An ultrasonic guided wave corrosion thinning imaging method and system for pipe elbows

Abstract

This invention relates to a method and system for ultrasonic guided wave corrosion thinning imaging of pipe elbows, belonging to the field of ultrasonic guided wave nondestructive testing technology. The method includes establishing a two-dimensional planar non-uniform mesh model of the pipe elbow; determining the positions of the annular transducer arrays at both ends of the area to be inspected in the pipe elbow; performing forward modeling based on the excitation signal; obtaining an objective function regarding sound velocity based on the simulation and observation results; determining whether the objective function satisfies the iteration termination condition; selecting whether to perform iterative calculation of the objective function based on the determination result; obtaining sound velocity distribution information based on the determination result and the iterative calculation results; and obtaining a corrosion thinning image of the pipe elbow based on the relationship between the frequency-thickness product and sound velocity, the sound velocity distribution information, and the two-dimensional planar non-uniform mesh model. This invention constructs a two-dimensional planar non-uniform mesh model that conforms to the actual dimensions of the pipe elbow, and accurately realizes corrosion thinning imaging of the pipe elbow on the two-dimensional planar non-uniform mesh model.

Description

Technical Field

[0001] This invention belongs to the field of ultrasonic guided wave nondestructive testing technology, and specifically relates to an ultrasonic guided wave corrosion thinning imaging method and system for pipe elbows. Background Technology

[0002] Throughout the oil and gas industry, monitoring pipeline corrosion and thinning is a crucial means of ensuring the safe and stable operation of upstream and downstream equipment.

[0003] Although various ultrasonic and electromagnetic thickness gauges are relatively mature, they require manual point-by-point scanning of the area to be inspected. This makes them unsuitable for long-distance pipeline monitoring and limits their applicability when physical obstacles prevent direct access to the pipeline. Furthermore, another major limitation of current manual thickness measurement is its reliance on highly skilled operators; inaccurate selection of points can easily lead to missed detections.

[0004] Ultrasonic guided wave-based detection offers a solution for long-distance, large-coverage inspections. An excitation sensor positioned at a single point can propagate over a considerable distance along the structure under test. Guided waves are currently used in defect detection in conventional pipelines. Current corrosion thinning imaging methods are mainly categorized into travel-time tomography, diffraction tomography, and tomography based on full-waveform inversion. Travel-time imaging resolution is limited by the first Snell's zone, while diffraction imaging requires multi-angle scattering field analysis; however, in pipeline structures, the incident and receiving angles of the guided wave signal are limited. Existing research on full-waveform inversion focuses on imaging straight pipe sections, lacking studies on bends. Overall, current long-distance ultrasonic guided wave defect detection methods cannot provide accurate estimates of defect depth, especially in cases involving complex morphologies such as bends. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides an ultrasonic guided wave corrosion thinning imaging method and system for pipe elbows, which solves the problem of quantitative detection of corrosion thinning in existing guided wave detection schemes.

[0006] The first objective of this invention is to provide an ultrasonic guided wave corrosion thinning imaging method for pipe bends, the method comprising:

[0007] A two-dimensional planar non-uniform mesh model of the pipe bend is established based on the dimensional information of the pipe bend.

[0008] Determine the positions of the annular transducer arrays arranged at both ends of the area to be inspected at the pipe bend, wherein one annular transducer array serves as the excitation source array and the other annular transducer array serves as the receiving array.

[0009] Forward modeling is performed based on the excitation signal from the excitation source;

[0010] Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, the objective function for the speed of sound is obtained;

[0011] Determine whether the objective function satisfies the iteration termination condition;

[0012] Based on the judgment result, decide whether to perform iterative calculation of the objective function;

[0013] Based on the judgment results and iterative calculation results, the sound velocity distribution information is obtained;

[0014] Based on the relationship between frequency thickness product and sound velocity, sound velocity distribution information, and a two-dimensional planar non-uniform network model, corrosion thinning images of pipe elbows are obtained.

[0015] In this embodiment of the invention, the dimensional information of the pipe elbow includes the inner bending radius, the outer bending radius, the bending angle, and the inner and outer diameters of the pipe.

[0016] In this embodiment of the invention, before the forward modeling, a sound-absorbing layer is added at the boundary of the two-dimensional planar non-uniform mesh model of the pipe bend.

