WO2022257661A1 - Procédé de traitement de données d'un système de dragage et de transport sur un site de transport par pipeline à longue distance - Google Patents

Procédé de traitement de données d'un système de dragage et de transport sur un site de transport par pipeline à longue distance Download PDF

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WO2022257661A1
WO2022257661A1 PCT/CN2022/091219 CN2022091219W WO2022257661A1 WO 2022257661 A1 WO2022257661 A1 WO 2022257661A1 CN 2022091219 W CN2022091219 W CN 2022091219W WO 2022257661 A1 WO2022257661 A1 WO 2022257661A1
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data
concentration
pipeline
flow velocity
transportation
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PCT/CN2022/091219
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English (en)
Chinese (zh)
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王费新
张晴波
周忠玮
程书凤
江帅
树伟
刘功勋
冒小丹
张忱
袁超哲
尹立明
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中交疏浚技术装备国家工程研究中心有限公司
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Publication of WO2022257661A1 publication Critical patent/WO2022257661A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass

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  • the invention belongs to the technical field of dredging engineering.
  • the main form of sediment transportation is hydraulic transportation, especially long-distance pipeline transportation of solid-liquid mixed multiphase flow, and multi-pump systems are often used.
  • Pipeline transportation has a great influence on the efficiency of dredging construction and consumes a lot of energy. For example, the energy consumption of pipeline transportation accounts for more than 80% of the total energy consumption in the construction of cutter suction dredgers. If the slurry flow rate is too fast, friction will be increased and power will be wasted. If the flow rate is too slow, sediment will be deposited, leading to a series of problems such as pipe blockage and pipe burst. Realizing efficient, stable and safe hydraulic transportation is the goal that has been pursued all along.
  • the conventional on-site test data usually adopts the processing scheme of averaging by period (such as 1 hour, 1 day, etc.), so that the processed data can be more accurate in terms of flow velocity and concentration distribution. It tends to be concentrated and cannot effectively reflect the actual conveying characteristics.
  • the data obtained on site is different from the stable and controllable data in the laboratory. It is highly volatile and complex, and the real-time changing flow rate, concentration and pressure difference data are interrelated.
  • the object of the present invention is to disclose a set of data processing methods, through which the basic pipeline data can be obtained, which can effectively replace the dense physical concentration sensors to obtain the basic pipeline data, which is used for engineering applications and dredging pipeline characteristics research.
  • the present invention considers adopting the processing method of classifying and averaging according to the flow velocity and concentration. Firstly, the measured flow velocity, concentration, and pressure data are pre-processed, and then combined with the layout of pipelines and measuring points, the real-time concentration of the test pipe section is deduced under reasonable assumptions. data.
  • test working condition data combinations flow rate, concentration and pressure difference
  • a certain number generally not less than 500 data combinations.
  • a data processing method for long-distance pipeline transportation on-site dredging and transportation system includes flow velocity data, pressure data, and concentration data; the flow velocity data and pressure data are obtained through existing methods, and it is characterized in that, first, the entire transportation pipeline is selected The initial point of the monitoring point is used as the monitoring point, and the flow velocity value of the sediment fluid at the monitoring point is regarded as the common flow velocity value of the entire downstream pipeline (including the target pipe section) at the same time, and the concentration value at the monitoring point is obtained.
  • the time-varying concentration value deduces the concentration distribution on the entire pipeline at any time, matches the pressure difference of the target pipe section, and constructs a data group; then, cleans the data group; then, uses the data group as two-dimensional spatial data, and separates them according to their size Sorting, grouping, averaging, weakening the concentration of data.
  • the data group processed by the method of the invention is representative and authentic, and provides reliable engineering data for follow-up research.
  • the innovation and advantage of the present invention are: a long-distance pipeline transportation field data processing method and its application are established, and the data can be classified and averaged according to the flow rate and concentration to form the final test condition data It can obtain the test results under a wide range of flow velocity and concentration distribution in actual construction, and reduce the test deviation of a small number of data and the deviation of test results caused by various abnormal situations on average; it can be used to study the pipeline transportation mechanism, improve the transportation theory, and establish the transportation Work such as theoretical models, such as studying the matching between different friction empirical formulas and the transportation project, and revising and optimizing them, and even proposing new calculation formulas, which can provide more accurate calculation results in related calculations, and have practical significance. Engineering significance.
