Acoustic emission damage mode identification method of composite material under thermal shock

Abstract

A kind of acoustic emission damage mode identification method of composite material under thermal shock, comprising the following steps;Step one: composite material sample is connected with acoustic emission sensor;Step two: acoustic emission sensor is connected with acoustic emission instrument, and the signal acquisition display of acoustic emission instrument is opened;Step three: lead breaking experiment is carried out on the surface of composite material sample, realize the stable collection of acoustic emission signal under the service environment of gas thermal shock;Step four: set the experimental parameters of gas thermal shock on simulation device;Step five: carry out gas thermal shock experiment, and the parameters of acoustic emission signal are shown on signal acquisition display;Step six: the damage mode identification of acoustic emission signal collected is carried out;Step seven: the acoustic emission signal collected is carried out k-means clustering, and the damage mode of composite material sample under gas thermal shock is identified after wavelet packet analysis to clustered acoustic emission signal.The present application realizes the damage mode identification of composite material under gas thermal shock environment.

Description

Technical Field

[0001] The invention belongs to the technical field of non-destructive testing of composite materials, and particularly relates to an acoustic emission damage pattern recognition method for composite materials under thermal shock. Background Art

[0002] Currently, most research still analyzes the failure mechanism of composite materials through the evolution of their microstructure. There is a lack of real-time detection methods to study the key parameters in the gas thermal shock failure process and thus derive the failure mechanism of composite materials. Conventional nondestructive testing methods have difficulty studying the damage evolution and failure modes of composite materials and are insensitive to defect detection. In addition, some studies focus on using acoustic emission testing technology to study the damage mechanism of composite materials at room temperature or under static loads, and have discovered the existence of damage modes. However, this still leaves a huge gap between this and the actual service environment of composite materials. In gas thermal shock environments, the failure behavior of composite materials is relatively unclear.

[0003] Ultrasonic testing, X-ray testing, infrared testing, and acoustic emission testing can all be used for non-destructive testing of composite materials. Ultrasonic testing can detect defects within materials, but ultrasonic signals are severely attenuated in composite materials and are not very sensitive to small-sized defects. XCT technology is used to detect changes in the porosity within the material, but it is insensitive to delamination defects in composite materials and is expensive. Infrared testing technology is mainly used to measure surface and internal defects, but its ability to detect defects in composite materials larger than 2mm is limited, and it is easily affected by the surface state of the material, interfering with the identification of defects. Acoustic emission testing technology can collect crack signals generated during material failure in real time. It has the advantages of online real-time dynamic detection, high sensitivity to defects, no need for external excitation, and a wide range of applications. The source of the acoustic emission signal is the material itself, and an acoustic emission signal is only generated when the material undergoes structural or state changes.

[0004] Patent CN116990119A discloses a composite material damage pattern recognition method based on acoustic emission. This method analyzes the damage mechanism of composite materials by monitoring and analyzing acoustic emission signals during the composite material's tensile process. However, this method still differs significantly from the actual service environment of composite materials. In a gas thermal shock environment, the failure mode of composite materials is relatively unclear. Summary of the Invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a method for acoustic emission damage pattern recognition of composite materials under thermal shock, that is, during the failure process of composite materials in a gas thermal shock environment, an acoustic emission detection method is used to collect damage signals by setting a threshold value, sampling frequency, preamplifier, etc. for the system, extract specified acoustic emission signal characteristic parameters, perform cluster analysis and wavelet packet decomposition on the acoustic signal, and realize damage pattern recognition of composite materials in a gas thermal shock environment.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for identifying acoustic emission damage patterns of composite materials under thermal shock comprises the following steps:

[0008] Step 1: Connect the composite material sample to the acoustic emission sensor;

[0009] Step 2: connecting the acoustic emission sensor to the acoustic emission instrument via a signal transmission line, turning on the signal acquisition display of the acoustic emission instrument, and setting the acoustic emission acquisition parameters of the composite material sample under the gas thermal shock service environment on the display;

