CN106482784A - Two phase flow spatial complex network visualization analysis method based on grid sensor - Google Patents
Two phase flow spatial complex network visualization analysis method based on grid sensor Download PDFInfo
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Abstract
A kind of two phase flow spatial complex network visualization analysis method based on grid sensor, spatial weighting complex network is built by the multi-channel data of Wire Mesh sensor, respectively visual analyzing is carried out to vertical biphase gas and liquid flow flow process from network node weights and two angles of network community topological structure, and merge two kinds of visualization result, and then the information such as bubble diameter size, oil vacuole distribution and flow behavior during acquisition two phase flow.Wherein Wire Mesh sensor is made up of the stainless steel wire of each 16 a diameter of 0.12mm of two-layer, and they are evenly distributed in the pipeline section that caliber is 50mm, and the axial distance of two-layer stainless steel wire is 1.5mm, and two-layer stainless steel wire interlocks at an angle of 90.Present invention is mainly applied to two phase flow visual analyzing.
Description
Technical field
The present invention relates to a kind of two phase flow spatial weighting complex network visual analysis method.More particularly to one kind is based on
The two phase flow spatial weighting complex network visual analysis method of grid (Wire-Mesh) sensor.
Background technology
The flow process of two-phase fluid is not only the common phenomenon of daily life and nature, and is widely present in energy
During the modern industries such as source, oil, chemical industry, metallurgy, refrigeration, aerospace, medicine, food.Due to two-phase flow complexity and
Randomness, the meteor trail echoes of biphase gas and liquid flow and phase content measurement are long-term in scientific research and industrial application all the time
Fail the measurement difficult problem being solved very well.Wire-Mesh imaging technique is that biphase flow containing rate measures hot research in recent years
One of problem.Different from traditional process tomographic imaging technology, the measurement that Wire-Mesh imaging technique uses intrusive mood becomes
As technology, there is the flow imaging effect of What You See Is What You Get, biphase flow containing rate certainty of measurement is also higher.At present, in two phase flow
Flow parameter measurement field is gradually able to research and development.
Complex Networks Theory, since founding, has nowadays been flourished multi-field, is research complication system
One important tool, especially it is made that major contribution in time series analysis in recent years.If certain system is by multiple
Entity is constituted, can each entity as a node, certain contact of inter-entity as even side, so this system just by
It has been abstracted into complex network.Complex network provides probability and foundation for the height combination of reality system and mathematical theory, from
And the mathematical model that can preferably set up various complication systems is analyzed with the mechanism to its inside.
Content of the invention
The technical problem to be solved be to provide one kind can obtain bubble diameter size during two phase flow,
The two phase flow spatial complex network visualization analysis method based on grid sensor of the information such as bubble distribution and flow behavior.
The technical solution adopted in the present invention is：A kind of two phase flow spatial complex network visualization based on grid sensor
Analysis method, obtains the experimental series of vertical biphase gas and liquid flows using 16 × 16 electrode Wire-Mesh sensors, by space plus
Power complex network carries out visual analyzing, realizes measuring and disclosing two phase flow Complex Flows characteristic to phase content；Specifically include
Following steps：
1) experimental series according to the N number of effective cross point being obtained by Wire-Mesh sensor in measurement planeBuild the t of corresponding operating modekThe spatial weighting complex network in momentDescribed experimental seriesRepresent i-th effective cross point n in Wire-Mesh sensoriFrom tkMoment starts the measurement data that length is L, space
Weighted Complex NetworksBuilding process include：
(1) with all effective cross point n in Wire-Mesh sensoriAs the node of network, and determine all-network
Node niNode weightsWhereinRepresent experimental seriesTime domain average value；
(2) select threshold value Sε, whenWhen, define network node ni∈ngas, wherein gas represents gas phase, ngasRepresent quilt
The set in effective cross point that gas phase is surrounded；WhenWhen, define network node ni∈nwater, wherein water represents aqueous phase,
nwaterRepresent the set in the effective cross point being surrounded by aqueous phase；
(3) in ngasInterior determination node niWith node njThe distance betweenIncluding：First pass through experimental series
Characteristic parameter structural features vectorBy experimental seriesCharacteristic parameter structural features vectorThen ask
Take characteristic vectorWith characteristic vectorThe distance betweenWherein | | | | represent the behaviour taking two norm value
Make；
(4) select threshold epsilon, in described ngasInterior all nodes between determine even side