  • Fig. 1 shows the flow chart of long-distance pipeline transportation field data processing and empirical formula correction method
  • Figure 2 shows the concentration distribution diagram on the pipeline at a certain moment
  • Figure 3 shows a two-dimensional schematic diagram of data classification
  • Figure 4 shows the comparison between the friction loss calculated by the common formula and the measured value of a steel pipe section
  • Figure 5 shows the comparison between the friction loss calculated by the new formula and the measured value of a certain steel pipe section.
  • This method is not limited to the pipeline transportation of the cutter suction dredger in this embodiment, and is also applicable to other multi-pump-pipeline solid-liquid two-phase flow transportation processes.
  • many specific details are set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here, so the protection scope of the present invention is not limited by the specific embodiments disclosed below. limits;
  • the present invention proposes a long-distance pipeline transportation on-site data processing method and its application.
  • the flow chart is shown in Figure 1.
  • the engineering condition used in this embodiment is pipeline transportation by a cutter suction dredger, which specifically includes the following steps:
  • S1 Long-distance pipeline transportation construction data collection
  • S1-1 Collect static data of pipeline transportation, that is, data that does not change with time within a certain period of time. These data often do not change in the short term during engineering construction, such as the length, diameter, type, roughness, and sediment transport of pipelines Particle size, bulk density, and excavation depth, elevation, etc., these data will be used in the following data processing and application examples;
  • S1-2 Collect dynamic data of pipeline transportation, that is, data that changes in real time over time, mainly including concentration at the starting point of transportation, flow velocity, and pipeline pressure at the test target section.
  • These three physical quantities are also the most important physical quantities for long-distance pipeline transportation in dredging projects , is a key parameter for analysis and research. The following is mainly for the processing and analysis of dynamic data.
  • the pressure on a single pipeline section is of little significance. It is often necessary to analyze the degree of pressure loss after the fluid flows through a section of pipeline, that is, the pressure difference between the head and tail of this section of pipeline. Therefore, in actual measurement, this Absolute pressure values at the start and end positions of the segment pipeline (test target segment).
  • S2-2 Preprocessing of concentration data.
  • the missing concentration data needs to be corrected, and secondly, the wet square concentration (also known as the ship display concentration) measured by the sensor is converted into the particle volume concentration;
  • the wet square concentration is a professional term for pipeline transportation in dredging engineering; when the dredger is digging the soil, the undisturbed soil is loose, and the concentration directly measured at this time is the wet square concentration, and its value is larger than the particle volume concentration .
  • S2-3 Preprocessing of pressure data. Calculate the pressure difference between the start position and the end position of the measurement target segment, and record missing or obviously wrong data as NaN;
  • the slurry flow rate is often controlled in the range higher than the critical flow rate to reduce sediment deposition and prevent pipe plugging. Therefore, the present invention assumes that the concentration value of the slurry does not change with the change of the spatial position during the conveying process.
  • the particle volume concentration of the slurry element is C vd
  • the slurry flow rate V changes with time, it can be considered as a function of time t, so The moving distance of the slurry micro-element with concentration value C vd is different in different time. After ⁇ t time, the slurry is located at a distance from the starting point x, which can be expressed as:
  • the distance from the starting point at any time can be calculated by integration.
  • its moving distance can be calculated by this integration method.
  • the concentration distribution of the slurry at any time is used to provide to step S4.
  • the invention discretizes the time in seconds, and tracks every moment, every concentration value and its displacement distance on the pipeline. Therefore, the interval distance between the traced concentration values is different, and the concentration at the interval length between two concentrations is obtained by interpolation.
  • the present invention takes the concentration and flow velocity measurement position as the initial zero point of the pipeline, and calculates the concentration distribution of the whole pipeline at different times sequentially based on the concentration and flow velocity measurement values measured at this point.
