Online laser ultrasonic nondestructive testing method for pipeline in high-temperature environment

Abstract

The invention belongs to the technical field of pipeline damage detection, and particularly discloses a pipeline online laser ultrasonic nondestructive testing method in a high-temperature environment, which comprises the following steps: carrying out heat treatment on a pipeline sample to simulate the ultra-long-term service state of the pipeline sample; the method comprises the following steps: acquiring a metallographic diagram of a pipeline sample by using a metallographic method, and extracting tissue information of the pipeline sample through JX-metallographic analysis software, including an average grain size and a spheroidization degree; performing ultrasonic testing on the pipeline sample by using a laser ultrasonic detection system, and analyzing laser ultrasonic information of the pipeline sample, including ultrasonic sound velocity, sound wave spectrum and sound wave attenuation coefficient; establishing a relationship between the laser ultrasonic information and the grain size, and measuring and calculating the grain size of the pipeline sample based on the laser ultrasonic information so as to realize nondestructive evaluation of the pipeline sample; by establishing the relationship between the ultrasonic signal and the grain size, the nondestructive evaluation of the grain size of the pipeline based on the laser ultrasonic surface wave technology is realized.

Description

Technical Field

[0001] The invention relates to the technical field of pipeline damage detection, and in particular to an online laser ultrasonic nondestructive detection method for pipelines in a high-temperature environment. Background Art

[0002] At present, the steam system of power station boilers in waste incineration power plants is mostly a centralized steam supply system for the main steam main pipe, that is, the main steam of several boilers is introduced into the main pipe and then to the steam turbine for power generation or other purposes. Therefore, the main steam main pipe can only be inspected and tested when the whole plant is shut down. Considering a series of factors such as the progress of waste treatment and economic losses, it is difficult to shut down the whole plant. Even if the whole plant is shut down, the reserved maintenance period is short. This has caused the main steam main pipe to serve for a long time. The main steam main pipes of many power plants have been running for more than 100,000 hours in total and have not been inspected and tested. Due to the ultra-long-term high temperature and high pressure operation, temperature changes, pressure fluctuations and vibrations, the steam pipelines are seriously damaged and deteriorated.

[0003] In recent years, the ultrasonic evaluation of the microstructure of polycrystalline materials has been obtained by finding the correspondence between the sound velocity and attenuation coefficient of ultrasonic waves and the microstructure of polycrystalline materials. Studies have shown that ultrasound can provide a new method for the rapid detection of the metallographic structure of boiler pipes. The current technical difficulties of the online non-destructive detection method of ultrasonic waves on pipes are: after the ultrasonic wave is emitted, it will be scattered when it encounters the grains, and it is difficult to collect the waveform after scattering, so it is impossible to quantify the relationship between the ultrasonic information of the ultrasonic wave and the grain size. Summary of the invention

[0004] The object of the present invention is to provide an online laser ultrasonic nondestructive testing method for pipelines in a high temperature environment to solve the technical problem in the prior art that the relationship between ultrasonic information of ultrasonic waves and grain size cannot be quantified.

[0005] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0006] A pipeline online laser ultrasonic nondestructive testing method under high temperature environment comprises the following steps:

[0007] Step 100, heat treating the pipeline sample to simulate its ultra-long-term service state;

[0008] Step 200, obtaining a metallographic image of the pipeline sample by metallographic method, and extracting the organizational information of the pipeline sample, including average grain size and spheroidization degree, by JX-Metallographic Analysis Software;

[0009] Step 300: Use a laser ultrasonic detection system to perform ultrasonic testing on the pipeline sample, and analyze the laser ultrasonic information of the pipeline sample, including ultrasonic sound velocity, sound wave spectrum, and sound wave attenuation coefficient;

[0010] Step 400: Establish a relationship between laser ultrasonic information and grain size, and calculate the grain size of the pipeline sample based on the laser ultrasonic information to achieve non-destructive testing of the pipeline sample.

[0011] As a preferred solution of the present invention, in step 100, the size selection of the pipeline sample includes the length selection and the diameter selection of the pipeline sample, and the specific implementation method is:

[0012] The pipeline sample is measured by a pulse reflection method, and the length of the pipeline sample is determined based on the near-field length of the pipeline sample. The near-field length N determined by the pulse reflection method is expressed as:

[0013] Among them, D S is the diameter of the laser ultrasonic probe, λ is the wavelength of the laser ultrasonic wave, and the diameter D of the laser ultrasonic probe S The larger the value is, the longer the near-field length N is. The length d of the pipeline sample is selected based on the near-field length N. The length d of the pipeline sample is greater than 1 times the near-field length N.

[0014] The expression formula for the ratio of the sample diameter D* to the sample length d is:

[0015]

[0016] and

[0017] Wherein, λ is the wavelength of the laser ultrasound, the sample diameter D* is determined based on the sample length, f is the frequency of the laser ultrasound, and c is the longitudinal wave speed of the laser ultrasound.

