A method for evaluating turbine blade life

Abstract

The present invention relates to the technical field of life assessment, and specifically to a turbine blade life assessment method, comprising the following steps: obtaining surface temperature gradient data of a gas turbine turbine blade, extracting the temperature change rate of each blade region, analyzing the effect of the temperature change rate on the local stress of the blade, and calculating the thermal stress synthesis effect by combining the blade's rotational inertia force with the aerodynamic load to obtain blade thermal stress distribution data. By obtaining blade surface temperature gradient data and calculating the thermal stress synthesis effect by combining the rotational inertia force with the aerodynamic load, the present invention can accurately reflect the thermal stress distribution characteristics of the blade under different operating conditions, calculate the life decay rate based on the current operating conditions, and enable life prediction to be adjusted according to the operating conditions. By analyzing the impact of the gas turbine's start-up and shutdown loads and comparing the creep-fatigue joint distribution to calculate the life offset, the impact of the gas turbine's operating conditions on the 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 in particular to a method for assessing the life of a turbine blade. Background Art

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

[0003] The turbine blade life assessment method is used to predict the lifespan and performance degradation of turbine blades under long-term high-temperature, high-pressure, and high-speed rotation environments. This method combines fluid dynamics analysis, thermomechanical coupling calculations, fatigue damage modeling, and failure probability assessment to determine the remaining lifespan of turbine blades and the optimal repair and replacement time. It also optimizes the turbine blade lifespan management strategy to improve gas turbine operating efficiency, reduce unplanned downtime, and ensure safe and stable unit operation.

[0004] Traditional assessment methods typically use finite element analysis, fracture mechanics analysis, and statistical life modeling to predict blade life during life assessment. While these methods offer a certain degree of accuracy when dealing with life assessments under stable operating conditions, they lack a real-time correction mechanism for the dynamic characteristics of temperature gradient changes, stress evolution, and creep-fatigue interactions during gas turbine operation. This makes it difficult to accurately characterize the actual thermal stress distribution in different regions of the blade, resulting in reduced accuracy in life predictions under extreme or variable operating conditions. Traditional methods typically employ independent assessments to address creep-fatigue interactions, failing to consider the cumulative effects of creep and fatigue damage under varying operating loads, making them prone to life prediction deviations. Under operating conditions where gas turbine loads frequently change during startup and shutdown, traditional methods are unable to accurately reflect the impact of extreme loads on blade fatigue damage rates, causing life assessment results to deviate from actual service conditions and increasing uncertainty in maintenance and replacement decisions. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a turbine blade life assessment method.

[0006] To achieve the above object, the present invention adopts the following technical solution: a method for evaluating the life of a turbine blade, comprising the following steps:

[0007] S1: Obtain the surface temperature gradient data of the gas turbine blade, extract the temperature change rate of each area of ​​the blade, analyze the impact of the temperature change rate on the local stress of the blade, calculate the thermal stress synthesis effect by combining the blade rotation inertia force and aerodynamic load, and obtain the blade thermal stress distribution data;

[0008] 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 exceedance probability of each area of ​​the blade in combination with the fatigue limit of the blade matrix to obtain the blade exceedance risk area;

[0009] S3: Calling the blade over-limit risk area, combining the creep rate, fatigue damage accumulation rate and operating load change rate to calculate the accumulation rate of high-temperature creep and fatigue damage of the blade, screening the area where the creep damage accumulation rate is higher than the set damage threshold, and obtaining the blade creep fatigue damage distribution information;

[0010] S4: Based on the creep fatigue damage distribution information of the blade, according to the creep and fatigue damage accumulation rate under the current working conditions, the life decay rate is adjusted, and the remaining life correction coefficient of the blade is calculated. In combination with the service stage of the blade, the corrected life prediction value is calculated and obtained.

[0011] The present invention has improvements in that the blade thermal stress distribution data includes the thermal stress change rate, the temperature change rate and the thermal stress synthesis effect; the blade overlimit risk area is specifically the stress overlimit probability, the stress change rate and the overlimit area distribution; the blade creep fatigue damage distribution information includes the creep stress influence coefficient, the fatigue damage accumulation rate and the joint probability density function; the acquisition of 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 life threshold influence and the operating condition life influence.

[0012] The present invention is improved in that the steps of acquiring the blade thermal stress distribution data are specifically as follows:

[0013] S111: Acquire surface temperature gradient data of gas turbine blades, extract the temperature change rate of each region of the blade according to the time series, calculate the temperature difference between adjacent time points, and obtain temperature change rate distribution data;

[0014] 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:

[0015] ;

[0016] Calculate the thermal stress change rate of the blade area and obtain the thermal stress change rate distribution data;

[0017] in, represents the rate of change of thermal stress in the blade area, represents the elastic modulus of the blade material, represents the thermal expansion coefficient of the material, represents the temperature difference between adjacent time points, represents adjacent time intervals;

[0018] S113: Based on the thermal stress change rate distribution data, combined with the blade rotation inertia force and aerodynamic load data, the thermal stress synthesis effect of each area is calculated to obtain the blade thermal stress distribution data.