  • the data within a certain 25000s is selected for analysis. Taking the 14000s time of this period as an example, the calculated concentration distribution results are shown in FIG. 3 .
  • the target pipeline section is a small section of the entire pipeline. Since the data is discretized, the average concentration of the target pipeline section needs to be calculated by numerical integration. In this way, the corresponding average concentration on the target pipe section can be calculated at all times
  • the average concentration at all times calculated on the target pipe section in S4 The data groups composed of the flow velocity V and the pressure difference P corresponding to each moment are collected together to form a data group set ⁇ , assuming that ⁇ has n data groups, it can be expressed as
  • the data set obtained in S5 is not a completely ideal data set. There are many "problem data” that will affect the follow-up research.
  • the obtained data group is screened and screened. If there is a problem with any data in the data group, or there is a contradiction between the data in the data group, the entire data group needs to be removed from the data group set.
  • the removed data group set is ⁇ , Suppose there are m data groups, then
  • the cutter head of the dredging ship needs to make a U-turn when it swings to the edge. At this time, the soil is no longer cut.
  • the concentration is very small, and even many data are close to zero.
  • the pressure difference value should also be very small, but sometimes the measured pressure difference is still not small. This is due to the sensor signal problem, and the data collected in the project will not decrease instantaneously;
  • the concentration meter For the data group with high concentration and normal pressure difference, the concentration meter has a range limit. When the actual concentration exceeds the maximum range of the concentration meter, the concentration meter only records the maximum value. At this time, the pressure difference measurement is accurate, which is also a kind of "Question Data";
  • the present invention's coping strategy sort and classify the flow rate and concentration, and take the average of the data in the same category, weaken the data error, weaken the data concentration, and make the new data group after processing more representative and authentic.
  • Research provides solid engineering data.
  • the specific processing method is as follows:
  • S7-1 Sort the m data groups in the data group set ⁇ according to the speed in the data group, and keep the corresponding relationship between the other two physical quantities and speeds. After sorting, each i data is a large category , classified in turn, provided to step S7-2;
  • S7-2 In each category, sort the data groups according to the concentration in the data groups, and take each j data group as a sub-category, classify them sequentially, and provide them to step S7-3;
  • the vertical columns and horizontal columns are The 98,000 data groups are grouped into 196 sub-categories, that is to say, each grid in Figure 3 is equivalent to a sub-category, and each sub-category has 500 data groups.
  • Data sets have similar properties and reflect similar physical phenomena. Then, take the average of the data sets in the same category, that is, take the average of the three physical quantities of the flow velocity, the average concentration of the pipeline, and the pressure difference between the head and the tail of the pipeline, and a new processed data set can be obtained
  • the principle of the technical solution of the present invention select a certain monitoring point through mathematical means, and deduce the concentration value of the monitoring point changing with time to the concentration distribution on the entire delivery pipeline at any time, so as to provide convenience for the pressure difference matching of the target pipe section , to construct a data group; according to the construction characteristics of dredging ships and related theories, analyze and identify the existing "problem data", and clean the data group, which improves the reliability of the final data group; Its size is sorted, grouped, and averaged separately, which weakens the concentration of data, makes the new processed data group more representative and authentic, and provides reliable engineering data for follow-up research.
  • This embodiment 2 carries out data analysis and application based on embodiment 1, which is regarded as S8:
  • the data provided by step S7 can be applied to the research of the pipeline transportation mechanism, the improvement of the transportation theory, the establishment of the transportation theory model and so on.
  • the three friction empirical formulas of Durand's formula, Jufin's formula and Fei Xiangjun's formula are firstly applied to the transportation project to calculate the frictional resistance, as shown in Figure 4, the abscissa is the field data acquired by S1-S7, namely The actual measured value, the ordinate is the theoretical value calculated by the empirical formula, the closer these scattered points are to the middle straight line, the higher the matching degree between the theoretical calculation value and the field measured value is, obviously, the matching degree between the calculation results of these empirical formulas and the project is better Difference. If it can be corrected, optimized, or even put forward a new calculation formula, more accurate calculation results can be provided in related calculations, which has practical engineering significance. For this reason, the present invention has done following processing:
  • the existing Fei Xiangjun formula divides friction into two parts: carrier friction and bed friction.
