CN106446441B - Two phase flow method for network visualization and application based on multiple dimensioned weighting recurrence network - Google Patents
Two phase flow method for network visualization and application based on multiple dimensioned weighting recurrence network Download PDFInfo
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Abstract
A kind of two phase flow method for network visualization and application based on multiple dimensioned weighting recurrence network: multi-scale transform is carried out to the multi channel signals obtained by the double-deck cycle motivation conductivity sensor；Multiple dimensioned weighting recurrence network is constructed under each scale factor；Multi channel signals under all scales are repeated with above-mentioned process, obtains multiple dimensioned weighting recurrence network；Selected threshold has even side if intersecting recurrence rate is greater than threshold value between two nodes in network, otherwise without even side, obtain multiple dimensioned having no right Recursive Networks；The multiple dimensioned community structure for having no right Recursive Networks is sought with Luwen's algorithm based on greedy optimisation strategy；Community structure is sought, two phase flow structure feature is disclosed, realizes the network visualization to complex flow structure.Vertical oil-water two-phase flow experiment, and the proportion of fixing oil phase and water phase are carried out using the double-deck cycle motivation conductivity sensor, changes oil and is mutually tested with the flow of water phase.The present invention can be realized the network visualization to two phase flow structure.
Description
Technical field
The present invention relates to a kind of two phase flow structural network method for visualizing.Added more particularly to one kind based on multiple dimensioned
Weigh two phase flow method for network visualization and the application of Recursive Networks.
Background technique
Two phase flow is widely present in petrochemical industry such as oil exploitation.In two-phase flow system, point of each phase
Cloth is constantly changing with time and space, forms different nowed formings, referred to as flow pattern.The flow pattern of two phase flow is complicated more
Become, local flow information is difficult to accurately capture, so that there are many difficult points for meteor trail echoes, for further measuring two phase flow
Parameter causes many influences.Currently, being mainly using observation method and the small baud based on measuring signal for the research of flow pattern
Sign analysis and fuzzy C-means clustering, fuzzy logic and genetic algorithm, Digital Image Processing algorithm etc..The annular that traditional measurement uses
Conductivity sensor and double helix capacitance sensor etc. are single channel sensors, are easily lost microcosmic local flow information.And divide
Conductivity sensor and excitation cycle stimulus sensor of cloth etc. can then acquire multi channel signals simultaneously, capture richer
Micro flow information provides important technology support for the announcement of two phase flow complex flow structure.
Nowadays Complex Networks Theory has been flourished multi-field since foundation, be research complication system
One important tool, especially major contribution has been made in its field in time series analysis in recent years.Practice have shown that complex web
Network includes to have apparent advantage with the important information in Kind of Nonlinear Dynamical System in Nonlinear Time Series for excavation,
Significant effect.Its succeed in single channel time series apply while, but also scientific research personnel begin to focus on how will
It is applied in the convergence analysis of multi-channel data.Recursive Networks are as an important branch in complex network research, more
Field is used widely, and especially for unstable, short time series, analytical effect is very significant.It can be used
In the kinetic characteristics probed into complication system and time series.
Summary of the invention
The technical problem to be solved by the invention is to provide one kind, and microcosmic fluidal texture can be disclosed from macroscopic perspective
Two phase flow method for network visualization and application based on multiple dimensioned weighting recurrence network.
