Online monitoring method for tiny corrosion defects of pipeline elbow

Abstract

The invention belongs to the technical field of pipeline structure health monitoring, and particularly relates to an online monitoring method for tiny corrosion defects of a pipeline elbow. Comprising the following steps of 1, building finite element models in batches through a control variable method; 2, extracting the strain size of the corrosion area, the corrosion depth and the area data, and establishing a relationship among the data; step 3, installing a miniature fiber grating strain sensing network on the pipeline elbow; 4, collecting and demodulating sensor network data, and reconstructing a pipeline outer wall strain field by using a difference value method; and 5, calculating the depth and the area of the corrosion region through the outer wall strain field and the established relation function. The method has the beneficial effects that micro deformation and strain data of the surface of the pipeline are acquired in real time through the micro fiber grating strain sensor network, and a strain field is reconstructed by using a difference method, so that the purpose of real-time monitoring is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of pipeline structure health monitoring, and in particular relates to an online monitoring method for tiny corrosion defects of pipeline elbows. Background Art

[0002] The impact of corrosion on pipelines is an important issue involving safety and economy. Pipeline corrosion will lead to thinning of the pipe wall, which may eventually exceed the designed load-bearing capacity, especially when the pipeline is subjected to high pressure during operation. When the pipeline is thinned to a certain extent, stress concentration points will be formed, which may cause local rupture of the pipeline, resulting in serious accidents.

[0003] Pipeline leakage causes certain economic losses, so it is very important to monitor and manage pipeline corrosion defects. Especially for the elbows used at the corners of the pipeline, these elbows can change the direction of the pipeline and also affect the flexibility and stress distribution of the pipeline system.

[0004] The existence of elbows increases the flexibility of the pipeline system and makes the pipeline easy to deform. In this way, the pipeline is more likely to deform to a certain extent when subjected to stress, which is conducive to reducing the temperature stress of the pipeline. However, unlike the straight pipe section, the stress distribution of the curved pipe section is uneven, and its maximum stress is usually higher than that of the straight pipe section. This makes the corrosion defects at the elbow an important and complex issue in the safe operation of the pipeline.

[0005] Therefore, it is necessary to conduct in-depth research on the stress distribution of corrosion defects at elbows, as well as the effects of factors such as remaining wall thickness, internal pressure, defect length, and defect width on stress at defects. Such research can provide a theoretical basis for engineering and technical personnel, enabling them to more accurately analyze, design, and calculate the safe operation of pipelines, and provide convenient and fast calculation formulas and practical guidance for the dynamic management of on-site pipelines. Summary of the invention

[0006] The purpose of the present invention is to provide an online monitoring method for micro-corrosion defects of pipeline elbows, which can solve the problem that micro-corrosion defects at existing pipeline elbows are difficult to monitor due to their concealment, and use non-destructive testing methods to achieve online monitoring of micro-corrosion defects in pipelines, thereby ensuring the importance of safe operation of pipelines.

[0007] The technical solution of the present invention is as follows: An online monitoring method for micro corrosion defects of pipeline elbows comprises the following steps:

[0008] Step 1: Batch establish finite element models through the control variable method;

[0009] Step 2: Extract the strain size, corrosion depth and area data of the corrosion area and establish the relationship between them;

[0010] Step 3: Install a micro fiber grating strain sensor network at the pipe elbow;

[0011] Step 4: Collect and demodulate sensor network data and reconstruct the pipe outer wall strain field using the difference method;

[0012] Step 5: Calculate the depth and area of ​​the corrosion zone through the outer wall strain field and the established relationship function.

[0013] The step 1 is to select general finite element software, clarify the influence of different corrosion depths and corrosion areas on pipeline stress and strain, then determine the key parameters to be studied, design a control variable comparison model, select fixed variables and variable variables, set different corrosion depths and different corrosion areas, establish a batch finite element numerical model, set initial conditions and boundary conditions; perform numerical simulation on multiple models to simulate the stress and strain under conditions of different corrosion depths, positions and shapes.

[0014] The key parameters include pipeline materials, including carbon steel, stainless steel, pipeline dimensions including diameter, wall thickness and corrosion parameters, including corrosion depth, corrosion shape, and corrosion location.