  • I m is the friction loss of the transported slurry (mH 2 O/m); ⁇ is the correction coefficient related to the relative viscosity coefficient of the slurry; ⁇ is the resistance coefficient along the pipeline when transporting clean water; V g is the acceleration of gravity (m/s 2 ); D is the inner diameter of the pipeline (m); ⁇ m is the bulk density of the slurry (t/m 3 ); ⁇ w is the bulk density of the transport medium, the The embodiment is mainly sea water, which is 1.025t/ m3 ; ⁇ s is the bulk density of the solid material to be transported, and the working condition used in this embodiment is mainly medium and coarse sand, which is 2.65t/ m3 ; K m is the test coefficient; ⁇ s is the friction Coefficient, generally taken as 0.44; C vd is the volume concentration of solid particles in the slurry, V c is the critical velocity (m/s), and is calculated using the standard formula (JTS 181-5-2012);
  • v is the hydrodynamic viscosity coefficient, which is taken as 10 6 m 2 /s.
  • Fei Xiangjun's formula is more suitable for pipeline transportation of solid-liquid two-phase flow in a thin bed state.
  • the present invention considers to improve Fei Xiangjun's formula from the angle of flow form, promptly take whether there is bed as critical condition, when lower than this critical condition, adopt existing Fei Xiangjun's formula; , the carrier friction remains unchanged, and the bed friction tends to disappear.
  • the revised Fei Xiangjun formula is established as follows:
  • Vc is the critical velocity of the slurry (m/s)
  • JTS 181-5-2012 is used here:
  • V c (90C vd ) 1/3 g 1/4 D 1/2 ⁇ 1/2 d 50 -1/4 (4)
  • the revised formula is an improvement based on the original Fei Xiangjun's empirical formula, the principle has changed, the structure of the formula has changed significantly, and the calculation results match well, so it can be regarded as a new formula. Then, in actual construction, when similar working conditions are encountered, the formula can be used to calculate the friction value of the transmission pipeline and guide the on-site operation, which has high application value.

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Abstract

Procédé de traitement de données d'un système de dragage et de transport sur un site de transport par pipeline à longue distance. Les données comprennent des données de vitesse d'écoulement, des données de pression et des données de concentration, et les données de vitesse d'écoulement et les données de pression sont obtenues par des procédés existants. Le procédé consiste : premièrement, à sélectionner le point de départ de l'ensemble du pipeline en tant que point de surveillance, à obtenir une valeur de vitesse d'écoulement d'un fluide sédimentaire au point de surveillance en tant que valeur de vitesse commune de l'ensemble du pipeline comprenant une section de tuyau cible aval à tester au même instant, à obtenir une valeur de concentration au niveau du point de surveillance, à déduire la distribution de concentration dans l'ensemble du pipeline à n'importe quel instant en fonction de la variation dans le temps de la valeur de concentration du point de surveillance, à mettre en correspondance la différence de pression de la section de tuyau cible, et à construire des ensembles de données ; à nettoyer ensuite les ensembles de données ; puis à utiliser les ensembles de données en tant que données spatiales bidimensionnelles, et à trier, à regrouper et à calculer la moyenne de ces derniers en fonction de leur taille de façon à affaiblir la centralité des données. L'ensemble de données traitées est représentatif et authentique, ce qui permet de fournir des données d'ingénierie fiables pour des recherches ultérieures.
PCT/CN2022/091219 2021-06-10 2022-05-06 Procédé de traitement de données d'un système de dragage et de transport sur un site de transport par pipeline à longue distance WO2022257661A1 (fr)

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CN113483805B (zh) * 2021-06-10 2022-04-01 中交疏浚技术装备国家工程研究中心有限公司 一种长距离管道输送现场疏浚输送系统数据处理方法
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