The technical scheme adopted by the invention is that: a kind of two phase flow network visualization based on multiple dimensioned weighting recurrence network
Method includes the following steps:
1) multi-scale transform is carried out to the multi channel signals obtained by the double-deck cycle motivation conductivity sensor, comprising:
(1) be to length C p channel signalCoarse is carried out respectively to obtain:
Wherein,μxk,i (β)It is any point of the signal obtained after coarse, β is scale factor, and μ indicates that data are to ask equal
What value obtained, yk,bIt is any point in signal Y,Expression pairIt is rounded, wherein the single pass number after coarse
It is L according to length,K is port number, and b indicates the number at any point of signal Y, and i indicates what coarse obtained later
The number at any point of signal；
(2) coarse variance is calculated to the original signal in each channel in p channel, obtains the multiple dimensioned of each channel
Signal xk,i (β):
Multiple dimensioned multi channel signals are obtained as a result,
2) multiple dimensioned weighting recurrence network is constructed at each scale factor β, comprising:
(1) to the multi channel signals obtained under any one fixed size factor-betaInto
Row phase space reconfiguration:
Wherein, N is the number of vector point on trajectory of phase space after carrying out phase space reconfiguration, and m is Embedded dimensions, takes mistake
Nearest neighbor method determines that τ is delay time, is determined using mutual information method,It is obtained after phase space reconfiguration for channel k
Trajectory of phase space, wherein t=1 ..., N；
(2) for the signal x of any two channels f and gf,i (β)And xg,i (β)F ≠ g is obtained mutually empty after carrying out (1) step
Between trackWithBy being carried out to trajectory of phase space described in two
Intersect recurrence, obtain the cross recurrence plots that a size is N × N:
Wherein, ε is threshold value, using 15% two channel signal standard deviations and selection,It indicates
The distance between any two vector in two trajectory of phase space；Indicate ifThen value is 1, ifThen value is 0；IfValue be
1, then it is corresponding position black in recurrence plot, ifValue be 0, then in recurrence plot corresponding position be it is white
Color；
(3) it in order to quantify the density of recursive point in each cross recurrence plots, calculates and intersects recurrence rate:
(4) multi channel signals for being obtained under fixed size factor-betaTo every two
Channel signal uses the processing mode of (1) Bu Zhi (3) step, obtains the intersection recurrence rate matrix that a size is p × p；
(5) each channel signal is regarded as node, using two interchannels formed cross recurrence plots intersection recurrence rate as
Connect the weight on side between node；The weighting recurrence network under any fixed size is obtained as a result,；
3) by the multi channel signals under all scalesThe process of step 2) is repeated,
Obtain multiple dimensioned weighting recurrence network；Selected threshold, if intersecting recurrence rate is greater than the threshold value, between two nodes in network
There is even side, otherwise without even side, to obtain multiple dimensioned having no right Recursive Networks；
4) the multiple dimensioned community structure for having no right Recursive Networks is sought using Luwen's algorithm based on greedy optimisation strategy；Pass through
Community structure is sought, two phase flow structure feature is disclosed, realizes the network visualization to complex flow structure.
A kind of application method of the two phase flow method for network visualization based on multiple dimensioned weighting recurrence network is using bilayer
Cycle motivation conductivity sensor carries out vertical oil-water two-phase flow experiment, and the proportion of fixing oil phase and water phase, changes oil phase and water
The flow of phase is tested；Experimentation is as follows:
1) proportion of fixed water phase and oily phase, a certain amount of water is passed through into vertical ascent pipeline, then gradually to pipeline
In be passed through oily phase, after water-oil phase sufficiently merges and is gradually stable, measured simultaneously using the double-deck cycle motivation conductivity sensor
Multi channel signals, and flow pattern is recorded with high-speed camera instrument；
2) change oil mutually with the flow of water phase, repeat step 1), until operating condition designed under fixed proportion is all complete
At；
3) change oil mutually with the proportion of water phase, repeat step 1) and step 2), until all operating conditions whole of design measures
It completes；
4) based on above-mentioned multi-channel measurement signal, multi-scale transform is carried out to the signal in every channel, then at each
Under scale, phase space reconfiguration is carried out to the signal in transformed every two channel, cross recurrence plots is drawn and calculates and intersect recurrence rate；
Using each channel signal as the node of network, the weight on side, structure are connected using the intersection recurrence rate between two channel signals as network
Build multiple dimensioned weighting recurrence network；Using false discovery rate algorithm picks threshold value, weighting recurrence network is converted to and haves no right recurrence
Network；
5) the multiple dimensioned evolution for having no right Recursive Networks community structure under different operating conditions is sought using Luwen's algorithm, discloses two-phase
Fluidal texture feature is flowed, realizes the network visualization to complex flow structure.
