CN107271493A - A kind of Air Concentration in Water Flow computational methods and system based on normal distribution - Google Patents
A kind of Air Concentration in Water Flow computational methods and system based on normal distribution Download PDFInfo
- Publication number
- CN107271493A CN107271493A CN201710548929.1A CN201710548929A CN107271493A CN 107271493 A CN107271493 A CN 107271493A CN 201710548929 A CN201710548929 A CN 201710548929A CN 107271493 A CN107271493 A CN 107271493A
- Authority
- CN
- China
- Prior art keywords
- data sample
- aeration
- normal distribution
- resistance
- water flow
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Analytical Chemistry (AREA)
- Algebra (AREA)
- Immunology (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pathology (AREA)
- Health & Medical Sciences (AREA)
- Electrochemistry (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
The invention discloses a kind of Air Concentration in Water Flow computational methods and system based on normal distribution, aeration concentrater calculating is carried out using the dynamic data sample of aerated flow electrical conductivity, in addition to calculating average aeration concentrater, the aeration concentrater for calculating different frequency can also be sought, aeration concentrater is measured achievement more horn of plenty, more can comprehensively reflect the air mixing corrosion reducing effect of air entraining facilities.
Description
Technical field
It is specifically that one kind measures aeration with conductance instrument the present invention relates to a kind of computational methods of Air Concentration in Water Flow
The conductivity data of water flow dynamic, and be converted into dynamic aeration resistance and when aeration resistance meets normal distribution, ask calculation
The computational methods and system of Air Concentration in Water Flow.
Background technology
In Hydraulic and Hydro-Power Engineering practice, cavitation erosion easily occurs for high-velocity flow outlet structure escape works concrete surface, leads to
Normal solution is that air entraining facilities is set in runner, using the effect forcing high-velocity current aeration of air entraining facilities, to reach
Reduce or remit the purpose of cavitation erosion.Air Concentration in Water Flow is to weigh air entraining facilities arrangement and the rational important indicator of structural style.
High-velocity flow aeration is a random process, and aeration concentrater is bigger, and (bubble is more in water body) aeration resistance is bigger,
Otherwise smaller, clear water resistance during non-aeration is minimum.Aeration resistance is used as the basic Normal Distribution of stochastic variable or Pearson came
III type is distributed.When the aeration resistance and clear water resistance of known current, aeration concentrater can be calculated with Maxell formula.
Maxell proposes the computation model for the whole ball resistivity for calculating two kinds of different resistivity materials, model
Assuming that one is to be dispersed with the small ball that several resistivity are k2 materials 2, roundlet in the big ball of k1 materials 1 full of resistivity
The distance between ball is much larger than the diameter of small ball, to ensure not producing influence each other.The calculation formula of ball resistivity
For：
In formula, K is the resistivity of whole ball；K1 is the resistivity of material 1；K2 is the resistivity of material 2；P is material 2
Percentage of the total volume.
Resistivity calculation formula is applied in aerated flow, material 1 is water, material 2 is bubble, takes the resistivity of air
K2=+ ∞, abbreviation formula obtains the percentage by volume of bubble in aerated flow, i.e. aeration concentrater C：
In formula, C is aeration concentrater；R0For the clear water resistance of not bubbles；RcFor the aeration resistance of gassiness current.
Now widely used aeration concentrater measuring instrument is resistance-type entrained air concentration meter, can only measure the average aeration of current
Resistance, and must have an aeration sensor (being referred to as clear water measuring point sensor, positioned at the upstream of air entraining facilities) when measuring same
The clear water resistance of non-aerated flow is measured in one period.Asked by the ratio measured value of aeration measuring point sensor and clear water measuring point sensor
The clear water resistance of each aeration measuring point is calculated, and then calculates the average aeration concentrater of aeration measuring point.
With the development of detecting instrument equipment and computer data acquiring technology, conductance instrument is mixed applied to current
In the measurement of gas concentration, and the dynamic measurement of current electrical conductivity is realized, closed because resistance and the electrical conductivity of current are inversely proportional
System, therefore the dynamic electric resistor of current can be calculated.Conductance instrument is used for aeration concentrater measurement and is still in starting developing stage,
So far without a kind of rational aeration concentrater computational methods, therefore in the urgent need to a kind of dynamic measuring data asks calculation current aeration dense
The computational methods of degree.
