Vibration fatigue analysis and evaluation method of compressor alloy blade in coastal environment

Abstract

The application discloses a method for analyzing and evaluating vibration fatigue of compressor alloy blades in a coastal environment, comprising Step 1. determining the relationship between the corrosion condition of the compressor alloy steel blade and the service environment and service time; Step 2. determining the vibration fatigue characteristics of the compressor alloy steel blade under load; Step 3. determining the fatigue life evolution law of the compressor alloy steel blade after pre-corrosion; Step 4. determining the corrosion influence coefficient of the compressor alloy steel blade; Step 5. performing verification analysis on the fatigue life of the compressor alloy steel blade and the corrosion damage in the coastal environment; the method can effectively analyze and evaluate the service life of the compressor blade structure in the coastal environment by comprehensively evaluating the mechanical life of the compressor blade in the coastal environment through the pre-corrosion test, the vibration fatigue test after pre-corrosion and the corrosion influence coefficient analysis method based on the test data.

Description

Technical Field

[0001] This invention relates to the field of aero-engine blade testing technology, specifically to a method for vibration fatigue analysis and evaluation of compressor alloy blades in coastal environments. Background Technology

[0002] Environmental adaptability is a crucial quality characteristic for all aviation equipment, including aero-engines, directly impacting engine performance, lifespan, and safety. Corrosion damage to typical aero-engine structures caused by environmental factors directly leads to decreased environmental adaptability and induces corrosion fatigue. As the service life increases, corrosion gradually worsens in typical aero-engine structures, becoming one of the main forms of damage to their external structures and airflow channels. This is especially true in the high-temperature, high-humidity, and high-salt-spray environments of coastal areas, where environmental corrosion ultimately becomes a prominent issue affecting engine environmental adaptability and lifespan. A typical example of corrosion on engine compressor blades in coastal areas is shown below. Figure 1-1 As shown;

[0003] Blades are one of the key components of aero-engines. Their performance and lifespan directly affect the safe operation of the aero-engine. Once the blades corrode and are damaged, it is very likely to cause catastrophic consequences. The fatigue life of the aero-engine blade structure is the most important component of its service life. It is a measure of the lifespan of the blade structure under its operating load spectrum and environmental effects. For a long time, fatigue tests for the life determination of full-size blade structures have been conducted in general environments without applying environmental effects. Therefore, the life determination conclusions are in principle applicable to "domestic land areas under general environments". For aero-engine blades used in coastal and humid and hot areas, if their fatigue life is still obtained by relying on the traditional fatigue test methods under general environments, the results are difficult to directly apply in engineering and need to be reasonably evaluated.

[0004] Therefore, considering the corrosion status of aero-engine compressor blades when used in coastal areas, how to analyze and evaluate the fatigue life of aero-engine blade structures under the highly corrosive environmental conditions of coastal areas is a key technical problem that urgently needs to be solved in the marine environmental adaptability engineering of aero-engine structures; only by solving this problem can the service life of engine structures used in coastal and humid areas be accurately given.

[0005] Taking the compressor blade structure of a certain type of aero-engine made of 0Cr16Ni5Mo1 alloy steel as the research object, a pre-corrosion test and a vibration fatigue test after the pre-corrosion test were carried out in a simulated coastal environment for 10 equivalent calendar years (each equivalent calendar year is represented by T1, T2, T3...T9, T10, the same below). Based on the test results, the evolution law of vibration fatigue life of the blade structure in the coastal environment was analyzed. The pre-corrosion influence coefficient method was used to establish the corrosion influence coefficient CT curve expression. The curve was used to evaluate the vibration fatigue life of the blade in the coastal service environment and under a certain service life.

[0006] Therefore, considering the aforementioned existing problems, there is an urgent need to design a method for analyzing and evaluating the vibration fatigue life of alloy steel blades for aero-engine compressors in coastal environments, in order to solve the problems existing in the above-mentioned technologies. Summary of the Invention

[0007] To address the aforementioned problems, this invention aims to provide a method for vibration fatigue analysis and evaluation of compressor alloy blades in coastal environments. This method evaluates the mechanical life of aero-engine compressor blades in coastal environments by combining pre-corrosion tests, post-pre-corrosion vibration fatigue tests, and corrosion influence coefficient analysis based on experimental data. The results show that the method combining the above-mentioned comprehensive pre-corrosion tests, post-pre-corrosion vibration fatigue tests, and corrosion influence coefficients can effectively analyze the fatigue life variation law of aero-engine compressor blades in coastal environments and effectively analyze and evaluate the service life of compressor blade structures in coastal environments.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] Vibration fatigue analysis and evaluation method for compressor alloy blades in coastal environments, including steps

[0010] Step 1. Determine the relationship between the corrosion status of the compressor alloy steel blades and the service environment and service duration;

[0011] Step 2. Determine the vibration fatigue characteristics of the pre-corroded compressor alloy steel blades under load;

[0012] Step 3. Determine the fatigue life evolution law of compressor alloy steel blades after pre-corrosion, and determine the linear relationship between logarithmic fatigue life and corrosion equivalent calendar years;

[0013] Step 4. Determine the corrosion influence coefficient of the compressor alloy steel blades, and conduct fatigue life assessment of the compressor alloy steel blades based on the corrosion influence coefficient;

[0014] Step 5. Conduct a correlation analysis between the fatigue life of compressor alloy steel blades and corrosion damage in coastal environments.

[0015] Preferably, in Step 1, a pre-corrosion test simulating the service environment of the compressor alloy steel blade is used to analyze the relationship between the corrosion of the compressor alloy steel blade and the service environment and service duration; it is determined that in a coastal environment, the compressor blade structure will be affected by the environment and suffer corrosion damage, and the corrosion damage will gradually worsen with the increase of equipment service time within a certain period.

[0016] Preferably, in Step 2, the vibration fatigue characteristics of the pre-corroded compressor alloy steel blade under load are analyzed using a fatigue test after pre-corrosion. The fatigue test after pre-corrosion is based on the finite element method. It is determined that during the accelerated corrosion process of the alloy steel blade over 10 equivalent calendar years, localized corrosion occurs at individual defect locations on the blade surface, manifesting as regionalized spots. As the corrosion cycle extends, up to the 9th equivalent calendar year, the corrosion area on the blade surface gradually increases, and the number of localized spot areas increases.

[0017] Preferably, in Step 3, the fatigue life evolution law of the compressor alloy steel blade after pre-corrosion is analyzed using experimental methods to determine the linear relationship between logarithmic fatigue life and corrosion equivalent calendar years. The linear relationship between logarithmic fatigue life LgN and corrosion equivalent calendar years T is obtained as: LgN = 6.83 - 0.14T, R 2 =0.96.

[0018] Preferably, in Step 4, the corrosion influence coefficient of the compressor alloy steel blade is determined by the fatigue life assessment method for aerospace equipment structures under environmental corrosion conditions, and a CT curve is established.

[0019] Preferably, the specific process for determining the corrosion influence coefficient of compressor alloy steel blades using the method for assessing the structural fatigue life of aerospace equipment under environmental corrosion conditions includes the following steps:

[0020] Step 4021. Define and obtain the corrosion influence coefficient C;

[0021] (1) Define the corrosion influence coefficient C

[0022] Based on the conclusions of fatigue life determination under normal environment, a corrosion influence coefficient C is introduced to convert the damage under corrosive conditions into equivalent flight hours under normal environment. The fatigue life under normal environment is used as the criterion for evaluating and monitoring fatigue life under corrosive conditions.

[0023] Corrosion Influence Coefficient C j Defined as:

[0024] Where, N 0j N is the lifetime value for corrosion cycle j, and N0 is the lifetime value of the uncorroded structure.

[0025] (2) Obtain the corrosion influence coefficient C;

[0026] Step 4022. Establish the curve of corrosion influence coefficient C versus corrosion time T.

