Turbine blade service life evaluation method

Abstract

The invention relates to the technical field of life evaluation, in particular to a turbine blade life evaluation method which comprises the following steps: acquiring surface temperature gradient data of a turbine blade of a gas turbine, extracting the temperature change rate of each area of the blade, analyzing the influence of the temperature change rate on the local stress of the blade, and evaluating the life of the turbine blade. And calculating a thermal stress synthetic effect by combining the blade rotation inertia force and the aerodynamic load to obtain blade thermal stress distribution data. According to the method, the thermal stress synthesis effect is calculated by acquiring the temperature gradient data of the surface of the blade and combining the rotary inertia force and the aerodynamic load, the thermal stress distribution characteristics of the blade in different operation states can be accurately reflected, and the life attenuation rate is calculated by combining the current working condition, so that life prediction can be adjusted along with the operation states; the service life offset is calculated by analyzing the influence of the start-stop load of the gas turbine and comparing creep fatigue joint distribution, so that the influence of the running state of the gas turbine on the service life prediction can be dynamically updated in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of life assessment, and particularly to a method for assessing the life of a turbine blade. Background Art

[0002] The technical field of life assessment involves predicting and analyzing the durability, reliability, and service life of equipment, structures, or components under specific operating conditions. This technology is widely applied in industries such as aerospace, energy, automotive, and nuclear industry. By means of physical modeling, data-driven methods, or a combination of both, it evaluates the damage evolution process of materials under conditions such as fatigue, creep, corrosion, and wear. Common methods include finite element analysis, fracture mechanics analysis, statistical life modeling, machine learning-based health monitoring, etc., aiming to optimize maintenance strategies, reduce operating costs, and improve equipment safety and reliability.

[0003] Among them, the method for assessing the life of a turbine blade is used to predict the life and performance degradation of a turbine blade in a long-term high-temperature, high-pressure, and high-speed rotating environment. This method combines technologies such as fluid mechanics analysis, thermo-mechanical coupling calculation, fatigue damage modeling, and failure probability assessment to determine the remaining life of the turbine blade and the optimal maintenance and replacement time, optimize the service life management strategy of the turbine blade, so as to improve the operating efficiency of the gas turbine, reduce unplanned shutdowns, and ensure the safe and stable operation of the unit.

[0004] Traditional assessment methods usually predict the blade life by means of finite element analysis, fracture mechanics analysis, and statistical life modeling during the life assessment process. They can provide a certain degree of accuracy when dealing with the life assessment under stable operating conditions. However, for the dynamic characteristics of temperature gradient changes, stress evolution, and creep-fatigue interaction during the operation of a gas turbine, there is a lack of a real-time correction mechanism, and it is difficult to finely depict the actual thermal stress distribution in different regions of the blade, resulting in a reduction in the accuracy of life prediction under extreme or variable operating conditions. When traditional methods deal with the creep-fatigue interaction, they usually adopt an independent assessment method and fail to consider the cumulative effect of creep damage and fatigue damage under different operating loads, which is prone to life prediction deviation. Under the condition of frequent load changes during the start-up and shutdown of a gas turbine, traditional methods cannot accurately reflect the influence of extreme loads on the fatigue damage rate of the blade, making the life assessment result deviate from the actual service situation and increasing the uncertainty of maintenance and replacement decisions. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a method for assessing the life of a turbine blade.

[0006] To achieve the above purpose, the present invention adopts the following technical scheme: A method for assessing the life of a turbine blade, comprising the following steps: S1: Obtain the surface temperature gradient data of the gas turbine turbine blade, extract the temperature change rate of each area of the blade, analyze the influence of the temperature change rate on the local stress of the blade, and calculate the combined effect of thermal stress by combining the blade rotational inertia force and aerodynamic load to obtain the blade thermal stress distribution data; S2: Based on the blade thermal stress distribution data, extract the stress change rate of the local area of the blade, screen the areas where the stress change rate is greater than the set stress change threshold, and calculate the stress overrun probability of each area of the blade in combination with the fatigue limit of the blade matrix to obtain the blade overrun risk area; S3: Call the blade overrun risk area, combine the creep rate, fatigue damage accumulation rate and operating load change rate to calculate the cumulative rate of high-temperature creep and fatigue damage of the blade, and screen the areas where the creep damage accumulation rate is higher than the set damage critical value to obtain the blade creep-fatigue damage distribution information; S4: Based on the blade creep-fatigue damage distribution information, adjust the life decay rate according to the creep and fatigue damage accumulation rate under the current working condition, calculate the residual life correction coefficient of the blade, and calculate and obtain the corrected life prediction value in combination with the service stage of the blade.

[0007] The improvements of the present invention are as follows: the blade thermal stress distribution data includes the thermal stress change rate, temperature change rate and combined effect of thermal stress; the blade overrun risk area specifically refers to the stress overrun probability, stress change rate and overrun area distribution; the blade creep-fatigue damage distribution information includes the creep stress influence coefficient, fatigue damage accumulation rate and joint probability density function; the obtaining of the corrected life prediction value specifically refers to the residual life correction coefficient, life decay rate and life decay trend; the blade remaining life interval includes the remaining life range, life threshold influence and operating condition life influence.

[0008] The improvements of the present invention are as follows: the specific steps for obtaining the blade thermal stress distribution data are as follows: S111: Obtain the surface temperature gradient data of the gas turbine turbine blade, extract the temperature change rate of each area of the blade according to the time series, calculate the temperature difference between adjacent time points to obtain the temperature change rate distribution data; S112: Based on the temperature change rate distribution data, calculate the thermal stress change rate of each area of the blade, call the thermal expansion coefficient and elastic modulus of the blade material, and use the formula: ; Calculate the thermal stress change rate of the blade area to obtain the thermal stress change rate distribution data; Among them, represents the thermal stress change rate of the blade area, represents the elastic modulus of the blade material, represents the material thermal expansion coefficient, represents the temperature difference between adjacent time points, represents the adjacent time interval; S113: Based on the thermal stress change rate distribution data, combining the blade rotation inertia force and the aerodynamic load data, calculate the thermal stress synthesis effect of each region, and obtain the blade thermal stress distribution data.