[0015] The step 2 is to divide the grid points in the finite element model, divide the grid points of the outer wall of the pipeline into separate sets, and then extract the strain data of the grid points of the outer wall of the pipeline based on post-processing. The data file is converted into the stress-strain results along the axial and circumferential directions of the pipeline elbow according to the strain conversion formula under different coordinate systems of elastic mechanics, and the pipeline strain data under different corrosion depths and different corrosion areas are extracted. The three-dimensional surface graph is drawn with the corrosion area and corrosion depth as independent variables and the strain as the strain variable, and the three-dimensional surface graph is projected on the XY plane to obtain a contour map of the strain changing with the corrosion area and corrosion depth.

[0016] The step 3 is to select micro fiber grating strain sensors to form a strain sensing network, and install them at pipe elbows or other locations that may be corroded.

[0017] The step 4 is to reconstruct the strain field of the outer wall of the pipeline by using the difference method. The interpolation method is as follows:

[0018] First, a series of strain data measured by the distributed sensor each time is expressed as a strain vector:

[0019] ε p =[ε p1 …ε pi …ε pN ] T (1)

[0020] ε piis the strain measured from point i (i = 1, 2, ..., N), N is the total number of data points, and the subscript p is the variable of direction, which is consistent with the direction of the distributed sensor;

[0021] The coordinates of the measurement points in the distributed sensor are expressed as:

[0022]

[0023] x i y i are the x and y coordinates of point i respectively;

[0024] The strain vector and coordinate matrix are used to describe the strain and coordinate of each point on the distributed sensor using the following matrix:

[0025]

[0026] The full-field strain distribution of the monitored structure is determined by performing regression analysis on the strain distribution function using the surface fitting method. The strain component at any point (x, y) is expressed as:

[0027]

[0028] m k is the coefficient of the kth fitting function (k=1, 2, ..., M), and M is the total number of fitting functions. The strain vector is expressed as:

[0029]

[0030] Using a polynomial function to fit the strain distribution curve, the strain component at any point (x, y) is expressed as:

[0031]

[0032] Integrate discrete sensor data into continuous strain field distribution.

[0033] The step 5 is to monitor the strain data of the outer wall of the pipeline through the strain sensor network, and then obtain the specific contour lines corresponding to the strain data size in the finite element model through the strain data of the outer wall of the pipeline.

[0034] The beneficial effects of the present invention are: Real-time: The method has the ability to monitor pipeline corrosion defects in real time. The micro deformation and strain data of the pipeline surface are collected in real time through the micro fiber grating strain sensor network, and the strain field is reconstructed by the difference method, so as to achieve the purpose of real-time monitoring. Accuracy: Through the rapid processing and analysis of the measured data, an immediate and accurate assessment of the pipeline corrosion state is provided, which helps to find problems early and take corresponding measures, highlighting the accuracy of the method. Non-invasive and non-destructive testing: The use of non-invasive sensing technologies such as micro fiber grating sensors does not require disassembly or destructive testing of the pipeline, avoiding additional damage to the pipeline operation. This non-destructive testing method not only ensures the continuity of pipeline operation, but also reduces maintenance costs and risks. Accurate prediction and management optimization: By establishing a relationship model between the strain and depth and area of ​​the corrosion area, the depth and area of ​​the actual corrosion area can be derived from the strain field of the outer wall of the pipeline. This accurate prediction capability provides an accurate assessment of the health status of the pipeline structure, helps to optimize the pipeline maintenance management strategy, and take necessary maintenance or replacement measures in time to extend the life of the pipeline and reduce the risk of accidents. Scientificity and practicality: Due to the adaptability of basic theory and structure, it is applicable to various types of pipelines and has strong scientific and practical significance. Low maintenance and low cost: This method has low application cost and maintenance cost. It only needs strain data to monitor the health status of pipelines. It is applicable to large pipeline systems and has strong practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the schematic diagram of the finite element elbow model;

[0036] Figure 2 Schematic diagram of corrosion defects;

[0037] Figure 3 This is the relationship diagram between the strain of the dangerous point in the corrosion area and the defect depth and area;

[0038] Figure 4 Schematic diagram of a micro fiber Bragg grating strain sensor;

[0039] Figure 5 This is a schematic diagram of the fiber Bragg grating strain sensor packaging;

[0040] Figure 6 It is a schematic diagram of multiple grids;

[0041] Figure 7 It is a schematic diagram of multi-grid packaging;

[0042] Figure 8 Schematic diagram of strain field restoration for strain sensing network;

[0043] Fig. 9 Schematic diagram of the reconstruction of the depth and area of ​​the corrosion area.