[0027] Preferably, the process of obtaining the corrosion influence coefficient C in step 4021(2) includes:

[0028] 1) Determine N0 by using simulated specimens of designated fatigue critical parts and conducting group fatigue tests under room temperature atmospheric environment and service load spectrum to obtain a set of fatigue life N. 0k (k = 1, ..., m0), let them follow a log-normal distribution, then

[0029] 2) Determination of N 0j Using a set of identical simulated specimens, they were first subjected to accelerated testing in an environment equivalent to ground parking for T... j Accelerated corrosion tests were conducted over several years, followed by group fatigue tests under ambient temperature and atmospheric conditions and operating load spectrum to obtain the fatigue life N. kj (k = 1, ..., m) j Median life

[0030] 3) To determine the CT curve, several T values ​​need to be selected. j (j=0,1,…,q), using (q+1) groups of simulated specimens, one group is used to determine N0, and the q groups are used to accelerate corrosion to the equivalent environmental corrosion T. j After the New Year, group fatigue tests were conducted under ambient temperature and atmospheric conditions and using load spectra, and the values ​​of q N were measured. 0j Thus, q groups (T) are obtained. j C j The data is fitted using a functional relationship to determine the CT curve.

[0031] Preferably, the process of establishing the corrosion influence coefficient C versus corrosion time T curve in step 4022 includes:

[0032] (1) From (T) j C j The CT curve fitted by the data and the curve obtained from (t) j C j The Ct curves fitted to the data have similar functional expressions;

[0033] (2) The establishment of CT curves is mainly based on the simulation specimens of key fatigue parts corresponding to several t j C below j The experimental results, i.e. (t) j C j (j = 1, ..., q) data;

[0034] (3) Based on (t) j C j The analysis of the data variation patterns and the variation patterns of Ct(CT) leads to the selection of several possible functional relationships corresponding to it, based on (t) j C j The data (j = 1, ..., q) were fitted, with the best correlation being the primary criterion; and C was referenced within the commonly used service life range. j The magnitude of the deviation between the fitted value and the experimental value determines the expression for the Ct(CT) curve;

[0035] (4) When establishing CT curves, the real physical context should be considered, namely:

[0036] As the environmental corrosion time T increases, the corrosion influence coefficient C value will continuously decrease; that is, in the early stage of equipment service, corrosion has no significant impact on fatigue life; as the service time is extended, the effect of environmental corrosion begins to appear, and as corrosion damage forms and intensifies, the fatigue life and C value decrease at a large gradient; the gradient of fatigue life and C value decreases gradually.

[0037] Based on the general physical background, the following properties of CT curves can be summarized:

[0038] ①When T=0, C(0)=1;

[0039] ②When T=∞, C(∞)=0;

[0040] ③ As the environmental corrosion time increases, the structural fatigue life continuously decreases, that is: C'(t)≤0;

[0041] Based on the above three points, the following expression for the CT curve is proposed:

[0042] C(t)=1.0-βt α

[0043]

[0044] In the formula: β is the coefficient; α is the exponent.

[0045] The beneficial effects of this invention are: This invention discloses a method for vibration fatigue analysis and evaluation of compressor alloy blades in coastal environments. Compared with the prior art, the improvement of this invention lies in:

[0046] This invention presents a method for vibration fatigue analysis and evaluation of compressor alloy blades in a coastal environment. First, an accelerated corrosion test scheme simulating a coastal environment is designed for the alloy steel blades of aero-engine compressors to obtain corrosion morphology and damage statistics of the blade structure under coastal conditions. Second, based on the mechanical property parameters of the alloy steel material and finite element analysis of the blade structure, a vibration fatigue test scheme after pre-corrosion is designed, and vibration fatigue tests are conducted on blades at pre-corrosion equivalent calendar years of T0, T4, T7, T9, and T10 to obtain fatigue life data after pre-corrosion. Third, based on the vibration fatigue test data, a fatigue life model for alloy steel blades in a coastal environment is established. The evolution law of service life with service life was obtained. Finally, the concept of corrosion influence coefficient was proposed, and a corrosion influence coefficient CT curve was established based on vibration fatigue test data. The fatigue life of typical pre-corroded compressor blades was evaluated and analyzed, with evaluation errors of about 5%. The fatigue life analysis results were verified by combining the correlation of corrosion damage depth. The results show that the method of combining pre-corrosion test, pre-corrosion vibration fatigue test and corrosion influence coefficient can better analyze the fatigue life variation law of aero-engine compressor blades in coastal environments, and can be used for life analysis and evaluation of compressor blade structures used in coastal environments. Attached Figure Description

[0047] Figure 1-1 This is a diagram showing corrosion spots on the blades of a certain type of aero-engine compressor used in coastal areas.

[0048] Figure 1-2 This is a flowchart of the method for vibration fatigue analysis and evaluation of compressor alloy blades in a coastal environment according to the present invention.

[0049] Figure 2 This is an accelerated corrosion curve of a 0Cr16Ni5Mo1 compressor blade from Embodiment 2 of the present invention.

[0050] Figure 3 This is a diagram of the original morphology of the 0Cr16Ni5Mo1 blade in Example 2 of the present invention.

[0051] Figure 4 This is a diagram showing the arrangement of the blade specimen in an environmental chamber according to Embodiment 2 of the present invention.

[0052] Figure 5 This is a typical equivalent calendar-year corrosion morphology image of a 0Cr16Ni5Mo1 blade from Example 2 of the present invention.

[0053] Figure 6 This is a diagram showing the definition of pitting morphology parameters in Embodiment 2 of the present invention.

[0054] Figure 7This is a microscopic measurement of corrosion damage depth in Embodiment 2 of the present invention.

[0055] Figure 8 This is a graph showing the minimum clamping torque in Embodiment 3 of the present invention.

[0056] Figure 9 The figure shows the finite element calculation results of Embodiment 3 of the present invention.

[0057] Figure 10 This is a blade assembly diagram of Embodiment 3 of the present invention.

[0058] Figure 11 The image shows the minimum clamping torque test result of Embodiment 3 of the present invention.

[0059] Figure 12 This is a graph showing the minimum clamping torque in Embodiment 3 of the present invention.

[0060] Figure 13 This is a schematic diagram and image showing the location of the stress distribution patch in Embodiment 3 of the present invention.

[0061] Figure 14 This is a stress monitoring diagram of the dynamic signal analysis system in Embodiment 3 of the present invention.

[0062] Figure 15 These are images of the stress calibration test in Embodiment 3 of the present invention.

[0063] Figure 16 The image shows a test piece from Embodiment 3 of the present invention.

[0064] Figure 17 This is an image of the experimental fixture from Embodiment 3 of the present invention.

[0065] Figure 18 This is a graph showing the evolution of fatigue life and pre-corrosion equivalent calendar years of the blade in Example 4 of the present invention.

[0066] Figure 19 This is a graph showing the CT curve changes of the 0Cr16Ni5Mo1 blade in Example 5 of the present invention.

[0067] Figure 20 The curve showing the relationship between corrosion depth and fatigue life in Example 6 of this invention.

[0068] Among them: Figure 5 In the figure, Figure (a) shows the typical appearance of corrosion at T6 years for 0Cr16Ni5Mo1 blades, Figure (b) shows the typical appearance of corrosion at T9 years for 0Cr16Ni5Mo1 blades, and Figure (c) shows the typical appearance of corrosion at T10 years for 0Cr16Ni5Mo1 blades.

[0069] exist Figure 9In the figure, Figure (a) shows the results of the mode shape finite element calculation, and Figure (b) shows the results of the stress distribution finite element calculation.

[0070] exist Figure 10 In the figure, Figure (a) is a top view of the vibration table, Figure (b) is a diagram showing the fixing effect of the fixed blades of the vibration table, Figure (c) is a top view of the vibration table, and Figure (d) is a diagram showing the arrangement of vibration monitoring points on the vibration table.