[0018] As a preferred solution of the present invention, in step 100, the heat treatment of the pipeline sample is implemented as follows:

[0019] and heating the pipeline sample by a high-temperature solid solution method to simulate a state where the pipeline sample is in ultra-long-term service;

[0020] The heat treatment conditions are as follows: the temperature is increased by 5°C per minute until the target temperature is reached. After the heating is completed, the temperature is kept in the furnace for 3 hours, and the oxide layer is removed by grinding to form the pipeline sample.

[0021] As a preferred solution of the present invention, in step 200, the method for obtaining the metallographic image of the pipeline sample by metallographic method is as follows:

[0022] Grinding and polishing the pipeline sample;

[0023] Using a prepared chemical corrosive agent to perform a candle treatment on the polished pipe sample, so that the pipe sample can show the tissue morphology, which is convenient for observation under a microscope;

[0024] After corrosion for 3s to 5s, rinse with clean water and dehydrate with alcohol to prevent the corroded surface from oxidizing in the air to form an oxide layer.

[0025] As a preferred embodiment of the present invention, in step 200, when the average grain size of all pipeline samples is calculated by JX-Metallographic Analysis Software, the value of the grain diameter or grain size is used to characterize the average grain size, wherein the specific implementation steps of calculating the average grain size are:

[0026] Placing the corrosion-treated pipeline sample under a metallographic microscope to obtain a metallographic structure image of the pipeline sample;

[0027] Using JX-metallographic analysis software to process the metallographic structure image of the pipeline sample, and obtain the average grain size and spheroidization degree of each pipeline sample;

[0028] The average grain size L is calculated by using the ratio of the total length of any section to the total number of grains. The calculation formula of the average grain size L is:

[0029]

[0030] Among them, N L is the number of grains per unit length of the cross section, L T is the length of any section, P is the total number of intersections between the section and the grain boundary, and M is the magnification of the microscope.

[0031] As a preferred solution of the present invention, in step 300, a laser ultrasonic detection system is used to perform ultrasonic testing on each of the pipeline samples, wherein three different test positions are selected for each of the pipeline samples, and multiple ultrasonic tests are performed on each test position to obtain the average sound velocity of each of the pipeline samples;

[0032] The formula for calculating the ultrasonic sound velocity corresponding to the pipeline sample is:

[0033] Where d is the wall thickness of the pipeline sample being tested, Δt is the time interval between the probe receiving the primary echo and the secondary echo, and c is the ultrasonic sound velocity corresponding to the pipeline sample.

[0034] As a preferred solution of the present invention, in step 400, a quantitative relationship between the laser ultrasonic sound wave attenuation coefficient and the average grain size is constructed to establish an average grain size calculation model, and the average grain size calculation model determines the grain size of the pipeline sample according to the laser ultrasonic surface wave information. After the ultrasonic pulse of the laser ultrasonic detection system is transmitted to the pipeline sample, the pulse will be reflected and propagated back and forth between the two end surfaces of the pipeline sample to form a series of echoes. The calculation formula for measuring the sound wave attenuation coefficient is:

[0035]

[0036] Wherein, B1 and B2 are the amplitudes of the first and second bottom surface reflection echoes, respectively, and H is the wall thickness of the pipeline sample.

[0037] As a preferred solution of the present invention, in step 400, the ultrasonic sound velocity and the sound wave attenuation coefficient corresponding to all pipeline samples are respectively fitted with the average grain size by least squares, wherein the sound velocity evaluation model between the ultrasonic sound velocity and the average grain size is:

[0038]

[0039] The attenuation evaluation model between the acoustic wave attenuation coefficient and the average grain size is:

[0040]

[0041] in, is the average grain size;

[0042] The ultrasonic sound velocity and sound wave attenuation coefficient corresponding to all pipeline samples were fitted with the degree of spheroidization by least squares. Based on the fitting results, it was found that the ultrasonic sound velocity decreased with the increase of the degree of spheroidization, and the sound wave attenuation coefficient increased with the increase of grain size.

[0043] As a preferred embodiment of the present invention, the specific method for constructing the attenuation evaluation model between the acoustic wave attenuation coefficient and the average grain size is:

[0044] According to the detection distance between the laser excitation point and the receiving point as a fixed distance, the pipeline test was ultrasonically tested, and the spectrum amplitudes of different pipeline samples were calculated respectively. Combined with the average grain size of each pipeline sample, the polynomial function calculation model between the acoustic wave attenuation coefficient and the average grain size was fitted:

[0045]

[0046] Where D is the average grain size; k i is the coefficient; α is the sound wave attenuation coefficient; n is 1 to 3.