[0019] The present invention is improved in that the step of obtaining the blade over-limit risk area is specifically as follows:

[0020] S211: Based on the blade thermal stress distribution data, calculating the transient thermal stress change trend of the blade under the current working condition at a preset time interval, and obtaining transient thermal stress change trend data;

[0021] 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 area where the stress change rate is greater than the set stress change threshold, using the formula:

[0022] ;

[0023] Calculate the probability of stress exceeding the limit in the local area of ​​the blade and obtain the stress exceeding probability distribution data;

[0024] in, represents the probability of stress exceeding the limit, represents the stress change rate in the local area, Represents the setting of stress change threshold, To avoid small positive numbers with zero denominator, represents the yield strength ratio of the blade material, is the base of natural logarithms, represents the stress change influence coefficient;

[0025] S213: Based on the stress excess probability distribution data and in combination with the fatigue limit of the blade substrate, regions where the stress excess probability is higher than the fatigue limit risk threshold are screened to obtain blade excess risk regions.

[0026] The present invention is improved in that the steps of obtaining the blade creep fatigue damage distribution information are specifically as follows:

[0027] S311: Calling the blade overrun risk area, extracting the temperature gradient change rate, stress change rate, and cyclic load frequency of the blade overrun area, calculating the local creep stress influence coefficient of the blade, constructing a creep stress influence factor matrix based on the combined influence of the temperature change rate and stress change rate of each area, and obtaining the blade creep stress influence coefficient distribution;

[0028] 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:

[0029] ;

[0030] The blade creep and fatigue damage accumulation rates are calculated;

[0031] in, 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 material fatigue strength index, 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 baseline creep rate;

[0032] S313: calling the blade creep and fatigue damage accumulation rate, screening the region where the creep damage accumulation rate is higher than the set damage threshold, and obtaining the blade creep fatigue damage distribution information.

[0033] The present invention is improved in that the step of obtaining the corrected life prediction value is specifically as follows:

[0034] S411: Based on the blade creep fatigue damage distribution information, according to the creep and fatigue damage accumulation rate under the current operating condition, calculating the life decay rate of each region of the blade, and adjusting the decay parameter in combination with the change trend of the damage accumulation rate to obtain the blade life decay rate distribution information;

[0035] S412: Call the blade life attenuation rate distribution using the formula:

[0036] ;

[0037] Calculate the remaining life correction factor of the blade;

[0038] in, 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 factor, represents the cyclic load frequency of the blade, Represents the load frequency attenuation parameter;

[0039] S413: calling the blade remaining life correction coefficient, combining the service stage of the blade, correcting the life, calculating the life prediction value, establishing blade life prediction data, and obtaining the blade corrected life prediction value.

[0040] The present invention is improved in that the method further comprises the following steps:

[0041] S5: calling the corrected life prediction value, analyzing the influence of the gas turbine start-up and shutdown loads on the blade damage accumulation rate, calculating the fatigue damage adjustment coefficient under extreme working conditions, comparing the creep fatigue joint distribution under the blade operating state, calculating the extreme working condition life offset, and obtaining the blade remaining life range;

[0042] The remaining life interval of the blade includes a remaining life range, a life threshold impact, and an operating condition life impact.

[0043] The present invention is improved in that the steps for obtaining the remaining life span of the blade are specifically as follows:

[0044] S511: Calling the corrected life prediction value, analyzing the impact of the gas turbine start-stop load on the blade damage accumulation rate, extracting the creep damage increment and fatigue damage increment caused by the start-stop load change, calculating the damage rate change trend of the blade under various start-stop cycles, and obtaining the blade start-stop load damage rate distribution information;

[0045] S512: Calling the blade start-stop load damage rate distribution information, calculating the fatigue damage adjustment coefficient under extreme working conditions, and using the improved formula:

[0046] ;

[0047] Calculate the fatigue damage adjustment coefficient of the blade under extreme working conditions;

[0048] in, represents the fatigue damage adjustment factor 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;

[0049] S513: calling the extreme working condition fatigue damage adjustment coefficient of the blade, comparing the creep fatigue joint distribution under the blade operation state, calculating the extreme working condition life offset, establishing the blade remaining life range data, and obtaining the blade remaining life interval.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, by acquiring the temperature gradient data of the blade surface, calculating the temperature change rate of the blade area, and combining the rotational inertia force and the aerodynamic load to calculate the thermal stress synthesis effect, the thermal stress distribution characteristics of the blade under different operating conditions can be accurately reflected, the stress change rate of the local area of ​​the blade can be extracted, the area where the stress change rate is greater than the set threshold can be screened, and the stress overlimit probability of the blade can be calculated in combination with the fatigue limit of the matrix, so that the stress overlimit risk of the blade can be finely judged according to different operating conditions. Based on the analysis of the overlimit risk area, the high-temperature creep and fatigue damage of the blade can be calculated in combination with the creep rate, fatigue damage accumulation rate and operating load change rate. The cumulative rate of creep damage is calculated, and the areas where the creep damage cumulative rate is higher than the set critical value are screened. During the fatigue damage evolution process, the impact of creep damage on the remaining life of the blade can be dynamically evaluated. According to the creep fatigue damage distribution information and the current operating conditions, the life attenuation rate is calculated, and the remaining life correction factor is adjusted, so that the life prediction can be adjusted with the operating status. By analyzing the impact of the start-up and shutdown loads of the gas turbine, the fatigue damage adjustment factor under extreme operating conditions is calculated, and the life offset is calculated by comparing the creep fatigue joint distribution, forming the remaining life range of the gas turbine under different load conditions, so that the impact of the gas turbine operating status on the life prediction can be dynamically updated in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the method of the present invention;