[0017] In this embodiment of the invention, the excitation signal is a narrowband signal.

[0018] In this embodiment of the invention, the specific process of the forward simulation is as follows:

[0019] Perform a fast Fourier transform on the excitation signal to obtain the transformed excitation signal;

[0020] Extract a frequency point near the center frequency of the transformed excitation signal that has a non-zero amplitude.

[0021] A matrix equation is established based on the frequency domain wave equation and the non-uniform grid finite difference method, and forward modeling is performed on each individual frequency point.

[0022] In this embodiment of the invention, the objective function for the speed of sound obtained based on the simulation results obtained from forward modeling and the observation results from the receiving position includes:

[0023] Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, calculate the residuals between the simulation results obtained from the forward modeling and the observation results from the receiving position.

[0024] Based on the residuals obtained from the forward modeling and the observations at the receiving location, the objective function for the speed of sound is calculated.

[0025] In this embodiment of the invention, the iteration termination condition is that the current value of the objective function is less than a set threshold; or the iteration stops after the target number of iterations is reached.

[0026] In this embodiment of the invention, the iterative calculation of the objective function includes:

[0027] Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, the gradient of the objective function with respect to the speed of sound is obtained.

[0028] The step size is calculated iteratively, and the step size is the increment of the objective function on the gradient;

[0029] Update the velocity of sound based on gradient and step size;

[0030] Based on the updated sound velocity, perform iterative forward modeling simulation;

[0031] Based on the results of iterative forward simulation, the objective function is calculated iteratively.

[0032] Repeat the steps of "iterative calculation of step size", "updating sound velocity", "iterative forward modeling" and "iterative calculation of objective function" until the objective function satisfies the iteration termination condition;

[0033] The final step size is determined to be the step size calculated in the last iteration.

[0034] In this embodiment of the invention, the step of selecting whether to perform iterative calculation of the objective function based on the judgment result includes:

[0035] The judgment result is:

[0036] The gradient is obtained based on the simulation results obtained from the forward modeling corresponding to the objective function and the observation results from the receiving position;

[0037] The step size is obtained using a step size search algorithm;

[0038] The speed of sound is obtained based on the gradient and step size.

[0039] Output sound speed;

[0040] or,

[0041] If the result is negative, perform iterative calculation of the objective function.

[0042] A second objective of this invention is to provide an ultrasonic guided wave corrosion thinning imaging system for pipe bends, the system comprising:

[0043] The model building module is used to build a two-dimensional planar non-uniform mesh model of the pipe bend based on its size information.

[0044] The determination module is used to determine the positions of the annular transducer arrays arranged at both ends of the area to be detected in the pipe bend, wherein one annular transducer array serves as the excitation source array and the other annular transducer array serves as the receiving array.

[0045] The forward modeling module is used to perform forward modeling based on the excitation signal from the excitation source;

[0046] The objective function module is used to derive the objective function for the speed of sound based on the simulation results obtained from the forward modeling and the observation results from the receiver location;

[0047] The iteration module is used to determine whether the objective function meets the iteration termination condition, and to select whether to perform iterative calculation of the objective function based on the determination result;

[0048] The sound velocity module is used to obtain sound velocity distribution information based on the judgment results and iterative calculation results;

[0049] The imaging module is used to obtain corrosion thinning images of pipe bends based on the relationship between frequency-thickness product and sound velocity, sound velocity distribution information, and a two-dimensional planar non-uniform network model.

[0050] A third object of the present invention is to provide an electronic device, the electronic device comprising: a processor coupled to a memory;

[0051] The memory is used to store computer programs;

[0052] The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the method described above.

[0053] A fourth object of the present invention is a computer-readable storage medium storing a program or instructions that, when executed on a computer, cause the computer to perform the method described above.

[0054] The beneficial effects of this invention are:

[0055] This invention provides an ultrasonic guided wave corrosion thinning imaging method and system for pipe elbows. It constructs a two-dimensional planar non-uniform grid model that conforms to the actual size of the pipe elbow, and performs forward modeling based on the non-uniform grid finite difference method in full waveform inversion and the frequency domain acoustic wave equation in the discrete matrix form. It realizes accurate corrosion thinning imaging of pipe elbows on the two-dimensional planar non-uniform grid model, which solves the problem that traditional diffraction imaging cannot realize corrosion thinning imaging of pipe elbows in pipe structure detection due to the limited incident and receiving angle of guided wave signals.