Represent tkThe spatial weighting complex network in momentInterior joint niAnd njCompany's side right value, whereinRepresent ifThenIt is worth for 1, ifThenIt is worth for 0, the spatial weighting of configuration node Weighted Coefficients is multiple
Miscellaneous network
2) algorithm is sought by application Girvan Newman corporations, seeks the spatial weighting complex network corresponding to different operating modesInternal community structure, determines experimental seriesCorresponding distributed mutually topological diagram；And combine complicated according to spatial weighting
NetworkThe weights of nodeThe distributed mutually audio-visual picture obtaining, determines tkSteep during two phase flow in moment measurement plane
The information such as footpath size, bubble distribution and fluid flow characteristics；
3) again select start time tk', take the data that length is L, repeat step 1) and step 2) in operating process,
Obtain distributed mutually topological diagram and distributed mutually audio-visual picture, determine tkDuring two phase flow in ' moment measurement plane, bubble diameter is big
Little, bubble distribution and fluid flow characteristics information；
4) carry out the measurement of correlation analysis of biphase gas and liquid flow flow parameter and structure, including：Respectively draw gas phase content with
The distribution curve of measurement pipe radius and bubble average equivalent radius, with the change curve of aqueous phase flow rate, analyze fluid-mixing stream
The influencing mechanism to Air Bubble Size and distribution for the speed；And based on described distribution curve and influencing mechanism, study two phase flow stream
The Dynamic Evolution of dynamic form, analyzes the generation evolution mechanism of various flow patterns.
Described Wire-Mesh sensor is made up of the stainless steel wire of each 16 a diameter of 0.12mm of two-layer, their average marks
Cloth is in caliber for, in the pipeline section of 50mm, the axial distance of two-layer stainless steel wire is 1.5mm, and two-layer stainless steel wire is staggered into
90 ° of angles.
Step 1) described in effective cross point number N be less than 16 × 16, this is that there is edge due in circular pipe
Cross point, wherein, spatial weighting complex network is being set up in edge cross pointWhen do not consider.
Step 1) (3rd) step described in characteristic vectorBuilding process be：Ask for experimental series first
Time domain charactreristic parameter, including time domain maximumTime domain average value
Temporal criterion deviationSteepness functionKurtosis functionAnd through the frequency domain sequence after fast discrete Fourier transformationFrequency domain special
Levy parameter, wherein F represents frequency domain, including frequency domain maximumFrequency-domain average valueFrequency domain criteria deviation
ObtainRightIn each element
It is normalized according to maximum respectively and obtain：
Wherein maxi
Maximum when () represents that the variable in bracket changes with i.
Step 1) (4th) step described in the value of threshold epsilon be taken as step 2) described in distributed mutually audio-visual picture in corresponding
Largest air bubbles equivalent diameter.
The two phase flow spatial complex network visualization analysis method based on grid sensor of the present invention, by grid
(Wire-Mesh) multi-channel data of sensor builds spatial weighting complex network, and then from network node weights and network society
Two angles such as group's topological structure carry out visual analyzing to biphase gas and liquid flow flow process respectively, and merge the information of the two,
And then the information such as bubble diameter size, bubble distribution and flow behavior during acquisition two phase flow.
Brief description
Fig. 1 a is Wire-Mesh sensor construction schematic diagram；
Fig. 1 b is the top view of Wire-Mesh sensor
In figure R represents pipe diameter, is 50mm；H represents spacing between electrodes, is 2.94mm；
Fig. 2 is the t of the present inventionkThe multichannel time serieses spatial weighting complex network creation analysis flow chart in moment；
Fig. 3 a is that the multichannel time serieses spatial weighting complex network fusion metrical information acquisition distributed mutually of the present invention is straight
See figure
In figure R represents pipe diameter, is 50mm；H represents spacing between electrodes, is 2.94mm；
Fig. 3 b is that the multichannel time serieses spatial weighting complex network fusion metrical information acquisition distributed mutually of the present invention is opened up
Flutter figure
In figure R represents pipe diameter, is 50mm；H represents spacing between electrodes, is 2.94mm.
Specific embodiment
With reference to embodiment and accompanying drawing, to the present invention, the two phase flow spatial complex network based on grid sensor is visual
Change analysis method to be described in detail.
Implication is consistent in this patent with wire-mesh sensor to point out initially that grid sensor, can exchange.
Based on the spatial multichannel measurement data of Wire-Mesh sensor, and add with reference to Complex Networks Theory structure space
Power complex network, on this basis, from node weights and two angles of network topology structure are carried out to two phase flow process can
Depending on changing analysis, and then the information such as bubble diameter size, bubble distribution and flow behavior during acquisition two phase flow.