[0047] Referring to the acoustic wave attenuation coefficient of the pipeline sample at different average grain sizes, a cubic polynomial function calculation model between the acoustic wave attenuation coefficient and the average grain size was fitted:

[0048] α=1.32761D-0.04634D 2 +5.50542×10 -4 D 3 -12.77432;

[0049] Where α is the acoustic wave attenuation coefficient and D is the average grain size.

[0050] As a preferred solution of the present invention, in step 400, based on the obtained sound velocity evaluation model and the attenuation evaluation model, a model verification test is performed again on another actual boiler pipe to be tested with a known average grain size to optimize the parameter settings of the sound velocity evaluation model and the attenuation coefficient model. The specific implementation method is:

[0051] Using metallographic method to obtain the metallographic image of the actual boiler pipe to be tested, and determine the actual average grain size of the actual boiler pipe to be tested;

[0052] Using a laser ultrasonic detection system to detect the ultrasonic sound velocity and the sound wave attenuation coefficient inside the actual boiler pipe to be tested;

[0053] The detected ultrasonic sound velocity is brought into the sound velocity evaluation model to obtain the average grain size predicted by the sound velocity evaluation model, and the relative error between the predicted average grain size obtained by the sound velocity evaluation model and the actual average grain size is calculated;

[0054] The detected acoustic wave attenuation coefficient is brought into the attenuation evaluation model to obtain the average grain size predicted by the attenuation evaluation model, and the relative error between the predicted average grain size obtained by the attenuation evaluation model and the actual average grain size is calculated;

[0055] Based on the relative error calculated above, the parameter settings of the sound velocity evaluation model and the attenuation coefficient model are adjusted.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention adopts a solution heating method to perform heat treatment on pipeline samples. By simulating different heating temperatures and heating times for different pipeline samples, the ultra-long service state of the pipeline samples can be simulated. The metallographic method is used to obtain the metallographic images of the pipeline samples with different heating temperatures and heating times. The organizational information of the pipeline samples with different heating temperatures and heating times, including the average grain size and the degree of spheroidization, is extracted by JX-metallographic analysis software. The ultrasonic information of the pipeline samples with different heating temperatures and heating times, including the ultrasonic sound velocity and the attenuation coefficient, is analyzed. A non-destructive evaluation method of the pipeline grain size using a laser ultrasonic surface wave is established. By establishing the relationship between the ultrasonic signal and the grain size, the non-destructive evaluation of the pipeline grain size based on the laser ultrasonic surface wave technology is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0059] Figure 1 It is a schematic diagram of the process of the pipeline online laser ultrasonic nondestructive testing method according to an embodiment of the present invention;

[0060] Figure 2 Metallographic microscope images of 12 types of pipeline samples provided in the embodiments of the present invention;

[0061] Figure 3 A fitting diagram between the ultrasonic velocity and the average grain size provided in an embodiment of the present invention;

[0062] Figure 4 A fitting diagram between the acoustic wave attenuation coefficient and the average grain size provided by an embodiment of the present invention;

[0063] Figure 5 A fitting diagram between the ultrasonic velocity and the degree of spheroidization provided in an embodiment of the present invention;

[0064] Figure 6 A fitting diagram between the acoustic wave attenuation coefficient and the degree of spheroidization provided in an embodiment of the present invention;

[0065] Figure 7 This is a fitting diagram of a polynomial function calculation model between the acoustic wave attenuation coefficient and the average grain size provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] like Figure 1 As shown, the present invention provides an online laser ultrasonic nondestructive testing method for pipelines in a high-temperature environment. Microstructure characteristics such as the grain size of metal materials can be used to evaluate the damage of the pipeline. When using ultrasonic nondestructive testing, the scattering and absorption effects of sound wave energy caused by changes in the microstructure state of the material cause changes in ultrasonic attenuation. Therefore, ultrasonic attenuation is related to the scattering generated when passing through the grains, so ultrasonic attenuation can be used to evaluate and characterize the grain size of the material. When the grains are coarse, the scattering attenuation is serious.

[0068] Moreover, the propagation speed of ultrasound is closely related to some organizational structural parameters of the material itself, such as phase structure and segregation, grain size and grain orientation, number and density of dislocations, etc. When the grain size is comparable to the wavelength of ultrasound, the interaction of the material microstructure will cause the sound speed to change. When the grain size or phase composition in the metal material is different, the sound speed will also change accordingly.

[0069] Therefore, based on the above basic principles, this embodiment is mainly used to perform nondestructive testing on pipelines through a laser ultrasonic nondestructive testing system to obtain the laser ultrasonic attenuation coefficient and the ultrasonic propagation velocity. The grain size of the pipeline is determined through the relationship model between the laser ultrasonic attenuation coefficient and the ultrasonic propagation velocity and the grain size, thereby evaluating the damage of the pipeline.