[0056] Furthermore, to avoid the influence of boundary echoes, a PML sound-absorbing layer is added at the boundary of the two-dimensional planar non-uniform mesh model of the pipe bend in this invention, which further improves the accuracy of corrosion thinning imaging obtained by forward modeling.

[0057] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 A flowchart of an ultrasonic guided wave corrosion thinning imaging method for a pipe elbow according to an embodiment of the present invention is shown;

[0060] Figure 2 A schematic diagram of the transducer array arrangement at both ends of a pipe bend according to an embodiment of the present invention is shown;

[0061] Figure 3 A schematic diagram of a two-dimensional non-uniform grid unfolded for a pipe elbow according to an embodiment of the present invention is shown.

[0062] Figure 4 A schematic diagram showing the position of point m in a non-uniform mesh model according to an embodiment of the present invention is shown;

[0063] Figure 5 A dispersion curve diagram according to an embodiment of the present invention is shown;

[0064] Figure 6 A frame diagram of an ultrasonic guided wave corrosion thinning imaging system for a pipe elbow according to an embodiment of the present invention is shown.

[0065] Figure 7 A frame diagram of an electronic device according to an embodiment of the present invention is shown;

[0066] In the picture:

[0067] Model building module 1; detection module 2; forward simulation module 3; objective function module 4; iteration module 5; sound velocity module 6; imaging module 7; electronic equipment 300; processor 301; memory 302. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] like Figure 1 As shown, an ultrasonic guided wave corrosion thinning imaging method for pipe elbows according to an embodiment of the present invention includes:

[0070] Step S1: Establish a two-dimensional planar non-uniform mesh model of the pipe elbow based on its size information;

[0071] Step S2: Determine the positions of the annular transducer arrays arranged at both ends of the area to be tested in the pipe bend, wherein one side of the annular transducer array serves as the excitation source array and the other side of the annular transducer array serves as the receiving array.

[0072] Step S3: Perform forward modeling based on the excitation signal from the excitation source;

[0073] Step S4: Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, obtain the objective function for the speed of sound;

[0074] Step S5: Determine whether the objective function satisfies the iteration termination condition;

[0075] Step S6: Based on the judgment result, decide whether to perform iterative calculation of the objective function;

[0076] Step S7: Based on the judgment results and iterative calculation results, obtain the sound speed distribution information;

[0077] Step S8: Based on the relationship between frequency thickness product and sound velocity, sound velocity distribution information, and two-dimensional planar non-uniform network model, obtain the corrosion thinning image of the pipe elbow.

[0078] In step S1, the dimensional information of the pipe elbow includes the inner bending radius r, the outer bending radius R, the bending angle α, and the inner and outer diameters of the pipe.

[0079] In step S1, to accurately reflect the corrosion thinning imaging of the pipe bend, a two-dimensional planar non-uniform network model conforming to the bend structure is established based on the dimensions of the pipe bend. Specifically, the two-dimensional planar non-uniform network model is discretized into a non-uniform mesh according to the actual dimensions of the bend. Taking the expansion along the inside of the bend as an example, the length of the left and right boundaries of the mesh is 2πr×(α / 2π), and the length of the outside of the bend is 2πR×(α / 2π). The length of each mesh in the middle region is determined using the linear interpolation method. During mesh generation, a non-uniform mesh is established to account for the changes in length dimensions in the Y direction on the inside and outside. After establishing the non-uniform mesh, each node represents the sound velocity parameter at that node, as detailed in [the following section is missing]. Figure 2 ;

[0080] In step S2, the specific operation is as follows:

[0081] A ring-shaped transducer array is arranged at both ends of the area to be inspected at the pipe bend. One ring-shaped transducer array serves as the excitation source array, and the other ring-shaped transducer array serves as the receiving array. After the array is fixed, the position of each excitation and receiving element is recorded. A schematic diagram of the specific transducer array arrangement is shown below. Figure 3 As shown;

[0082] In step S3, before the forward modeling, a PML (Perfectly Matched Layer) sound-absorbing layer is added at the boundary of the two-dimensional plane non-uniform mesh model of the pipe bend to avoid the influence of boundary echoes. The acoustic wave equation of the best matching layer is shown in equation (1).