[0071] exist Figure 13 In the figure, Figure (a) is a schematic diagram of the stress distribution patch location, and Figure (b) is an image of the stress distribution patch location;

[0072] exist Figure 18 In the figure, Figure (a) shows the evolution curve of logarithmic fatigue life versus pre-corrosion equivalent calendar years, and Figure (b) shows the evolution curve of original fatigue life data versus pre-corrosion equivalent calendar years.

[0073] exist Figure 20 In the figure, Figure (a) shows the exponential relationship curve between corrosion depth and fatigue life, Figure (b) shows the linear relationship curve between corrosion depth and fatigue life, Figure (c) shows the quadratic relationship curve between corrosion depth and fatigue life, and Figure (d) shows the cubic relationship curve between corrosion depth and fatigue life. Detailed Implementation

[0074] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0075] Example 1: A method for vibration fatigue analysis and evaluation of compressor alloy blades in a coastal environment, as shown in Figures 1-20, including the following steps.

[0076] Step 1. Determine the relationship between the corrosion status of the compressor alloy steel blades and the service environment and service duration;

[0077] Step 2. Analyze the vibration fatigue characteristics of pre-corroded compressor alloy steel blades under load;

[0078] Step 3. Study the fatigue life evolution law of compressor alloy steel blades after pre-corrosion and determine the linear relationship between logarithmic fatigue life and corrosion equivalent calendar years;

[0079] Step 4. Determine the corrosion influence coefficient of the compressor alloy steel blades, and conduct fatigue life assessment of the compressor alloy steel blades based on the corrosion influence coefficient;

[0080] Step 5. Conduct a correlation verification analysis on the fatigue life of compressor alloy steel blades and corrosion damage in coastal environments.

[0081] Preferably, the specific process of determining the relationship between the corrosion status of the compressor alloy steel blades and the service environment and service duration in Step 1 is based on the pre-corrosion test of the compressor alloy steel blades under simulated service environment.

[0082] Preferably, the vibration fatigue characteristics of the pre-corroded compressor alloy steel blades under load described in Step 2 are based on fatigue tests of the pre-corroded compressor alloy steel blades.

[0083] Preferably, the determination of the linear relationship between the logarithmic fatigue life and the corrosion equivalent calendar years of the compressor alloy steel blade after pre-corrosion, as described in Step 3, is based on the experimental analysis of the fatigue life evolution law of the compressor alloy steel blade after pre-corrosion.

[0084] Preferably, the corrosion influence coefficient of the compressor alloy steel blade and the fatigue life assessment process of the compressor alloy steel blade based on the corrosion influence coefficient described in Step 4 are based on the fatigue life assessment process of the compressor alloy steel blade based on the corrosion influence coefficient.

[0085] Preferably, the process of verifying and analyzing the correlation between the fatigue life of the compressor alloy steel blades and corrosion damage in the coastal environment in Step 5 includes the verification and analysis process of the correlation between the fatigue life of the compressor alloy steel blades and corrosion damage in the coastal environment.

[0086] Example 2: To study the relationship between the corrosion of compressor alloy steel blades and the service environment and service duration, the pre-corrosion test process for the compressor alloy steel blades under simulated service environment includes:

[0087] Based on the environmental characteristics of coastal areas and following the equivalent corrosion damage relationship, an accelerated corrosion test scheme for the compressor blade structure of a certain type of aero-engine made of 0Cr16Ni5Mo1 alloy steel was designed, such as... Figure 2 As shown;

[0088] The test environment is as follows:

[0089] (1) Soaking in acidic NaCl solution: Add dilute H2SO4 to a 5% NaCl solution to make the pH of the solution (4.0 ± 0.2) and the solution temperature T (40 ± 2)℃ during soaking;

[0090] (2) During baking, the relative humidity of the air shall not be less than 90%, and the temperature T = (40±2)℃;

[0091] (3) During the accelerated corrosion test, the blade specimens are suspended horizontally in the test chamber. A gap should be left between the blade specimens. In order to avoid the influence of uneven working environment of the environmental chamber on the specimens, the specimen positions are randomly changed once every 24 hours.

[0092] During the test, the environmental intensity for each equivalent calendar year was 335 cycles of wet and dry alternation, including immersion for 5.07 minutes and baking for 15.81 minutes each time, with a total corrosion test time of 116.63 hours;

[0093] A total of 88 specimens were randomly divided into 11 groups, with 8 specimens in each group. The specimens in each group were numbered from 01 to 08. The original morphology is as follows: Figure 3 As shown;

[0094] Using a periodic immersion test chamber, according to the experimental protocol, the cleaned blade specimens were suspended on the test rack of the periodic immersion environment test chamber, such as... Figure 4 As shown, an accelerated corrosion test was carried out for 10 equivalent calendar years. After each test cycle, the blade specimens were photographed before and after cleaning, and corrosion damage was detected. The detection contents included: (1) whether the surface of the blade specimens was blistering, wrinkling, peeling and cracking; whether the metal substrate was discolored and whether there was corrosion damage; (2) the corrosion morphology and corrosion damage size were observed and measured using a KH-7700 three-dimensional microscope in an atmospheric environment, including the three-dimensional morphology of the corrosion spots.

[0095] Step 101. Corrosion Morphology Observation

[0096] Corrosion morphology of 0Cr16Ni5Mo1 compressor blade structure under typical equivalent calendar years, as shown in the figure. Figure 5 As shown, during the experiment, corrosion gradually emerged, multiplied, and expanded on the blade surface from scratch. By the 9th equivalent calendar year, the corrosion area on the blade surface began to increase, and large-area local spots appeared. Therefore, it can be concluded that in a coastal environment, the compressor blade structure will be affected by the environment and suffer corrosion damage, and the corrosion damage will gradually worsen with the increase of equipment service time within a certain period.

[0097] Step 102. Corrosion Damage Measurement

[0098] Corrosion damage is defined using three morphological parameters: surface length, width, and depth. Specifically, each parameter is defined as follows: the maximum dimension of the damaged surface parallel to the specimen axis is defined as the damage length, denoted by L; the maximum dimension of the damaged surface perpendicular to the specimen axis is defined as the damage width, denoted by W; and the maximum dimension of the damage perpendicular to the specimen surface and extending into the specimen depth is defined as the damage depth, denoted by D. The unit for all three parameters is μm. The definition of corrosion damage is as follows: Figure 6 As shown;

[0099] As mentioned above, accelerated corrosion tests were conducted for a total of 10 equivalent calendar years. Microscopic measurements of typical corrosion damage on the blade surface are shown below. Figure 7As shown, corrosion damage morphology parameters (length, width, and depth) of corroded blade specimens under different equivalent corrosion years were obtained by observation and measurement using a KSTAR microscope. In the analysis process, corrosion damage depth is taken as an example. The analysis methods for length and width are similar and will not be listed here.

[0100] Statistical analysis yielded the parameters of surface corrosion damage depth for typical calendar years, as shown in Table 1.

[0101] Table 1: Statistical values ​​of corrosion damage depth on blade surface

[0102] Equivalent calendar years (years) Statistical significance of mean (μm) T1 58.8924 T2 61.94785 T3 81.0732 T4 87.3834 T5 95.6405 T6 110.2886 T7 104.0225 T8 141.6903 T9 144.457 T10 158.0785

[0103] As can be seen from this embodiment, in a coastal environment, the compressor blade structure will be affected by environmental factors and suffer corrosion damage, and the corrosion damage will gradually worsen over a certain period of time as the equipment service time increases.

[0104] Example 3: To analyze the vibration fatigue characteristics of pre-corroded compressor alloy steel blades under load, the fatigue test process of the pre-corroded compressor alloy steel blades includes...