The content of the invention
The present invention is intended to provide a kind of Air Concentration in Water Flow computational methods and system based on normal distribution, flat except calculating
Outside equal aeration concentrater, the aeration concentrater for calculating different frequency can also be sought, aeration concentrater is measured achievement more horn of plenty, more can be comprehensive
Ground reflects the air mixing corrosion reducing effect of air entraining facilities.
In order to solve the above technical problems, the technical solution adopted in the present invention is：A kind of current based on normal distribution are mixed
Gas density calculating method, comprises the following steps：
1) the conductivity data sample of dynamic acquisition aerated flow；
2) conductivity data sample is converted into resistance data sample, and according to normal distribution " 3 σ rules " to data sample
This progress is pre-processed；
3) parameter and graphing of pretreated data sample are calculated；
4) parameter and figure to data sample are analyzed：When coefficient of skew Cs → 0, mode M0 ≈ median Me, generally
Rate density curve tends to ± ∞ in two ends, and during using x-axis as the unimodal curve of asymptote, judges that data sample meets normal state point
Cloth, into step 5)；Otherwise, terminate；
5) the clear water resistance R of aeration measuring point is calculatedi0；
6) by given frequency P, the aeration resistance Rcp that frequency is P is calculated；
7) the aeration concentrater Cp that frequency is P is calculated using following formula：
Step 1) in, utilize the conductivity data sample of conductance instrument dynamic acquisition aerated flow.Cost is low, realizes
Simply.
Step 1) in, the sample frequency of conductance instrument is 10Hz, and conductivity data sample is no less than at 1024 points.Ensure
Measurement accuracy.
Step 3) in, probability density curve is drawn with NORMDIST functions, drawing process is simple, easily realizes.
Step 5) the process that implements be：The clear water electricity of clear water measuring point is calculated with the data sample of clear water measuring point sensor
Hinder R0, when the ratio measured value of aeration measuring point sensor and clear water measuring point sensor is ρ i, then the clear water resistance R of aeration measuring pointi0=
R0*ρi.The computational methods are simple, and the result of calculation degree of accuracy is high.
Step 6) in, aeration resistance Rcp is calculated with NORMINV functions, calculates simple, easily realizes.
Correspondingly, present invention also offers a kind of Air Concentration in Water Flow computing system based on normal distribution, including：
Acquisition module：Conductivity data sample for dynamic acquisition aerated flow；
Pretreatment module：Conductivity data sample for acquisition module to be gathered is converted to resistance data sample, and according to
Data sample is pre-processed according to " the 3 σ rules " of normal distribution；
Graphic plotting module：Parameter and graphing for calculating pretreated data sample；
Analytic unit：Analyzed for parameter and figure to data sample：When coefficient of skew Cs → 0, mode M0 ≈
Median Me, probability density curve tends to ± ∞ in two ends, and during using x-axis as the unimodal curve of asymptote, judges data sample
Meet normal distribution；
First computing unit：Following parameter calculating is carried out for the data sample to normal distribution：Calculate aeration measuring point
Clear water resistance Ri0With the aeration resistance Rcp that frequency is P；
Second computing unit：For the output according to the first computing unit, the aeration concentrater Cp that frequency is P is calculated：
In the present invention, in order to react sample properties more fully hereinafter, the parameter of the data sample is flat including aeration resistance
Average Rc, standard deviation sigma, coefficient variation Cv, coefficient of skew Cs, mode M0, median Me and time graph, probability density are bent
Line.
Compared with prior art, the advantageous effect of present invention is that：The present invention is averaged in addition to aeration concentrater except calculating,
The aeration concentrater for calculating different frequency can also be sought, aeration concentrater is measured achievement more horn of plenty, more can comprehensively reflect that aeration is set
The air mixing corrosion reducing effect applied.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the time course line of the embodiment of the present invention；
Fig. 3 is the probability density curve of the embodiment of the present invention；
Fig. 4 is the fit curves of the embodiment of the present invention.
