CN114139469B - Optimal sensor arrangement method based on gradient distribution algorithm - Google Patents
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Abstract
The invention discloses an optimal sensor arrangement method based on a gradient distribution algorithm, which comprises the following steps: 1) Induction of pipe parameters: 2) Calculating parameters of medium in the tube: 3) Generating boundary conditions according to the operation conditions: 4) Establishing a numerical model to uniformly solve the distribution of the speed field and the pressure field of the whole pipeline system; 5) Solving the two unified equations established in the steps 1) to 4); 6) Calculating different pressure/speed gradient distributions corresponding to different pipe section positions according to the result obtained in the step 5); 7) Substituting the result obtained in the step 6) into a gradient distribution algorithm; the method of the invention calculates reasonable sensor arrangement under different conditions by combining the pipeline flow numerical model and the gradient distribution algorithm, is a convenient, quick and effective method in the practical implementation of intelligent pipelines, and has important engineering significance.
Description
Technical Field
The invention relates to the technical field of intelligent pipelines, in particular to an optimal sensor arrangement method based on a gradient distribution algorithm.
Background
At present, in the intelligent pipeline application field, the integration and design of a sensor become key of pipeline digitization and informatization. In the present stage, continuous measurement can be realized under a specific sensor aiming at specific parameters, and most other sensor data acquisition adopts point measurement, so that the problem of how to arrange point sensors in the whole pipe network is related, and an effective system method is lacking in the field of intelligent pipe networks.
For the sensor arrangement method in the smart pipeline field, most of the concerns are the integration method of the sensor and the pipeline, and the arrangement position and the arrangement density of the sensor are inevitably involved in the experimental discussion, so the following problems mainly exist at present: 1) The sensor arrangement is too sparse, the accuracy of obtaining the whole data is not high, and the parameter error of a single sensor has a great influence on the system parameter discreteness. 2) The sensor arrangement is too dense, while in theory the denser the sensors the higher the accuracy of the system parameters, the more sensors will also produce redundancy in information, and secondly the more sensors the higher the cost of the overall system. 3) The sensor arrangement parameters have low correlation, and the sensor measures the parameters but the arrangement is separated from the parameters. Taking pressure sensors in pipelines as an example, different pipelines, different medium in pipes and different operating conditions, the pressure distribution in the pipes is large and different, even in different pipe sections, the pressure distribution and the change rate are different, so that the single uniformly distributed sensors are arranged, the measurement accuracy is insufficient in the areas with large partial pressure gradient, and the sensor arrangement waste is caused in the areas with small partial pressure gradient. 4) There are some sensor arrangement algorithms based on parameter optimization, but these algorithms first need to sample parameter samples and then calculate, which falls into a logic paradox, that is, the arrangement algorithm for determining the sensors needs to first arrange part of the sensors to sample samples, and the implementation steps and subsequent calculation are too complex.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a reasonable-design optimal sensor arrangement method based on a gradient distribution algorithm.