[0009] The improvement of the present invention is that the obtaining step of the blade over-limit risk area is specifically as follows: S211: Based on the blade thermal stress distribution data, calculate the transient thermal stress change trend under the current working condition of the blade at a preset time interval, and obtain the transient thermal stress change trend data; S212: Based on the transient thermal stress change trend data, extract the stress change rate of the local area of the blade, and screen the areas where the stress change rate is greater than the set stress change threshold, and use the formula: ; Calculate the stress over-limit probability of the local area of the blade, and obtain the stress over-limit probability distribution data; Among them, represents the stress over-limit probability, represents the stress change rate of the local area, represents the set stress change threshold, is a small positive number to avoid the denominator being zero, represents the yield strength ratio of the blade material, is the base of the natural logarithm, represents the stress change influence coefficient; S213: Based on the stress over-limit probability distribution data, combining the fatigue limit of the blade matrix, screen the areas where the stress over-limit probability is higher than the fatigue limit risk threshold, and obtain the blade over-limit risk area.

[0010] The improvement of the present invention is that the obtaining step of the blade creep-fatigue damage distribution information is specifically as follows: S311: Call the blade over-limit risk area, extract the temperature gradient change rate, stress change rate and cyclic load frequency of the blade over-limit area, calculate the creep stress influence coefficient of the local area of the blade, and construct a creep stress influence factor matrix according to the combined influence of the temperature change rate and stress change rate of each region, and obtain the blade creep stress influence coefficient distribution; S312: Call the blade creep stress influence coefficient distribution, combine the creep rate, fatigue damage accumulation rate and operating load change rate, and use the formula: ; Calculate the creep and fatigue damage accumulation rate of the blade; Among them, represents the creep and fatigue damage accumulation rate of the blade, represents the transient thermal stress value of the blade, represents the fatigue limit of the blade material, represents the fatigue strength index of the material, represents the load frequency influence coefficient, represents the cyclic load frequency of the blade, represents the creep rate adjustment factor, represents the creep rate of the blade, represents the reference creep rate; S313: Call the creep and fatigue damage accumulation rate of the blade, screen the areas where the creep damage accumulation rate is higher than the set damage critical value, and obtain the creep-fatigue damage distribution information of the blade.

[0011] The present invention is improved in that the step of obtaining the corrected life prediction value is specifically as follows: S411: Based on the creep-fatigue damage distribution information of the blade, calculate the life attenuation rate of each area of the blade according to the creep and fatigue damage accumulation rate under the current working condition, and adjust the attenuation parameter in combination with the change trend of the damage accumulation rate to obtain the life attenuation rate distribution information of the blade; S412: Call the life attenuation rate distribution of the blade and use the formula: ; Calculate the remaining life correction coefficient of the blade; Wherein, represents the remaining life correction coefficient of the blade, represents the creep and fatigue damage accumulation rate of the blade, represents the damage accumulation adjustment coefficient, represents the cyclic load frequency of the blade, represents the load frequency attenuation parameter; S413: Call the remaining life correction coefficient of the blade, combine the service stage of the blade, correct the life, calculate the life prediction value, establish the blade life prediction data, and obtain the corrected life prediction value of the blade.

[0012] The present invention is improved in that the method further includes the following steps: S5: Call the corrected life prediction value, analyze the influence of the start-stop load of the gas turbine on the blade damage accumulation rate, calculate the fatigue damage adjustment coefficient under extreme working conditions, compare the creep-fatigue joint distribution in the blade operation state, calculate the extreme working condition life offset, and obtain the remaining life interval of the blade; The remaining life interval of the blade includes the remaining life range, the influence of the life threshold, and the influence of the operation condition life.