[0070] The specific steps include:

[0071] Step 100: heat-treat the pipeline sample to simulate its ultra-long-term service state.

[0072] From the perspective of ultrasonic testing, the size selection of the pipeline sample includes the length selection and diameter selection of the pipeline sample. The size design of the pipeline sample should consider three factors. The specific implementation method is:

[0073] 1) Near field zone A series of areas with maximum and minimum sound pressure values ​​appear near the wave source due to the interference of waves, which is called the near field zone of the ultrasonic field, also known as the Fresnel zone. Due to the different distances between each point of the wave source and a certain point on the axis, there is a difference in wave path. When they are superimposed on each other, there is a phase difference and mutual interference, so that the sound pressure in some places is strengthened and in other places is weakened, resulting in uneven sound pressure distribution in the near field zone. Therefore, the near field zone is not conducive to flaw detection, and it is easy to misjudge or miss detection, so the near field zone should be avoided.

[0074] The pipeline sample is measured by a pulse reflection method, and the length of the pipeline sample is determined based on the near-field length of the pipeline sample. The near-field length N determined by the pulse reflection method is expressed as:

[0075] Among them, D S is the diameter of the laser ultrasonic probe, λ is the wavelength of the laser ultrasonic wave, and the diameter D of the laser ultrasonic probe S The larger the value, the longer the near-field length N is. The length d of the pipe sample is selected based on the near-field length N. The length d of the pipe sample is greater than 1 times the near-field length N. That is, in order to avoid the influence of the near-field zone, the sample length must be ensured.

[0076] 2) Directivity of Ultrasonic Waves

[0077] The ratio of the sound pressure P(r,0) at any point far enough in front of the wave source to the sound pressure P(r,0) at the same distance on the wave source axis is called the directivity coefficient. The first zero-value divergence angle of the longitudinal wave sound field radiated by the disk source is called the main sound beam half-divergence angle.

[0078] The expression formula for the ratio of the sample diameter D* to the sample length d is:

[0079]

[0080] and

[0081] Wherein, λ is the wavelength of the laser ultrasound, the sample diameter D* is determined based on the sample length, f is the frequency of the laser ultrasound, and c is the longitudinal wave speed of the laser ultrasound.

[0082] 3) Side wall of the specimen

[0083] When ultrasonic longitudinal waves are used to test a sample, if the probe is close to the side wall, the longitudinal waves reflected by the side boundary surface will interfere with the sound beam, changing the directivity and symmetry of the probe. This phenomenon is generally called the "boundary effect". In order to avoid the boundary effect, it is necessary to ensure that the sound path of the side wall reflected sound beam is 4λ greater than the sound path of the direct sound beam. Then the formula for the minimum distance from the probe sound beam axis to the side wall without side wall interference is:

[0084]

[0085] Where c is the longitudinal wave speed, b is the distance from the incident point to the detection point, that is, the radius of the pipeline sample, and f is the frequency.

[0086] If it is ensured that there is no interference with the bottom wave of the sample, b in formula 3 needs to be changed to 2b, then L = 2b, d = D / 2, and the aspect ratio formula is obtained:

[0087]

[0088] The probe frequency, chip size, and sample length and diameter should meet the ranges in Table 1-1 below:

[0089] Table 1-1 Relationship between sample and probe parameters

[0090] f / MH z D / mm λ / mm N / mm <![CDATA[θ HB / °]]> d d / D <![CDATA[d / D 2 ]]> 2.5 6 2.32 3.88 27.1 >3.88 <1.96 <0.054

[0091] Since the wall thickness of the pipe sample is 8mm, in order to keep the size of the controlled variable material the same, the dimensions of the pipe sample are all 8mm thick, 35mm long and 25mm wide.

[0092] In step 100, a pipe sample with a size of 35 mm*25 mm*8 mm is cut from a new 12Cr1MoV boiler steam main pipe. Assuming that 12 pipe samples are used in total, the heat treatment of the 12 pipe samples is implemented as follows:

[0093] and heating the pipeline sample by a high-temperature solid solution method to simulate a state where the pipeline sample is in ultra-long-term service;

[0094] The heat treatment conditions are as follows: the temperature is increased by 5°C per minute until the target temperature is reached. After the heating is completed, the temperature is kept in the furnace for 3 hours, and the oxide layer is removed by grinding to form the pipeline sample. The heating temperature of the pipeline sample is shown in Table 1-2.

[0095] Table 1-2 Heat treatment temperature

[0096]

[0097]

[0098] Step 200: Obtain a metallographic image of the pipeline sample using a metallographic method, and extract the organizational information of the pipeline sample, including the average grain size and the degree of spheroidization, using JX-Metallographic Analysis Software.