[0083]

[0084] In equation (1), L PML Where A is the matching layer thickness and A is the attenuation coefficient.

[0085] make Equations (2)-(4) can be obtained;

[0086]

[0087]

[0088]

[0089] In equation (4), P represents the wave field in the frequency domain, ω represents the angular frequency, c represents the speed of sound, and F(ω) represents the source.

[0090] The treatment of the boundary and corner regions of the mesh model is somewhat different. In the corner regions, the attenuation of the wave field is a superposition of the attenuation in the x and z directions.

[0091] In step S3, the excitation signal is a narrowband signal. Taking a sine wave modulated by a Hanning window as an example, the Hanning window function is shown in equation (5). The guided wave information at the receiving sensor array is obtained through the acquisition circuit connected to the receiving transducer.

[0092]

[0093] In equation (5), α = 0.53836;

[0094] In step S3, the specific process of the forward simulation is as follows:

[0095] Step A1: Perform a fast Fourier transform on the excitation signal to obtain the transformed excitation signal;

[0096] Step A2: Extract a frequency band with non-zero amplitude near the center frequency from the transformed excitation signal (i.e., the target frequency);

[0097] Step A3: Establish matrix equations based on the frequency domain wave equation and the non-uniform grid finite difference method, and perform forward modeling simulations for each individual frequency point.

[0098] Specifically, in step A3, the frequency domain wave equation is a frequency domain acoustic wave equation in the form of a discrete matrix, as shown in equation (6).

[0099] A a (c,ω)Y a (ω)=F a (ω) (6)

[0100] In equation (6), the number of grids in the region to be calculated is N = Nx × Ny, then Aa is an N × N impedance matrix, Ya is the wave field value to be solved, Fa represents the excitation source, ω is the angular frequency, and c represents the speed of sound.

[0101] In step A3, the non-uniform mesh finite difference method is established for the two-dimensional planar non-uniform mesh model established in step S1;

[0102] The forward modeling simulation of this invention is based on the finite difference method used in the forward and inverse calculations of the full waveform inversion calculation. It utilizes the difference approximation to realize the partial differential calculation in the partial differential equation, and the difference equation approximates the frequency domain finite difference of the differential equation.

[0103] Conventional finite difference methods based on uniform grids are difficult to solve the problem of bends. In reality, the dimensions of the inner side of the bend are significantly smaller than those of the outer side. Using the general uniform grid difference method to establish a forward model inevitably leads to positioning errors and errors in the quantitative measurement of defects, and may even cause uncompensable deviations between the forward model and the design structure. Therefore, for bends, a non-uniform grid discretization method for bends is proposed in step 1. Based on the dimensional constraints in the actual physical model, a non-uniform grid is established for the actual shape of the composite bend.

[0104] During mesh generation, a non-uniform mesh is established to account for the changes in length dimensions on the inner and outer sides of the Y direction. Linear interpolation is used to determine the Y-axis dimensions of each mesh and to establish a forward model.

[0105] Based on this, the embodiments of the present invention select the non-uniform grid finite difference method and perform forward modeling on the frequency domain acoustic wave equation in the form of the discretized matrix. The specific formulas involved in the non-uniform grid finite difference method are shown in equations (7)-(9).

[0106]

[0107]

[0108] and

[0109]

[0110] Equations (7)-(9) represent the analytical equations for points (m,n) in a non-uniform grid, and in equations (7)-(9), α1, α2, β1, β2 and c ij P is the weighting coefficient. m,n Represents the wave field at any point, where (m, n) is any point in a non-uniform grid. See details. Figure 4 A non-uniform grid can be viewed as being composed of nine-point grids with different grid spacing ratios.

[0111] In step S4, the objective function for the speed of sound is obtained based on the simulation results obtained from the forward modeling and the observation results from the receiving position, including:

[0112] Step B1: Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, calculate the residuals between the simulation results obtained from the forward modeling and the observation results from the receiving position.

[0113] Step B2: Based on the residuals of the simulation results obtained from the forward modeling and the observation results from the receiving position, calculate the objective function with respect to the speed of sound.

[0114] Specifically, in step B1, the residual between the simulation results obtained from the forward simulation and the observation results from the receiving position, the residual s δ The expression is shown in equation (10);

[0115] s δ =s cal -s obs (10)

[0116] In equation (10), s cal The simulation results obtained from forward modeling, s obs For the observation results of the receiving location.