[0044] In the figure: 1. micro fiber Bragg grating strain sensor, 2. optical fiber, 3. epoxy resin, 4. glass fiber ribbon. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] The present invention provides an online monitoring method for micro corrosion defects of pipeline elbows. First, the optical fiber sensor is packaged and calibrated so that it can be used in harsh on-site environments (high temperature and vibration). Then, the corresponding relationship between strain and wall thickness is obtained by finite element method, and defect identification and positioning tests are performed for verification. Finally, strain field reconstruction is realized based on test and simulation data, and software functions are developed to realize functions such as three-dimensional strain cloud map display and wall thickness identification.

[0047] An online monitoring method for micro corrosion defects of pipeline elbows comprises the following steps:

[0048] Step 1: Batch build finite element models by controlling variable method

[0049] In the real-time nondestructive monitoring of pipeline micro-corrosion defects, the establishment of finite element model is one of the key steps. First, select general finite element software to clarify the key issues to be studied, that is, the influence of different corrosion depths and corrosion areas on pipeline stress and strain; then determine the key parameters to be studied, including pipeline materials (such as carbon steel, stainless steel), pipeline dimensions (diameter, wall thickness) and corrosion parameters (corrosion depth, corrosion shape, corrosion location); then design a control variable comparison model, select fixed variables (pipeline materials and dimensions) and variable variables (corrosion depth and corrosion area), systematically change the variable variables, set different corrosion depths (such as 5%, 10%, 20%, etc.) and different corrosion areas (such as 5%, 10%, 20%, etc.), establish a batch finite element numerical model, set initial conditions and boundary conditions; perform numerical simulations on multiple models to simulate stress and strain conditions under different corrosion depths, locations and shapes. Numerical simulation can obtain the strain response of the pipeline under different conditions and determine the influence of corrosion on the strain field. The key to establishing a finite element model lies in the accuracy and reliability of the model. The selection of model parameters should be based on a deep understanding of the properties of pipeline materials and corrosion conditions. In addition, for different corrosion forms, such as pitting, line corrosion or surface corrosion, the model needs to be adjusted accordingly. The control variable method can help verify the robustness of the model and ensure that the established model has a high degree of credibility. By establishing these finite element models, the stress and strain distribution of pipeline corrosion defects under different conditions can be simulated, providing basic data for subsequent steps to better understand the pipeline corrosion process and strain characteristics.

[0050] Step 2: Extract the strain size, corrosion depth and area data of the corrosion area and establish the relationship between them;

[0051] For the internal corrosion area of ​​the pipeline, the simulation can provide the Mises strain and uniaxial strain of the corrosion area. The actual monitored values ​​should be along the axial and circumferential directions of the pipeline, so the strain needs to be converted. First, the grid points in the finite element model are divided, and the grid points of the outer wall of the pipeline are divided into separate sets. Then, based on post-processing, the strain data of the grid points of the outer wall of the pipeline are extracted from the data file. Then, according to the strain conversion formula in different coordinate systems of elastic mechanics, the uniaxial strain results of the outer wall are converted into stress-strain results along the axial and circumferential directions of the pipeline elbow. According to the control variable method shown in the previous article, the pipeline strain data under different corrosion depths and different corrosion areas are extracted. A three-dimensional surface map can be drawn with the corrosion area and corrosion depth as independent variables and the strain as the strain variable. The three-dimensional surface map is projected on the XY plane to obtain a contour map of the strain changing with the corrosion area and corrosion depth. The three-dimensional surface map and the contour map fully represent the correlation between the strain information that can be monitored on the outer wall of the pipeline and the internal corrosion information of the pipeline, namely the corrosion area and corrosion depth. Based on this relationship, the depth and area of ​​the corrosion area can be deduced by monitoring the strain data on the pipeline surface, providing basic support for real-time monitoring.