Of the invention two phase flow method for network visualization and application based on multiple dimensioned weighting recurrence network, have has as follows
Beneficial effect:
(1) by seeking to network community structure, complicated two phase flow structure is disclosed；
(2) it can be realized the network visualization to two phase flow structure, microcosmic fluidal texture disclosed from macroscopic perspective.
Detailed description of the invention
Fig. 1 is the two phase flow method for network visualization flow chart the present invention is based on multiple dimensioned weighting recurrence network；
Fig. 2 is the signal acquiring system schematic diagram of the method for the present invention.
In figure
A: cycle motivation conductivity sensor b: signal acquisition circuit
C: computer 1,5,9,13,17,21,25,29: electrode
Specific embodiment
It is visual to the two-phase flow network of the invention based on multiple dimensioned weighting recurrence network below with reference to embodiment and attached drawing
Change method and application are described in detail.
Two phase flow method for network visualization based on multiple dimensioned weighting recurrence network of the invention is by recycling to bilayer
The multi-channel data that exciting conductance sensor obtains is in the multiple dimensioned upper intersection recurrence rate calculated between every a pair of of signal, with recurrence
Rate determines company's side right weight of Recursive Networks, using the signal in each channel as the node of network, establishes multiple dimensioned weighting recurrence net
Network.Multiple dimensioned weighting recurrence network is converted to by selected threshold and haves no right Recursive Networks, multiple dimensioned haves no right recurrence by seeking
The community structure of network realizes the network visualization to two phase flow complex flow structure.
As shown in Figure 1, the two phase flow method for network visualization of the invention based on multiple dimensioned weighting recurrence network, including such as
Lower step:
1) multi-scale transform is carried out to the multi channel signals obtained by the double-deck cycle motivation conductivity sensor, comprising:
(1) be to length C p channel signalCoarse is carried out respectively to obtain:
Wherein,μxk,i (β)It is any point of the signal obtained after coarse, β is scale factor, and μ indicates that data are to ask equal
The y that value obtainsk,bIt is any point in signal Y,Expression pairIt is rounded, wherein the single pass number after coarse
It is L according to length,K is port number, and b indicates the number at any point of signal Y, and i indicates what coarse obtained later
The number at any point of signal；
(2) coarse variance is calculated to the original signal in each channel in p channel, obtains the multiple dimensioned of each channel
Signal xk,i (β):
Multiple dimensioned multi channel signals are obtained as a result,
2) multiple dimensioned weighting recurrence network is constructed at each scale factor β, comprising:
(1) to the multi channel signals obtained under any one fixed size factor-betaInto
Row phase space reconfiguration:
Wherein, N is the number of vector point on trajectory of phase space after carrying out phase space reconfiguration, and m is Embedded dimensions, takes mistake
Nearest neighbor method determines that τ is delay time, is determined using mutual information method,It is obtained after phase space reconfiguration for channel k
Trajectory of phase space, wherein t=1 ..., N；
(2) for the signal x of any two channels f and gf,i (β)And xg,i (β)F ≠ g is obtained mutually empty after carrying out (1) step
Between trackWithBy being carried out to trajectory of phase space described in two
Intersect recurrence, obtain the cross recurrence plots that a size is N × N:
Wherein, ε is threshold value, using 15% two channel signal standard deviations and selection,It indicates
The distance between any two vector in two trajectory of phase space；Indicate ifThen value is 1, ifThen value is 0；IfValue be
1, then corresponding position is black in recurrence plot, ifValue be 0, then in recurrence plot corresponding position be it is white
Color；
(3) it in order to quantify the density of recursive point in each cross recurrence plots, calculates and intersects recurrence rate:
(4) multi channel signals for being obtained under fixed size factor-betaTo every two
Channel signal uses the processing mode of (1) Bu Zhi (3) step, obtains the intersection recurrence rate matrix that a size is p × p；
(5) each channel signal is regarded as node, using two interchannels formed cross recurrence plots intersection recurrence rate as
Connect the weight on side between node；The weighting recurrence network under any fixed size is obtained as a result,；
3) by the multi channel signals under all scalesThe process of step 2) is repeated,
Obtain multiple dimensioned weighting recurrence network；Under each scale, for the weight matrix that the intersection recurrence rate is formed, threshold is chosen
Value has even side if intersecting recurrence rate is greater than the threshold value between two nodes in network, otherwise without even side, to obtain more
Scale haves no right Recursive Networks；
4) the multiple dimensioned community structure for having no right Recursive Networks is sought using Luwen's algorithm based on greedy optimisation strategy；Pass through
Community structure is sought, two phase flow structure feature is disclosed, realizes the network visualization to complex flow structure.