[0013] The improvement of the present invention is that the steps for obtaining the remaining life interval of the blade are specifically as follows: S511: Call the corrected life prediction value, analyze the influence of the start-stop load of the gas turbine on the blade damage accumulation rate, extract the creep damage increment and fatigue damage increment caused by the change of the start-stop load, calculate the change trend of the damage rate of the blade under various start-stop cycles, and obtain the blade start-stop load damage rate distribution information; S512: Call the blade start-stop load damage rate distribution information, calculate the fatigue damage adjustment coefficient under extreme conditions, and use the improved formula: ; Calculate the fatigue damage adjustment coefficient of the blade under extreme conditions; Wherein, represents the fatigue damage adjustment coefficient of the blade, represents the stress change amount caused by extreme conditions, represents the reference stress, represents the stress response coefficient under extreme conditions, represents the damage accumulation adjustment coefficient under extreme conditions, represents the load frequency sensitivity under extreme conditions, represents the blade cyclic load frequency under extreme conditions; S513: Call the fatigue damage adjustment coefficient of the blade under extreme conditions, compare the creep-fatigue joint distribution in the blade operating state, calculate the extreme condition life offset, establish the blade remaining life range data, and obtain the blade remaining life interval.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by obtaining the temperature gradient data on the blade surface, calculating the temperature change rate in the blade area, and combining the rotational inertial force and the aerodynamic load to calculate the synthetic effect of thermal stress, it is possible to accurately reflect the thermal stress distribution characteristics of the blade under different operating conditions, extract the stress change rate in the local area of the blade, screen the areas where the stress change rate is greater than the set threshold, and calculate the stress overrun probability of the blade in combination with the matrix fatigue limit, so that the stress overrun risk of the blade can be finely determined for different operating conditions. Based on the analysis of the overrun risk area, the cumulative rate of high-temperature creep and fatigue damage of the blade is calculated by combining the creep rate, the fatigue damage accumulation rate, and the operating load change rate, and the areas where the creep damage accumulation rate is higher than the set critical value are screened. During the evolution process of fatigue damage, the impact of creep damage on the remaining life of the blade can be dynamically evaluated. According to the creep-fatigue damage distribution information, the life attenuation rate is calculated in combination with the current working condition, and the remaining life correction coefficient is adjusted, so that the life prediction can be adjusted with the operating condition. By analyzing the impact of the start-stop load of the gas turbine, the fatigue damage adjustment coefficient under extreme working conditions is calculated, and the life offset is calculated by comparing the creep-fatigue joint distribution, forming the remaining life interval of the gas turbine under different load conditions, so that the impact of the gas turbine operating condition on the life prediction can be updated in real time and dynamically. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the flowchart of the method of the present invention; Figure 2 is the flowchart of obtaining the blade thermal stress distribution data of the present invention; Figure 3 is the flowchart of obtaining the blade overrun risk area of the present invention; Figure 4 is the flowchart of obtaining the creep-fatigue damage distribution information of the blade of the present invention; Figure 5 is the flowchart of obtaining the corrected life prediction value of the present invention; Figure 6 is the flowchart of obtaining the remaining life interval of the blade of the present invention; Figure 7 is the schematic diagram of the blade fatigue damage distribution of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Please refer to Figure 1 , the present invention provides a technical solution: a method for evaluating the life of a turbine blade, including the following steps: S1: Obtain the surface temperature gradient data of the gas turbine turbine blade, extract the temperature change rate of each area of the blade, calculate the thermal stress change rate of each area, analyze the influence of the temperature change rate on the local stress of the blade, and combine the rotational inertia force and aerodynamic load of the blade according to the thermal stress distribution characteristics of each area of the blade to calculate the thermal stress synthesis effect, and obtain the blade thermal stress distribution data; S2: Based on the blade thermal stress distribution data, calculate the transient thermal stress change trend of the blade under the current working condition, extract the stress change rate of the local area of the blade, screen the areas where the stress change rate is greater than the set stress change threshold, and combine the fatigue limit of the blade matrix to calculate the stress overrun probability of each area of the blade to obtain the blade overrun risk area; S3: Call the blade overrun risk area, extract the temperature gradient change rate, stress change rate and cyclic load frequency of the blade overrun area, calculate the creep stress influence coefficient of the local area of the blade, and combine the creep rate, fatigue damage accumulation rate and operating load change rate to calculate the cumulative rate of high-temperature creep and fatigue damage of the blade, and screen the areas where the creep damage accumulation rate is higher than the set damage critical value to obtain the blade creep fatigue damage distribution information; S4: Based on the blade creep fatigue damage distribution information, adjust the life attenuation rate according to the cumulative rate of creep and fatigue damage under the current working condition, calculate the remaining life correction coefficient of the blade, and combine the service stage of the blade to calculate and obtain the corrected life prediction value; S5: Call the corrected life prediction value, analyze the influence of the start-stop load of the gas turbine on the blade damage accumulation rate, calculate the fatigue damage adjustment coefficient under extreme working conditions, compare the creep-fatigue joint distribution of the blade operating state, calculate the extreme working condition life offset, and obtain the blade remaining life interval.

[0019] The blade thermal stress distribution data includes the thermal stress change rate, the temperature change rate, and the thermal stress synthesis effect. The specific areas of blade overlimit risk are the stress overlimit probability, the stress change rate, and the overlimit area distribution. The information distribution of the blade creep-fatigue damage distribution includes the creep stress influence coefficient, the fatigue damage accumulation rate, and the joint probability density function. Obtaining the corrected life prediction value specifically refers to the remaining life correction coefficient, the life decay rate, and the life decay trend. The blade remaining life interval includes the remaining life range, the influence of the life threshold, and the influence of the operating condition life.

[0020] Please refer to Figure 2 , and the steps for obtaining the blade thermal stress distribution data are specifically as follows: S111: Obtain the surface temperature gradient data of the gas turbine turbine blade, extract the temperature change rate of each area of the blade according to the time series, calculate the temperature difference between adjacent time points, and obtain the temperature change rate distribution data; When obtaining the surface temperature gradient data of the gas turbine turbine blade, temperature sensors need to be arranged in different areas of the blade, and an appropriate sampling interval, such as 0.01 s, should be set to ensure that the temperature change over time can be accurately captured. The temperature data is recorded in the database, and each measurement point corresponds to a timestamp. The recorded data format includes time, position, and temperature value. In the data processing process, first, interpolate the collected temperature data to compensate for the accuracy loss caused by uneven sampling point distribution. The commonly used method is linear interpolation, and the calculation method is , where is the known temperature, is the known coordinate, is the interpolated temperature value. After interpolation, based on the time series data, calculate the temperature change rate of each area. The finite difference method is used to calculate the temperature change rate, and the calculation method is , where and represent the temperatures at two adjacent time points respectively, and represent the corresponding time points respectively. To ensure the stability of the calculation results, when calculating the temperature change rate, sliding window averaging is used. The setting of the sliding window size is based on the blade thermal response time. Usually, the value range is 3 - 7 data points. If the data sampling interval is 0.01 s, the window length range should be set between 0.03 s and 0.07 s. In this embodiment, is selected, that is, the window length is 0.05 s. This value ensures that the calculation results are smooth and can reflect the short-term temperature change situation. The specific calculation method is , where represents the size of the sliding window, such as N = 5; after the above processing, the temperature change rate distribution data is obtained, and the temperature change rate of each region can be calculated by the average value of the temperature change rates at different time points. For example, if the temperature change rates at different time points are , then the temperature change rate of this region can be expressed as °C / s, as shown in Table 1.