Embodiment
As shown in figure 1, the method flow of the present invention is as follows：
Step 1, with the electrical conductivity number of conductance instrument coupled computer data collecting system dynamic acquisition aerated flow
According to sample, sample frequency 10Hz, sample data is no less than at 1024 points；
Step 2, conductivity data sample is converted into resistance data sample, and according to normal distribution " 3 σ rules " to sample
Notebook data is pre-processed；
Step 3, the parameter and graphing of data sample are calculated, including：Aeration resistance average value Rc, standard deviation are (square
Difference) it is σ, coefficient variation (coefficient of dispersion) Cv, the coefficient of skew (deviation factor) Cs, mode M0, median Me and time graph, general
Rate density curve etc. (as shown in table 1 and Fig. 2, Fig. 3)；
The embodiment data sample parameter table of table 1
| Parameter name | Numerical value |
| Aeration resistance average value Rc | 1156 |
| Standard deviation sigma | 131 |
| Coefficient variation Cv | 0.114 |
| Coefficient of skew Cs | 0.112 |
| Mode M0 | 1284 |
| Median Me | 1167 |
Step 4, parameter and figure to data sample carries out comprehensive analysis：The time course line of data sample reflects number
According to the rule that changes with time, the general statistical property with stationary random process may determine that the reasonability of data accordingly.By
In Cs → 0, M0 ≈ Me, probability density curve tends to ± ∞ in two ends, and the unimodal curve by asymptote of x-axis is (such as Fig. 3 institutes
Show), so can judge that data sample meets normal distribution substantially；
Step 5, clear water resistance R is calculated with the data sample of clear water measuring point sensor0=829 Ω；
Step 6, measurement is compared with aeration measuring point sensor and clear water measuring point sensor, obtained ratio measured value ρ i are
0.9891, calculate the clear water resistance Ri0=820 Ω of aeration measuring point；
Step 7, by given frequency P, the aeration resistance Rcp (as shown in Figure 4) that frequency is P is calculated with NORMINV functions；
Step 8, the aeration concentrater Cp that frequency is P is calculated：
Embodiment calculating achievement is as shown in table 2.
The embodiment aeration concentrater calculating achievement table of table 2
Claims (9)
1. a kind of Air Concentration in Water Flow computational methods based on normal distribution, it is characterised in that comprise the following steps：
1) the conductivity data sample of dynamic acquisition aerated flow；
2) conductivity data sample is converted into resistance data sample, and data sample entered according to " the 3 σ rules " of normal distribution
Row pretreatment；
3) parameter and graphing of pretreated data sample are calculated；
4) parameter and figure to data sample are analyzed：When coefficient of skew Cs → 0, mode M0 ≈ median Me, probability is close
Line of writing music tends to ± ∞ in two ends, and during using x-axis as the unimodal curve of asymptote, judges that data sample meets normal distribution, enter
Enter step 5)；Otherwise, terminate；
5) the clear water resistance R of aeration measuring point is calculatedi0；
6) by given frequency P, the aeration resistance Rcp that frequency is P is calculated；
7) the aeration concentrater Cp that frequency is P is calculated using following formula：
2. the Air Concentration in Water Flow computational methods according to claim 1 based on normal distribution, it is characterised in that step 1)
In, utilize the conductivity data sample of conductance instrument dynamic acquisition aerated flow.
3. the Air Concentration in Water Flow computational methods according to claim 2 based on normal distribution, it is characterised in that step 1)
In, the sample frequency of conductance instrument is 10Hz, and conductivity data sample is no less than at 1024 points.
4. the Air Concentration in Water Flow computational methods according to claim 1 based on normal distribution, it is characterised in that step 3)
In, the parameter of the data sample includes aeration resistance average value Rc, standard deviation sigma, coefficient variation Cv, coefficient of skew Cs, mode
M0, median Me and time graph, probability density curve.
5. the Air Concentration in Water Flow computational methods according to claim 1 based on normal distribution, it is characterised in that step 3)
In, draw probability density curve with NORMDIST functions.
6. the Air Concentration in Water Flow computational methods according to claim 1 based on normal distribution, it is characterised in that step 5)
The process that implements be：The clear water resistance R of clear water measuring point is calculated with the data sample of clear water measuring point sensor0, when aeration is surveyed
When the ratio measured value of point sensor and clear water measuring point sensor is ρ i, then the clear water resistance R of aeration measuring pointi0=R0*ρi。
7. the Air Concentration in Water Flow computational methods according to claim 1 based on normal distribution, it is characterised in that step 6)
In, calculate aeration resistance Rcp with NORMINV functions.