[0010] Step 3: After setting the acoustic emission acquisition parameters, conduct a lead-breaking test on the surface of the composite material sample and check the attenuation of the transmission signal on the signal acquisition display to achieve stable acquisition of the acoustic emission signal under the gas thermal shock service environment;

[0011] Step 4: After completing the acoustic emission signal collection preparation work of steps 1 to 3, set the gas thermal shock experimental parameters on the gas thermal shock service environment simulation device;

[0012] Step 5: Turn on the ignition button on the simulation device to conduct a gas thermal shock test. When the gas impacts the surface of the composite material sample, damage will occur inside the composite material sample. Then, the acoustic emission signal is collected through steps 1 to 3, and the amplitude, peak frequency, duration, rise time, counts, and energy parameters of the acoustic emission signal are displayed on the signal acquisition display.

[0013] Step 6:

[0014] Damage pattern recognition is performed on the acoustic emission signals collected in step five. Tensile tests are performed on the substrate material, fiber bundle, and composite material samples at room temperature on a high-temperature universal mechanical testing machine, and acoustic emission detection is performed using an acoustic emission instrument. The specific process repeats steps two to three to obtain the amplitude, peak frequency, duration, rise time, count, and energy parameters of the three samples of the substrate material, fiber bundle, and composite material. These parameters are then subjected to k-means cluster analysis and wavelet packet analysis to obtain the frequency characteristic ranges of the four damage modes of matrix cracking, interface slip, interface debonding, and fiber breakage.

[0015] Step 7: Perform k-means clustering on the acoustic emission signals collected in step 5, perform wavelet packet analysis on the clustered acoustic emission signals, and compare them with the results of step 6 to identify the damage mode of the composite material sample under gas thermal shock.

[0016] The step 1 is specifically as follows:

[0017] A circular composite material specimen is fixed on a thermal shock fixture. The top surface facing the spray gun is used for the gas thermal shock test. The bottom surface facing away from the spray gun is mechanically or welded to one end of a high-temperature waveguide wire made of nickel-chromium alloy. The other end of the high-temperature waveguide wire is connected to the acoustic emission sensor by means of a grease coupling fixed with a rubber plug.

[0018] The step 2 is specifically as follows:

[0019] The parameters set are specifically preamplifier 20 / 40 / 60dB, sampling threshold 0-100dB, sampling frequency 2MHz, frequency range 0.1-1MHz and impact length 2k;

[0020] When a composite material sample is damaged, a stress wave, namely an acoustic emission signal, is generated, which is then transmitted to the sensor and finally collected on the signal display.

[0021] The step three is specifically as follows:

[0022] Lead is used to fracture composite material specimens, and the emitted acoustic emission signals are collected to observe the amplitude attenuation. The higher the amplitude, the better the coupling with the sensor. Ultimately, stable acquisition of acoustic emission signals in a gas thermal shock service environment is achieved.

[0023] The lead breaking experiment is carried out by slowly pressing a pencil at an angle of 30 degrees at a position 30 mm away from the contact point of the high-temperature waveguide wire on the surface of the composite material sample to break the lead core. The emitted acoustic emission signal is collected and the amplitude attenuation is observed. The higher the amplitude, the better the coupling with the sensor.

[0024] The step 4 is specifically as follows:

[0025] The parameters are specifically gas shock temperature, oxygen outlet pressure, oxygen flow rate, kerosene outlet pressure, kerosene flow rate, cooling gas outlet pressure and cooling gas flow rate;

[0026] Among them, the gas impact temperature is 0-1300℃, the oxygen outlet pressure is above 1.3MPa, the oxygen flow rate is 230-500L / min, the kerosene outlet pressure is above 0.5MPa, the kerosene flow rate is 5-15L / h, the cooling gas outlet pressure is about 0.7MPa, and the cooling gas flow rate is about 60L / min.

[0027] The step six specifically comprises: performing a substrate tensile test at room temperature using an acoustic emission device to obtain a characteristic range of acoustic emission frequencies for substrate cracking;

[0028] The fiber bundle tensile test was carried out at room temperature using an acoustic emission device to obtain the characteristic range of the acoustic emission frequency of fiber breakage.