[0021] Table 1 Temperature change rates of different regions of the turbine blade: ; As shown in Table 1, there are differences in the temperature change rates of different blade regions, and this data will be used for subsequent thermal stress calculations.

[0022] S112: Based on the temperature change rate distribution data, calculate the thermal stress change rate of each region of the blade, call the thermal expansion coefficient and elastic modulus of the blade material, and use the formula: ; Calculate the thermal stress change rate of the blade region to obtain the thermal stress change rate distribution data; Among them, represents the thermal stress change rate of the blade region, represents the elastic modulus of the blade material, represents the material thermal expansion coefficient, represents the temperature difference between adjacent time points, represents the adjacent time interval; Based on the temperature change rate distribution data, to calculate the thermal stress change rate of each region of the blade, it is necessary to call the thermal expansion coefficient and elastic modulus of the blade material. Among them, the thermal expansion coefficient depends on the blade material. For example, the thermal expansion coefficient of nickel-based superalloy is , and the elastic modulus is 200 GPa; the formula is used to calculate the thermal stress change rate, and the thermal stress change rate of each region is calculated according to this formula. For example, for the leading edge region of the blade, the temperature change rate is 2 °C / s, and substituting it into the formula to calculate obtains MPa / s; for the middle part of the blade, the temperature change rate is 2.5 °C / s, and the calculated result is MPa / s; for the trailing edge of the blade, the temperature change rate is 1 °C / s, and the calculated result is MPa / s, as shown in Table 2. In the above calculations, the elastic modulus and the thermal expansion coefficient Depending on the blade material, to ensure the rationality of the calculation, it is necessary to refer to the standard data of the material. According to the mechanical property parameters of Inconel 718, in the temperature range of 800°C - 1000°C, its elastic modulus is taken as 200 GPa, and the coefficient of thermal expansion is taken as , and this data is from the test results of the mechanical properties of the alloy material and can be used for the calculation of this embodiment.

[0023] Table 2 Thermal stress change rate in different regions of the blade: ; As shown in Table 2, the thermal stress change rates in different regions have been calculated, and the data is used for subsequent blade stress analysis.

[0024] S113: Based on the thermal stress change rate distribution data, combined with the blade rotational inertia force and aerodynamic load data, calculate the thermal stress synthesis effect in each region to obtain the blade thermal stress distribution data; Based on the thermal stress change rate distribution data, combined with the blade rotational inertia force and aerodynamic load data, calculate the thermal stress synthesis effect in each region to obtain the blade thermal stress distribution data. The calculation of the blade rotational inertia force is based on the angular velocity and the blade mass distribution, where the blade mass is set to 5 kg, the blade radius is taken as 0.3 m, and the angular velocity is set to 300 rad / s. Then the formula for calculating the rotational inertia force is calculated to obtain N; The aerodynamic load uses the aerodynamic pressure data obtained from fluid mechanics analysis. For example, the aerodynamic pressure at the leading edge of the blade is 2 MPa, at the middle of the blade is 2.5 MPa, and at the trailing edge of the blade is 1.8 MPa. When calculating the combined stress, it is necessary to superimpose the stress changes caused by the loads on each part of the blade, and the stress superposition method is used, where calculate the blade rotational stress. For example, if the force area at the leading edge of the blade is 0.002 m2, then MPa, and the final combined stress at the leading edge of the blade MPa, and the blade stress calculation is completed.

[0025] Please refer to Figure 3 , and the steps for obtaining the blade overlimit risk area are specifically as follows: S211: Based on the blade thermal stress distribution data, calculate the transient thermal stress change trend under the current working condition of the blade at a preset time interval to obtain the transient thermal stress change trend data; Based on the blade thermal stress distribution data, calculate the transient thermal stress change trend under the current working condition of the blade at a preset time interval. First, select a suitable time interval , such as 0.01 s, to ensure capturing the dynamic changes of the blade thermal stress. Within the set time interval, sample the thermal stress of each area of the blade, record the thermal stress data at different time points, such as the stresses at the leading edge, middle, and trailing edge of the blade at time and are respectively , , and , , . Calculate the transient thermal stress change rate . Adopt the finite difference calculation method, and the calculation formula is . For example, if the stress of the leading edge of the blade at 0.01 s is 80 MPa and the stress at 0.02 s is 82 MPa, then the transient thermal stress change rate is calculated as MPa / s. Adopt a sliding window average to process the transient thermal stress change rate, reduce the influence of measurement errors, and finally obtain the transient thermal stress change trend data, as shown in Table 3. In the above process, the time interval is selected as 0.01 s. The reason is that the thermal stress change period of the gas turbine blade in a high-speed rotating environment is usually in the millisecond level. For example, the time for the blade to rotate one week is about 0.02 s. At least two samples need to be taken within a single cycle to ensure sufficient time resolution. Therefore, set to ensure the timeliness and continuity of data acquisition; the size of the sliding window is selected as 5. The setting basis is that it is necessary to smooth the transient stress change rate. If the window is too small, local fluctuations may interfere with the analysis. If the window is too large, the sensitivity to sudden stress changes will be reduced. Considering the thermal inertia and thermal stress conduction characteristics of the blade material comprehensively, select as the best setting value to balance stability and sensitivity.

[0026] Table 3 Blade transient thermal stress change rate data: ; As shown in Table 3, the transient thermal stress change rates of different areas of the blade are calculated, and this data is used for subsequent stress overrun analysis.