The two phase flow spatial complex network visualization analysis method based on grid sensor of the present invention, using 16 × 16 electricity
Pole Wire-Mesh sensor obtains the experimental series of vertical biphase gas and liquid flow, is visualized by spatial weighting complex network
Analysis, realizes measuring and disclosing two phase flow Complex Flows characteristic to phase content.Described Wire-Mesh sensor such as Fig. 1 a, figure
Shown in 1b, it is made up of the stainless steel wire of each 16 a diameter of 0.12mm of two-layer, they are evenly distributed in the pipeline that caliber is 50mm
In section, the axial distance of two-layer stainless steel wire is 1.5mm, and two-layer stainless steel wire interlocks at an angle of 90.
The two phase flow spatial complex network visualization analysis method based on grid sensor of the present invention, specifically includes as follows
Step：
1) experimental series according to the N number of effective cross point being obtained by Wire-Mesh sensor in measurement planeBuild the t of corresponding operating modekThe spatial weighting complex network in momentDescribed experimental seriesRepresent i-th effective cross point n in Wire-Mesh sensoriFrom tkMoment starts the measurement data that length is L, and L is
The taken data length of single visualization, the determination of its value needs to be determined according to fluid-flow rate and fluid flow structure.
The number N in described effective cross point is less than 16 × 16, and this is due to there is edge cross point, wherein, edge in circular pipe
Spatial weighting complex network is being set up in cross pointWhen do not consider.Spatial weighting complex networkBuilding process include：
(1) with all effective cross point n in Wire-Mesh sensoriAs the node of network, and determine all-network
Node niNode weightsWhereinRepresent experimental seriesTime domain average value；
(2) measurement signal according to full water selectes threshold value Sε, whenWhen, represent when having longer in this measuring section
Between gas phase pass through this node, define network node ni∈ngas, wherein gas represents gas phase, ngasRepresent by gas phase surround effective
The set in cross point；WhenWhen, it is relatively long to represent the aqueous phase persistent period at this node in this measuring section, defines net
Network node ni∈nwater, wherein water represents aqueous phase, nwaterRepresent the set in the effective cross point being surrounded by aqueous phase；Follow-up mistake
Journey is only in ngasDetermine between node that network connects side, the connection of such nodes just can more intuitively reflect gas
The distribution of phase, and weaken the impact of aqueous phase；
(3) in ngasInterior determination node niWith node njThe distance betweenIncluding：First pass through experimental series
Characteristic parameter structural features vectorBy experimental seriesCharacteristic parameter structural features vectorThen ask
Take characteristic vectorWith characteristic vectorThe distance betweenWherein | | | | represent the behaviour taking two norm value
Make；
Described characteristic vectorBuilding process be：Ask for experimental series firstTime domain charactreristic parameter, bag
Include time domain maximumTime domain average valueTemporal criterion deviationSteepness functionKurtosis function
And through the frequency domain sequence after fast discrete Fourier transformationFrequency domain character parameter, wherein F represents frequency domain, including
Frequency domain maximumFrequency-domain average valueFrequency domain criteria deviation
ObtainRightIn each element
It is normalized according to maximum respectively and obtain：
Wherein maxiMaximum when () represents that the variable in bracket changes with i.
(4) select threshold epsilon, in described ngasInterior all nodes between determine even side
Represent tkThe spatial weighting complex network in momentInterior joint niAnd njCompany's side right value, whereinRepresent ifThenIt is worth for 1, ifThenIt is worth for 0, the spatial weighting of configuration node Weighted Coefficients is complicated
NetworkThe value of wherein threshold epsilon is taken as step 2) described in distributed mutually audio-visual picture in corresponding largest air bubbles equivalent diameter.