[0105] Step 201. Modal Finite Element Analysis and Experimental Calibration Verification of Compressor Alloy Steel Blades

[0106] Based on the solid model of the 0Cr16Ni5Mo1 blade, finite element analysis was performed on the blade using ANSYS to obtain the first-order bending mode of the blade, and to obtain the stress cloud diagram and the location of the stress maximum point of the blade, so as to determine the strain gauge patching direction and position.

[0107] Three blades were selected for a minimum clamping torque test. The minimum clamping torque was determined by gradually increasing the clamping torque. The minimum clamping torque met the following requirements. Figure 8 The curve shown requires that the clamping torque of the fixture be gradually increased under constant acceleration and frequency sweeping is performed until the measured change of the first-order bending frequency of the blade does not exceed 0.5% (without damaging the blade). This torque is then used as the clamping torque for the blade assembly in this test, with an additional 10% margin.

[0108] Based on the finite element analysis results, strain gauges were attached to each type of 0Cr16Ni5Mo1 blade after T0, T4, T7, T9, and T10 years to complete stress distribution tests.

[0109] The blade was excited by an electromagnetic vibration test system, and the strain was monitored by a dynamic signal test and analysis system. The location of the maximum vibration stress under the first-order bending mode was measured. Strain gauges were attached to the point of maximum stress to conduct stress and amplitude calibration tests. The least squares method was used to fit the linear relationship curve of stress and amplitude to obtain the maximum vibration stress-af relationship.

[0110] (1) Finite element analysis calculation

[0111] The results of the first-order modal finite element calculation of the 0Cr16Ni5Mo1 blade are as follows: Figure 9 As shown, its first mode shape is a bending mode shape, and the location of maximum stress is at the radius of the blade root.

[0112] (2) Minimum clamping torque test

[0113] 1) Blade assembly

[0114] Blade assembly method as follows Figure 10 As shown, the sample is rigidly fixed to the moving coil of the vibration table by a clamp. An accelerometer is placed at the rigid connection between the clamp and the vibration table as the control point for the vibration test, and a laser displacement sensor is placed at the blade tip (3 mm away from the blade tip) as the response point for the vibration test.

[0115] 2) Minimum clamping torque test

[0116] Minimum clamping torque test sweep parameters: frequency range (700~900)Hz, excitation magnitude 1g, sweep time 1.5min, sweep times 1;

[0117] Images from the experiment, as shown Figure 11 As shown, the minimum clamping torque test data are shown in Table 2 and Figure 12 As shown, the minimum clamping torque for the test was determined to be 70 N·m;

[0118] Table 2: Tightening Torque Test Data

[0119]

[0120] (3) Stress distribution and calibration test

[0121] Stress distribution

[0122] 1) Patch placement

[0123] Based on the finite element analysis results of the blade stress distribution and the actual blade structure, the stress distribution monitoring locations were determined as follows: Figure 13 As shown, the actual bonding position of the strain gauge is as follows: Figure 13 As shown;

[0124] 2) Stress distribution detection results

[0125] The stress distribution test data are shown in Table 3. The maximum stress location is at strain monitoring point #1, which is the middle position of the blade root. The location of the maximum stress is consistent with the finite element calculation results. The stress in the dynamic signal analysis system is as follows: Figure 14 As shown;

[0126] Table 3: Test data on stress distribution

[0127]

[0128] (4) Stress calibration test

[0129] The maximum stress-af calibration test data are shown in Table 4. The calibration is as follows: Figure 15 As shown;

[0130] Table 4: Stress Calibration Test Data

[0131]

[0132]

[0133] Step 202. Vibration fatigue test of compressor alloy steel blades after typical pre-corrosion equivalent calendar years.

[0134] Vibration fatigue tests were conducted on 0Cr16Ni5Mo1 blades after different equivalent calendar years of pre-corrosion. The vibration fatigue tests were carried out on 0Cr16Ni5Mo1 blades in accordance with HB5277-84 "Vibration Fatigue Test Method for Engine Blades and Materials".

[0135] Specifically, calibration tests were conducted on 0Cr16Ni5Mo1 blades after pre-corrosion years (T0, uncorroded, the same below), T4, T7, T9, and T10 years. The least squares method was used to fit the stress-amplitude linear curve to obtain the maximum vibration stress-af relationship for each blade. Then, vibration fatigue comparative tests were conducted on the blades after T0, T4, T7, T9, and T10 years.

[0136] The test subjects were uncorroded blades and some blades after typical pre-corrosion equivalent calendar years, namely 8 specimens each at T0, T4, T7, T9, and T10 years, for a total of 40 specimens. The mechanical parameters of the 0Cr16Ni5Mo1 alloy steel blades are shown in Table 5.

[0137] Table 5: Technical Data Sheet for Test Specimens

[0138] Blade material 0Cr16Ni5Mo1 Elastic modulus E 204.14 GPa <![CDATA[Tensile strength σ b > 947.2 MPa (0℃) material Poisson's ratio 0.3μ Number of test pieces 40 items

[0139] The equipment and instruments used in the experiment are shown in Table 6:

[0140] Table 6: Basic Configuration of Test Equipment and Instruments

[0141]

[0142]

[0143] The test fixtures used in the vibration fatigue test after pre-corrosion are as follows: Figure 16 As shown.

[0144] (1) Vibration fatigue testing was carried out

[0145] Referring to standards HB5277-84 and HB / Z112-1986, for blades after T0, T4, T7, T9, and T10 years, N0 = 1 × 10 7 Vibration fatigue tests were conducted to determine the baseline for infinite life cycles.

[0146] 1) Adjustment of test load

[0147] Based on the tensile strength σ of the blade material b and fatigue limit σ -1 One to three blades were selected to explore the blade stress level and determine the test load. Specifically, one blade with a corrosion cycle of T0 was first selected for vibration fatigue test estimation measurement: an electric vibration testing system and a laser displacement sensor were used. After the frequency was adjusted to the first-order vibration frequency and stabilized, the excitation force was increased uniformly while the frequency was finely adjusted. When the blade amplitude reached 80% of the initial stress level calculated by the stress-amplitude linear relationship (to avoid inaccurate stress values ​​due to the stress gauge loosening and falling off during high-level tests, the stress value was monitored), timing was started, and the test magnitude was slowly adjusted to the initial load. Fatigue tests were first conducted at a lower stress level, reaching 1×10⁻⁶. 7 During cycle life testing, if the blade does not fail, the stress level should be increased as needed to repeat the test until blade failure, in order to obtain the high-cycle fatigue limit estimate δ1. Then, 1-2 blades should be selected and verified using δ1 as the test load. If the blade does not reach 1×10⁻⁶, the test is considered complete. 7 If failure occurs during cycle life, δ1 will be used as the subsequent test load; if the blade reaches 1×10 7 If the cycle life is not damaged, the stress level should be appropriately increased to δ2 and the verification should be continued.

[0148] Table 7 shows the test load adjustment of the 0Cr16Ni5Mo1 blade, and the subsequent test load is determined to be 360 ​​MPa.

[0149] Table 7: Test Load Adjustment Data for 0Cr16Ni5Mo1 Blades

[0150]

[0151] 2) Vibration fatigue test

[0152] Vibration fatigue tests were conducted on 0Cr16Ni5Mo1 blades after T0, T4, T7, T9, and T10 years based on the obtained stress levels.

[0153] a. During the test, the excitation force should be increased slowly. The test should begin when the amplitude at the measuring point reaches 80% of the given test amplitude, and then the amplitude should be slowly adjusted to the test amplitude. The amplitude and frequency of the specimen should be kept stable, and test data should be recorded every 20 minutes. Instantaneous overload should be recorded. Long-term overload (meaning the load on the blade exceeds the given stress level by 10% and exceeds the required number of test cycles by more than 1 / 5) will render the specimen invalid and the test ineffective. b. During the test, the test status should be monitored at all times, and photos should be taken to preserve the test data. c. Stop the test: Stop the test if the specimen is not damaged when the specified number of cycles is reached. During normal testing, if the vibration frequency of the specimen decreases by about 1% or visible cracks appear on the blade, the test should be stopped. If any abnormality is found during the test, the test should be stopped immediately, and the fault should be checked and eliminated.