[0053] Figure 2 A flow chart for obtaining blade thermal stress distribution data according to the present invention;

[0054] Figure 3 A flow chart for obtaining a blade over-limit risk area according to the present invention;

[0055] Figure 4 This is a flow chart of obtaining blade creep fatigue damage distribution information according to the present invention;

[0056] Figure 5 A flow chart for obtaining a modified life prediction value for the present invention;

[0057] Figure 6 A flowchart for obtaining the remaining life span of a blade according to the present invention;

[0058] Figure 7 This is a schematic diagram of blade fatigue damage distribution according to the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 The present invention provides a technical solution: a method for evaluating the life of a turbine blade, comprising the following steps:

[0062] S1: Obtain the surface temperature gradient data of the gas turbine blade, extract the temperature change rate of each area of ​​the blade, calculate the thermal stress change rate of each area, analyze the impact of the temperature change rate on the local stress of the blade, and calculate the thermal stress synthesis effect based on the thermal stress distribution characteristics of each area of ​​the blade, combining the blade rotation inertia force and aerodynamic load to obtain the blade thermal stress distribution data;

[0063] S2: Based on the blade thermal stress distribution data, calculate the transient thermal stress change trend under the current operating condition 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, and calculate the stress exceedance probability of each area of ​​the blade in combination with the fatigue limit of the blade matrix to obtain the blade exceedance risk area;

[0064] S3: Call the blade overrun risk area, extract the temperature gradient change rate, stress change rate, and cyclic load frequency in the blade overrun area, calculate the local creep stress influence coefficient of the blade, and calculate the accumulation rate of high-temperature creep and fatigue damage of the blade by combining the creep rate, fatigue damage accumulation rate, and operating load change rate. Filter the areas where the creep damage accumulation rate is higher than the set damage threshold to obtain the blade creep fatigue damage distribution information;

[0065] S4: Based on the creep fatigue damage distribution information of the blade, according to the creep and fatigue damage accumulation rate under the current working conditions, the life decay rate is adjusted to calculate the remaining life correction factor of the blade. Combined with the service stage of the blade, the corrected life prediction value is calculated;

[0066] S5: Call the corrected life prediction value, analyze the impact of the gas turbine start-up and shutdown loads on the blade damage accumulation rate, calculate the fatigue damage adjustment coefficient under extreme working conditions, compare the creep fatigue joint distribution under the blade operating state, calculate the extreme working condition life offset, and obtain the remaining life range of the blade.

[0067] The blade thermal stress distribution data includes the thermal stress change rate, temperature change rate and thermal stress synthesis effect. The blade over-limit risk area is specifically the stress over-limit probability, stress change rate and over-limit area distribution. The blade creep fatigue damage distribution information includes the creep stress influence coefficient, fatigue damage accumulation rate and joint probability density function. Obtaining the corrected life prediction value specifically refers to the remaining 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.

[0068] See also Figure 2 ,The specific steps for obtaining blade thermal stress distribution data are:

[0069] S111: Acquire surface temperature gradient data of gas turbine blades, extract the temperature change rate of each region of the blade according to the time series, calculate the temperature difference between adjacent time points, and obtain temperature change rate distribution data;

[0070] When obtaining the surface temperature gradient data of gas turbine blades, temperature sensors need to be arranged in different areas of the blades and an appropriate sampling interval, such as 0.01s, needs to be set to ensure that the temperature changes 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, location, and temperature value. During the data processing process, the collected temperature data is first interpolated to compensate for the accuracy loss caused by the uneven distribution of sampling points. The commonly used method is linear interpolation, which is calculated as follows: ,in is a known temperature, are known coordinates, is the temperature value after interpolation; after interpolation, the temperature change rate of each region is calculated based on the time series data. The finite difference method is used to calculate the temperature change rate. The calculation method is: ,in and Represent the temperature at two adjacent time points, and Represent the corresponding time points respectively; in order to ensure the stability of the calculation results, when calculating the temperature change rate, the sliding window average processing is adopted, and the sliding window size The setting of is based on the blade thermal response time, which is usually in the range of 3-7 data points. If the data sampling interval is 0.01s, the window length range should be set between 0.03s and 0.07s. In this embodiment, , that is, the window length is 0.05s. This value ensures that the calculation results are smooth and can reflect short-term temperature changes. The specific calculation method is: ,in Represents the size of the sliding window, such as N=5; after the above processing, the temperature change rate distribution data is obtained, where the temperature change rate of each area can be calculated by the average temperature change rate at different time points, such as the temperature change rate at different time points , then the temperature change rate of this area can be expressed as °C / s, as shown in Table 1.