[0029] Using an acoustic emission device to conduct tensile tests on composite materials at room temperature, failure modes were obtained, mainly matrix cracking, interface slip, interface debonding, and fiber fracture damage. K-means cluster analysis was performed on the obtained acoustic emission characteristic parameters. Based on the acoustic emission frequency characteristic ranges of matrix cracking and fiber fracture obtained above, the frequency characteristic ranges of interface slip and interface debonding were obtained.

[0030] The step seven is specifically as follows:

[0031] Amplitude, peak frequency, duration, rise time, count and energy parameters are selected as characteristic parameters for cluster analysis, and the mean variance normalization method is adopted to make the selected characteristic parameters dimensionless so that the measured data are defined between [0,1]. The expression is as shown in formula (1);

[0032]

[0033] Where A i is the original signal parameter, A μ is the average value of the original signal parameter, is the normalized signal parameter, A σ is the standard deviation of the original signal parameter;

[0034] Secondly, the silhouette coefficient value of the dimensionless characteristic parameters is calculated, as shown in formula (2), to determine the optimal clustering number k;

[0035]

[0036] Where a(i) represents the average distance between the i-th vector and other vectors in the same category, and b(i,k) represents the average distance between the i-th vector and other vectors in different categories.

[0037] Finally, perform k-means clustering analysis on the processed feature parameters, select k cluster centers, calculate the distance from the initial cluster centroid to the input vector, and then classify the input vector into the category of the nearest cluster centroid; calculate the position of the cluster center according to the shortest distance principle; repeat the above steps until the position of the initial cluster centroid no longer changes.

[0038] Perform j-layer wavelet packet transform on an acoustic emission signal f(t) to obtain the sum of a series of decomposition parts, as follows:

[0039]

[0040] f(t) refers to an acoustic emission signal, is the decomposed signal at frequency band i and scale j, and the energy of each part after decomposition is Defined as:

[0041]

[0042] R i The wavelet packet energy spectrum coefficient is the ratio of the energy of each node to the total energy of all nodes after decomposition;

[0043]

[0044] Beneficial effects of the present invention:

[0045] While damage mechanisms in composite materials are primarily studied at room temperature, the present invention utilizes the technology presented here to establish a method for identifying damage patterns in composite materials exposed to gas thermal shock using acoustic emission technology. This method not only enables the acquisition of acoustic emission signals from composite material damage under gas thermal shock conditions but also provides a novel solution for identifying failure modes in composite materials under extreme service conditions. This approach holds broad prospects for development and possesses high practical value.

[0046] 1. The present invention realizes real-time detection of composite materials under high temperature conditions by introducing a high-temperature waveguide rod and connecting the waveguide rod, sensor and sample through a mechanical device. It also sets the acquisition parameters of acoustic emission, and finally realizes the stable acquisition of acoustic emission signals of composite materials under gas thermal shock environment, solving the technical problem that composite materials cannot perform acoustic emission signal detection in high temperature environment.

[0047] 2. The present invention identifies the damage mode of composite materials based on a single load. By conducting tensile tests on the matrix, fiber, and composite materials respectively, the acoustic emission characteristic parameter ranges of matrix cracking, fiber breakage, interface debonding, and interface slip are obtained, solving the difficult problem of identifying the damage mode of composite materials.

[0048] 3. The present invention implements k-means clustering and wavelet packet analysis on the acoustic emission signals collected under a gas thermal shock environment to realize damage pattern recognition of composite materials under a gas thermal shock environment, and realizes qualitative and quantitative evaluation of composite material damage, providing rich data for the diagnosis of composite material failure mechanism and failure process, and also providing a direct basis for the life prediction of composite materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the process of the present invention.

[0050] Figure 2 Schematic diagram of gas thermal shock experiment based on acoustic emission test equipment.

[0051] Figure 3 Schematic diagram of the Sk curve of the AE signal of the composite material under gas thermal shock.

[0052] Figure 4 Schematic diagram of the clustering results of AE signals of composite materials under gas thermal shock.