[0154] (2) Vibration fatigue test data

[0155] Based on the above process, 0Cr16Ni5Mo1 blade tests were conducted at various pre-corrosion equivalent calendar years. The test data are shown in Table 8.

[0156] Table 8: Test data of 0Cr16Ni5Mo1 blades

[0157]

[0158]

[0159] In this embodiment, an accelerated corrosion test of 0Cr16Ni5Mo1 alloy steel blades was carried out for 10 equivalent calendar years by designing a periodic immersion test scheme. During the test, local corrosion occurred at individual defect locations on the blade surface, mainly manifested as regional spots. As the corrosion period was extended, until the 9th equivalent calendar year, the corrosion area on the blade surface began to gradually increase, and the number of local spot areas increased.

[0160] Example 4: To study the fatigue life evolution law of compressor alloy steel blades after pre-corrosion and determine the linear relationship between logarithmic fatigue life and corrosion equivalent calendar years, the specific process of the experimental analysis of the fatigue life evolution law of compressor alloy steel blades after pre-corrosion includes:

[0161] Step 301. Statistical Analysis of Fatigue Life Test Data

[0162] According to HB / Z112-1986 "Statistical Analysis Methods for Material Fatigue Testing", statistical analysis was performed on the life data of 0Cr16Ni5Mo1 blades obtained from the tests. The specific method and procedure include:

[0163] 1) Take the leaves of the five different ages as a group and calculate the average value of the samples according to HB / Z112-1986.

[0164] 2) Take each of the five leaf ages as a group and calculate the sample variance S according to HB / Z112-1986. 2 ;

[0165] 3) The five types of leaves with different ages were compared as a group, and the F-test was performed with reference to HB / Z112-1986.

[0166] 4) The five types of leaves with different ages were compared as a group, and the seven tests were performed according to HB / Z112-1986.

[0167] 5) The five types of leaves with different ages were compared as a group, and interval estimation was calculated with reference to HB / Z112-1986;

[0168] 6) Take each of the five leaf ages as a group and calculate the t' test according to HB / Z112-1986;

[0169] The median fatigue life of blades with five different service lifes was calculated. SPSS software was used to process the data and a regression model was established to show the evolution of the vibration fatigue life of the blades with the pre-corrosion service life.

[0170] Comparative test data of vibration fatigue of 0Cr16Ni5Mo1 blades after different pre-corrosion years (see Table 8 for details). Following the above procedure, the average value of each sample was calculated. Variance S 2 For details, please refer to Table 9:

[0171] Table 9: Statistical Analysis of Fatigue Life Data for 0Cr16Ni5Mo1 Blades

[0172]

[0173]

[0174] Calculation of the statistic F shows that, regardless of whether debug data is excluded, the F value is much larger than F. α This indicates that the differences in the average values ​​of the five corrosion cycle subsamples are significant, and it cannot be assumed that all subsamples come from the same parent body. In other words, the corrosion cycle has a significant impact on the fatigue performance of the blade.

[0175] Step 302. Analysis of the Evolution of Fatigue Life with Pre-Corrosion Age

[0176] The fatigue life data in Table 8 were first logarithmized, and then fitted with the pre-corrosion equivalent calendar years, as shown below. Figure 17 The figures show the measured data and fitting formulas for logarithmic fatigue life and fatigue life under different pre-corrosion equivalent calendar years, respectively. It can be seen that:

[0177] The logarithmic fatigue life of the blade decreases linearly with the corrosion cycle, indicating that corrosion damage weakens the fatigue life of the blade structure.

[0178] The linear relationship between logarithmic fatigue life LgN and corrosion equivalent calendar years T, obtained by least squares fitting, is: LgN = 6.83 - 0.14T, R 2 =0.96;

[0179] Within the same corrosion equivalent calendar years, the fatigue life of the blade exhibits a certain degree of dispersion; Table 9 presents the average fatigue life under different corrosion equivalent calendar years. Standard deviation S and coefficient of variation; with the 00-5 test piece data retained, the maximum coefficient of variation is in state T4 (0.037492098); after removing the 00-5 test piece data, the maximum coefficient of variation is still in state T4 (0.037492098); according to Appendix 1 of HB / Z112-1986, when the confidence level is 95%, the minimum number of observations of the data meets the requirements (required value 5, actual minimum value 7), indicating that its data error limit does not exceed 5%, which meets the standard requirements;

[0180] This embodiment combines finite element analysis and stress calibration to design a vibration fatigue test scheme after pre-corrosion. Vibration fatigue tests were carried out on 0Cr16Ni5Mo1 blades with five typical pre-corrosion equivalent calendar years: T0, T4, T7, T9, and T10. Different pre-corrosion years have a significant impact on fatigue life. With the increase of pre-corrosion years, the fatigue life generally shows a decreasing trend, with the decreasing law being: LgN = 6.83 - 0.14T.

[0181] Example 5: To determine the corrosion influence coefficient of compressor alloy steel blades, the fatigue life assessment process for compressor alloy steel blades based on the corrosion influence coefficient includes:

[0182] The service life of an aerospace equipment structure includes two main indicators: one is fatigue life, expressed in flight hours or number of takeoffs and landings; the other is calendar life, expressed in years of service. The development goal is usually to achieve the total life of the combined fatigue life and calendar life. Once either of these two lifespans reaches the design target, the service life of the aerospace equipment structure ends.

[0183] Modern aviation equipment requires long service life, high reliability, and good economy. To achieve this comprehensive requirement, key components and parts that determine the service life of aviation equipment should be designed to allow for appropriate inspection and economical repair throughout their entire service life. The total service life of aviation equipment structures is usually allowed to be achieved through a certain number of major overhauls. Therefore, both fatigue life and calendar life consist of the corresponding first overhaul period, repair (major overhaul) interval, and total service life, and include the corresponding repair outline, i.e., the components, parts, and repair methods for each repair. When aviation equipment reaches the first overhaul period of fatigue life or calendar life, it needs to undergo its first overhaul. After the first overhaul, regardless of whether the next fatigue life repair interval or calendar life repair interval is reached first, a second major overhaul is required, and so on, until the total fatigue life or total calendar life is reached, indicating the end of the service life of the aviation equipment structure. The first overhaul period, repair interval, and corresponding number of repairs for fatigue life or calendar life are given by the life assessment (including analysis and testing) after design, with the aim of achieving the development indicators of structural fatigue and total calendar life of aviation equipment. However, the increase in the number of repairs will have a significant impact on the availability and combat readiness of aviation equipment, and will also increase repair costs and affect economic efficiency. Therefore, the allowable number of repairs usually must be approved by the user, and the user can also make certain requirements in advance.

[0184] In principle, the fatigue life and calendar life of aircraft equipment structures both include the first overhaul period, repair interval, and total lifespan, with the first-come-first-served principle controlling the service life of the aircraft equipment structure. However, the corrosive environment affects both fatigue life and calendar life, and is closely related to the annual flight hours (annual flight intensity) of the aircraft equipment; therefore, there is a certain mutual constraint between calendar life and fatigue life. Under different annual flight intensities and operating environments, sometimes the first overhaul, major overhaul, and total lifespan are all determined by the service life, and sometimes they are all determined by the fatigue life; sometimes, more complex situations arise, such as the first overhaul depending on the service life, while the second major overhaul depends on the fatigue life after considering the corrosion effect, and the end of the total lifespan may also depend on the fatigue life; or the first overhaul and the second major overhaul depend on the service life, while the end of the total lifespan depends on the fatigue life; of course, there are other possibilities; at the same time, the different requirements for repairing structural cracks and corrosion during the first overhaul and major overhaul will also have a certain impact on the subsequent lifespan; considering this situation, the first-come-first-served principle is no longer suitable for the requirement of clearly judging the first overhaul, major overhaul, and total lifespan of aircraft equipment structures. To facilitate users' management of the overhaul and total lifespan of aviation equipment, it is necessary to establish a service life (first overhaul, major overhaul and total lifespan) system based on calendar years, taking into account calendar life and fatigue life that takes into account the effects of corrosion. We call this the calendar life system.