[0117] In step B2, the expression for the objective function of the speed of sound is given in equation (11);

[0118]

[0119] In equation (11), the superscript T denotes matrix transpose, the superscript * denotes complex conjugate, E(c) is the objective function with respect to the speed of sound, and c is the speed of sound.

[0120] In step S5, the iteration termination condition is that the current value of the objective function is less than a set threshold (which is a set value, empirically selected as 0.1); or the iteration stops after reaching the target number of iterations (the target number of iterations is a set value).

[0121] In step S6, the iterative calculation of the objective function includes:

[0122] Step C1: Based on the simulation results obtained from the reverse propagation forward modeling and the observation results from the receiving position, obtain the gradient of the objective function with respect to the speed of sound;

[0123] Step C2: Iteratively calculate the step size, where the step size is the increment of the objective function on the gradient;

[0124] Step C3: Update the speed of sound based on the gradient and step size;

[0125] Step C4: Perform iterative forward modeling simulation based on the updated sound velocity;

[0126] Step C5: Based on the results of the iterative forward simulation, iteratively calculate the objective function;

[0127] Step C6: Repeat the steps of "iterative calculation of step size", "update sound velocity", "iterative forward modeling" and "iterative calculation of objective function" until the objective function satisfies the iteration termination condition;

[0128] Step C7: Determine the final step size as the step size calculated in the last iteration.

[0129] In step C1, firstly, based on the simulation results obtained from the reverse propagation forward modeling and the observation results of the receiving position, the residual is obtained, as shown in equation (10).

[0130] Then, the residual is back propagated to obtain the adjoint wave field, and the gradient of the objective function with respect to the speed of sound is calculated using the adjoint wave field, as shown in equation (12).

[0131]

[0132] Transforming equation (12), we can obtain equation (13) relating the gradient to the adjoint wave field. The specific derivation is as follows:

[0133] make have to Substituting into equation (12), we obtain the relationship between the gradient and the adjoint wave field (13);

[0134]

[0135] In equation (13), let the accompanying wave field but Equivalent to the reverse propagation of the residual wavefield, by performing reverse propagation on the conjugate of the residual wavefield, we can calculate... This yields the gradient, which is the calculation process of step C1 above.

[0136] In steps C2-C6, the iterative calculation step size mentioned in step C2 refers to assigning values ​​to the step size and calculating iteratively through different methods, and repeating the iterative calculations in steps C3, C4, and C5 (i.e., step C6).

[0137] In this invention, a simple calculation method is used as an example. The initial step size is set to l. The speed of sound at the step size l is obtained by using the calculated gradient and the step size (i.e., step C3). The step size is the increment of the objective function on the gradient.

[0138] Perform the forward modeling calculation again (forward simulation and calculation of simulation results) to obtain the objective function under this velocity model (i.e., steps C4 and C5), that is, update the objective function under this step size l;

[0139] And determine whether the updated objective function satisfies the iteration termination condition:

[0140] When the updated objective function satisfies the iteration termination condition, it indicates that the objective function is optimized at the step size d, and this step size is taken as the final step size.

[0141] If the updated objective function does not meet the iteration termination condition, it means that the objective function has not been optimized. Take half of the step size d as the step size after iteration, and proceed to step C6 until the objective function meets the iteration termination condition. Then determine the last iteration step size as the final step size.

[0142] In step S6, selecting whether to perform iterative calculation of the objective function based on the judgment result includes:

[0143] If the judgment result is yes, perform the following operations:

[0144] Step D1: Based on the simulation results obtained from the forward modeling of the objective function and the observation results of the receiving position, obtain the gradient of the objective function with respect to the speed of sound, as in step C1 above.

[0145] Step D2: Obtain the step size according to the step size search algorithm, wherein the step size search condition is derived from equation (14);

[0146]

[0147] In equation (14), m0 represents the initial model parameters, and r1 represents the step size of the first iteration (taking the first iteration as an example);

[0148] Step D3: Based on the gradient and step size, obtain the speed of sound, the same as step C3;

[0149] Step D4: Output sound velocity;

[0150] or,

[0151] If the judgment result is negative, perform iterative calculation of the objective function, that is, perform the above steps C1-C6.

