMD decomposition, it is thus achieved that one group of new IMF component, the frequency wherein comprised is close with surface wave
Component, show the interaction between wave and two different frequency signals of turbulent flow；IMFj is carried out Hilbert-Huang spectrum
Analyzing, time available, the energy spectrum matrix of m-amplitude modulation frequency-modulation frequency, is then integrated the time, obtains the tune of correspondence
Swing frequency-modulation frequency (AM-FM) energy spectrum；
(5) all turbulent flow eigen mode components are repeated step 3-4, finally give comprehensive amplitude modulation frequency-modulation frequency (AM-FM)
Energy spectrum, by this AM-FM energy spectrum, analyzes the different frequency motion effect to turbulent flow, and the numerical value of energy spectrum shows that high frequency is rapid
Flow the relative intensity by low frequency wave PROCESS MODULATE effect.
The concrete grammar of described step (2) is: Xt carries out Empirical Mode Decomposition (EMD), it is thus achieved that eigen mode component
(IMF)；Calculate the instantaneous frequency of each modal components, find out the frequency range all eigen mode components in w1 ~ w2, i.e. turbulent flow
Component；Envelop data after taking absolute value turbulent flow component carries out EMD decomposition and Hilbert-Huang analysis of spectrum, is combined
Amplitude modulation frequency-modulation frequency (AM-FM) energy spectrum closed, thus the modulating action that quantitative analysis fluctuation is to turbulent flow, by right
Should be able to the intensity that interacts of big small quantization wave and turbulent flow.
The invention have the advantage that the frequency modulation (Frequency Modulation, FM) in energy signal acquisition simultaneously and amplitude modulation
Nonlinear interaction between (Amplitude Modulation, AM) information, and the motion of quantized data different scale.Remove
It is used for analyzing ripple rapids nonlinear interaction data analysis, it may also be used for other multiple dimensioned dynamic process phases of air and ocean
The analysis of interaction and geophysical day long data analysis etc..The method can help it is found that and be hidden in data
Nonlinear interaction between different scale, have a very wide range of applications field and application prospect.
Accompanying drawing explanation
Fig. 1. signalPower Spectrum Distribution figure, wherein block curve represents signalPower spectrum, dashed curve
Represent 95% confidence interval of power spectrum.
Fig. 2. signalEMD decomposition result oscillogram, wherein for high fdrequency component (being equivalent to " rapid "),For low frequency division
Amount (is equivalent to fluctuation).
Fig. 3. (a) is the instantaneous frequency figure of high fdrequency component, and (b) is low frequency componentInstantaneous frequency figure, wherein IF=
Instantaneous Frequency(instantaneous frequency).
Fig. 4. (a) is the envelope diagram of high fdrequency component absolute value, and (b) is the envelope diagram of low frequency component absolute value.
Fig. 5. high fdrequency componentThe envelope of absolute value carries out EMD exploded view (d1, d2 and d3 are eigen mode component).
Fig. 6. componentPower spectrum.
Fig. 7. the fluctuation AM-FM energy spectrum scattergram to turbulent flow, colour code represents the size of energy.
Fig. 8. the Energy distribution that high frequency turbulence signal changes with wave phase.
Detailed description of the invention
The present invention proposes a kind of new data analysing method: decompose the Hilbert of former T/F-energy based on EMD
Spectrum expands to frequency-frequency (AM-FM) energy spectrum of multilamellar, mould carrier frequencies change in FM frequency representation Rapid Variable Design carrier,
AM frequency representation changes at a slow speed the change of intermediate die frequency, by data non-linear, astable are carried out full spectrum information expression, amount
Change the nonlinear interaction between the internal different scale signal of data.