The technical scheme of the invention is as follows:
An optimal sensor arrangement method based on a gradient distribution algorithm comprises the following steps:
1) Induction of pipe parameters: the method comprises two parts of material parameters and geometric parameters, wherein the geometric parameters comprise a pipeline through-flow sectional area A, a wet perimeter Z and an average hydraulic diameter d h; the material parameters comprise the surface friction coefficient e of the inner wall of the pipe;
wherein the calculation formula of the average hydraulic diameter d h is as follows:
2) Calculating parameters of medium in the tube: calculating a darcy friction coefficient f D and a reynolds number Re in the pipe for different media fluids;
2.1 If the fluid in the conduit is newtonian:
the calculation formula is as follows:
Where ρ is the density of the fluid, μ is the viscosity coefficient, re is the Reynolds number of the fluid flow, u is the cross-sectional velocity;
2.2 If the fluid within the conduit is a non-newtonian fluid:
the calculation formula is as follows:
Wherein ρ is the density of the fluid, m is the fluid consistency coefficient, n is the flow characteristic coefficient, reMR is the Reynolds number correction parameter for the non-Newtonian fluid;
3) Generating boundary conditions according to the operation conditions: for solving the speed field and the pressure field of a complex pipeline system, the boundary condition is selected as the speed or the pressure of an inlet and the speed or the pressure of an outlet;
4) Geometric model, flow model and boundary conditions obtained according to steps 1) -2): establishing a numerical model to uniformly solve the distribution of the speed field and the pressure field of the whole pipeline system, wherein the uniform equation consists of a continuity equation and a momentum conservation equation, and the method is as follows:
Wherein F D is the Darcy coefficient of friction obtained in step 2), A is the cross-sectional area obtained in step 1), F is the bulk force obtained in step 3), Representing a Laplacian, and t represents time;
5) Solving the two unified equations established in the steps 1) to 4) to obtain a simulation result of the velocity field or pressure field distribution of the fluid flow in the pipeline;
6) Calculating different pressure/speed gradient distributions corresponding to different pipe section positions according to the result obtained in the step 5); the whole pipeline system is divided into i different pipeline sections with the length of L 1,L2,L3……Li, and the speed/pressure gradient corresponding to each pipeline section is T 1,T2,T3……Ti;
7): substituting the result obtained in the step 6) into a gradient distribution algorithm, and assuming that the total number of planned sensors of the whole system is K, obtaining the number G (T i) of sensors which are required to be arranged in the corresponding pipe section L i as follows:
wherein s represents a weight coefficient, and refers to the influence degree of the length dimension on the whole algorithm.
The beneficial effects of the invention are as follows:
1) The method is a parameter correlation arrangement algorithm, and is mainly based on two indexes of parameter gradient and geometric dimensions of different pipe sections, and different weight algorithms are added to obtain reasonable arrangement aiming at different parameter sensors, so that the accuracy of overall parameters is ensured, and the performance waste of redundant sensors is avoided.
2) The method is a pre-parameter model, a numerical model of the pipeline is pre-established through pipeline parameters, medium in the pipeline and operation conditions, the distribution situation of corresponding parameters is solved, no additional sensor is needed to be arranged for data acquisition, and subsequent parameterization is simpler and more convenient.
3) The method of the invention calculates reasonable sensor arrangement under different conditions by combining the pipeline flow numerical model and the gradient distribution algorithm, is a convenient, quick and effective method in the practical implementation of intelligent pipelines, and has important engineering significance.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a piping system according to embodiment 1 of the present invention;
FIG. 3 is a pressure distribution diagram of a piping system according to embodiment 1 of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples of the specification.
As shown in fig. 1-3, an optimal sensor arrangement method based on a gradient distribution algorithm includes
Step one: the pipeline parameters are summarized and mainly comprise two parts, namely material parameters and geometric parameters. The geometric parameters comprise the through-flow cross-sectional area A, the wet perimeter Z and the average hydraulic diameter d h of the pipeline; the length and space, reducing and shunting information is mainly used for constructing a geometric model of the pipeline system, and the calculation model is carried out on the basis of the geometric model and is shown in figure 2), and the material parameters comprise the surface friction coefficient e of the inner wall of the pipeline; wherein the calculation formula of the average hydraulic diameter is as follows:
step two: and calculating medium parameters in the pipe, and calculating the Darcy friction coefficient f D and the Reynolds number Re in the pipe for different medium fluids.
If the fluid in the pipeline is newtonian (such as water, light oil, most low molecular compound solutions), the density ρ and viscosity coefficient μ of the fluid need to be counted, and the following formula is calculated:
Where Re is the Reynolds number of the fluid flow and u is the cross-sectional velocity. In general, water is used as the flow medium for most pipes, and the value ρ=1000 kg/m 3,μ＝8.01*10-4 is available.
If the fluid in the pipeline is a non-Newtonian fluid (such as heavy oil, coal water slurry, mortar, etc.), the density ρ, the fluid consistency coefficient m and the flow characteristic coefficient n of the fluid need to be counted, and the following calculation formula is adopted:
Wherein ReMR is a reynolds number correction parameter under the non-newtonian fluid, and in general, crude oil is typical of the non-newtonian fluid transported in a pipeline, where the value m=1.4 and n=0.4 can be taken.