[0152] In step S7, the information based on the judgment result and the iterative calculation result, that is, based on the sound velocity distribution information corresponding to some frequency points output by step D4 in step S6 (the judgment result is negative),

[0153] and

[0154] Based on the final step size determined by step C7 (iterative calculation result) in step S6, and combined with the gradient, update the sound velocity distribution information corresponding to some other frequency points;

[0155] This yields information on the sound velocity distribution at the target frequency.

[0156] In step S8, based on the relationship between the frequency-thickness product and sound velocity, sound velocity distribution information, and a two-dimensional planar non-uniform mesh model, a corrosion thinning image of the pipe elbow is obtained. The relationship between the frequency-thickness product and sound velocity is expressed as a dispersion curve relationship, as detailed in [link to details]. Figure 5 , Figure 5In diagram (a), the phase velocity dispersion curve is shown. Figure 5 (b) is the group velocity dispersion curve;

[0157] Based on the relationship between frequency-thickness product and guided wave velocity, and the sound velocity distribution information at the target frequency obtained through the above steps, substitute into equation (15) to obtain the thickness distribution information;

[0158] c(r) = c ph {f·d[r]} (15)

[0159] In equation (15), C' represents the sound velocity at the node, r represents the location information (the position of all points in each calculation during the forward and inverse processes), C ph The phase velocity is represented by f, and the frequency and thickness are represented by d, respectively.

[0160] The thickness distribution information and corresponding location information obtained above are plotted on a two-dimensional non-uniform mesh model to obtain the corrosion thinning image of the pipe elbow.

[0161] like Figure 6 As shown, an ultrasonic guided wave corrosion thinning imaging system for pipe elbows according to an embodiment of the present invention includes:

[0162] Model building module 1 is used to build a two-dimensional planar non-uniform mesh model of the pipe bend based on the size information of the pipe bend;

[0163] Detection module 2 is used to arrange a ring transducer array at both ends of the area to be detected in the pipe bend, with one side serving as the excitation source and the other side serving as the receiver;

[0164] Forward simulation module 3 is used to perform forward simulation based on the excitation signal from the excitation source;

[0165] Objective function module 4 is used to obtain the objective function for the speed of sound based on the simulation results obtained from the forward modeling and the observation results from the receiving position;

[0166] Iteration module 5 is used to determine whether the objective function meets the iteration termination condition, and to select whether to perform iterative calculation of the objective function based on the determination result;

[0167] Sound velocity module 6 is used to obtain sound velocity distribution information based on the judgment results and iterative calculation results;

[0168] Imaging module 7 is used to obtain corrosion thinning images of pipe bends based on the relationship between frequency thickness product and sound velocity, sound velocity distribution information, and a two-dimensional planar non-uniform network model.

[0169] like Figure 7 As shown, in some embodiments of the present invention, an electronic device is provided, the electronic device 300 including: a processor 301 coupled to a memory 302;

[0170] The memory 302 is used to store computer programs;

[0171] The processor 301 is configured to execute the computer program stored in the memory 302, so that the electronic device performs the method described in the above embodiments.

[0172] In some embodiments of the present invention, a computer-readable storage medium is provided that stores a program or instructions that, when executed on a computer, cause the computer to perform the methods described in the above embodiments.

[0173] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, electronic device, or apparatus.

[0174] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for ultrasonic guided wave corrosion thinning imaging of pipe elbows, characterized in that, include: A two-dimensional planar non-uniform mesh model of the pipe bend is established based on the dimensional information of the pipe bend. Determine the positions of the annular transducer arrays arranged at both ends of the area to be inspected at the pipe bend, wherein one annular transducer array serves as the excitation source array and the other annular transducer array serves as the receiving array. Forward modeling is performed based on the excitation signal from the excitation source; Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, the objective function for the speed of sound is obtained; Determine whether the objective function satisfies the iteration termination condition; Based on the judgment result, decide whether to perform iterative calculation of the objective function; Based on the judgment results and iterative calculation results, the sound velocity distribution information is obtained; Based on the relationship between frequency thickness product and sound velocity, sound velocity distribution information, and a two-dimensional planar non-uniform network model, corrosion thinning images of pipe elbows are obtained. The specific process of the forward modeling is as follows: Perform a fast Fourier transform on the excitation signal to obtain the transformed excitation signal; Extract a frequency point near the center frequency of the transformed excitation signal that has a non-zero amplitude. A matrix equation is established based on the frequency domain wave equation and the non-uniform grid finite difference method, and forward modeling is performed for each individual frequency point. Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, the objective function regarding the speed of sound is derived, including: Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, calculate the residuals between the simulation results obtained from the forward modeling and the observation results from the receiving position. Based on the residuals obtained from the forward modeling and the observations at the receiving location, the objective function for the speed of sound is calculated.