(this is high to utilize sea observation or laboratory experiment gained wave and the Velocity Time sequence of turbulent flow interaction
The routine data of frequency observation), the detailed process carrying out ripple rapids transactional analysis based on EMD method is as follows:
(1) determination of turbulence power spectrum frequency range.Primordial time series data Xt is carried out power spectrumanalysis, and finding out slope is-5/3
Frequency range w1 ~ w2, i.e. inertia district；
(2) frequency eigen mode component in this inertia district is obtained；
(3) the eigen mode component IMFi of each turbulent flow component is taken absolute value, it is thus achieved that corresponding time series Yt, find out each Yt
All maximum, obtained time series At of coenvelope line by matching, for analyzing the wave pair comprised in turbulent flow component
The nonlinear interaction of turbulent flow；
(4) time series At is carried out EMD decomposition, it is thus achieved that one group of new IMF component, the frequency wherein comprised is close with surface wave
Component, show the interaction between wave and two different frequency signals of turbulent flow；IMFj is carried out Hilbert-Huang spectrum
Analyzing, time available, the energy spectrum matrix of m-amplitude modulation frequency-modulation frequency, is then integrated the time, obtains the tune of correspondence
Swing frequency-modulation frequency (AM-FM) energy spectrum；
(5) all turbulent flow eigen mode components are repeated step 3-4, finally give comprehensive amplitude modulation frequency-modulation frequency (AM-FM)
Energy spectrum, by this AM-FM energy spectrum, analyzes the different frequency motion effect to turbulent flow, and the numerical value of energy spectrum shows that high frequency is rapid
Flow the relative intensity by low frequency wave PROCESS MODULATE effect.
With a concrete example, construct a low-frequency fluctuation and high frequency turbulent flow is had the number of obvious Modulation and Amplitude Modulation effect
According to, further illustrate how the method carries out low frequency (wave) and high frequency (turbulent flow) Signal separator, and analyze wave to turbulent flow work
With.We input one and have an amplitude-modulated signal:
According to step 1, first this time series is carried out Fourier analysis of spectrum decomposition, power as shown in Figure 1 can be obtained
Spectrum.Wherein solid black lines represents that power spectrum, dotted line represent 95% confidence interval of power spectrum.Power spectrum shows in these data and comprises
The low-frequency fluctuation of 1Hz and the turbulence signal of about 10Hz.
According to step 2, original time series is carried out EMD decomposition, obtain three components (Fig. 2) such as C1, C2 and C3, in conjunction with
Their instantaneous frequency (Fig. 3), it can be seen that C1 is high frequency turbulent flow component, and C2 is low-frequency fluctuation component, and cannot differentiate C1 and divide
Amount comprises the information of 1Hz fluctuation.
It follows that C1 is taken absolute value according to step 3, find out all of maximum, and matching C1 take absolute value after upper
Envelope (Fig. 4).Then according to step 4 carries out EMD decomposition to the time series of coenvelope line, obtain the IMF component shown in Fig. 5
D1, d2 and d3.This it appears that the fluctuation information that comprises of C1 in the power spectrumanalysis (Fig. 6) of component d3.By step 5, can
To show that corresponding AM-FM energy spectrum (Fig. 7) and turbulent flow are along with the Energy distribution (Fig. 8) of wave phase place.It can be seen that it is rapid
Stream energy is equally distributed at crest and the trough of wave, also show the energy quantity set to turbulent flow that fluctuates in energy spectrum
In at (10Hz, 2Hz) around.The low-frequency fluctuation that in primary signal comprise is clearly seen to high frequency turbulent flow by these analysis processes
Modulating action, and the value of energy spectrum quantifies to indicate the relative intensity of modulating action further.