2) algorithm is sought by application Girvan Newman corporations, finds the spatial weighting complex network corresponding to different operating modesInternal community structure, determines experimental seriesCorresponding distributed mutually topological diagram；And combine complicated according to spatial weighting
NetworkThe weights of nodeThe distributed mutually audio-visual picture obtaining, determines tkSteep during two phase flow in moment measurement plane
The information such as footpath size, bubble distribution and flow behavior；
As shown in Figure 3 a, it is a certain distributed mutually audio-visual picture schematic diagram visualizing time period fluid, basis in practical operation
Meansigma methodss within this time period for each cross point experimental data to determine the weights of each point, then draws coloured picture according to weights size
Reflection gas-liquid distribution of each phase, the black region of in figure represents gas phase；Fig. 3 b is that the distributed mutually of identical visualization time period fluid is opened up
Flutter diagram to be intended to, pass through in practical operation to use Girvan Newman to calculate this time period corresponding spatial weighting complex network
Method carries out corporations and seeks, and determines community structure to obtain；The core concept of wherein Girvan Newman algorithm is constantly to delete
Except the company side that intermediary of network number centrality is maximum, and then by nodes with the arrangement of tree graph form, binding modules degree Q and phase
Distribution audio-visual picture just can select suitable community number, determines community structure.Although distributed mutually audio-visual picture can effectively recognize
The presence of big bubble structure, but limited for the fluid distribution of each phase identification capability between air pocket, and utilization space weights
The community structure of complex network, can be very good to make up this deficiency, as shown in Figure 3 a, although the two of top bubbles divide in phase
Sharpness of border in cloth audio-visual picture, but but belong to same community structure in distributed mutually topological diagram shown in Fig. 3 b, illustrate this two
More minute bubbles are probably distributed between air pocket；Distributed mutually audio-visual picture and topological diagram are combined, can be more efficient
Determine the information such as bubble diameter size during two phase flow in measurement plane, bubble distribution and flow behavior；
3) again select start time tk', take the data that length is L, repeat step 1) and step 2) in operating process,
Obtain distributed mutually topological diagram and distributed mutually audio-visual picture, determine tkDuring two phase flow in ' moment measurement plane, bubble diameter is big
The information such as little, bubble distribution and fluid flow characteristics；
4) carry out the measurement of correlation analysis of biphase gas and liquid flow flow parameter and structure, including：Respectively draw gas phase content with
The distribution curve of measurement pipe radius and bubble average equivalent radius, with the change curve of aqueous phase flow rate, analyze fluid-mixing stream
The influencing mechanism to Air Bubble Size and distribution for the speed；And based on described distribution curve and influencing mechanism, study two phase flow stream
The Dynamic Evolution of dynamic form, analyzes the generation evolution mechanism of various flow patterns.
It is not limited to this, the description in embodiment is only the reality of the present invention for description to the present invention and embodiment above
Apply one of mode, in the case of without departing from the invention objective, any design and this technical scheme without creative
Similar structure or embodiment, all belong to protection scope of the present invention.
Claims (5)
1. a kind of two phase flow spatial complex network visualization analysis method based on grid sensor is it is characterised in that adopt 16
× 16 electrode Wire-Mesh sensors obtain the experimental series of vertical biphase gas and liquid flow, are carried out by spatial weighting complex network
Visual analyzing, realizes measuring and disclosing two phase flow Complex Flows characteristic to phase content；Specifically include following steps：
1) experimental series according to the N number of effective cross point being obtained by Wire-Mesh sensor in measurement planei
=1,2 ..., N, build the t of corresponding operating modekThe spatial weighting complex network in momentDescribed experimental seriesTable
Show i-th effective cross point n in Wire-Mesh sensoriFrom tkMoment starts the measurement data that length is L, and spatial weighting is complicated
NetworkBuilding process include：
(1) with all effective cross point n in Wire-Mesh sensoriAs the node of network, and determine all-network node
niNode weightsWhereinRepresent experimental seriesTime domain average value；
(2) select threshold value Sε, whenWhen, define network node ni∈ngas, wherein gas represents gas phase, ngasRepresent by gas phase
The set in the effective cross point surrounding；WhenWhen, define network node ni∈nwater, wherein water represents aqueous phase,
nwaterRepresent the set in the effective cross point being surrounded by aqueous phase；
(3) in ngasInterior determination node niWith node njThe distance betweenIncluding：First pass through experimental seriesSpy
Levy parametric configuration characteristic vectorBy experimental seriesCharacteristic parameter structural features vectorThen ask for feature
VectorWith characteristic vectorThe distance betweenWherein | | | | represent the operation taking two norm value；
(4) select threshold epsilon, in described ngasInterior all nodes between determine even side Represent
tkThe spatial weighting complex network in momentInterior joint niAnd njCompany's side right value, whereinRepresent if
ThenIt is worth for 1, ifThenIt is worth for 0, the spatial weighting complex network of configuration node Weighted Coefficients
2) algorithm is sought by application Girvan Newman corporations, seeks the spatial weighting complex network corresponding to different operating modesInterior
Portion's community structure, determines experimental seriesCorresponding distributed mutually topological diagram；And combine according to spatial weighting complex networkThe weights of nodeThe distributed mutually audio-visual picture obtaining, determines tkDuring two phase flow in moment measurement plane, bubble diameter is big
The information such as little, bubble distribution and fluid flow characteristics；
3) again select start time tk', take the data that length is L, repeat step 1) and step 2) in operating process, obtain
Distributed mutually topological diagram and distributed mutually audio-visual picture, determine tkBubble diameter size, gas during two phase flow in ' moment measurement plane
Bubble distribution and fluid flow characteristics information；
4) carry out the measurement of correlation analysis of biphase gas and liquid flow flow parameter and structure, including：Draw gas phase content respectively with measurement
The distribution curve of pipe radius and bubble average equivalent radius, with the change curve of aqueous phase flow rate, analyze fluid-mixing flow velocity pair
Air Bubble Size and the influencing mechanism of distribution；And based on described distribution curve and influencing mechanism, study two phase flow shape
The Dynamic Evolution of state, analyzes the generation evolution mechanism of various flow patterns.