[0071] Table 1 Temperature change rate of different areas of turbine blades:

[0072] ;

[0073] As shown in Table 1, the temperature change rates of different blade regions are different, and this data will be used for subsequent thermal stress calculations.

[0074] 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:

[0075] ;

[0076] Calculate the thermal stress change rate of the blade area and obtain the thermal stress change rate distribution data;

[0077] in, represents the rate of change of thermal stress in the blade area, represents the elastic modulus of the blade material, represents the thermal expansion coefficient of the material, represents the temperature difference between adjacent time points, represents adjacent time intervals;

[0078] Based on the temperature change rate distribution data, the thermal stress change rate of each area of ​​the blade is calculated. The thermal expansion coefficient and elastic modulus of the blade material need to be called. Depends on the blade material, such as the thermal expansion coefficient of nickel-based high-temperature alloys , elastic modulus is 200GPa; the thermal stress change rate is calculated using the formula The thermal stress change rate of each area is calculated according to this formula. For example, for the leading edge area of ​​the blade, the temperature change rate is 2°C / s, and the formula is substituted into the calculation. get MPa / s; for the middle of the blade, the temperature change rate is 2.5°C / s, and the calculation is MPa / s; for the blade trailing edge, the temperature change rate is 1°C / s, and the calculation is MPa / s, as shown in Table 2. In the above calculation, the elastic modulus and thermal expansion coefficient It depends 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 properties of Inconel718, its elastic modulus is 800°C-1000°C. Take 200GPa, thermal expansion coefficient Pick , which is derived from the test results of the mechanical properties of the alloy material and can be used for calculation in this embodiment.

[0079] Table 2. Change rate of thermal stress in different regions of the blade:

[0080] ;

[0081] As shown in Table 2 , the thermal stress change rates in different regions were calculated, and the data were used for subsequent blade stress analysis.

[0082] S113: Based on the thermal stress change rate distribution data, combined with the blade rotation inertia force and aerodynamic load data, the thermal stress composite effect of each region is calculated to obtain the blade thermal stress distribution data;

[0083] Based on the thermal stress change rate distribution data, combined with the blade rotation inertia force and aerodynamic load data, the thermal stress synthesis effect of each area is calculated to obtain the blade thermal stress distribution data. The blade rotation inertia force is calculated based on the angular velocity and blade mass distribution, where the blade mass Assuming 5kg, the blade radius Take 0.3m, angular velocity Assuming 300rad / s, the formula for calculating the rotational inertia force is Calculated N; The aerodynamic load uses the aerodynamic pressure data obtained by fluid mechanics analysis. For example, the aerodynamic pressure at the leading edge of the blade is 2MPa, the middle of the blade is 2.5MPa, and the trailing edge of the blade is 1.8MPa. When calculating the composite stress, the stress changes caused by the loads of each part on the blade need to be superimposed, and the stress superposition method is used. ,in Calculate the blade rotation stress. If the force area of ​​the blade leading edge is 0.002m2, then MPa, final blade leading edge composite stress MPa, blade stress calculation is completed.

[0084] See also Figure 3 ,The specific steps for obtaining the blade over-limit risk area are:

[0085] S211: Based on the blade thermal stress distribution data, calculating the transient thermal stress change trend of the blade under the current working condition at a preset time interval, and obtaining transient thermal stress change trend data;

[0086] Based on the blade thermal stress distribution data, the transient thermal stress change trend of the blade under the current working condition is calculated according to the preset time interval. First, select the appropriate time interval , for example 0.01s, to ensure that the dynamic changes of blade thermal stress are captured, the thermal stress of each area of ​​the blade is sampled within the set time interval, and the thermal stress data at different time points are recorded, such as the leading edge, middle and trailing edge of the blade at time and The stresses at the moment are 、 、 and 、 、 , calculate the transient thermal stress change rate , using finite difference calculation method, the calculation formula is If the stress at the leading edge of the blade at 0.01s is 80MPa and the stress at 0.02s is 82MPa, the transient thermal stress change rate is calculated as MPa / s, the transient thermal stress change rate is processed by sliding window average to reduce the influence of measurement error, and finally the transient thermal stress change trend data is obtained, as shown in Table 3. The value of 0.01s is chosen because the thermal stress variation cycle of gas turbine blades in a high-speed rotating environment is usually in the millisecond level. For example, the time it takes for a blade to rotate once is about 0.02s. At least two samples are required in a single cycle to ensure sufficient time resolution. Therefore, To ensure the timeliness and continuity of data collection; sliding window size Select 5. The setting is based on the need to smooth the transient stress change rate. If the window is too small, it may cause local fluctuations to interfere with the analysis. If the window is too large, it will reduce the sensitivity to sudden stress changes. Considering the thermal inertia and thermal stress conduction characteristics of the blade material, select As the optimal setting value for balancing stability and sensitivity.