Step three: boundary conditions are generated according to the operation conditions, and for solving the speed field and the pressure field of a complex pipeline system, the boundary conditions can be selected as the speed or the pressure of an inlet and the speed or the pressure of an outlet.
Step four: according to the geometric model, the flow model and the boundary conditions obtained in the steps one to three, a numerical model is established to uniformly solve the distribution of the speed field and the pressure field of the whole pipeline system, and a uniform equation consists of a continuity equation and a momentum conservation equation, wherein the following steps are shown as follows:
Wherein F D is the Darcy coefficient of friction obtained in step two, A is the cross-sectional area obtained in step one, and F is the volumetric force obtained in step three.
Step five: solving the two unified equations established in the first to fourth steps to obtain a simulation result of the velocity field or pressure field distribution of the fluid flow in the pipeline.
Step six: and D, calculating different pressure/speed gradient distributions corresponding to different pipe section positions according to the result obtained in the step five. The entire tubing is divided into i different tube sections of length L 1,L2,L3……Li, each of which corresponds to a velocity/pressure gradient T 1,T2,T3……Ti.
Step seven: substituting the result obtained in the step six into a gradient distribution algorithm, and assuming that the total number of planned sensors of the whole system is K, obtaining the number G (T i) of sensors which are required to be arranged in the corresponding pipe section L i as follows:
Wherein s represents a weight coefficient, and refers to the influence degree of the length dimension on the whole algorithm, generally 0.6-1 is preferable for the water pipeline, 0.4-0.6 is preferable for the oil pipeline, and 0.3-0.5 is preferable for mortar, coal water slurry and the like for conveying solid-liquid two phases.
Example 1:
A conventional pipeline system for conveying the coal water slurry is shown in FIG. 1, the density of the coal slurry is 1030kg/m 3, and the coal slurry enters a pipeline with the diameter of 150mm at the flow rate of 3.4m 3/min. The main pipe is divided into two loops, wherein the pipe diameter of the upper loop is 100mm, and the pipe diameter of the lower loop is 75mm. Then, the branches are recombined in a pipeline with the diameter of 175mm, the budget number of sensors of the whole pipeline system is 100, and how the pressure sensors of the system are distributed is more reasonable.
Step one: to summarize the pipeline parameters, in this case, assuming that the conveying pipeline of the coal water slurry is in a full state, the hydraulic diameter can be equivalent to the nominal diameter of the pipeline, and the following table data are provided:
Step two: calculating medium parameters in a pipe, wherein the coal water slurry belongs to a solid-liquid two-phase non-Newtonian fluid, substituting rho=1030 kg/m 3, m=1.4 and n=0.4 by adopting an algorithm of equivalent Reynolds number, and substituting the velocity field u into a unified equation to calculate later because the velocity field u is unknown.
Step three: boundary conditions were calculated, no pump and external fluid forces were applied in the tubing, the inlet flow was 3.4m 3/min, and the outlet was the pressure outlet boundary conditions.