[0027] S212: Based on the transient thermal stress change trend data, extract the stress change rate of the local area of the blade, and screen the areas where the stress change rate is greater than the set stress change threshold. Use the formula: ; Calculate the stress overrun probability of the local area of the blade, and obtain the stress overrun probability distribution data; Among them, represents the stress overrun probability, represents the stress change rate of the local area, represents the set stress change threshold, is a small positive number to avoid a zero denominator, represents the yield strength ratio of the blade material, is the base of the natural logarithm, represents the stress change influence coefficient; Based on the transient thermal stress change trend data, extract the stress change rate of the local area of the blade, and screen the areas where the stress change rate is greater than the set stress change threshold. First, set the stress change threshold , for example, take 180 MPa / s, and screen out the areas where the stress change rate is greater than this threshold. For example, the stress change rate at the leading edge of the blade is 200 MPa / s, which is greater than 180 MPa / s and meets the screening conditions. The stress change rate at the trailing edge of the blade is 250 MPa / s, which also meets the screening conditions. The stress change rate in the middle of the blade is 300 MPa / s, which also meets the screening conditions; within the screened areas, calculate the stress overrun probability , in the formula , the parameters are set as follows: is the stress change rate, is used as a small positive number to prevent the denominator from being zero, is set as the yield strength ratio of the material. For example, the yield strength ratio of the nickel-based alloy Inconel718 , the stress change influence coefficient is set to 0.05. Calculate the stress overrun probability at the leading edge of the blade, substitute the data , and calculate to get , and calculate the stress overrun probability at the trailing edge of the blade in the same way , and the stress overrun probability in the middle of the blade , as shown in Table 4. In the above calculation, the stress change threshold MPa / s is set based on the fatigue limit and thermal stress fluctuation tolerance of the blade material, and is usually set to 0.75 times the yield strength to avoid early crack propagation caused by high stress fluctuations. The yield strength ratio is set based on the fact that the yield stress of nickel-based alloy materials is generally about 10% higher than the normal operating stress to ensure a safety margin. The stress change influence coefficient is set based on empirical analysis to ensure that the amplitude of adjusting the stress overrun probability by the exponential term is appropriate and avoid over-amplifying or shrinking the probability value.

[0028] Table 4 Stress overrun probability data for different areas of the blade: ; As shown in Table 4, the stress overrun probabilities for different areas have been calculated, and this data is used to identify the overrun risk areas of the blade.

[0029] S213: Based on the stress overrun probability distribution data, combined with the fatigue limit of the blade matrix, screen the areas where the stress overrun probability is higher than the fatigue limit risk threshold to obtain the blade overrun risk areas.

[0030] Based on the stress overrun probability distribution data, combined with the fatigue limit of the blade matrix, screen the areas where the stress overrun probability is higher than the fatigue limit risk threshold. Set the fatigue limit of the blade material to 250 MPa, compare the calculated stress data, and the stress overrun probability at the leading edge of the blade , is lower than the fatigue limit risk threshold of 0.15 and does not belong to the overrun risk area. The stress overrun probability at the trailing edge of the blade , is still lower than 0.15 and also does not belong to the overrun risk area. However, the stress overrun probability at the middle of the blade , is higher than 0.15 and belongs to the overrun risk area. Mark the middle of the blade as the risk area to obtain the blade overrun risk area data. In the above calculation, the fatigue limit risk threshold is set to 0.15, and its basis lies in the crack growth rate critical value in the engineering standard. Usually, 60%-80% of the material fatigue limit is taken as the threshold. In this example, the 60% standard is adopted to ensure the conservativeness of fatigue damage assessment.

[0031] Please refer to Figure 4 , and the specific steps for obtaining the creep fatigue damage distribution information of the blade are as follows: S311: Call the blade overrun risk areas, extract the temperature gradient change rate, stress change rate, and cyclic load frequency in the blade overrun areas, calculate the creep stress influence coefficient of the local part of the blade, and construct a creep stress influence factor matrix based on the combined influence of the temperature change rate and stress change rate in each area to obtain the creep stress influence coefficient distribution of the blade; Call the blade overrun risk area data, extract the temperature gradient change rate, stress change rate, and cyclic load frequency in the blade overrun areas. First, based on the temperature data of the local part of the blade, calculate the temperature gradient change rate. Select the temperature values of the blade overrun areas at different time points. For example, the temperature at the middle of the blade at is , and the temperature at is , then the temperature gradient change rate is calculated as ; Based on the stress change rate, select the stress data of the overrun areas. For example, the stress at the middle of the blade at is 100 MPa, and the stress at is 105 MPa, then calculate the stress change rate MPa / s; When calculating the cyclic load frequency, use the rotational speed data of the blade. For example, if the blade rotational speed is 3000 rpm, then the cyclic load frequency Hz; When constructing the creep stress influence factor matrix, the creep stress influence coefficients in each region need to be jointly determined by the temperature gradient change rate, stress change rate, and cyclic load frequency. Among them, the creep stress influence factor weights need to be adjusted according to the high-temperature endurance performance curve of the material. For example, in nickel-based alloy materials, the influence weight of temperature on creep can be set to 0.6, the influence weight of stress change rate can be set to 0.3, and the influence weight of cyclic load frequency can be set to 0.1. This setting is based on the high-temperature endurance strength experimental data of the material. Among them, high-temperature creep deformation is mainly dominated by temperature, followed by stress rate and cyclic load. Therefore, different weighting values are given to construct the creep stress influence factor matrix , the matrix element is calculated by weighting the temperature gradient change rate, stress change rate, and cyclic load frequency in each region according to the weights. For example, in the middle of the blade , at the trailing edge of the blade , and finally the distribution of the creep stress influence coefficient of the blade is obtained