[0087] Table 3 Blade transient thermal stress change rate data:

[0088] ;

[0089] As shown in Table 3, the transient thermal stress change rates of different regions of the blade are calculated, and the data are used for subsequent stress overlimit analysis.

[0090] 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, using the formula:

[0091] ;

[0092] Calculate the probability of stress exceeding the limit in the local area of ​​the blade and obtain the stress exceeding probability distribution data;

[0093] in, represents the probability of stress exceeding the limit, represents the stress change rate in the local area, Represents the setting of stress change threshold, To avoid small positive numbers with zero denominator, represents the yield strength ratio of the blade material, is the base of natural logarithms, represents the stress change influence coefficient;

[0094] Based on the transient thermal stress change trend data, the stress change rate of the local area of ​​the blade is extracted, and the area where the stress change rate is greater than the set stress change threshold is screened. First, the stress change threshold is set For example, take 180MPa / s and filter out 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 200MPa / s, which is greater than 180MPa / s and meets the screening conditions. The stress change rate at the trailing edge of the blade is 250MPa / s, which also meets the screening conditions. The stress change rate in the middle of the blade is 300MPa / s, which also meets the screening conditions. In the filtered area, calculate the probability of stress exceeding the limit. ,formula In the example, the parameters are set as follows: is the stress change rate, As a small positive number prevents the denominator from being zero, Set to the material yield strength ratio, such as the yield strength ratio of nickel-based alloy Inconel718 , stress change influence coefficient Set it to 0.05, calculate the probability of stress exceeding the limit at the leading edge of the blade, and substitute the data , calculated Similarly, the probability of blade trailing edge stress exceeding the limit is calculated , probability of stress exceeding limit 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. It is usually set to 0.75 times the yield strength to avoid early crack growth caused by high stress fluctuations. The setting is based on the fact that the yield stress of nickel-based alloy materials is generally about 10% higher than the normal operating stress, in order to ensure the safety margin and the stress change influence coefficient. The setting is based on empirical analysis to ensure that the exponential term adjusts the probability of stress exceeding the limit to a moderate extent and avoids excessive amplification or reduction of the probability value.

[0095] Table 4 Data on probability of stress exceeding limit in different areas of the blade:

[0096] ;

[0097] As shown in Table 4, the stress exceedance probability of different regions is calculated, and the data is used to identify the blade exceedance risk area.

[0098] S213: Based on the stress excess probability distribution data and in combination with the fatigue limit of the blade substrate, regions where the stress excess probability is higher than the fatigue limit risk threshold are screened to obtain blade excess risk regions.

[0099] Based on the stress overlimit probability distribution data and the fatigue limit of the blade matrix, the area where the stress overlimit probability is higher than the fatigue limit risk threshold is screened. The fatigue limit of the blade material is set to 250MPa. By comparing the calculated stress data, the stress overlimit probability at the leading edge of the blade is , which is lower than the fatigue limit risk threshold of 0.15 and does not belong to the over-limit risk area. The probability of stress over-limit at the trailing edge of the blade is , is still lower than 0.15, and does not belong to the over-limit risk area, while the probability of stress over-limit in the middle of the blade is If the value is higher than 0.15, it is considered an over-limit risk zone. The middle of the blade is marked as a risk zone, and data for this over-limit risk zone is obtained. In the above calculations, the fatigue limit risk threshold is set at 0.15. This is based on the critical crack growth rate in engineering standards, which typically uses 60%-80% of the material's fatigue limit as the threshold. In this example, 60% is used to ensure a conservative fatigue damage assessment.

[0100] See also Figure 4 ,The specific steps for obtaining blade creep fatigue damage distribution information are:

[0101] S311: Calling the blade overrun risk area, extracting the temperature gradient change rate, stress change rate, and cyclic load frequency of the blade overrun area, calculating the local creep stress influence coefficient of the blade, constructing a creep stress influence factor matrix based on the combined influence of the temperature change rate and stress change rate of each area, and obtaining the blade creep stress influence coefficient distribution;

[0102] Call the blade over-limit risk area data, extract the temperature gradient change rate, stress change rate and cyclic load frequency of the blade over-limit area, first calculate the temperature gradient change rate based on the temperature data of the local area of ​​the blade, select the temperature value of the blade over-limit area at different time points, such as the middle of the blade at Temperature ,exist Temperature , then the temperature gradient change rate is calculated as Based on the stress change rate, the stress data of the over-limit area is selected, such as the stress data of the middle part of the blade. The stress at the moment is 100 MPa. The stress at the moment is 105 MPa, so calculate the stress change rate MPa / s; When calculating the cyclic load frequency, the blade speed data is used. For example, if the blade speed is 3000rpm, the cyclic load frequency Hz; When constructing the creep stress influence factor matrix, the creep stress influence coefficient of each region needs to be determined by the temperature gradient change rate, stress change rate and cyclic load frequency. Among them, the creep stress influence factor weight needs to be adjusted according to the high-temperature durability 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 durability 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 weighted values ​​are given to construct the creep stress influence factor matrix. , the matrix elements The temperature gradient change rate, stress change rate and cyclic load frequency of each region are weighted and calculated, such as the middle of the blade , blade trailing edge , and finally the distribution of blade creep stress influence coefficient is obtained.