Steps four to five: substituting the results of the first to third steps into a unified equation, and solving in software to obtain the pressure distribution of the pipeline system as shown in fig. 3:
Step six: the pressure gradient distribution of each tube segment was calculated as shown in the following table:
| Pipe section numbering | Pressure gradient (Pa/m) | Pipe section numbering | Pressure gradient (Pa/m) |
| 1 | 624 | 5 | 303 |
| 2 | 1078 | 6 | 1228 |
| 3 | 768 | 7 | 762 |
| 4 | 964 | 8 | 1331 |
Step seven: substituting a gradient distribution algorithm, and taking a weight coefficient s=0.5 to obtain the reasonable sensor arrangement number of each pipe section:
| Pipe section numbering | Number of sensors | Pipe section numbering | Number of sensors |
| 1 | 14 | 5 | 6 |
| 2 | 12 | 6 | 14 |
| 3 | 10 | 7 | 18 |
| 4 | 11 | 8 | 15 |
。
Claims (1)
1. The optimal sensor arrangement method based on the gradient distribution algorithm is characterized by comprising the following steps of:
1) Induction of pipe parameters: the method comprises two parts of material parameters and geometric parameters, wherein the geometric parameters comprise a pipeline through-flow sectional area A, a wet perimeter Z and an average hydraulic diameter d h; the material parameters comprise the surface friction coefficient e of the inner wall of the pipe;
wherein the calculation formula of the average hydraulic diameter d h is as follows:
2) Calculating parameters of medium in the tube: calculating a darcy friction coefficient f D and a reynolds number Re in the pipe for different media fluids;
2.1 If the fluid in the conduit is newtonian:
the calculation formula is as follows:
Where ρ is the density of the fluid, μ is the viscosity coefficient, re is the Reynolds number of the fluid flow, u is the cross-sectional velocity;
2.2 If the fluid within the conduit is a non-newtonian fluid:
the calculation formula is as follows:
Wherein ρ is the density of the fluid, m is the fluid consistency coefficient, n is the flow characteristic coefficient, reMR is the Reynolds number correction parameter for the non-Newtonian fluid;
3) Generating boundary conditions according to the operation conditions: for solving the speed field and the pressure field of a complex pipeline system, the boundary condition is selected as the speed or the pressure of an inlet and the speed or the pressure of an outlet;
4) Geometric model, flow model and boundary conditions obtained according to steps 1) -2): establishing a numerical model to uniformly solve the distribution of the speed field and the pressure field of the whole pipeline system, wherein the uniform equation consists of a continuity equation and a momentum conservation equation, and the method is as follows:
Wherein F D is the Darcy coefficient of friction obtained in step 2), A is the cross-sectional area obtained in step 1), F is the bulk force obtained in step 3), Representing a Laplacian, and t represents time;
5) Solving the two unified equations established in the steps 1) to 4) to obtain a simulation result of the velocity field or pressure field distribution of the fluid flow in the pipeline;
6) Calculating different pressure/speed gradient distributions corresponding to different pipe section positions according to the result obtained in the step 5); the whole pipeline system is divided into i different pipeline sections with the length of L 1,L2,L3……Li, and the speed/pressure gradient corresponding to each pipeline section is T 1,T2,T3……Ti;
7): substituting the result obtained in the step 6) into a gradient distribution algorithm, and assuming that the total number of planned sensors of the whole system is K, obtaining the number G (T i) of sensors which are required to be arranged in the corresponding pipe section L i as follows:
wherein s represents a weight coefficient, and refers to the influence degree of the length dimension on the whole algorithm.
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| PCT/CN2021/137809 WO2023082398A1 (en) | 2021-11-10 | 2021-12-14 | Gradient distribution algorithm-based optimal sensor arrangement method |
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| CN110119581A (en) * | 2019-05-17 | 2019-08-13 | 合肥工业大学 | A kind of electromagnetism sensor of water meter construction design method improving Small flowrate measurement performance |
| CN112632676A (en) * | 2020-12-23 | 2021-04-09 | 华能澜沧江水电股份有限公司 | Concrete dam stress gradient monitoring method |
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| US7603222B2 (en) * | 2005-11-18 | 2009-10-13 | General Electric Company | Sensor diagnostics using embedded model quality parameters |
| CN102346058B (en) * | 2010-07-29 | 2014-07-09 | 中国石油大学(华东) | Model method for measuring flow rate of air-assisted liquid by Coriolis mass flowmeter (CMF) |
| CN102374960B (en) * | 2010-08-10 | 2013-11-06 | 中国石油化工集团公司 | Variable-diameter-tube drilling fluid rheology measuring method |
| CN107748273B (en) * | 2017-09-29 | 2019-12-20 | 中国石油大学(华东) | Pipeline pressure wave velocity testing device and method based on pipe flow test loop |
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| CN110119581A (en) * | 2019-05-17 | 2019-08-13 | 合肥工业大学 | A kind of electromagnetism sensor of water meter construction design method improving Small flowrate measurement performance |
| CN112632676A (en) * | 2020-12-23 | 2021-04-09 | 华能澜沧江水电股份有限公司 | Concrete dam stress gradient monitoring method |
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