[0053] Figure 5 Three typical wavelet packet energy spectra of composite materials during gas thermal shock process; among them, (a) substrate cracking; (b) interface slip; (c) interface debonding.

[0054] 1. Acoustic emission sensor; 2. Waveguide rod (nickel-chromium alloy wire); 3. Clamping fixture; 4. Composite material sample. DETAILED DESCRIPTION

[0055] The present invention will be described in further detail below with reference to the accompanying drawings.

[0056] like Figure 1 、 Figure 2 As shown, a method for identifying acoustic emission damage patterns of composite materials under thermal shock comprises the following steps:

[0057] Step 1: Connect the composite material sample 4 and the acoustic emission sensor 1. Since the composite material is in service under a gas thermal shock environment, and the operating temperature of the acoustic emission sensor is generally room temperature, in order to realize the application of acoustic emission in the composite material service environment simulation experiment, a high-temperature waveguide rod 2 is introduced to realize real-time detection of the composite material sample 4 under high temperature conditions. Nickel-chromium alloy wire is selected as the high-temperature waveguide rod 2 through the lead breaking experiment. One end of the waveguide rod 2 is mechanically connected to the back of the composite material sample 4 and fixed by a clamping fixture 3. The other end adopts the holding device of the acoustic emission probe and is tightly connected to the sensor under the action of the butter coupling agent, ensuring a tight connection between the acoustic emission sensor 1 and the waveguide rod 2, i.e., the nickel-chromium wire.

[0058] Step 2: Connect the acoustic emission system and set the acoustic emission acquisition parameters suitable for composite materials serving in a gas thermal shock environment, including preamplifier, sampling threshold, sampling frequency, frequency range, and impact length.

[0059] Step 3: Check whether the acoustic emission system's sensor is properly coupled to the composite material. Perform a lead-breaking test on the composite sample surface to check the signal attenuation. Ultimately, achieve stable acquisition of the composite sample's acoustic emission signal under a gas thermal shock environment.

[0060] Step 4: Set the main parameters of the gas thermal shock experiment on the gas thermal shock service environment simulation device, specifically the gas shock temperature, oxygen outlet pressure, oxygen flow rate, kerosene outlet pressure, kerosene flow rate, cooling gas outlet pressure and cooling gas flow rate.

[0061] Step 5: After setting the gas thermal shock test parameters, conduct a gas thermal shock test on the composite material sample and use an acoustic emission instrument to perform acoustic emission detection. The parameters such as the amplitude, peak frequency, duration, rise time, count and energy of the acoustic emission signal are collected on the signal acquisition display.

[0062] Step 6: The frequency characteristics of the acoustic emission signals of different damage modes and the energy distribution of the signals in each frequency band are different, and the frequency characteristics and energy distribution of the signals are almost independent of the size of the damage and the load. Therefore, acoustic emission detection of the tensile failure process of the base material, fiber bundle, and composite material was performed respectively, and a large number of acoustic emission signals with known damage modes (matrix cracking, interface slip, interface debonding, and fiber breakage) were obtained. The measured acoustic emission signals were subjected to k-means cluster analysis and wavelet packet analysis to establish the acoustic emission signal characteristics of the four damage modes. Finally, the frequency ranges of matrix cracking damage, fiber breakage damage, interface slip damage, and interface debonding damage were determined.

[0063] Step 7: Using an acoustic emission-based composite material damage pattern recognition method, the received acoustic emission signals undergo k-means cluster analysis and wavelet packet analysis. Before performing the k-means cluster analysis, the silhouette coefficient value is used to determine the number of clusters, k. The characteristic parameters of each clustered acoustic emission signal are then used to correlate the cluster analysis results with the damage pattern. A three-layer wavelet packet decomposition of the acoustic emission signal using the "db5" wavelet basis is performed to determine the frequency bands for each damage pattern, and then a wavelet energy spectrum coefficient distribution map is generated. This map is compared with the characteristic energy spectrum coefficient distribution map of the damage pattern identified in Step 5 to determine the damage pattern of the composite material specimen damage signal.