[0103] 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:

[0104] ;

[0105] The blade creep and fatigue damage accumulation rates are calculated;

[0106] in, 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 material fatigue strength index, 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 baseline creep rate;

[0107] Call the blade creep stress influence coefficient distribution data, combine the creep rate, fatigue damage accumulation rate and operating load change rate to calculate the blade creep and fatigue damage accumulation rate. First, set the transient thermal stress value , such as the transient thermal stress value of the middle part of the blade is 105MPa, the fatigue limit Set to 250MPa. The fatigue limit is set based on the material's SN curve (stress-life curve). The fatigue limit of nickel-based alloy materials at a high temperature of 650°C is about 250MPa. This value is obtained based on fatigue experiments. The material fatigue strength index Take 3. This value is set according to the Paris fatigue crack growth equation. The Paris index of nickel-based alloys is usually between 2.5 and 3.5, so the median value is 3. The load frequency influence coefficient Take 0.02, which is obtained from the frequency sensitivity test of the material, usually between 0.01-0.03. Nickel-based alloy materials take 0.02 due to the grain boundary expansion mechanism, and the cyclic load frequency Take 50Hz, creep rate adjustment factor Take 0.1, which is adjusted according to creep test data. The high temperature creep strain rate of nickel-based alloy material is about The influence factor is usually set between 0.05-0.15, so 0.1 is selected, and the blade creep rate Pick , baseline creep rate Pick , substitute into the formula to calculate:

[0108] ;

[0109] Calculate the blade creep and fatigue damage accumulation rate .

[0110] S313: Calling the blade creep and fatigue damage accumulation rate, screening the area where the creep damage accumulation rate is higher than the set damage threshold, and obtaining the blade creep fatigue damage distribution information;

[0111] Call the blade creep and fatigue damage accumulation rate data, filter the area where the creep damage accumulation rate is higher than the set damage threshold, and set the damage threshold , usually set between 3.0-4.0 to ensure that the cumulative damage does not reach the material damage threshold. The typical critical value of nickel-based alloy materials is set at 3.5. Comparing the calculated creep and fatigue damage accumulation rates , which is higher than the set damage critical value, indicating that there is a serious creep damage risk in the middle of the blade. The middle of the blade is marked as the creep fatigue damage area, and the creep fatigue damage distribution information of the blade is obtained.

[0112] See also Figure 5 , the specific steps for obtaining the corrected life expectancy prediction value are:

[0113] S411: Based on the blade creep fatigue damage distribution information, according to the creep and fatigue damage accumulation rate under the current operating condition, the life decay rate of each region of the blade is calculated. In combination with the change trend of the damage accumulation rate, the decay parameter is adjusted to obtain the blade life decay rate distribution information;

[0114] Based on the creep and fatigue damage distribution information of the blade, the life attenuation rate of each area of ​​the blade is calculated according to the creep and fatigue damage accumulation rate under the current working conditions. First, the creep and fatigue damage accumulation rate of the over-limit area is selected. As an initial variable, such as the middle of the leaf , the trailing edge of the blade , the leading edge of the blade , and then analyze the changing trend of damage accumulation rate in different areas and calculate the time Damage change rate within , for example, the damage accumulation rate of the middle part of the blade after 1000 hours of operation changes to , then the damage change rate / h, combined with the damage accumulation rate change trend, adjust the life attenuation parameters This parameter is used to adjust the life attenuation rate under different working conditions. Its value should be based on the high temperature endurance strength test data of the material. In a typical nickel-based high temperature alloy blade, It can be derived from the experimentally measured endurance strength curve, usually with a value range of 0.05-0.15. Take 0.1 to calculate the blade life decay rate , calculated as , such as the life decay rate of the middle part of the blade / h, and finally the blade life attenuation rate distribution information is calculated.

[0115] S412: Call the blade life decay rate distribution using the formula:

[0116] ;

[0117] Calculate the remaining life correction factor of the blade;

[0118] in, 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 factor, represents the cyclic load frequency of the blade, Represents the load frequency attenuation parameter;

[0119] Call the blade life attenuation rate distribution to calculate the blade's remaining life correction coefficient , set the damage accumulation adjustment coefficient This coefficient is used to correct the nonlinear effect of different stress levels on damage accumulation. The specific value can be obtained by fitting the material SN curve. In the typical blade material Inconel718, this coefficient is usually between 0.03-0.07. The blade cyclic load frequency Hz, load frequency attenuation parameter This parameter is used to reflect the additional attenuation effect of high-frequency load on blade life. The value is set based on the calculation of the cyclic stress amplitude under fatigue limit. Generally, when the cycle frequency is higher than 30Hz, Take the value 0.015-0.025 and substitute it into the formula:

[0120] ;

[0121] Finally, the remaining life correction coefficient of the middle part of the blade is calculated .