[0185] Step 401. The Impact of Environmental Corrosion on the Service Life of Aviation Equipment

[0186] The service conditions that determine the lifespan of aerospace equipment structures mainly include the load-time history experienced by the structure during use, and the environmental-time history during ground parking and flight, referred to as load conditions and corrosion conditions. The load-time history described by the load spectrum (power spectrum for aero-engines, the same below) is the main factor determining the fatigue life of aerospace equipment structures, while the environmental-time history described by the environmental spectrum is the main factor determining the calendar life of aerospace equipment structures. Environmental corrosion affects the fatigue life of aerospace equipment structures, thereby affecting the calendar life of critical fatigue components. In particular, for critical components that may fail or become unrepairable due to corrosion (corrosion failure critical components), their calendar life directly depends on corrosion conditions.

[0187] (1) The impact of environmental corrosion on the fatigue life of aerospace equipment structures

[0188] Environmental corrosion has a significant impact on the fatigue life of aerospace equipment structures. Generally, this impact manifests in two aspects: First, when aerospace equipment is parked on the ground, factors such as the airport's natural environment place critical fatigue components and parts in a localized corrosive environment. As the years of ground parking increase, corrosion continuously degrades the fatigue quality of these components, thus reducing their fatigue life. Second, during flight, the combined effects of the air environment and loads exacerbate fatigue damage, further reducing fatigue life. For aerospace equipment with an average annual flight time of 300-400 flight hours, the impact of ground-based environmental corrosion is dominant. This is noteworthy. The impact of corrosion on the fatigue life of aircraft equipment structures depends not only on the severity of the environment but also on the annual flight intensity of the aircraft equipment. Therefore, for a particular type of aircraft equipment structure, its fatigue life is related to environmental conditions and annual flight intensity. As a design indicator for fatigue life, it is necessary to not only clarify the corresponding operating environment (region) limitations but also to clarify the requirements for the annual flight intensity of the model. As a refined and improved fatigue life indicator, it is necessary to distinguish the environmental conditions of several different typical operating regions and several different operating conditions (annual flight intensity), and give different first overhaul periods, repair intervals, and total lifespans.

[0189] (2) The impact of environmental corrosion conditions on the structural calendar life of aerospace equipment

[0190] First, corrosion conditions affect the fatigue life of aircraft equipment structures. The calendar life of critical fatigue components is related to fatigue life and annual flight intensity. Therefore, corrosion conditions will correspondingly reduce the calendar life repair interval and total life of critical fatigue components. Second, for critical components that fail due to corrosion, the repair interval of their calendar life must ensure that the structure does not suffer from corrosion that causes functional failure in the environment where the critical component (part) is located, or that the structure cannot be effectively and economically repaired due to corrosion. Therefore, environmental corrosion conditions are the decisive factor in determining the calendar life of such critical components (parts). For some aircraft equipment, the ground parking time is much longer than the flight time, the latter usually not exceeding 2% to 3% of the total service time, and the air environment is usually weaker than the ground environment. Therefore, the calendar life of such critical components (parts) mainly depends on ground parking corrosion. Therefore, the determination of the calendar life index of a type of aircraft equipment structure should mainly be based on the user's requirements for the service life of the aircraft type, and the number of calendar repairs (including the first overhaul period and each major overhaul) should be coordinated with the number of fatigue life repairs.

[0191] (3) The impact of environmental corrosion conditions on the structural lifespan of aerospace equipment

[0192] Since environmental corrosion conditions affect both the fatigue life and calendar life of aircraft equipment structures, there is a certain constraint between the fatigue life and calendar life indicators. Under the condition that the operating area, corrosion conditions and annual flight intensity do not change significantly during the service life of aircraft equipment, in some cases the life system is mainly based on fatigue life, that is, the first overhaul, major overhaul and total life of aircraft equipment structures are mainly controlled by flight hours; while in other cases the calendar life is mainly based, that is, the first overhaul, major overhaul and total life of aircraft equipment structures are controlled by the service life.

[0193] The main factors determining the different situations mentioned above are environmental corrosion conditions and annual flight intensity. Therefore, it is necessary to clarify the impact of environmental corrosion conditions and annual flight intensity on the life system of aviation equipment structures, and to give the first overhaul period, repair interval and total life of fatigue life and calendar life respectively. Under given corrosion conditions, within what range of annual flight intensity should the life system use fatigue life or calendar life as the main control indicator, or should both be judged comprehensively? Such a comprehensive life system will enable users to more proactively and rationally control the overhaul and service life of aviation equipment structures.

[0194] Step 402. Methods for assessing the structural fatigue life of aircraft equipment under environmental corrosion conditions.

[0195] Step 4021. Definition and Acquisition Process of Corrosion Influence Coefficient C

[0196] Based on the conclusion of fatigue life (first overhaul period, repair interval and total life) under normal environment (room temperature and atmosphere), a corrosion influence coefficient C is introduced to convert the damage such as flight hours under corrosive conditions into equivalent flight hours under normal environment. The fatigue life under normal environment is used as the criterion for evaluating and monitoring fatigue life (first overhaul period, repair interval and total life) under corrosive conditions.

[0197] Corrosion Influence Coefficient C j Defined as:

[0198] Where, N 0j Let J be the lifetime value for corrosion period j, and N0 be the lifetime value of the uncorroded structure. As can be seen from the definition, the corrosion influence coefficient C is a function related to the corrosion time T.

[0199] Corrosion Influence Coefficient C j The acquisition and calculation process is as follows:

[0200] (1) To determine N0, simulated specimens of designated fatigue key parts can be used to conduct group fatigue tests under room temperature atmospheric environment and operating load spectrum to obtain a set of fatigue life N. 0k (k = 1, ..., m0), assuming it follows a log-normal distribution, then

[0201] (2) Determination of N 0j A set of identical simulated specimens can be used, first subjected to accelerated testing under a harmonic environment equivalent to ground parking for T... j Accelerated corrosion tests were conducted over several years, followed by group fatigue tests under ambient temperature and atmospheric conditions and operating load spectrum to obtain the fatigue life N. kj (k = 1, ..., m) j Median life

[0202] (3) To determine the CT curve, several T values ​​need to be selected. j (j=0,1,…,q), using (q+1) groups of simulated specimens, one group is used to determine N0, and the q groups are used to accelerate corrosion to the equivalent environmental corrosion T. j After the New Year, group fatigue tests were conducted under ambient temperature and atmospheric conditions and using load spectra, and the values ​​of q N were measured. 0j Thus, q groups (T) are obtained. j C j The data is fitted using a suitable functional relationship to determine the CT curve;

[0203] Step 4022. Physical background and expression establishment of the curve of corrosion influence coefficient C versus corrosion time T

[0204] (1) The CT curve is composed of (T jC j The data was obtained through fitting, which is equivalent to parking T on the ground. j The corrosion of (year) was determined through accelerated testing under environmental spectra. j (hours), obtained according to the equivalent acceleration relationship T = βt; therefore, from (T j C j The CT curve fitted by the data and the curve obtained from (t) j C j The Ct curves fitted to the data have similar functional expressions;

[0205] (2) The establishment of CT curves is mainly based on the simulation specimens of key fatigue parts corresponding to several t j C below j The experimental results, i.e. (t) j C j (j = 1, ..., q) data, where the number of data points q must be sufficient, t j The range should be wide in order to better show the changes in CT scans.