[0099] In step 200, the method for obtaining the metallographic image of the pipeline sample by metallographic method is as follows:

[0100] The pipeline sample is ground and polished. Before polishing, the polishing disc should be wetted with water, and then the metallographic polishing agent should be sprayed on the polishing disc. During polishing, the grinding surface of the sample is in close contact with the polishing disc. In order to keep the polishing cloth moist, water should be continuously poured on the polishing cloth. After polishing for a period of time, the direction of the sample is changed by an angle, and the above process is repeated until the grinding scratches are no longer visible to the naked eye, the sample becomes a mirror surface, and the polishing is completed;

[0101] The polished pipe sample is corroded by using a prepared chemical corrosive agent, so that the pipe sample can show the tissue morphology, which is convenient for observation under a microscope. The selected corrosive agent formula is: 10 mL of nitric acid and 100 ml of anhydrous ethanol are mixed in equal proportions;

[0102] After corrosion for 3s to 5s, rinse with clean water and dehydrate with alcohol to prevent the corroded surface from oxidizing in the air to form an oxide layer.

[0103] The surface of the pipe sample cut from the original material is rough and has obvious deformation. The purpose of grinding is to restore the deformed part of the pipe sample to smoothness. It is necessary to use water-abrasive sandpaper of different degrees for rough grinding and then use metallographic sandpaper for dry grinding.

[0104] In step 200, when the average grain size of all pipeline samples is calculated by JX-Metallographic Analysis Software, the value of the grain diameter or grain size is used to characterize the average grain size, wherein the specific implementation steps for calculating the average grain size are:

[0105] Placing the corrosion-treated pipeline sample under a metallographic microscope to obtain a metallographic structure image of the pipeline sample;

[0106] Using JX-metallographic analysis software to process the metallographic structure image of the pipeline sample, and obtain the average grain size and spheroidization degree of each pipeline sample;

[0107] The average grain size L is calculated by using the ratio of the total length of any section to the total number of grains. The calculation formula of the average grain size L is:

[0108]

[0109] Among them, N L is the number of grains per unit length of the cross section, L T is the length of any section, P is the total number of intersections between the section and the grain boundary, and M is the magnification of the microscope. The microscope images of the 12 pipe samples measured by metallographic method are shown in Figure 2 shown.

[0110] In this embodiment, metallographic examination is performed on 12Cr1MoV pipe samples with 12 different heating degrees to obtain the microstructural characteristics of the 12 pipe samples with different heating degrees, as well as the corresponding average grain size and spheroidization degree. The specific metallographic examination results are shown in Tables 1-3 below.

[0111] Table 1-3 Microstructure characteristics of pipeline samples

[0112] serial number Average grain size / μm Spheroidization degree 1 27.2 0％ 2 27.6 25％ 3 29.5 50％ 4 30.7 75％ 5 31.2 75％ 6 32.6 100％ 7 33.1 100％ 8 35.3 100％ 9 37.7 100％ 10 35.1 100％ 11 36.9 100％ 12 35.6 100％

[0113] Step 300: Use a laser ultrasonic detection system to perform ultrasonic testing on the pipeline sample, and analyze the laser ultrasonic information of the pipeline sample, including ultrasonic sound velocity, sound wave attenuation coefficient and spectrum.

[0114] In step 300, a laser ultrasonic detection system is used to perform ultrasonic testing on each of the pipeline samples, wherein three different test positions are selected for each of the pipeline samples, and ultrasonic testing is performed multiple times on each test position to obtain an average sound velocity of each of the pipeline samples;

[0115] The formula for calculating the ultrasonic sound velocity corresponding to the pipeline sample is:

[0116] Where d is the wall thickness of the pipeline sample being tested, Δt is the time interval between the probe receiving the primary echo and the secondary echo, and c is the ultrasonic sound velocity corresponding to the pipeline sample.

[0117] The laser ultrasonic detection system was used to test the sound velocity of 12 pipeline samples, and the ultrasonic sound velocity parameters of the pipeline samples were obtained as shown in Table 3-4.

[0118] Table 1-4 Sound velocity values ​​of pipeline samples

[0119]

[0120] After the ultrasonic pulse of the laser ultrasonic detection system is transmitted to the pipe sample, the pulse will be reflected and propagated back and forth between the two end surfaces of the pipe sample to form a series of echoes. The calculation formula for measuring the sound wave attenuation coefficient is:

[0121]

[0122] Wherein, B1 and B2 are the amplitudes of the first and second bottom surface reflection echoes, respectively, and H is the wall thickness of the pipeline sample.

[0123] The laser ultrasonic detection system was used to test the acoustic attenuation of 12 pipeline samples, and the ultrasonic attenuation coefficients of the pipeline samples were obtained as shown in Table 3-4.