2. The ultrasonic guided wave corrosion thinning imaging method for pipe elbows according to claim 1, characterized in that, The dimensional information of the pipe elbow includes the inner bending radius, outer bending radius, bending angle, and inner and outer diameters of the pipe.

3. The ultrasonic guided wave corrosion thinning imaging method for pipe elbows according to claim 1, characterized in that, Before the forward modeling, a sound-absorbing layer is added at the boundary of the two-dimensional planar non-uniform mesh model of the pipe bend.

4. The ultrasonic guided wave corrosion thinning imaging method for pipe elbows according to claim 1, characterized in that, The excitation signal is a narrowband signal.

5. The ultrasonic guided wave corrosion thinning imaging method for pipe elbows according to claim 1, characterized in that, The iteration termination condition is that the current value of the objective function is less than a set threshold; or that the iteration stops after the target number of iterations is reached.

6. The ultrasonic guided wave corrosion thinning imaging method for pipe elbows according to claim 1, characterized in that, The iterative calculation of the objective function includes: Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, the gradient of the objective function with respect to the speed of sound is obtained. The step size is calculated iteratively, and the step size is the increment of the objective function on the gradient; Update the velocity of sound based on gradient and step size; Based on the updated sound velocity, perform iterative forward modeling simulation; Based on the results of iterative forward simulation, the objective function is calculated iteratively. Repeat the steps of "iterative calculation of step size", "updating sound velocity", "iterative forward modeling" and "iterative calculation of objective function" until the objective function satisfies the iteration termination condition; The final step size is determined to be the step size calculated in the last iteration.

7. The ultrasonic guided wave corrosion thinning imaging method for pipe elbows according to claim 1, characterized in that, The step of selecting whether to perform iterative calculation of the objective function based on the judgment result includes: The judgment result is: The gradient is obtained based on the simulation results obtained from the forward modeling corresponding to the objective function and the observation results from the receiving position; The step size is obtained using a step size search algorithm; The speed of sound is obtained based on the gradient and step size. Output sound speed; or, If the result is negative, perform iterative calculation of the objective function.

8. An ultrasonic guided wave corrosion thinning imaging system for pipe elbows, characterized in that, include: The model building module is used to build a two-dimensional planar non-uniform mesh model of the pipe bend based on its size information. The determination module is used to determine the positions of the annular transducer arrays arranged at both ends of the area to be detected in the pipe bend, wherein one annular transducer array serves as the excitation source array and the other annular transducer array serves as the receiving array. The forward modeling module is used to perform forward modeling based on the excitation signal from the excitation source; The objective function module is used to derive the objective function for the speed of sound based on the simulation results obtained from the forward modeling and the observation results from the receiver location; The iteration module is used to determine whether the objective function meets the iteration termination condition, and to select whether to perform iterative calculation of the objective function based on the determination result; The sound velocity module is used to obtain sound velocity distribution information based on the judgment results and iterative calculation results; The imaging module is used to obtain corrosion thinning images of pipe elbows based on the relationship between frequency-thickness product and sound velocity, sound velocity distribution information, and a two-dimensional planar non-uniform network model. The specific process of the forward modeling is as follows: Perform a fast Fourier transform on the excitation signal to obtain the transformed excitation signal; Extract a frequency point near the center frequency of the transformed excitation signal that has a non-zero amplitude. A matrix equation is established based on the frequency domain wave equation and the non-uniform grid finite difference method, and forward modeling is performed for each individual frequency point. Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, the objective function for the speed of sound is derived, including: Based on the simulation results obtained from the forward modeling and the observation results from the receiving position, calculate the residuals between the simulation results obtained from the forward modeling and the observation results from the receiving position. Based on the residuals obtained from the forward modeling and the observations at the receiving location, the objective function for the speed of sound is calculated.

9. An electronic device, characterized in that, include: Processor, the processor being coupled to memory; The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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