[0064] Implementation Cases:

[0065] Prepare a C / SiC composite material sample with a diameter of 30 mm and a thickness of 4 mm. The back of the sample is mechanically connected with a high-temperature waveguide wire made of nickel-chromium alloy with a diameter of 1 mm and a length of 60 cm. The other end of the high-temperature waveguide wire is connected to the acoustic emission sensor by a grease coupling method fixed with a rubber plug.

[0066] The AE signals were collected and processed using a 16-channel PCI-2 acoustic emission instrumentation system manufactured by PAC (USA). The AE detection system parameters were set as follows: preamplifier 40 dB, sampling threshold 40 dB, sampling frequency 2 MHz, frequency range 0.1-1 MHz, and impact length 2 k.

[0067] A lead-breaking test was conducted on the surface of the composite material sample, and the attenuation of the transmission signal was checked on the signal acquisition display. The amplitude of the measured acoustic emission signal was 95, indicating that stable acquisition of the acoustic emission signal was achieved under the gas thermal shock service environment.

[0068] A service simulation device was used to simulate the gas thermal shock environment, and the equipment parameters were set as follows: gas shock temperature of 1200°C, oxygen outlet pressure of 1.3 MPa, oxygen flow rate of 260 L / min, kerosene outlet pressure of 0.6 MPa, kerosene flow rate of 5 L / h, cooling gas outlet pressure of 0.7 MPa, and cooling gas flow rate of 60 L / min.

[0069] While the sample is being impacted by gas, click the acquisition button on the signal acquisition display to collect the acoustic emission signal.

[0070] The experiment conducted acoustic emission testing on the tensile failure process of the base material, fiber bundle and composite material respectively. First, the acoustic emission signal of the base material during tension was subjected to wavelet packet decomposition, and it was determined that the frequency range of matrix cracking damage was 0-150kHz. Secondly, the acoustic emission signal of the fiber bundle material during tension was subjected to wavelet packet decomposition, and it was determined that the frequency range of fiber breakage damage was above 350kHz. K-means clustering analysis was performed on the acoustic emission signal of the composite material during tension to find the acoustic emission signal that was different from the previous two damage modes and perform wavelet packet decomposition to determine that the frequency ranges of interface slip and debonding damage correspond to 150-250kHz and 250-350kHz, respectively.

[0071] The acoustic emission signals collected in the gas thermal shock experiment were subjected to k-means cluster analysis and wavelet packet analysis. Before applying the kmeans cluster analysis, the k value should be determined.

[0072] Firstly, amplitude, peak frequency, duration, rise time, count and energy parameters were selected as characteristic parameters for cluster analysis.

[0073] Secondly, the mean-variance normalization method is adopted to make the selected characteristic parameters dimensionless, so that the measured data are defined between [0, 1], and the expression is as shown in formula (1).

[0074] Finally, the processed feature parameters were subjected to k-means clustering analysis. In order to determine the optimal cluster number k, the silhouette coefficient values ​​under different cluster numbers k were calculated, as shown in the following example: Figure 3 As shown. It is known that the larger the silhouette coefficient value, the better the classification effect. Generally speaking, when the silhouette coefficient value is greater than 0.6, the classification effect is very good. Figure 3As shown in the figure, the silhouette coefficient value is the largest when the AE signals of C / SiC composite materials during the 1200℃ gas impact process are divided into three categories, that is, when k=3, S is 0.64, which shows that when performing k-means clustering analysis, the clustering effect is best when three categories are selected.

[0075] Perform k-means cluster analysis on the acoustic emission signals after the above processing, as shown in the following example: Figure 4 shown. Figure 4 The figure shows the clustering results of the AE signal amplitude and peak frequency. The peak frequencies of the three types of signals, matrix cracking, interface slip, and interface debonding, show little overlap. The amplitudes are distributed between 40-80 dB, and the peak frequencies are 0-150 kHz, 150-250 kHz, and 250-350 kHz, respectively. This demonstrates that the peak frequency of the AE signal can be used to classify damage signals in C / SiC composites. Clear differences can be seen between the clusters, with little overlap between samples.