[0124] Table 1-5 Attenuation coefficient of pipeline samples

[0125] Sample No. <![CDATA[Attenuation coefficient / dB·mm -1 > Sample No. <![CDATA[Attenuation coefficient / dB·mm -1 > 1 0.4425 7 0.5771 2 0.4631 8 0.6071 3 0.5023 9 0.6214 4 0.4965 10 0.6056 5 0.5207 11 0.6116 6 0.5451 12 0.5944

[0126] Step 400: Establish a relationship between laser ultrasonic information and grain size, and calculate the grain size of the pipeline sample based on the laser ultrasonic information to achieve non-destructive testing of the pipeline sample.

[0127] In step 400, a quantitative relationship between the laser ultrasonic sound wave attenuation coefficient and the average grain size is constructed, a sound velocity evaluation model and an attenuation evaluation model are established, and the grain size of the pipeline sample is determined according to the laser ultrasonic surface wave information.

[0128] In step 400, the ultrasonic sound velocity and the sound wave attenuation coefficient corresponding to all pipeline samples are respectively fitted with the average grain size by least squares, wherein the sound velocity evaluation model between the ultrasonic sound velocity and the average grain size is:

[0129]

[0130] The attenuation evaluation model between the acoustic wave attenuation coefficient and the average grain size is:

[0131]

[0132] in, is the average grain size.

[0133] The fitting effect diagrams are as follows Figure 3 and Figure 4 As shown in the figure, it can be seen that the sound velocity decreases with the increase of grain size, and the attenuation coefficient increases with the increase of grain size.

[0134] The ultrasonic velocity and acoustic attenuation coefficient corresponding to all pipeline samples were fitted with the spheroidization degree by the least square method. The fitting results are shown in Figure 5 and Figure 6 As shown, based on the fitting results, the ultrasonic sound velocity decreases with the increase of spheroidization degree, and the attenuation coefficient increases with the increase of grain size.

[0135] In step 400, the ultrasonic sound velocity and the sound wave attenuation coefficient corresponding to all pipeline samples are respectively fitted with the average grain size by least squares, wherein the sound velocity evaluation model between the ultrasonic sound velocity and the average grain size is:

[0136]

[0137] The attenuation evaluation model between the acoustic wave attenuation coefficient and the average grain size is:

[0138]

[0139] in, is the average grain size;

[0140] The ultrasonic sound velocity and sound wave attenuation coefficient corresponding to all pipeline samples were fitted with the degree of spheroidization by least squares. Based on the fitting results, it was found that the ultrasonic sound velocity decreased with the increase of the degree of spheroidization, and the sound wave attenuation coefficient increased with the increase of grain size.

[0141] The specific method for constructing the attenuation evaluation model between the acoustic wave attenuation coefficient and the average grain size is:

[0142] According to the method that the detection distance between the laser excitation point and the receiving point is fixed, the pipeline test is ultrasonically detected, and the spectrum amplitudes of different pipeline samples are calculated respectively.

[0143] With the detection distance of 4mm as the fixed test distance, the sample was ultrasonically tested, and the ultrasonic attenuation parameters were calculated respectively. The results are listed in Table 5-1;

[0144] Table 1-6 Acoustic attenuation coefficients of reference samples with different grain sizes

[0145]

[0146]

[0147] Combined with the average grain size of each pipeline sample, a polynomial function calculation model between the acoustic attenuation coefficient and the average grain size was fitted:

[0148]

[0149] Where D is the average grain size; k i is the coefficient; α is the sound wave attenuation coefficient; n is 1 to 3.

[0150] Referring to the acoustic wave attenuation coefficient of the pipeline sample at different average grain sizes, a cubic polynomial function calculation model between the acoustic wave attenuation coefficient and the average grain size was fitted:

[0151] α=1.32761D-0.04634D 2 +5.50542×10 -4 D 3 -12.77432.

[0152] The fitting results of the acoustic wave attenuation coefficient and the average grain size are as follows: Figure 7 shown.

[0153] Furthermore, in step 400, based on the obtained sound velocity evaluation model and the attenuation evaluation model, a model verification test is performed again on another actual boiler pipe to be tested with a known average grain size to optimize the parameter settings of the sound velocity evaluation model and the attenuation coefficient model. The specific implementation method is:

[0154] Using metallographic method to obtain the metallographic image of the actual boiler pipe to be tested, and determine the actual average grain size of the actual boiler pipe to be tested;

[0155] Using a laser ultrasonic detection system to detect the ultrasonic sound velocity and the sound wave attenuation coefficient inside the actual boiler pipe to be tested;

[0156] The detected ultrasonic sound velocity is brought into the sound velocity evaluation model to obtain the average grain size predicted by the sound velocity evaluation model, and the relative error between the predicted average grain size obtained by the sound velocity evaluation model and the actual average grain size is calculated;

[0157] The detected acoustic wave attenuation coefficient is brought into the attenuation evaluation model to obtain the average grain size predicted by the attenuation evaluation model, and the relative error between the predicted average grain size obtained by the attenuation evaluation model and the actual average grain size is calculated;

[0158] Based on the relative error calculated above, the parameter settings of the sound velocity evaluation model and the attenuation coefficient model are adjusted.