[0122] S413: Calling the blade remaining life correction coefficient, combining the blade's service stage, correcting the life, calculating the life prediction value, establishing blade life prediction data, and obtaining the blade corrected life prediction value;

[0123] Call the blade remaining life correction coefficient, combine the blade's service stage, correct the life, calculate the life prediction value, and select the blade design life , using the modified calculation The life expectancy of the middle part of the blade is h, in this calculation The blade durability test data is set based on the benchmark working conditions (temperature 950℃, stress 200MPa, cycle frequency 30Hz) for 10^ 7The life test value for failure after the first cycle is taken as 20,000 hours. This benchmark value is usually set according to the manufacturer or engineering standards. The value can be adjusted according to the long-term reliability data of different materials to finally obtain the corrected life prediction value of the blade.

[0124] See also Figure 6 , the specific steps for obtaining the remaining life span of the blade are:

[0125] S511: Calling the corrected life prediction value, analyzing the impact of the gas turbine start-stop load on the blade damage accumulation rate, extracting the creep damage increment and fatigue damage increment caused by the start-stop load change, calculating the damage rate change trend of the blade under various start-stop cycles, and obtaining the blade start-stop load damage rate distribution information;

[0126] Call the corrected life prediction value and 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 working conditions to After shutdown, the temperature drops to , then the temperature change , based on which the creep damage increment is calculated, using Calculate temperature change rate, such as cooling time s, then , combined with the thermal expansion coefficient of the blade material and elastic modulus GPa, calculate thermal stress changes , substitute into numerical calculation get MPa, calculate creep damage increment under multiple start-stop cycles and fatigue damage increment , such as the creep damage increment of a single start-stop cycle The setting is based on the ASTM E139 standard, which is based on the creep behavior of high-temperature alloys at 900°C. , fatigue damage increment According to the blade cyclic load stress amplitude calculation, the Goodman formula is used to correct it and get After 1000 starts and stops, the creep damage accumulated is , fatigue damage accumulation is , calculate the damage rate change trend of the blade under various start-stop cycles, and obtain the damage rate distribution information of the blade start-stop load.

[0127] S512: Call the blade start-stop load damage rate distribution information to calculate the fatigue damage adjustment coefficient under extreme working conditions, using the improved formula:

[0128] ;

[0129] Calculate the fatigue damage adjustment coefficient of the blade under extreme working conditions;

[0130] in, represents the fatigue damage adjustment factor 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;

[0131] Call the blade start-stop load damage rate distribution information to calculate the fatigue damage adjustment coefficient under extreme working conditions , set the stress change MPa, reference stress The selection is based on the ASME BPVCIII high temperature component design specification, the value is 1200MPa, and the extreme working condition stress response coefficient The thermal mechanical fatigue coefficient determined by the experiment is taken as 0.1, and the damage accumulation adjustment coefficient under extreme working conditions is The parameters of the Paris-Erdogan crack growth model are modified and set to 0.02, and the extreme load frequency sensitivity According to ISO12110-2 material fatigue strength standard, it is selected as 0.05, and the blade cyclic load frequency under extreme working conditions Hz, substitute into the formula:

[0132] ;

[0133] Finally, the fatigue damage adjustment coefficient of the blade under extreme working conditions is calculated .

[0134] S513: Calling the extreme working condition fatigue damage adjustment coefficient of the blade, comparing the creep fatigue joint distribution under the blade operation state, calculating the extreme working condition life offset, establishing the blade remaining life range data, and obtaining the blade remaining life interval.

[0135] Call the fatigue damage adjustment coefficient of the blade under extreme working conditions, compare the creep fatigue joint distribution under the blade operating state, calculate the extreme working condition life offset, and select the initial life prediction value of the blade h, this value is set based on the GE90 blade life test data and the ASTM E1049-85 high-temperature alloy life prediction method to calculate the life offset According to the MIL-HDBK-5H standard, the reduction ratio of material life under extreme loads is calculated using the formula:

[0136] ;

[0137] Substituting the values:

[0138] ;

[0139] Calculate the remaining life of blades under extreme operating conditions :

[0140] ;

[0141] Finally, the blade remaining life range data is established to obtain the blade remaining life interval h. This range is set according to ASTM E917-15 high temperature fatigue life assessment method to ensure the rationality of the calculation results in engineering applications.