[0052] Step 3: Install the micro FBG strain sensor network at the pipe elbow

[0053] Pipeline corrosion is a relatively long process, which requires sensors to have good stability, robustness and anti-interference ability. Fiber Bragg grating sensors are light in weight, small in size, highly sensitive, corrosion-resistant, and anti-electromagnetic interference. They have good long-term stability and durability, and meet the requirements of pipeline safety monitoring for sensors. Therefore, micro fiber Bragg grating strain sensors are selected to form a strain sensing network and installed at pipeline elbows or other parts that may corrode. Fiber Bragg grating sensors have high sensitivity and high spatial resolution, and can monitor the tiny deformation and strain of the pipeline surface in real time and continuously. The installation process needs to ensure the stability and accuracy of the sensor, as well as good contact with the pipeline surface. The layout and density of the sensor grid need to be reasonably designed according to the characteristics of the pipeline and the monitoring requirements. Finally, the sensor is encapsulated with high-temperature resistant glass fiber cloth to protect the sensor.

[0054] Step 4: Collect and demodulate sensor network data and reconstruct the pipe outer wall strain field using the difference method

[0055] The installed sensor grid can collect strain data on the pipeline surface. These data need to be demodulated and processed to obtain accurate information on the strain field of the pipeline outer wall. The collected data needs to be preprocessed, cleaned and calibrated to ensure data quality. Then, based on the spatial distribution characteristics of the data, the strain field of the pipeline outer wall can be reconstructed using the difference method. The interpolation method is described as follows:

[0056] First, a series of strain data measured by the distributed sensor each time is expressed as a strain vector:

[0057] ε p =[ε p1 …ε pi …ε pN ] T (1)

[0058] ε pi is the strain measured from point i (i=1, 2, ..., N), N is the total number of data points. The subscript p is the variable of direction, which is consistent with the direction of the distributed sensor.

[0059] The coordinates of the measurement points in the distributed sensor are expressed as:

[0060]

[0061] x i y i are the x and y coordinates of point i respectively.

[0062] With the strain vector and coordinate matrix, the strain and coordinate of each point on the distributed sensor can be described using the following matrix:

[0063]

[0064] Although the distributed sensors cover a large area of ​​the monitored structure, they do not provide measurements at every point because there are gaps between adjacent paths of the distributed sensors. The full-field strain distribution of the monitored structure is determined by performing regression analysis on the strain distribution function using a surface fitting method. The strain component at any point (x, y) is expressed as:

[0065]

[0066] m k is the coefficient of the kth fitting function (k=1, 2, ..., M), and M is the total number of fitting functions. Then, the strain vector can be expressed as:

[0067]

[0068] Using a polynomial function to fit the strain distribution curve, the strain component at any point (x, y) is expressed as:

[0069]

[0070] Through the above operations, the discrete sensor data can be integrated into a continuous strain field distribution, such as Figure 8 As shown, it provides a basis for further analysis and calculation.

[0071] Step 5: Obtain the depth and area of ​​the corrosion zone through the outer wall strain field and the established relationship function;

[0072] The reconstructed pipe outer wall strain field data will be combined with the previously established corrosion area strain, depth, and area relationship model. The specific steps are as follows:

[0073] First, the strain data of the outer wall of the pipeline is monitored through the strain sensor network, and then the specific contour line corresponding to the strain data size in the finite element model can be obtained through the strain data of the outer wall of the pipeline. Since this contour line corresponds to data points of different corrosion thickness and different corrosion areas, the next step is to determine the corrosion area and corrosion depth.

[0074] According to the approximate relationship between thickness and strain in elastic mechanics, it can be concluded that when corrosion occurs inside the pipeline, that is, when the thickness changes, the strain of the outer wall of the pipeline will suddenly change. Therefore, the specific area of ​​corrosion can be determined by the strain inflection point data on the strain sensor network, that is, the boundary range of the corrosion area can be determined, and then the specific corrosion area can be determined based on the boundary area.

[0075] Finally, the corresponding corrosion depth is determined according to the specific strain contours and corrosion areas, and the reconstruction and restoration of the corrosion area and the corrosion depth of each point in the pipeline corrosion zone are completed.

[0076] This step combines mathematical models with actual data, and can accurately estimate the size and depth of the corrosion area through the actual strain field of the pipeline outer wall, providing important data for the evaluation of the pipeline operating status.