[0032] S312: Call the distribution of the creep stress influence coefficient of the blade, combine the creep rate, fatigue damage accumulation rate, and operating load change rate, and use the formula: ; Calculate the creep and fatigue damage accumulation rate of the blade; Among them, represents the creep and fatigue damage accumulation rate of the blade, represents the transient thermal stress value of the blade, represents the fatigue limit of the blade material, represents the fatigue strength index of the material, represents the load frequency influence coefficient, represents the cyclic load frequency of the blade, represents the creep rate adjustment factor, represents the creep rate of the blade, represents the reference creep rate; Call the data of the distribution of the creep stress influence coefficient of the blade, combine the creep rate, fatigue damage accumulation rate, and operating load change rate, and calculate the creep and fatigue damage accumulation rate of the blade. First, set the transient thermal stress value , such as the transient thermal stress value in the middle of the blade is 105 MPa, and the fatigue limit is set to 250 MPa. The setting of the fatigue limit is based on the S-N curve (stress-life curve) of the material. Among them, the fatigue limit of nickel-based alloy materials at a high temperature of 650 °C is about 250 MPa. This value is obtained based on fatigue experiments. The fatigue strength index of the material Take 3. This value is set according to the Paris fatigue crack growth equation. The Paris exponent of nickel-based alloys is usually between 2.5 and 3.5. Therefore, the median value 3 is taken. The load frequency influence coefficient Take 0.02. This value is obtained from the frequency sensitivity experiment of the material and is usually between 0.01 and 0.03. Due to the grain boundary propagation mechanism of nickel-based alloy materials, take 0.02. The cyclic load frequency Take 50Hz. The creep rate adjustment factor Take 0.1. This value is adjusted according to the creep experiment data. The high-temperature creep strain rate of nickel-based alloy materials is about When, the influence factor is usually set between 0.05 and 0.15. Therefore, select 0.1. The blade creep rate Take , the reference creep rate Take , substitute into the formula for calculation: ; Calculate the blade creep and fatigue damage accumulation rate .

[0033] S313: Call the blade creep and fatigue damage accumulation rate, screen the areas where the creep damage accumulation rate is higher than the set damage critical value, and obtain the blade creep fatigue damage distribution information; Call the blade creep and fatigue damage accumulation rate data, screen the areas where the creep damage accumulation rate is higher than the set damage critical value, and set the damage critical value , usually set between 3.0 and 4.0 to ensure that the cumulative damage has not reached the material failure threshold. The typical critical value of nickel-based alloy materials is set at 3.5. Compare the calculated creep and fatigue damage accumulation rate , which is higher than the set damage critical value, indicating that there is a relatively serious creep damage risk in the middle of the blade. Mark the middle of the blade as the creep fatigue damage area and obtain the blade creep fatigue damage distribution information.

[0034] Please refer to Figure 5 , the specific steps for obtaining the corrected life prediction value are as follows: S411: Based on the blade creep fatigue damage distribution information, calculate the life decay rate of each area of the blade according to the creep and fatigue damage accumulation rate under the current working conditions, and adjust the decay parameters in combination with the change trend of the damage accumulation rate to obtain the blade life decay rate distribution information; Based on the blade creep fatigue damage distribution information, calculate the life decay rate of each area of the blade according to the creep and fatigue damage accumulation rate under the current working conditions. First, select the creep and fatigue damage accumulation rate of the over-limit area as the initial variable. For example, the in the middle of the blade, the , at the leading edge of the blade , and then analyze the change trend of the damage accumulation rate in different regions, and calculate the damage change rate within the time For example, the damage accumulation rate of the middle part of the blade changes to after 1000h of operation, then the damage change rate / h. Combining the change trend of the damage accumulation rate, adjust the life decay parameter . This parameter is used to adjust the life decay rate under different working conditions, and its value should be based on the high-temperature creep strength test data of the material. In typical nickel-based superalloy blades, it can be derived from the creep strength curve measured by experiments, and the usual value range is 0.05 - 0.15. Set to 0.1, and calculate the life decay rate of the blade . The calculation method is . For example, the life decay rate of the middle part of the blade / h. Finally, the distribution information of the life decay rate of the blade is calculated. / h, and finally the distribution information of the blade life decay rate is calculated.

[0035] S412: Call the distribution of the blade life decay rate and use the formula: ; Calculate the remaining life correction factor of the blade; Among them, represents the remaining life correction factor of the blade, represents the creep and fatigue damage accumulation rate of the blade, represents the damage accumulation adjustment coefficient, represents the cyclic load frequency of the blade, represents the load frequency decay parameter; Call the distribution of the blade life decay rate and calculate the remaining life correction factor of the blade . Set the damage accumulation adjustment coefficient . This coefficient is used to correct the non-linear effect of different stress levels on damage accumulation. The specific value can be obtained by fitting the S-N curve of the material. In typical blade material Inconel718, this coefficient usually ranges between 0.03 - 0.07. The cyclic load frequency of the blade Hz, and the load frequency decay parameter . This parameter is used to reflect the additional decay effect of high-frequency load on the blade life. The value is set based on the cyclic stress amplitude calculation under the fatigue limit. Generally, when the cyclic frequency is higher than 30Hz, takes a value of 0.015 - 0.025 and substitutes it into the formula: ; Finally, calculate the remaining life correction factor of the middle part of the blade 。

[0036] S413: Call the blade remaining life correction coefficient, combine with the service stage of the blade, correct the life, calculate the life prediction value, establish the blade life prediction data, and obtain the corrected blade life prediction value; Call the blade remaining life correction coefficient, combine with the service stage of the blade, correct the life, calculate the life prediction value, and select the blade design life and adopt the correction calculation The life prediction value at the middle part of the blade is h, and the in this calculation is 7 Set according to the durability test data of the blade. The life test value that fails after 10^