The application side of two phase flow structural network method for visualizing based on multiple dimensioned weighting recurrence network of the invention
Method is to carry out vertical oil-water two-phase flow experiment, and the proportion of fixing oil phase and water phase using the double-deck cycle motivation conductivity sensor,
Change oil mutually to be tested with the flow of water phase；Signal acquiring system is as shown in Figure 2.Each layer of cycle motivation conductivity sensor by
16 electrodes form, totally 32 electrodes.Every time in measurement, one of electrode of a certain layer is as excitation end, such as the electricity in Fig. 2
Pole 1, opposite another layer of electrode ground connection, such as the electrode 25 in figure, remaining 30 electrode is received, and one cycle can measure
The signal in the channel 32 × 30=960 is obtained, so as to capture local flow information abundant.Fixing oil phase and water phase are matched
Than changing oil and mutually being tested with the flow of water phase.Experimentation is as follows:
1) proportion of fixed water phase and oily phase, a certain amount of water is passed through into vertical ascent pipeline, then gradually to pipeline
In be passed through oily phase, after water-oil phase sufficiently merges and is gradually stable, measured simultaneously using the double-deck cycle motivation conductivity sensor
Multi channel signals, and flow pattern is recorded with high-speed camera instrument；
2) change oil mutually with the flow of water phase, repeat step 1), until operating condition designed under fixed proportion is all complete
At；
3) change oil mutually with the proportion of water phase, repeat step 1) and step 2), until all operating conditions whole of design measures
It completes；
4) based on above-mentioned multi-channel measurement signal, multi-scale transform is carried out to the signal in every channel, then at each
Under scale, phase space reconfiguration is carried out to the signal in transformed every two channel, cross recurrence plots is drawn and calculates and intersect recurrence rate；
Using each channel signal as the node of network, the weight on side, structure are connected using the intersection recurrence rate between two channel signals as network
Build multiple dimensioned weighting recurrence network；Using false discovery rate algorithm picks threshold value, weighting recurrence network is converted to and haves no right recurrence
Network；
5) the multiple dimensioned Recursive Networks corporations that have no right under different operating conditions are sought using Luwen's algorithm (Louvain method) to tie
The evolution of structure discloses two phase flow structure feature, realizes the network visualization to complex flow structure.
The multi channel signals obtained from the double-deck cycle motivation conductivity sensor measurement carry out multi-scale transform, calculate each ruler
The cross recurrence plots between lower signal are spent, obtain intersecting recurrence rate matrix.Intersection recurrence rate using signal as node, between signal
As company's side right weight of network, constructs multiple dimensioned weighting recurrence network and be translated into be converted to and have no right Recursive Networks.It seeks
The evolution of different operating condition lower network community structures discloses two phase flow structure feature, realizes the net to complex flow structure
Network visualization.
Above to the description of the present invention and embodiment, it is not limited to which this, the description in embodiment is only reality of the invention
One of mode is applied, it is without departing from the spirit of the invention, any not inventively to design and the technical solution
Similar structure or embodiment, category protection scope of the present invention.