[0076] The above methods were all run using MATLAB software.

[0077]

[0078] Where A i is the original signal parameter, A μ is the average value of the original signal parameter, is the normalized signal parameter, A σ is the standard deviation of the original signal parameter.

[0079]

[0080] Where a(i) represents the average distance between the i-th vector and other vectors in the same category, and b(i,k) represents the average distance between the i-th vector and other vectors in different categories.

[0081] The collected acoustic emission signals are transformed by wavelet packet, and the "db5" wavelet basis is selected to perform three-layer wavelet packet decomposition on the acoustic emission signals. The wavelet energy spectrum coefficients are extracted as characteristic parameters for identifying the acoustic emission signals of composite material damage modes, such as Figure 5 shown.

[0082] The wavelet packet energy spectrum coefficient can intuitively, simply and effectively reflect the situation of various types of signals in each frequency band, and then identify the failure mode of AE signals. The maximum values ​​of the wavelet packet energy spectrum coefficients of the three typical failure modes are concentrated in Node (0-0.125MHz), Node (0.125-0.25MHz), Nodes (0.25-0.375MHz). Obviously, they represent substrate cracking, interface slip, and interface debonding, respectively.

Claims

1. A method for identifying acoustic emission damage patterns of composite materials under thermal shock, characterized in that: The following steps are included: Step 1: Connect the composite material sample to the acoustic emission sensor; Step 2: connecting the acoustic emission sensor to the acoustic emission instrument via a signal transmission line, turning on the signal acquisition display of the acoustic emission instrument, and setting the acoustic emission acquisition parameters of the composite material sample under the gas thermal shock service environment on the display; Step 3: After setting the acoustic emission acquisition parameters, conduct a lead-breaking test on the surface of the composite material sample and check the attenuation of the transmission signal on the signal acquisition display to achieve stable acquisition of the acoustic emission signal under the gas thermal shock service environment; Step 4: After completing the acoustic emission signal collection preparation work of steps 1 to 3, set the gas thermal shock experimental parameters on the gas thermal shock service environment simulation device; Step 5: Turn on the ignition button on the simulation device to conduct a gas thermal shock test. When the gas impacts the surface of the composite material sample, damage will occur inside the composite material sample. Then, the acoustic emission signal is collected through steps 1 to 3, and the amplitude, peak frequency, duration, rise time, counts, and energy parameters of the acoustic emission signal are displayed on the signal acquisition display. Step 6: Damage pattern recognition is performed on the acoustic emission signals collected in step five. Tensile tests are performed on the substrate material, fiber bundle, and composite material samples at room temperature on a high-temperature universal mechanical testing machine, and acoustic emission detection is performed using an acoustic emission instrument. The specific process repeats steps two to three to obtain the amplitude, peak frequency, duration, rise time, count, and energy parameters of the three samples of the substrate material, fiber bundle, and composite material. These parameters are then subjected to k-means cluster analysis and wavelet packet analysis to obtain the frequency characteristic ranges of the four damage modes of matrix cracking, interface slip, interface debonding, and fiber breakage. Step 7: Perform k-means clustering on the acoustic emission signals collected in step 5, perform wavelet packet analysis on the clustered acoustic emission signals, and compare them with the results of step 6 to identify the damage mode of the composite material sample under gas thermal shock.

2. The method for identifying acoustic emission damage patterns of composite materials under thermal shock according to claim 1, characterized in that: The step 1 is specifically as follows: A circular composite material specimen is fixed on a thermal shock fixture. The top surface facing the spray gun is used for the gas thermal shock test. The bottom surface on the other side facing away from the spray gun is mechanically or welded to one end of a high-temperature waveguide wire made of nickel-chromium alloy. The other end of the high-temperature waveguide wire is connected to the acoustic emission sensor by means of a rubber plug fixed with butter coupling.

3. The method for identifying acoustic emission damage patterns of composite materials under thermal shock according to claim 1, characterized in that: The step 2 is specifically as follows: The parameters set are specifically preamplifier 20 / 40 / 60dB, sampling threshold 0-100dB, sampling frequency 2MHz, frequency range 0.1-1MHz and impact length 2k; When a composite material sample is damaged, a stress wave, namely an acoustic emission signal, is generated, which is then transmitted to the sensor and finally collected on the signal display.