[0037] Please refer to Figure 6 For the steps to obtain the blade remaining life interval, specifically: S511: Call the corrected life prediction value, analyze the influence of the start-stop load of the gas turbine on the blade damage accumulation rate, extract the creep damage increment and fatigue damage increment caused by the change of the start-stop load, calculate the change trend of the damage rate of the blade under multiple start-stop cycles, and obtain the blade start-stop load damage rate distribution information; Call the corrected life prediction value, analyze the influence of the start-stop load of the gas turbine on the blade damage accumulation rate. First, obtain the stress change of the blade under different start-stop cycles, extract the temperature change data of the blade during the start-stop process, and set the operating temperature of the blade under normal conditions as and the temperature drops to after shutdown. Then the temperature change amount . Based on this, calculate the creep damage increment, and use to calculate the temperature change rate. If the cooling time is s, then . Combine with the thermal expansion coefficient and elastic modulus GPa of the blade material to calculate the thermal stress change . Substitute the numerical value into the calculation to obtain MPa. Under multiple start-stop cycles, calculate the creep damage increment and fatigue damage increment . For example, the creep damage increment of a single start-stop cycle The settings refer to the ASTM E139 standard. Based on the creep behavior of superalloys at 900 °C, , the fatigue damage increment is calculated according to the stress amplitude of the blade cyclic load and corrected using the Goodman formula to obtain . After the blade undergoes 1000 start-stop cycles, the creep damage accumulation is , and the fatigue damage accumulation is . Calculate the change trend of the damage rate of the blade under various start-stop cycles to obtain the distribution information of the start-stop load damage rate of the blade.

[0038] S512: Call the distribution information of the start-stop load damage rate of the blade and calculate the fatigue damage adjustment coefficient under extreme conditions using the improved formula: ; Calculate the fatigue damage adjustment coefficient of the blade under extreme conditions; Among them, represents the fatigue damage adjustment coefficient of the blade, represents the stress change amount caused by extreme conditions, represents the reference stress, represents the stress response coefficient under extreme conditions, represents the damage accumulation adjustment coefficient under extreme conditions, represents the load frequency sensitivity under extreme conditions, represents the blade cyclic load frequency under extreme conditions; Call the distribution information of the start-stop load damage rate of the blade and calculate the fatigue damage adjustment coefficient under extreme conditions , set the stress change amount MPa, the selection of the reference stress is based on the ASME BPVC III high-temperature component design specification and takes a value of 1200 MPa. The stress response coefficient under extreme conditions is the thermo-mechanical fatigue coefficient determined by experiments and takes a value of 0.1. The damage accumulation adjustment coefficient under extreme conditions is corrected by the Paris-Erdogan crack growth model parameters and is set to 0.02. The load frequency sensitivity under extreme conditions is selected as 0.05 according to the ISO 12110-2 material fatigue strength standard. The blade cyclic load frequency Hz under extreme conditions is substituted into the formula: ; Finally, calculate the fatigue damage adjustment coefficient of the blade under extreme conditions.

[0039] S513: Invoke the fatigue damage adjustment coefficient for extreme operating conditions of the blade, compare the combined distribution of creep and fatigue under the blade's operating state, calculate the life offset for extreme operating conditions, establish data on the remaining life range of the blade, and obtain the remaining life interval of the blade.

[0040] Invoke the fatigue damage adjustment coefficient for extreme operating conditions of the blade, compare the combined distribution of creep and fatigue under the blade's operating state, calculate the life offset for extreme operating conditions, and select the initial life prediction value of the blade h, which is set with reference to the blade life test data of GE90 and the life prediction method for superalloys in ASTM E1049 - 85, and calculate the life offset Calculate the reduction ratio of the material life under extreme loads according to the MIL - HDBK - 5H standard, and use the formula: ; Substitute the values: ; Calculate the remaining life of the blade under extreme operating conditions : ; Finally, establish data on the remaining life range of the blade and obtain the remaining life interval of the blade h, and the setting of this range is based on the ASTM E917 - 15 high - temperature fatigue life assessment method to ensure the rationality of the calculation results in engineering applications.

[0041] The above is only a preferred embodiment of the present invention and does not impose other forms of limitation on the present invention. Any person skilled in the relevant art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above - mentioned embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating the life of a turbine blade, characterized in that, It includes the following steps: S1: Obtain the surface temperature gradient data of the gas turbine turbine blade, extract the temperature change rate of each area of the blade, analyze the influence of the temperature change rate on the local stress of the blade, combine the rotational inertia force and aerodynamic load of the blade to calculate the synthetic effect of thermal stress, and obtain the thermal stress distribution data of the blade; S2: Based on the thermal stress distribution data of the blade, extract the stress change rate of the local area of the blade, screen the areas where the stress change rate is greater than the set stress change threshold, combine the fatigue limit of the blade matrix, calculate the stress overrun probability of each area of the blade, and obtain the overrun risk area of the blade; S3: Call the overrun risk area of the blade, combine the creep rate, fatigue damage accumulation rate and operating load change rate, calculate the cumulative rate of high-temperature creep and fatigue damage of the blade, screen the areas where the creep damage accumulation rate is higher than the set damage critical value, and obtain the creep-fatigue damage distribution information of the blade; S4: Based on the creep-fatigue damage distribution information of the blade, adjust the life decay rate according to the cumulative rate of creep and fatigue damage under the current working condition, calculate the remaining life correction coefficient of the blade, and combine the service stage of the blade to calculate and obtain the corrected life prediction value.

2. The turbine blade life assessment method according to claim 1, wherein The thermal stress distribution data of the blade includes the thermal stress change rate, temperature change rate and synthetic effect of thermal stress. The overrun risk area of the blade specifically refers to the stress overrun probability, stress change rate and overrun area distribution. The creep-fatigue damage distribution information of the blade includes the creep stress influence coefficient, fatigue damage accumulation rate and joint probability density function. The obtaining of the corrected life prediction value specifically refers to the remaining life correction coefficient, life decay rate and life decay trend. The remaining life interval of the blade includes the remaining life range, life threshold influence and operating condition life influence.