8. a kind of Air Concentration in Water Flow computing system based on normal distribution, it is characterised in that including：
Acquisition module：Conductivity data sample for dynamic acquisition aerated flow；
Pretreatment module：Conductivity data sample for acquisition module to be gathered is converted to resistance data sample, and according to just
" the 3 σ rules " of state distribution is pre-processed to data sample；
Graphic plotting module：Parameter and graphing for calculating pretreated data sample；
Analytic unit：Analyzed for parameter and figure to data sample：When coefficient of skew Cs → 0, mode M0 ≈ middle positions
Number Me, probability density curve tends to ± ∞ in two ends, and during using x-axis as the unimodal curve of asymptote, judges that data sample meets
Normal distribution；
First computing unit：Following parameter calculating is carried out for the data sample to normal distribution：Calculate the clear water of aeration measuring point
Resistance Ri0With the aeration resistance Rcp that frequency is P；
Second computing unit：For the output according to the first computing unit, the aeration concentrater Cp that frequency is P is calculated：
9. the Air Concentration in Water Flow computing system according to claim 8 based on normal distribution, it is characterised in that the number
Include aeration resistance average value Rc, standard deviation sigma, coefficient variation Cv, coefficient of skew Cs, mode M0, median according to the parameter of sample
Me and time graph, probability density curve.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710548929.1A CN107271493A (en) | 2017-07-07 | 2017-07-07 | A kind of Air Concentration in Water Flow computational methods and system based on normal distribution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710548929.1A CN107271493A (en) | 2017-07-07 | 2017-07-07 | A kind of Air Concentration in Water Flow computational methods and system based on normal distribution |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107271493A true CN107271493A (en) | 2017-10-20 |
Family
ID=60072368
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710548929.1A Pending CN107271493A (en) | 2017-07-07 | 2017-07-07 | A kind of Air Concentration in Water Flow computational methods and system based on normal distribution |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107271493A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108875223A (en) * | 2018-06-25 | 2018-11-23 | 中国电建集团中南勘测设计研究院有限公司 | A kind of judgment method of air mixing corrosion reducing facility validity |
| CN110866043A (en) * | 2019-10-12 | 2020-03-06 | 上海上湖信息技术有限公司 | Data preprocessing method and device, storage medium and terminal |
| CN111898071A (en) * | 2020-07-30 | 2020-11-06 | 江西理工大学 | Method for calculating in-path velocity of high-pressure submerged water jet |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1589399A (en) * | 2001-11-19 | 2005-03-02 | 松下电器产业株式会社 | Measurement device for measuring electric signal emitted by biological sample and measurement method |
| US20070134651A1 (en) * | 2002-05-13 | 2007-06-14 | Matsushita Electric Industrial Co., Ltd. | Apparatus and method for measuring activity signals of biological samples |
| CN101324908A (en) * | 2008-08-01 | 2008-12-17 | 重庆大学 | Multi-dimension analogy method of solid-oxide fuel battery |
| CN102004816A (en) * | 2010-11-03 | 2011-04-06 | 昆明理工大学 | Method for representing multiphase mixing effect by utilizing histogram |
| CN202903723U (en) * | 2012-11-22 | 2013-04-24 | 中国水电顾问集团中南勘测设计研究院 | Verifiable sensor capable of measuring high aeration concentration |
| CN103148836A (en) * | 2013-03-15 | 2013-06-12 | 中国水电顾问集团中南勘测设计研究院 | Measuring method of outlet structure aerated flow water level elevation |
| CN104007142A (en) * | 2014-06-04 | 2014-08-27 | 浙江省水利河口研究院 | Tide head reoxygenation capability testing method for strong tidal bore spreading process |
| CN105929016A (en) * | 2016-04-20 | 2016-09-07 | 浙江中烟工业有限责任公司 | Value determination method for selenium content of quality control sample of tobacco leaf |
| CN106777928A (en) * | 2016-12-01 | 2017-05-31 | 中国人民解放军国防科学技术大学 | Towards the Bayes prior distribation building methods of normal distribution data sample |
-
2017
- 2017-07-07 CN CN201710548929.1A patent/CN107271493A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1589399A (en) * | 2001-11-19 | 2005-03-02 | 松下电器产业株式会社 | Measurement device for measuring electric signal emitted by biological sample and measurement method |
| US20070134651A1 (en) * | 2002-05-13 | 2007-06-14 | Matsushita Electric Industrial Co., Ltd. | Apparatus and method for measuring activity signals of biological samples |