[0159] After the above processing of 12 pipeline samples, the following conclusions can be drawn:

[0160] 1) As the grain size and spheroidization degree of the sample increase, the ultrasonic sound velocity will gradually decrease, and the ultrasonic sound wave attenuation coefficient will gradually increase;

[0161] 2) According to the ultrasonic parameters and grain size characteristics, the sound velocity model was established: The relative error value is -11.9%; the attenuation coefficient model is established: The relative error is -1.5%.

[0162] 3) Establish a grain size calculation model based on the acoustic wave attenuation coefficient: α = 1.32761D-0.04634D 2 +5.50542×10 -4 D 3 -12.77432. The calculation model was used to test the main steam mother pipe of the 12Cr1MoV boiler, and the error of the test result was 2.9%.

[0163] This embodiment adopts the solution heating method to perform heat treatment on the pipeline sample. By simulating different heating temperatures and heating times for different pipeline samples, the ultra-long-term service state of the pipeline sample can be simulated. The metallographic method is used to obtain the metallographic images of the pipeline samples with different heating temperatures and heating times. The organizational information of the pipeline samples with different heating temperatures and heating times, including the average grain size and the degree of spheroidization, is extracted by JX-Metallographic Analysis Software. The ultrasonic information of the pipeline samples with different heating temperatures and heating times, including the ultrasonic sound velocity and the attenuation coefficient, is analyzed. A non-destructive evaluation method of the pipeline grain size using laser ultrasonic surface waves is established, and the relationship between the ultrasonic signal and the grain size is established, thereby realizing the non-destructive evaluation of the pipeline grain size based on laser ultrasonic surface wave technology.

[0164] The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention. The protection scope of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present invention, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. A pipeline online laser ultrasonic nondestructive testing method under high temperature environment, characterized in that: The following steps are involved: Step 100, heat treating the pipeline sample to simulate its ultra-long-term service state; Step 200, obtaining a metallographic image of the pipeline sample by metallographic method, and extracting the organizational information of the pipeline sample, including average grain size and spheroidization degree, by JX-Metallographic Analysis Software; Step 300: Use a laser ultrasonic detection system to perform ultrasonic testing on the pipeline sample, and analyze the laser ultrasonic information of the pipeline sample, including ultrasonic sound velocity, sound wave spectrum, and sound wave attenuation coefficient; Step 400: Establish a relationship between laser ultrasonic information and grain size, and calculate the grain size of the pipeline sample based on the laser ultrasonic information to achieve non-destructive evaluation of the pipeline sample.

2. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 1, characterized in that: In step 100, the size selection of the pipeline sample includes the length selection and the diameter selection of the pipeline sample, which is implemented as follows: The pipeline sample is measured by a pulse reflection method, and the length of the pipeline sample is determined based on the near-field length of the pipeline sample. The near-field length N determined by the pulse reflection method is expressed as: Among them, D S is the diameter of the laser ultrasonic probe, λ is the wavelength of the laser ultrasonic wave, and the diameter D of the laser ultrasonic probe S The larger the value is, the longer the near-field length N is. The length d of the pipeline sample is selected based on the near-field length N. The length d of the pipeline sample is greater than 1 times the near-field length N. The expression formula for the ratio of the sample diameter D* to the sample length d is: and Wherein, λ is the wavelength of the laser ultrasound, the sample diameter D* is determined based on the sample length, f is the frequency of the laser ultrasound, and c is the longitudinal wave speed of the laser ultrasound.

3. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 2, characterized in that: In step 100, the heat treatment of the pipeline sample is implemented as follows: and heating the pipeline sample by a high-temperature solid solution method to simulate a state where the pipeline sample is in ultra-long-term service; The heat treatment conditions are as follows: the temperature is increased by 5°C per minute until the target temperature is reached. After the heating is completed, the temperature is kept in the furnace for 3 hours, and the oxide layer is removed by grinding to form the pipeline sample.

4. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 1, characterized in that: In step 200, the method for obtaining the metallographic image of the pipeline sample by metallographic method is as follows: Grinding and polishing the pipeline sample; Using a prepared chemical corrosive agent to perform a candle treatment on the polished pipe sample, so that the pipe sample can show the tissue morphology, which is convenient for observation under a microscope; After corroding for 3s to 5s, rinse with clean water and dehydrate with alcohol to prevent the corroded surface from oxidizing in the air to form an oxide layer.

5. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 4, characterized in that: In step 200, when the average grain size of all pipeline samples is calculated by JX-Metallographic Analysis Software, the value of the grain diameter or grain size is used to characterize the average grain size, wherein the specific implementation steps for calculating the average grain size are: Placing the corrosion-treated pipeline sample under a metallographic microscope to obtain a metallographic structure image of the pipeline sample; Using JX-metallographic analysis software to process the metallographic structure image of the pipeline sample, and obtain the average grain size and spheroidization degree of each pipeline sample; The average grain size L is calculated by using the ratio of the total length of any section to the total number of grains. The calculation formula of the average grain size L is: Among them, N L is the number of grains per unit length of the cross section, L T is the length of any section, P is the total number of intersections between the section and the grain boundary, and M is the magnification of the microscope.

6. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 5, characterized in that: In step 300, a laser ultrasonic detection system is used to perform ultrasonic testing on each of the pipeline samples, wherein three different test positions are selected for each of the pipeline samples, and ultrasonic testing is performed multiple times on each test position to obtain an average sound velocity of each of the pipeline samples; The formula for calculating the ultrasonic sound velocity corresponding to the pipeline sample is: Where d is the wall thickness of the pipeline sample being tested, Δt is the time interval between the probe receiving the primary echo and the secondary echo, and c is the ultrasonic sound velocity corresponding to the pipeline sample.

7. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 6, characterized in that: In step 400, a quantitative relationship between the laser ultrasonic sound wave attenuation coefficient and the average grain size is constructed to establish an average grain size calculation model. The average grain size calculation model determines the grain size of the pipeline sample according to the laser ultrasonic surface wave information. After the ultrasonic pulse of the laser ultrasonic detection system is transmitted to the pipeline sample, the pulse will be reflected and propagated back and forth between the two end surfaces of the pipeline sample to form a series of echoes. The calculation formula for measuring the sound wave attenuation coefficient is: Wherein, B1 and B2 are the amplitudes of the first and second bottom surface reflection echoes, respectively, and H is the wall thickness of the pipeline sample.

8. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 1, characterized in that: In step 400, the ultrasonic sound velocity and the sound wave attenuation coefficient corresponding to all pipeline samples are respectively fitted with the average grain size by least squares, wherein the sound velocity evaluation model between the ultrasonic sound velocity and the average grain size is: in, is the average grain size; The ultrasonic sound velocity and sound wave attenuation coefficient corresponding to all pipeline samples were fitted with the degree of spheroidization by least squares. Based on the fitting results, it was found that the ultrasonic sound velocity decreased with the increase of the degree of spheroidization, and the sound wave attenuation coefficient increased with the increase of grain size.

9. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 8, characterized in that: The specific method for constructing the attenuation evaluation model between the acoustic wave attenuation coefficient and the average grain size is: According to the detection distance between the laser excitation point and the receiving point as a fixed distance, the pipeline test was ultrasonically tested, and the spectrum amplitudes of different pipeline samples were calculated respectively. Combined with the average grain size of each pipeline sample, the polynomial function calculation model between the acoustic wave attenuation coefficient and the average grain size was fitted: Where D is the average grain size; k i is the coefficient; α is the sound wave attenuation coefficient; n is 1 to 3; Referring to the acoustic wave attenuation coefficient of the pipeline sample at different average grain sizes, a cubic polynomial function calculation model between the acoustic wave attenuation coefficient and the average grain size was fitted: α=1.32761D-0.04634D 2 +5.50542×10 -4 D 3 -12.77432; Among them, α is the acoustic wave attenuation coefficient, and D is the average grain size.

10. The method for online laser ultrasonic nondestructive testing of pipelines in a high temperature environment according to claim 9, characterized in that: In step 400, based on the obtained sound velocity evaluation model and the attenuation evaluation model, a model verification test is performed again on another actual boiler pipe to be tested with a known average grain size to optimize the parameter settings of the sound velocity evaluation model and the attenuation coefficient model, which is implemented as follows: Using metallographic method to obtain the metallographic image of the actual boiler pipe to be tested, and determine the actual average grain size of the actual boiler pipe to be tested; Using a laser ultrasonic detection system to detect the ultrasonic sound velocity and the sound wave attenuation coefficient inside the actual boiler pipe to be tested; The detected ultrasonic sound velocity is brought into the sound velocity evaluation model to obtain the average grain size predicted by the sound velocity evaluation model, and the relative error between the predicted average grain size obtained by the sound velocity evaluation model and the actual average grain size is calculated; The detected acoustic wave attenuation coefficient is brought into the attenuation evaluation model to obtain the average grain size predicted by the attenuation evaluation model, and the relative error between the predicted average grain size obtained by the attenuation evaluation model and the actual average grain size is calculated; Based on the calculated relative error, the parameter settings of the sound velocity evaluation model and the attenuation coefficient model are adjusted.

Images