Claims (2)
1. a kind of two phase flow method for network visualization based on multiple dimensioned weighting recurrence network, which is characterized in that including walking as follows
It is rapid:
1) multi-scale transform is carried out to the multi channel signals obtained by the double-deck cycle motivation conductivity sensor, comprising:
(1) be to length C p channel signalCoarse is carried out respectively to obtain:
Wherein,μxk,i (β)It is any point of the signal obtained after coarse, β is scale factor, and μ indicates that data are to average
It arrives, yk,bIt is any point in signal Y,Expression pairIt is rounded, wherein the single pass data after coarse are long
Degree is L,K is port number, and b indicates the number at any point of signal Y, and i indicates the signal obtained after coarse
Any point number；
(2) coarse variance is calculated to the original signal in each channel in p channel, obtains the multiple dimensioned signal in each channel
xk,i (β):
Multiple dimensioned multi channel signals are obtained as a result,
2) multiple dimensioned weighting recurrence network is constructed at each scale factor β, comprising:
(1) to the multi channel signals obtained under any one fixed size factor-betaCarry out phase
Space Reconstruction:
Wherein, N is the number of vector point on trajectory of phase space after carrying out phase space reconfiguration, and m is Embedded dimensions, is taken wrong nearest
Adjacent method determines that τ is delay time, is determined using mutual information method,The phase obtained after phase space reconfiguration for channel k is empty
Between track, wherein t=1 ..., N；
(2) for the signal x of any two channels f and gf,i (β)And xg,i (β)F ≠ g obtains phase space rail after carrying out (1) step
MarkWithIt is passed by intersect to trajectory of phase space described in two
Return, obtain the cross recurrence plots that a size is N × N:
Wherein, ε is threshold value, using 15% two channel signal standard deviations and selection,Indicate two-phase
The distance between any two vector in space tracking；Indicate ifThen value is 1, ifThen value is 0；IfValue be
1, then it is corresponding position black in recurrence plot, ifValue be 0, then in recurrence plot corresponding position be it is white
Color；
(3) it in order to quantify the density of recursive point in each cross recurrence plots, calculates and intersects recurrence rate:
(4) multi channel signals for being obtained under fixed size factor-betaTo every two channel
Signal uses the processing mode of (1) Bu Zhi (3) step, obtains the intersection recurrence rate matrix that a size is p × p；
(5) each channel signal is regarded as node, the intersection recurrence rate for the cross recurrence plots that two interchannels are formed is as node
Between connect the weight on side；The weighting recurrence network under any fixed size is obtained as a result,；
3) by the multi channel signals under all scalesThe process for repeating step 2), obtains
Multiple dimensioned weighting recurrence network；Selected threshold has company between two nodes in network if intersecting recurrence rate is greater than the threshold value
Side, otherwise without even side, to obtain multiple dimensioned having no right Recursive Networks；
4) the multiple dimensioned community structure for having no right Recursive Networks is sought using Luwen's algorithm based on greedy optimisation strategy；By to society
Unity structure is sought, and two phase flow structure feature is disclosed, and realizes the network visualization to complex flow structure.
2. a kind of application side of the two phase flow method for network visualization described in claim 1 based on multiple dimensioned weighting recurrence network
Method, which is characterized in that be vertical oil-water two-phase flow experiment is carried out using the double-deck cycle motivation conductivity sensor, and fixing oil phase and
The proportion of water phase changes oil and is mutually tested with the flow of water phase；Experimentation is as follows:
1) proportion of fixed water phase and oily phase, a certain amount of water is passed through into vertical ascent pipeline, is then gradually led into pipeline
Enter oily phase, after water-oil phase sufficiently merges and is gradually stable, multi-pass is measured using the double-deck cycle motivation conductivity sensor simultaneously
Road signal, and flow pattern is recorded with high-speed camera instrument；
2) change oil mutually with the flow of water phase, repeat step 1), until operating condition designed under fixed proportion is all completed；
3) change oil mutually with the proportion of water phase, repeat step 1) and step 2), until all operating conditions of design are all measured；
4) based on above-mentioned multi-channel measurement signal, multi-scale transform is carried out to the signal in every channel, then in each scale
Under, phase space reconfiguration is carried out to the signal in transformed every two channel, cross recurrence plots is drawn and calculates and intersect recurrence rate；With every
Node of one channel signal as network connects the weight on side using the intersection recurrence rate between two channel signals as network, constructs more
Scale weighting recurrence network；Using false discovery rate algorithm picks threshold value, weighting recurrence network is converted to and haves no right Recursive Networks；
5) the multiple dimensioned evolution for having no right Recursive Networks community structure under different operating conditions is sought using Luwen's algorithm, discloses two phase flow stream
Dynamic structure feature, realizes the network visualization to complex flow structure.
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