[0206] (3) Based on (t) j C j The analysis of the data variation patterns and the variation patterns of Ct(CT) leads to the selection of several possible functional relationships corresponding to it, based on (t) j C j The data (j = 1, ..., q) were fitted, with the best correlation (correlation coefficient closest to 1) as the primary criterion; and C was referenced within the commonly used service life range. j The magnitude of the deviation between the fitted value and the experimental value determines the expression for the Ct(CT) curve;

[0207] (4) When establishing CT curves, the actual physical context must be considered, namely:

[0208] As the environmental corrosion time T increases, the corrosion influence coefficient C value continuously decreases. That is, in the early stage of equipment service, corrosion has no significant impact on fatigue life; as the service time extends, the effects of environmental corrosion begin to appear, and with the formation and aggravation of corrosion damage, the fatigue life and C value decrease at a large gradient; however, after the equipment has been in service for a long time, due to the formation, deepening and merging of multiple corrosion damages on the equipment structure, the depth of the deepest pit relative to the depth of the corroded surface increases more and more slowly, therefore, the gradient of fatigue life and C value decreases gradually.

[0209] Based on the general physical background, we can conclude that CT curves should possess the following properties:

[0210] ①When T=0, C(0)=1;

[0211] ②When T=∞, C(∞)=0;

[0212] ③ As the environmental corrosion time increases, the structural fatigue life continuously decreases, that is: C'(t)≤0;

[0213] Based on the above three points, the following expression for the CT curve is proposed:

[0214] C(t)=1.0-βt α

[0215]

[0216] In the formula: β is the coefficient; α is the exponent;

[0217] Based on existing experimental data, the above expression shows a good local fitting effect, and can accurately reflect the influence of environmental corrosion within the range of 0≤T≤30. The physical meaning is relatively clear, strictly meets the above constraints, and has a good fitting effect.

[0218] Step 403. Fatigue life assessment and analysis of compressor alloy steel blades after pre-corrosion.

[0219] The fatigue test data of 0Cr16Ni5Mo1 blades are shown in Table 8 (raw data) and Table 9 (statistical data). The corrosion influence coefficient under typical pre-corrosion years is shown in Table 10.

[0220] Table 10: Calculation results of corrosion influence coefficient of 0Cr16Ni5Mo1 blade under typical pre-corrosion years.

[0221]

[0222] Two forms of CT curves, such as Figure 19 (a) Figure 19 As shown in (b);

[0223] The accuracy of the life assessment method was analyzed using the obtained corrosion influence coefficient expression and vibration fatigue life data after 10 years of pre-corrosion. The specific results are shown in Table 11.

[0224] Table 11: Calculation results of life of 0Cr16Ni5Mo1 blades under typical pre-corrosion years based on corrosion influence coefficient.

[0225]

[0226]

[0227] As can also be seen from Table 11, the life analysis results of 0Cr16Ni5Mo1 blades under typical pre-corrosion equivalent calendar years obtained by the above method have good prediction accuracy.

[0228] Using the corrosion impact coefficient to predict fatigue life after 15 years of service, compared with the uncorroded state, the blade structure is affected by the coastal environment, and its fatigue life is reduced by about 24%, which is consistent with the engineering background.

[0229] This embodiment proposes the concept of corrosion influence coefficient C based on statistical analysis of vibration fatigue test data, and establishes two forms of CT curve expressions based on experimental data, namely C = 1 - 0.0166T. 1.05 C = exp(-0.0158T) 1.12 Two CT curves were used to evaluate the vibration fatigue life of compressor blades after 10 years of service, and the evaluation error was about 4%.

[0230] Example 6: To verify the correlation between the fatigue life of compressor alloy steel blades and corrosion damage in a coastal environment, the verification analysis process for the correlation between the fatigue life of compressor alloy steel blades and corrosion damage in a coastal environment includes:

[0231] In Example 5, a method for analyzing the fatigue life of compressor blade structures using the corrosion influence coefficient based on environmental impact was proposed. It can be seen that the core of the corrosion influence coefficient analysis method is to explain the impact of environmental corrosion on fatigue life from the perspective of the corrosion influence coefficient C. This coefficient C changes with the corrosion equivalent calendar years T. The apparent relationship is that fatigue life is related to the corrosion years, and the corrosion years determine the corrosion damage state of the blade structure. That is, the evolution of fatigue life is essentially related to the corrosion damage state. This example verifies and analyzes the relationship between fatigue life and the material corrosion damage state.

[0232] The impact of corrosion damage on the fatigue life of aerospace equipment metal structures can be viewed physically as an alternating process of ground-based corrosion damage and in-flight fatigue damage, i.e., corrosion…fatigue…re-corrosion…re-fatigue…until failure. In engineering, this process is simplified by separating corrosion and fatigue, employing post-corrosion fatigue analysis methods. This problem can be studied using experimental or experimental analysis methods. Regardless of the method, the specific circumstances of the corrosion damage must be considered, taking into account its impact on fatigue life. Currently, it is generally accepted in relevant research fields that the corrosion damage depth parameter has the most significant impact on fatigue life. Related literature equates the corrosion damage depth parameter to the initial crack size during fatigue life calculations.

[0233] Therefore, when conducting the correlation analysis between fatigue life and corrosion damage, the corrosion damage depth parameter is used for explanation.

[0234] Based on the pre-corrosion test results, the average values ​​of the surface corrosion damage depth of the blades under various equivalent calendar years were obtained, as shown in Table 1. It can be seen that the average value of the surface corrosion damage depth of 0Cr16Ni5Mo1 alloy steel blades shows a linear increasing trend with the increase of the corrosion equivalent years. Moreover, as the corrosion years extend, the corrosion depth increases and gradually tends to stabilize.

[0235] The correlation between the mean corrosion depth and the mean vibration fatigue life was tested using SPSS software. First, the correlation between corrosion depth (Table 1) and fatigue life (Table 9) was analyzed, and the results are shown in Table 12.

[0236] Table 12: Correlation test between mean corrosion depth and mean blade vibration fatigue life

[0237] Linear correlation (Pearson) Logarithmic mean of vibration fatigue life Corrosion damage depth Log-lifetime removed average 1 <![CDATA[-.975 ** ]]>

[0238] The analysis results show that the mean corrosion depth is strongly negatively correlated with the mean fatigue life;

[0239] Based on this, exponential, linear, quadratic, power, and logarithmic fitting methods were used to further explore the correlation between fatigue life and corrosion depth, and the coefficient of determination was used to characterize the strength of the correlation.

[0240] The coefficient of determination, also known as the factor of determination, is a statistic reflecting the goodness of fit of a model. It indicates whether the regression of two random variables on one other is linear or nonlinear; the coefficient of determination η of random variable X1 with respect to X2. 2 12 It equals the ratio of the variance of the regression μ1(X2) = E(X1|X2) on X1 to the variance of X1.

[0241]

[0242] That is, the proportion of the total variance of X1 regressed by μ1(X2) has already been explained;

[0243] R 2 Values ​​range from 0 to 1, and are dimensionless; the magnitude of the value reflects the relative degree of the regression contribution, that is, the percentage of the total variation in the dependent variable Y that can be explained by the regression relationship; R 2 The closer R is to 1, the better the fitted regression equation; generally, R is considered to be... 2 >0.3 is meaningful, R 2 A value of around 0.8 indicates a relatively good model fit.

[0244] Four fitting methods were used, and the analysis results are shown in Table 13. The fitting correlation results are as follows: Figure 20 As shown.