[0077] The above steps constitute a real-time nondestructive monitoring method for pipeline micro-corrosion defects that comprehensively applies finite element models, sensor technology, data processing and mathematical models. Through this method, accurate monitoring and evaluation of pipeline corrosion defects can be achieved, providing important support for the safe operation of pipelines.

[0078] Matters not covered in the present invention are known technologies. The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. An online monitoring method for micro corrosion defects of pipeline elbows, characterized in that: The steps include: Step 1: Batch establish finite element models through the control variable method; Step 2: Extract the strain size, corrosion depth and area data of the corrosion area and establish the relationship between them; Step 3: Install a micro fiber grating strain sensor network at the pipe elbow; Step 4: Collect and demodulate sensor network data and reconstruct the pipe outer wall strain field using the difference method; Step 5: Calculate the depth and area of ​​the corrosion zone through the outer wall strain field and the established relationship function.

2. The method for online monitoring of micro corrosion defects of pipeline elbows according to claim 1, characterized in that: The step 1 is to select a general finite element software, clarify the influence of different corrosion depths and corrosion areas on pipeline stress and strain, then determine the key parameters to be studied, design a control variable comparison model, select fixed variables and variable variables, set different corrosion depths and different corrosion areas, establish a batch finite element numerical model, and set initial conditions and boundary conditions; Numerical simulations were performed on a variety of models to simulate the stress-strain conditions under different corrosion depths, locations, shapes, etc.

3. The method for online monitoring of micro corrosion defects of pipeline elbows according to claim 2, characterized in that: The key parameters include pipeline materials, including carbon steel, stainless steel, pipeline dimensions including diameter, wall thickness and corrosion parameters, including corrosion depth, corrosion shape, and corrosion location.

4. The method for online monitoring of micro corrosion defects of pipeline elbows according to claim 1, characterized in that: The step 2 is to divide the grid points in the finite element model, divide the grid points of the outer wall of the pipeline into separate sets, and then extract the strain data of the grid points of the outer wall of the pipeline based on post-processing. The data file is converted into the stress-strain results along the axial and circumferential directions of the pipeline elbow according to the strain conversion formula under different coordinate systems of elastic mechanics, and the pipeline strain data under different corrosion depths and different corrosion areas are extracted. The three-dimensional surface graph is drawn with the corrosion area and corrosion depth as independent variables and the strain as the strain variable, and the three-dimensional surface graph is projected on the XY plane to obtain a contour map of the strain changing with the corrosion area and corrosion depth.

5. The method for online monitoring of micro corrosion defects of pipeline elbows according to claim 1, characterized in that: The step 3 is to select micro fiber grating strain sensors to form a strain sensing network, and install them at pipe elbows or other locations that may be corroded.

6. The method for online monitoring of micro corrosion defects of pipeline elbows according to claim 1, characterized in that: The step 4 is to reconstruct the strain field of the outer wall of the pipeline by using the difference method. The interpolation method is as follows: First, a series of strain data measured by the distributed sensor each time is expressed as a strain vector: e p =[e p1 ...he pi ...he pN ] T (1) ε pi is the strain measured from point i (i = 1, 2, ..., N), N is the total number of data points, and the subscript p is the variable of direction, which is consistent with the direction of the distributed sensor; The coordinates of the measurement points in the distributed sensor are expressed as: x i y i are the x and y coordinates of point i respectively; The strain vector and coordinate matrix are used to describe the strain and coordinate of each point on the distributed sensor using the following matrix: The full-field strain distribution of the monitored structure is determined by performing regression analysis on the strain distribution function using the surface fitting method. The strain component at any point (x, y) is expressed as: m k is the coefficient of the kth fitting function (k=1, 2, ..., M), and M is the total number of fitting functions. The strain vector is expressed as: Using a polynomial function to fit the strain distribution curve, the strain component at any point (x, y) is expressed as: Integrate discrete sensor data into continuous strain field distribution.

7. The method for online monitoring of micro corrosion defects of pipeline elbows according to claim 1, characterized in that: The step 5 is to monitor the strain data of the outer wall of the pipeline through the strain sensor network, and then obtain the specific contour lines corresponding to the strain data size in the finite element model through the strain data of the outer wall of the pipeline.

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