[0142] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for evaluating the life of a turbine blade, characterized in that: The following steps are involved: S1: Obtain the surface temperature gradient data of the gas turbine blade, extract the temperature change rate of each area of ​​the blade, analyze the impact of the temperature change rate on the local stress of the blade, calculate the thermal stress synthesis effect by combining the blade rotation inertia force and aerodynamic load, and 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 exceedance probability of each area of ​​the blade in combination with the fatigue limit of the blade matrix to obtain the blade exceedance risk area; S3: Calling the blade over-limit risk area, combining the creep rate, fatigue damage accumulation rate and operating load change rate to calculate the accumulation rate of high-temperature creep and fatigue damage of the blade, screening the area where the creep damage accumulation rate is higher than the set damage threshold, and obtaining the blade creep fatigue damage distribution information; S4: Based on the creep fatigue damage distribution information of the blade, according to the creep and fatigue damage accumulation rate under the current working condition, the life decay rate is adjusted, and the remaining life correction coefficient of the blade is calculated. In combination with the service stage of the blade, a corrected life prediction value is calculated; The steps for obtaining the corrected life prediction value are specifically as follows: S411: Based on the blade creep fatigue damage distribution information, according to the creep and fatigue damage accumulation rate under the current operating condition, calculating the life decay rate of each region of the blade, and adjusting the decay parameter in combination with the change trend of the damage accumulation rate to obtain the blade life decay rate distribution information; S412: Call the blade life attenuation rate distribution using the formula: ； Calculate the remaining life correction factor of the blade; S413: calling the blade remaining life correction coefficient, combining the service stage of the blade, correcting the life, calculating the life prediction value, establishing blade life prediction data, and obtaining the blade corrected life prediction value.

2. The turbine blade life assessment method according to claim 1, characterized in that: The blade thermal stress distribution data includes the thermal stress change rate, the temperature change rate and the thermal stress synthesis effect. The blade over-limit risk area is specifically the stress over-limit probability, the stress change rate and the over-limit area distribution. The blade creep fatigue damage distribution information includes the creep stress influence coefficient, the fatigue damage accumulation rate and the joint probability density function. The obtained corrected life prediction value specifically refers to the remaining life correction coefficient, the life decay rate and the life decay trend.

3. The turbine blade life assessment method according to claim 1, characterized in that: The steps for obtaining the blade thermal stress distribution data are specifically as follows: S111: Acquire surface temperature gradient data of gas turbine blades, extract the temperature change rate of each region of the blade according to the time series, calculate the temperature difference between adjacent time points, and obtain temperature change rate distribution data; 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 area and obtain the thermal stress change rate distribution data; ； S113: Based on the thermal stress change rate distribution data, combined with the blade rotation inertia force and aerodynamic load data, the thermal stress synthesis effect of each area is calculated to obtain the blade thermal stress distribution data.

4. The turbine blade life assessment method according to claim 1, characterized in that: The steps for obtaining the blade over-limit risk area are specifically as follows: S211: Based on the blade thermal stress distribution data, calculating the transient thermal stress change trend of the blade under the current working condition at a preset time interval, and obtaining 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 area where the stress change rate is greater than the set stress change threshold, using the formula: ； Calculate the probability of stress exceeding the limit in the local area of ​​the blade and obtain the stress exceeding probability distribution data; ； S213: Based on the stress excess probability distribution data and in combination with the fatigue limit of the blade substrate, regions where the stress excess probability is higher than the fatigue limit risk threshold are screened to obtain blade excess risk regions.

5. The turbine blade life assessment method according to claim 1, characterized in that: The steps for obtaining the blade creep fatigue damage distribution information are specifically as follows: S311: Calling the blade overrun risk area, extracting the temperature gradient change rate, stress change rate, and cyclic load frequency of the blade overrun area, calculating the local creep stress influence coefficient of the blade, constructing a creep stress influence factor matrix based on the combined influence of the temperature change rate and stress change rate of each area, and obtaining 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: ； The blade creep and fatigue damage accumulation rates are calculated; ； S313: calling the blade creep and fatigue damage accumulation rate, screening the region where the creep damage accumulation rate is higher than the set damage threshold, and obtaining the blade creep fatigue damage distribution information.

6. The turbine blade life assessment method according to claim 1, characterized in that: The method further comprises the following steps: S5: calling the corrected life prediction value, analyzing the influence of the gas turbine start-up and shutdown loads on the blade damage accumulation rate, calculating the fatigue damage adjustment coefficient under extreme working conditions, comparing the creep fatigue joint distribution under the blade operating state, calculating the extreme working condition life offset, and obtaining the blade remaining life range; The remaining life interval of the blade includes a remaining life range, a life threshold impact, and an operating condition life impact.

7. The turbine blade life assessment method according to claim 5, characterized in that: The steps for obtaining the remaining life span of the blade are specifically as follows: S511: Calling the corrected life prediction value, analyzing the impact of the gas turbine start-stop load on the blade damage accumulation rate, extracting the creep damage increment and fatigue damage increment caused by the start-stop load change, calculating the damage rate change trend of the blade under various start-stop cycles, and obtaining the blade start-stop load damage rate distribution information; S512: Calling the blade start-stop load damage rate distribution information, calculating the fatigue damage adjustment coefficient under extreme working conditions, and using the improved formula: ； Calculate the fatigue damage adjustment coefficient of the blade under extreme working conditions; S513: calling the extreme working condition fatigue damage adjustment coefficient of the blade, comparing the creep fatigue joint distribution under the blade operation state, calculating the extreme working condition life offset, establishing the blade remaining life range data, and obtaining the blade remaining life interval.

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