Claims (2)
1. the analysis method that a fluctuation interacts with turbulent flow, it is characterised in that utilize sea observation or laboratory experiment
The Velocity Time sequence that measured wave interacts with turbulent flow, carries out ripple-rapids transactional analysis based on EMD method, including
Following steps:
(1) determination of turbulence power spectrum frequency range；
Primordial time series data Xt is carried out power spectrumanalysis, finds out frequency range w1 that slope is-5/3 ~ w2, i.e. inertia
District；
(2) frequency eigen mode component in this inertia district is obtained；
(3) the eigen mode component IMFi of each turbulent flow component is taken absolute value, it is thus achieved that corresponding time series Yt, find out each Yt
All maximum, obtained time series At of coenvelope line by matching, for analyzing the wave pair comprised in turbulent flow component
The nonlinear interaction of turbulent flow；
(4) time series At is carried out EMD decomposition, it is thus achieved that one group of new IMF component, the frequency wherein comprised is close with surface wave
Component, illustrate between the signal of two different frequencies exist interact；IMFj is carried out Hilbert-Huang analysis of spectrum,
Time available, the energy spectrum matrix of m-amplitude modulation frequency-modulation frequency, is then integrated the time, obtains the amplitude modulation frequency of correspondence
Rate-modulation frequency (AM-FM) energy spectrum；
(5) all turbulent flow eigen mode components are repeated step 3-4, finally give comprehensive amplitude modulation frequency-modulation frequency (AM-FM)
Energy spectrum, by this AM-FM energy spectrum, analyze the interaction between different frequency signals, the numerical value of energy spectrum shows height
Frequently turbulent flow is by the relative intensity of low frequency wave PROCESS MODULATE effect.
The analysis method that wave the most according to claim 1 interacts with turbulent flow, it is characterised in that described step
(2) concrete grammar is: Xt carries out Empirical Mode Decomposition (EMD), it is thus achieved that eigen mode component (IMF)；Calculate each modal components
Instantaneous frequency, find out the frequency range all eigen mode components in w1 ~ w2, i.e. turbulent flow component；Turbulent flow component is taken definitely
Envelop data after value carries out EMD and decomposes and Hilbert-Huang analysis of spectrum, obtains comprehensive amplitude modulation frequency-modulation frequency
(AM-FM) energy spectrum, thus clarify the fluctuation modulating action to turbulent flow, by big small quantization wave and the turbulent flow of corresponding energy
The intensity interacted.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110580392A (en) * | 2019-09-06 | 2019-12-17 | 大连理工大学 | Polynomial spectrum fitting method for representing near-island reef shallow water wave energy characteristics |
| CN115453143A (en) * | 2022-09-30 | 2022-12-09 | 华东师范大学 | Turbulence and wave separation method based on high-frequency water level and three-dimensional flow velocity data |
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| CN102930172A (en) * | 2012-11-15 | 2013-02-13 | 江苏科技大学 | Extraction method of multi-scale characteristic and fluctuation parameter of sea wave based on EMD |
| CN103577877A (en) * | 2013-11-19 | 2014-02-12 | 北京航空航天大学 | Ship motion prediction method based on time-frequency analysis and BP neural network |
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| US5317383A (en) * | 1992-09-18 | 1994-05-31 | Shell Oil Company | Array retroreflector apparatus for remote seismic sensing |
| US20060181451A1 (en) * | 2005-02-14 | 2006-08-17 | Honeywell International Inc. | System and method for combining displaced phase center antenna and space-time adaptive processing techniques to enhance clutter suppression in radar on moving platforms |
| CN102930172A (en) * | 2012-11-15 | 2013-02-13 | 江苏科技大学 | Extraction method of multi-scale characteristic and fluctuation parameter of sea wave based on EMD |
| CN103577877A (en) * | 2013-11-19 | 2014-02-12 | 北京航空航天大学 | Ship motion prediction method based on time-frequency analysis and BP neural network |
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| CN110580392A (en) * | 2019-09-06 | 2019-12-17 | 大连理工大学 | Polynomial spectrum fitting method for representing near-island reef shallow water wave energy characteristics |
| CN110580392B (en) * | 2019-09-06 | 2021-10-22 | 大连理工大学 | Polynomial spectrum fitting method for representing near-island reef shallow water wave energy characteristics |
| CN115453143A (en) * | 2022-09-30 | 2022-12-09 | 华东师范大学 | Turbulence and wave separation method based on high-frequency water level and three-dimensional flow velocity data |
| CN115453143B (en) * | 2022-09-30 | 2024-07-09 | 华东师范大学 | Turbulence and wave separation method based on high-frequency water level and three-dimensional flow velocity data |
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