4. The method for identifying acoustic emission damage patterns of composite materials under thermal shock according to claim 1, characterized in that: The step three is specifically as follows: Lead is used to fracture composite material specimens, and the emitted acoustic emission signals are collected to observe the amplitude attenuation. The higher the amplitude, the better the coupling with the sensor. Ultimately, stable acquisition of acoustic emission signals in a gas thermal shock service environment is achieved. The lead breaking experiment is carried out by slowly pressing a pencil at an angle of 30 degrees at a position 30 mm away from the contact point of the high-temperature waveguide wire on the surface of the composite material sample to break the lead core. The emitted acoustic emission signal is collected and the amplitude attenuation is observed. The higher the amplitude, the better the coupling with the sensor.

5. The method for identifying acoustic emission damage patterns of composite materials under thermal shock according to claim 1, characterized in that: The step 4 is specifically as follows: The parameters are specifically gas shock temperature, oxygen outlet pressure, oxygen flow rate, kerosene outlet pressure, kerosene flow rate, cooling gas outlet pressure and cooling gas flow rate; Among them, the gas impact temperature is 0-1300℃, the oxygen outlet pressure is above 1.3MPa, the oxygen flow rate is 230-500L / min, the kerosene outlet pressure is above 0.5MPa, the kerosene flow rate is 5-15L / h, the cooling gas outlet pressure is 0.7MPa, and the cooling gas flow rate is 60L / min.

6. The method for identifying acoustic emission damage patterns of composite materials under thermal shock according to claim 1, characterized in that: The step six specifically comprises: performing a substrate tensile test at room temperature using an acoustic emission device to obtain a characteristic range of acoustic emission frequencies for substrate cracking; The fiber bundle tensile test was carried out at room temperature using an acoustic emission device to obtain the characteristic range of the acoustic emission frequency of fiber breakage. Tensile tests of composite materials were conducted at room temperature using an acoustic emission device, and failure modes mainly characterized by matrix cracking, interface slip, interface debonding, and fiber fracture were obtained. K-means cluster analysis was performed on the obtained acoustic emission characteristic parameters. Based on the acoustic emission frequency characteristic ranges of matrix cracking and fiber fracture obtained above, the frequency characteristic ranges of interface slip and interface debonding were obtained.

7. The method for identifying acoustic emission damage patterns of composite materials under thermal shock according to claim 1, characterized in that: The step seven is specifically as follows: Amplitude, peak frequency, duration, rise time, count and energy parameters are selected as characteristic parameters for cluster analysis, and the mean variance normalization method is adopted to make the selected characteristic parameters dimensionless so that the measured data are defined between [0,1]. The expression is as shown in formula (1); Where A i is the original signal parameter, A μ is the average value of the original signal parameter, is the normalized signal parameter, A σ is the standard deviation of the original signal parameter; Secondly, the silhouette coefficient value of the dimensionless characteristic parameters is calculated, as shown in formula (2), to determine the optimal clustering number k; Where a(i) represents the average distance between the i-th vector and other vectors in the same category, and b(i,k) represents the average distance between the i-th vector and other vectors in different categories. Finally, perform k-means clustering analysis on the processed feature parameters, select k cluster centers, calculate the distance between the initial cluster centroid and the input vector, and then classify the input vector into the cluster with the nearest cluster centroid; calculate the position of the cluster center according to the shortest distance principle; repeat the above steps until the position of the initial cluster centroid no longer changes. Perform j-layer wavelet packet transform on an acoustic emission signal f(t) to obtain the sum of a series of decomposition parts, as follows: f(t) refers to an acoustic emission signal, is the decomposed signal at frequency band i and scale j, and the energy of each part after decomposition is Defined as: The calculation formula of total energy E(t) is as follows: R i The wavelet packet energy spectrum coefficient is the ratio of the energy of each node to the total energy of all nodes after decomposition;

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