[0245] Table 13: Coefficient of determination for different modeling methods of depth mean and logarithmic mean of lifetime

[0246] Fitting methods <![CDATA[Coefficient of determination (R 2 Index)]]> index 0.9329 linear 0.9499 Square 0.9922 cubed 0.9924

[0247] As shown in Table 13, the coefficients of determination for exponential, linear, quadratic, and cubic methods are respectively the exponential coefficient of determination R. 2 Index = 0.9329, Linearity of Determination R 2 Linear = 0.9499, quadratic coefficient of determination R 2 Quadratic = 0.9922, cubic coefficient of determination R 2 Cubic = 0.9924. The coefficients of determination for all four modeling methods are greater than 0.8, indicating that all four models meet statistical requirements. This demonstrates a significant correlation between the degree of corrosion damage and the degree of fatigue life decay, and R... 2 Cubic>R 2 Quadratic>R 2 Linear>R 2 The index can be considered as a cubic function that indicates a decrease in the fatigue life of the specimen as the corrosion depth increases.

[0248] The above correlation analysis results show that corrosion in coastal environments has a significant weakening effect on the fatigue life of compressor blade structures. Therefore, the corrosion influence coefficient method proposed in this invention has a realistic physical background for conducting fatigue life analysis under real service environments.

[0249] This embodiment demonstrates a strong negative correlation between corrosion damage depth and blade vibration fatigue life; that is, increased corrosion damage depth leads to a significant decrease in fatigue life. Correlation quantification analysis was performed using exponential, linear, quadratic, and cubic functions. The results show that the coefficients of determination are all above 0.8, with some functions exceeding 0.9.

[0250] As can be seen from the above analysis, the fatigue life correction assessment method based on the corrosion influence coefficient proposed in this study has good prediction accuracy, can meet the needs of life assessment applications in engineering, and can provide technical support for subsequent high reliability life studies of blades / structural components.

[0251] This invention focuses on the practical problem of fatigue life assessment for alloy steel compressor blades used in coastal environments for aero-engines. It proposes a comprehensive method integrating pre-corrosion testing, post-corrosion vibration fatigue testing, and corrosion influence coefficient analysis based on experimental data. This method provides a reasonable and accurate assessment of the mechanical life of aero-engine compressor blades in coastal environments. To conduct pre-corrosion and post-corrosion vibration fatigue tests, accelerated corrosion and vibration fatigue test schemes were designed respectively. Based on the test results, CT curve expressions for the two corrosion influence coefficients were established. These expressions were then used to analyze and assess the fatigue life of the blades after a certain service life, and the correlation between corrosion damage and fatigue life was verified. The analysis results show that the comprehensive method proposed in this invention yields reasonable and accurate results, demonstrating promising engineering application prospects.

[0252] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for vibration fatigue analysis and evaluation of compressor alloy blades in coastal environments, characterized by: Including steps Step 1. Determine the relationship between the corrosion status of the compressor alloy steel blades and the service environment and service duration; Step 2. Determine the vibration fatigue characteristics of the pre-corroded compressor alloy steel blades under load; Step 3. Determine the fatigue life evolution law of compressor alloy steel blades after pre-corrosion, and determine the linear relationship between logarithmic fatigue life and corrosion equivalent calendar years; Step 4. Determine the corrosion influence coefficient of the compressor alloy steel blades, and conduct fatigue life assessment of the compressor alloy steel blades based on the corrosion influence coefficient; Step 5. Conduct a correlation analysis between the fatigue life of compressor alloy steel blades and corrosion damage in a coastal environment; In Step 1, a pre-corrosion test simulating the service environment of compressor alloy steel blades is used to analyze the relationship between the corrosion of compressor alloy steel blades and the service environment and service duration. It is determined that in a coastal environment, the compressor blade structure will be affected by the environment and suffer corrosion damage, and the corrosion damage will gradually worsen with the increase of equipment service time within a certain period. In Step 2, fatigue tests were conducted on pre-corroded compressor alloy steel blades to analyze their vibration fatigue characteristics under load. These tests were based on the finite element method. The results showed that during accelerated corrosion of the alloy steel blades over 10 equivalent calendar years, localized corrosion occurred at individual defect locations on the blade surface, manifesting as regionalized spots. As the corrosion cycle lengthened, up to the 9th equivalent calendar year, the number of corroded areas on the blade surface gradually increased, along with the number of localized spot areas. In Step 3, the fatigue life evolution law of compressor alloy steel blades after pre-corrosion is analyzed using experimental methods to determine the linear relationship between logarithmic fatigue life and corrosion equivalent calendar years. The linear relationship between logarithmic fatigue life LgN and corrosion equivalent calendar years T is obtained as follows: ， ； In Step 4, the corrosion influence coefficient of the compressor alloy steel blades is determined using the fatigue life assessment method for aerospace equipment structures under environmental corrosion conditions, and a CT curve is established. The specific process for determining the corrosion influence coefficient of compressor alloy steel blades using the fatigue life assessment method for aerospace equipment structures under environmental corrosion conditions includes the following steps: Step 4021. Define and obtain the corrosion influence coefficient C; (1) Define the corrosion influence coefficient C Based on the conclusions of fatigue life determination under normal environment, a corrosion influence coefficient C is introduced to convert the damage under corrosive conditions into equivalent flight hours under normal environment. The fatigue life under normal environment is used as the criterion for evaluating and monitoring fatigue life under corrosive conditions. Corrosion Influence Coefficient Defined as: ; in, Let j be the lifetime value for a corrosion cycle. This represents the lifespan of the uncorroded structure. (2) Obtain the corrosion influence coefficient C; Step 4022. Establish the curve of corrosion influence coefficient C versus corrosion time T; The process of obtaining the corrosion influence coefficient C in step 4021 (2) includes: 1) Measurement Using simulated specimens of specified fatigue critical parts, group fatigue tests were conducted under room temperature atmospheric environment and service load spectrum to obtain a set of fatigue life results. Assuming it follows a log-normal distribution, then ; 2) Measurement Using a set of identical simulated specimens, they were first subjected to accelerated testing in an environment equivalent to ground parking. Accelerated corrosion tests were conducted over several years, followed by group fatigue tests under ambient temperature and atmospheric conditions and operating load spectrum to obtain fatigue life. (k=1,…, Median life ; 3) To determine the CT curve, several [specific parameters] need to be selected. (q+1) groups of simulated specimens were used, and 1 group was used for measurement. Group q accelerates corrosion to a level comparable to environmental corrosion. After the New Year, group fatigue tests were conducted under room temperature and atmospheric conditions and using load spectra, and q samples were measured. Thus, q groups are obtained. The data is fitted using a functional relationship to determine the CT curve; Step 4022 describes the process of establishing the corrosion influence coefficient C versus corrosion time T curve, which includes... (1) By ( , The CT curve fitted by the data and the curve obtained by ( , The Ct curve fitted to the data has a similar functional expression. For hours; (2) The establishment of CT curves is mainly based on several simulated specimens corresponding to key fatigue locations. Below The experimental results, namely ( , (j=1, ..., q) data; (3) Based on ( , The patterns of data change and their impact The analysis of the changing pattern involves selecting several possible functional relationships corresponding to it, from ( , The data (j=1, ..., q) were fitted, with the best correlation being the primary criterion; and the data within the commonly used service life range were also referenced. The magnitude of the deviation between the fitted value and the experimental value, and the selection The expression for the curve; (4) When establishing CT curves, the real physical background should be considered, that is: As the environmental corrosion time T increases, the corrosion influence coefficient C value will continuously decrease; that is, in the early stage of equipment service, corrosion has no significant impact on fatigue life; as the service time is extended, the effect of environmental corrosion begins to appear, and as corrosion damage forms and intensifies, the fatigue life and C value decrease at a large gradient; the gradient of fatigue life and C value decreases gradually. Based on the general physical background, the following properties of CT curves are summarized: ① hour, ; ② hour, ; ③ As environmental corrosion and corrosion time increase, the structural fatigue life continuously decreases, that is: ; Based on the above three points, the following expression for the CT curve is proposed: ； In the formula: For coefficients; It is an index.

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