3. The turbine blade life assessment method according to claim 1, wherein The specific steps for obtaining the thermal stress distribution data of the blade are as follows: S111: Obtain the surface temperature gradient data of the gas turbine turbine blade, extract the temperature change rate of each area of the blade according to the time series, calculate the temperature difference between adjacent time points, and obtain the temperature change rate distribution data; S112: Based on the temperature change rate distribution data, calculate the thermal stress change rate of each area of the blade, call the thermal expansion coefficient and elastic modulus of the blade material, and use the formula: ； Calculate the thermal stress change rate of the blade area and obtain the thermal stress change rate distribution data; Among them, represents the rate of change of the thermal stress in the blade area, represents the elastic modulus of the blade material, represents the coefficient of thermal expansion of the material, represents the temperature difference between adjacent time points, represents the adjacent time interval; S113: Based on the thermal stress change rate distribution data, combine the rotational inertia force and aerodynamic load data of the blade, calculate the synthetic effect of thermal stress of each area, and obtain the thermal stress distribution data of the blade.

4. The turbine blade life assessment method according to claim 1, characterized in that The specific steps for obtaining the overrun risk area of the blade are as follows: S211: Based on the thermal stress distribution data of the blade, calculate the transient thermal stress change trend of the blade under the current working condition at a preset time interval, and obtain the transient thermal stress change trend data; S212: Based on the transient thermal stress change trend data, extract the stress change rate of the local area of the blade, and screen the areas where the stress change rate is greater than the set stress change threshold. Use the formula: ； Calculate the stress overrun probability of the local area of the blade and obtain the stress overrun probability distribution data; Among them, represents the probability of stress overrun, represents the stress change rate in the local area, represents the set stress change threshold, is a small positive number to avoid a zero denominator, represents the yield strength ratio of the blade material, is the base of the natural logarithm, represents the stress change influence coefficient; S213: Based on the stress over - limit probability distribution data, combined with the fatigue limit of the blade matrix, screen the areas where the stress over - limit probability is higher than the fatigue limit risk threshold to obtain the blade over - limit risk areas.

5. The turbine blade life assessment method according to claim 1, characterized in that, The specific steps for obtaining the creep - fatigue damage distribution information of the blade are as follows: S311: Call the blade over - limit risk areas, extract the temperature gradient change rate, stress change rate, and cyclic load frequency in the blade over - limit areas, calculate the creep stress influence coefficient of the blade local area, construct a creep stress influence factor matrix according to the combined influence of the temperature change rate and stress change rate in each area, and obtain the creep stress influence coefficient distribution of the blade. S312: Call the creep stress influence coefficient distribution of the blade, combine the creep rate, fatigue damage accumulation rate, and operating load change rate, and use the formula: ； Calculate the creep and fatigue damage accumulation rate of the blade. Among them, represents the creep and fatigue damage accumulation rate of the blade, represents the transient thermal stress value of the blade, represents the fatigue limit of the blade material, represents the fatigue strength index of the material, represents the load frequency influence coefficient, represents the cyclic load frequency of the blade, represents the creep rate adjustment factor, represents the creep rate of the blade, represents the reference creep rate; S313: Call the creep and fatigue damage accumulation rate of the blade, screen the areas where the creep damage accumulation rate is higher than the set damage critical value to obtain the creep - fatigue damage distribution information of the blade.

6. The turbine blade life assessment method according to claim 1, characterized in that, The specific steps for obtaining the corrected life prediction value are as follows: S411: Based on the creep - fatigue damage distribution information of the blade, calculate the life decay rate of each area of the blade according to the creep and fatigue damage accumulation rate under the current working conditions, and adjust the decay parameter in combination with the change trend of the damage accumulation rate to obtain the life decay rate distribution information of the blade. S412: Call the life decay rate distribution of the blade and use the formula: ； Calculate the remaining life correction coefficient of the blade. Among them, represents the remaining life correction coefficient of the blade, represents the creep and fatigue damage accumulation rate of the blade, represents the damage accumulation adjustment coefficient, represents the cyclic load frequency of the blade, represents the load frequency decay parameter; S413: Call the remaining life correction coefficient of the blade, combine the service stage of the blade, correct the life, calculate the life prediction value, establish the blade life prediction data, and obtain the corrected life prediction value of the blade.

7. The turbine blade life assessment method according to claim 1, characterized in that, The method further includes the following steps: S5: Call the corrected life prediction value, analyze the influence of the start - stop load of the gas turbine on the blade damage accumulation rate, calculate the fatigue damage adjustment coefficient under extreme working conditions, compare the creep - fatigue joint distribution in the blade operating state, calculate the extreme - working - condition life offset, and obtain the remaining life interval of the blade. The remaining life interval of the blade includes the remaining life range, the influence of the life threshold, and the influence of the operating condition life.

8. The turbine blade life assessment method according to claim 7, characterized in that, The specific steps for obtaining the remaining life interval of the blade are as follows: S511: Call the corrected life prediction value, analyze the influence of the start - stop load of the gas turbine on the blade damage accumulation rate, extract the creep damage increment and fatigue damage increment caused by the change of the start - stop load, calculate the change trend of the damage rate of the blade under multiple start - stop cycles, and obtain the start - stop load damage rate distribution information of the blade. S512: Call the start - stop load damage rate distribution information of the blade, calculate the fatigue damage adjustment coefficient under extreme working conditions, and use the improved formula: ； Calculate the extreme - working - condition fatigue damage adjustment coefficient of the blade. Among them, represents the fatigue damage adjustment coefficient of the blade, represents the stress change caused by extreme working conditions, represents the reference stress, represents the stress response coefficient under extreme working conditions, represents the damage accumulation adjustment coefficient under extreme working conditions, represents the load frequency sensitivity under extreme working conditions, represents the blade cyclic load frequency under extreme working conditions; S513: Call the extreme - working - condition fatigue damage adjustment coefficient of the blade, compare the creep - fatigue joint distribution in the blade operating state, calculate the extreme - working - condition life offset, establish the remaining life range data of the blade, and obtain the remaining life interval of the blade.

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