2. the two phase flow spatial complex network visualization analysis method based on grid sensor according to claim 1, its
It is characterised by, described Wire-Mesh sensor is made up of the stainless steel wire of each 16 a diameter of 0.12mm of two-layer, their average marks
Cloth is in caliber for, in the pipeline section of 50mm, the axial distance of two-layer stainless steel wire is 1.5mm, and two-layer stainless steel wire is staggered into
90 ° of angles.
3. the two phase flow spatial complex network visualization analysis method based on grid sensor according to claim 1, its
Be characterised by, step 1) described in effective cross point number N be less than 16 × 16, this is that there is edge due in circular pipe
Cross point, wherein, spatial weighting complex network is being set up in edge cross pointWhen do not consider.
4. the two phase flow spatial complex network visualization analysis method based on grid sensor according to claim 1, its
Be characterised by, step 1) (3rd) step described in characteristic vectorBuilding process be：Ask for experimental series firstTime domain charactreristic parameter, including time domain maximumTime domain average valueTemporal criterion deviationSteepness function
Kurtosis functionAnd through the frequency domain sequence after fast discrete Fourier transformation
Frequency domain character parameter, wherein F represents frequency domain, including frequency domain maximumFrequency-domain average valueFrequency domain criteria deviation
ObtainRightIn each element respectively root
It is normalized according to maximum and obtain：
Wherein
maxiMaximum when () represents that the variable in bracket changes with i.
5. the two phase flow spatial complex network visualization analysis method based on grid sensor according to claim 1, its
Be characterised by, step 1) (4th) step described in the value of threshold epsilon be taken as step 2) described in distributed mutually audio-visual picture in corresponding
Largest air bubbles equivalent diameter.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109033226A (en) * | 2018-06-29 | 2018-12-18 | 长安大学 | Vibrating sensor optimizing method for disposing based on Complex Networks Theory |
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| CA2803066A1 (en) * | 2010-07-29 | 2012-02-02 | Exxonmobil Upstream Research Company | Methods and systems for machine-learning based simulation of flow |
| CN104049001A (en) * | 2014-05-27 | 2014-09-17 | 天津大学 | Vertical oil-water phase content measurement and verification method based on frequency complex network |
| CN104049000A (en) * | 2014-05-27 | 2014-09-17 | 天津大学 | Gas-liquid phase content measurement method based on modal migration complex network and verification method thereof |
| CN105806780A (en) * | 2016-03-10 | 2016-07-27 | 浙江大学 | Multi-vision-based system and method for measuring gas-liquid two-phase flow phase fraction of small channel |
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| CA2803066A1 (en) * | 2010-07-29 | 2012-02-02 | Exxonmobil Upstream Research Company | Methods and systems for machine-learning based simulation of flow |
| CN104049001A (en) * | 2014-05-27 | 2014-09-17 | 天津大学 | Vertical oil-water phase content measurement and verification method based on frequency complex network |
| CN104049000A (en) * | 2014-05-27 | 2014-09-17 | 天津大学 | Gas-liquid phase content measurement method based on modal migration complex network and verification method thereof |
| CN105806780A (en) * | 2016-03-10 | 2016-07-27 | 浙江大学 | Multi-vision-based system and method for measuring gas-liquid two-phase flow phase fraction of small channel |
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| CN109033226A (en) * | 2018-06-29 | 2018-12-18 | 长安大学 | Vibrating sensor optimizing method for disposing based on Complex Networks Theory |
| CN109033226B (en) * | 2018-06-29 | 2021-08-31 | 长安大学 | Vibration sensor arrangement optimization method based on complex network theory |
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