| CN101324908A (en) * | 2008-08-01 | 2008-12-17 | 重庆大学 | Multi-dimension analogy method of solid-oxide fuel battery |
| CN102004816A (en) * | 2010-11-03 | 2011-04-06 | 昆明理工大学 | Method for representing multiphase mixing effect by utilizing histogram |
| CN202903723U (en) * | 2012-11-22 | 2013-04-24 | 中国水电顾问集团中南勘测设计研究院 | Verifiable sensor capable of measuring high aeration concentration |
| CN103148836A (en) * | 2013-03-15 | 2013-06-12 | 中国水电顾问集团中南勘测设计研究院 | Measuring method of outlet structure aerated flow water level elevation |
| CN104007142A (en) * | 2014-06-04 | 2014-08-27 | 浙江省水利河口研究院 | Tide head reoxygenation capability testing method for strong tidal bore spreading process |
| CN105929016A (en) * | 2016-04-20 | 2016-09-07 | 浙江中烟工业有限责任公司 | Value determination method for selenium content of quality control sample of tobacco leaf |
| CN106777928A (en) * | 2016-12-01 | 2017-05-31 | 中国人民解放军国防科学技术大学 | Towards the Bayes prior distribation building methods of normal distribution data sample |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108875223A (en) * | 2018-06-25 | 2018-11-23 | 中国电建集团中南勘测设计研究院有限公司 | A kind of judgment method of air mixing corrosion reducing facility validity |
| CN110866043A (en) * | 2019-10-12 | 2020-03-06 | 上海上湖信息技术有限公司 | Data preprocessing method and device, storage medium and terminal |
| CN111898071A (en) * | 2020-07-30 | 2020-11-06 | 江西理工大学 | Method for calculating in-path velocity of high-pressure submerged water jet |
| CN111898071B (en) * | 2020-07-30 | 2024-01-30 | 江西理工大学 | Calculation method for high-pressure submerged water jet flow along-distance speed |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107271493A (en) | A kind of Air Concentration in Water Flow computational methods and system based on normal distribution | |
| CN106443285B (en) | Multiple-harmonic-source harmonic responsibility quantitative analysis method based on total least square method | |
| CN101556234A (en) | Gas-water two phase flow multiparameter measuring method and device | |
| CN206656765U (en) | A kind of combination type multifunction water leveling ruler | |
| CN110411521B (en) | Oil well multiphase flow split-phase content on-line metering method based on double nozzles | |
| Deng et al. | Fusion research of electrical tomography with other sensors for two-phase flow measurement | |
| CN103091226A (en) | Device and method for detecting porosity of saturated soil | |
| CN107341352B (en) | A kind of Air Concentration in Water Flow calculation method and system based on Pearson III distribution | |
| CN113780807B (en) | Calculation method for electric water diversion conversion coefficient | |
| CN103148836B (en) | Measuring method of outlet structure aerated flow water level elevation | |
| Chen et al. | Flowrate estimation of horizontal gas–water slug flow based on venturi tube and conductance sensor | |
| CN107064224A (en) | A kind of transformer oil micro-water on-line measurement system and its measuring method | |
| CN104881564B (en) | The construction method of structural plane roughness coefficient dimensional effect probability density estimation | |
| CN212646969U (en) | Calibration device for dynamic response time characteristic of electric energy meter | |
| CN108280300B (en) | Big data flowmeter development method based on computational fluid dynamics | |
| CN106969756A (en) | River suspended sediment sedimentary loading correction computational methods | |
| CN116187053A (en) | Method and device for measuring uncertainty of arm support stress based on Monte Carlo method | |
| Nakayama et al. | Similarity of instantaneous and filtered velocity fields in the near wall region of zero-pressure gradient boundary layer | |
| Gao et al. | Measurement of crude oil water content based on cross-correlation method | |
| CN113536711A (en) | Olive-shaped flow meter structure optimization design method based on Taguchi method | |
| RU86751U1 (en) | MEASURING AERODYNAMIC INSTALLATION | |
| CN107832935A (en) | A kind of determination method and device of hydrology Variational Design value | |
| Lapin et al. | Investigation of conversion function for vortex sonic flowmeter using monte-carlo method | |
| Farina et al. | Estimating discharge in rivers through the combined use of dimensionless isovels and point velocity measurements | |
| Jiang et al. | Simulation Research on Optimal Installation Position of partially-filled pipe Electromagnetic Flowmeter sensor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171020 |
|
| RJ01 | Rejection of invention patent application after publication |