Method for calculating reliability of turbine of small gas turbine

Abstract

The invention relates to a turbine reliability calculation method for a small gas turbine. The method comprises the steps of dividing typical operation condition modules; defining a minimum basic unit body module; a blade temperature field calculation module; a structure static analysis module; the random load spectrum generation module is used for randomly generating a working load spectrum in a certain time period according to the load statistical table; a high-low cycle fatigue damage calculation module; a creep damage calculation module; a composite damage calculation module; a turbine overall composite damage probability distribution module; and a reliability calculation module. The device is compact and reasonable in structure and convenient to operate, and by comprehensively considering various factors such as working condition division, multi-dimensional damage prediction and accurate service life prediction, the accuracy of service life and reliability evaluation of the turbine blade is remarkably improved. According to the method, optimization design and improvement of the performance of the gas turbine are facilitated, and a powerful guarantee can be provided for safe and stable operation of the gas turbine.

Description

Technical Field

[0001] The invention relates to the technical field of turbine reliability analysis, in particular to a method for calculating the reliability of a small gas turbine turbine. Background Art

[0002] As the core component of a gas turbine, the reliability and service life of the turbine are crucial to the performance and safety of the entire system. However, under complex loads and high temperature environments, turbine blades often face serious challenges, such as high and low cycle fatigue, creep, fracture and other failure modes. These failure modes will not only lead to a decline in turbine performance, but may even cause serious safety accidents. Therefore, accurately predicting the life and reliability of turbine blades is of great significance to ensure the safe and stable operation of gas turbines.

[0003] Although existing technologies can predict the fatigue and creep resistance of turbine blades to a certain extent, their prediction accuracy is still limited in the case of complex loads and lack of statistical data. This is mainly because existing methods are often based on simplified models or assumptions, and it is difficult to fully consider the complex state and multiple influencing factors of turbine blades in actual work. Therefore, developing a more accurate and comprehensive turbine reliability calculation method is of great significance to improving the performance and safety of gas turbines.

[0004] To this end, we propose a small gas turbine turbine reliability calculation method of the present invention. By comprehensively considering multiple damage mechanisms, random loads, and the randomness of material properties, we can more accurately evaluate the life and reliability of turbine blades, providing strong support for optimizing design and improving gas turbine performance. Summary of the invention

[0005] In response to the shortcomings in the above-mentioned existing production technologies, the applicant provides a reliability calculation method for a small gas turbine turbine. By comprehensively considering multiple factors, such as operating condition division, multi-dimensional damage prediction, and precise life prediction, the accuracy of turbine blade life and reliability assessment is significantly improved.

[0006] The technical solution adopted by the present invention is as follows:

[0007] 1. Small gas turbine turbine reliability calculation method, including:

[0008] The module for dividing the typical operating conditions is used to divide the operating conditions into several typical operating conditions according to the actual operating conditions of the gas turbine, and determine the inlet and outlet boundary conditions of each typical operating condition and the load statistics table of the expected occurrence frequency and operating time;

[0009] Define the minimum basic unit module, which is used to evenly divide the turbine into several parts along the circumference according to the number of blades, define it as the minimum basic unit, and obtain the model of the entire turbine by rotating the array;

[0010] The module for calculating blade temperature field is used to select the blade surface, form the fluid domain, divide the high-quality grid, and use computational fluid dynamics tools to calculate the flow field under each working condition, and output the temperature field and pressure field of the turbine blade;

[0011] Structural static analysis module, used to perform finite element calculation on the solid domain of the turbine, taking the temperature field and pressure field as boundary conditions to solve the total strain field and equivalent stress field of the blade;

[0012] A random load spectrum generation module is used to randomly generate a working load spectrum for a certain period of time according to a load statistics table;

[0013] High-low cycle fatigue damage calculation module, used to count the rain flow of the generated random load spectrum and calculate fatigue damage using the linear damage accumulation method;

[0014] Creep damage calculation module, used to calculate creep life using Larson-Miller parameter equation and creep damage using linear damage accumulation law;

[0015] Composite damage calculation module, used to accumulate fatigue damage and creep damage to obtain composite damage of a single blade under random load spectrum;

[0016] The turbine overall composite damage probability distribution module is used to obtain the composite damage frequency histogram of the turbine blades during a certain period of operation, and to perform distribution estimation and calculate the composite damage probability distribution of n time periods of operation;

[0017] The reliability calculation module is used to define reliability according to the probability that the total material damage is less than the material strength, and obtain an analytical solution through the Monte Carlo method or function calculation.

[0018] In one of the embodiments, in the typical operating condition module, typical conditions include but are not limited to assembly conditions, low load conditions, medium load conditions and rated load conditions.

[0019] In one of the embodiments, in the module for defining the minimum basic unit body, the minimum basic unit body is obtained by evenly dividing the turbine into n parts along the circumferential direction, where n is the number of blades.

[0020] In one of the embodiments, in the module for calculating blade temperature field, the high-quality grid includes a structured grid or a hybrid grid, and the accuracy of the boundary layer grid is ensured to capture flow characteristics.

[0021] In one of the embodiments, in the structural static analysis module, the boundary conditions applied also include limiting the axial and circumferential degrees of freedom of the turbine, and applying rotational symmetry conditions.

[0022] In one of the embodiments, in the random load spectrum generation module, a rejection sampling method is used to randomly generate a working load spectrum according to the frequency and time of occurrence of the working condition.

[0023] In one of the embodiments, in the high-low cycle fatigue damage calculation module, a linear damage accumulation method is used to calculate fatigue damage, and elastic strain and plastic strain terms are considered, which is applicable to high-cycle and low-cycle fatigue life calculations;

[0024] The fatigue damage calculation formula is:

[0025]

[0026] Where: D H-L is high-low cycle combined fatigue damage, N i is the number of cycles under fatigue load i, N f, is the total number of cycles under fatigue load i.

[0027] In one embodiment, in the creep damage calculation module, the Larson-Miller parameter equation is:

[0028]

[0029] Where T is the blade temperature, A, B, C are material constants;

[0030] The creep damage accumulation also uses the linear damage accumulation law:

[0031]

[0032] Where: D creep Creep damage, t i is the running time under creep load i, t c, is the total operating time under creep load i.

[0033] In one embodiment, in the probability distribution of the overall composite damage of the turbine, the composite damage D of running for n time periods is n The probability distribution is calculated as follows:

[0034] f(D n )=n[F(D)] n-1 f(D)

[0035] Where F(D) is the cumulative distribution function of the composite damage of a single blade.

[0036] In one embodiment, the reliability calculation module includes:

[0037] Reliability calculation, reliability is defined as the total material damage D nLess than material strength S r Probability:

[0038] R=P(D n r )

[0039] The residual strength distribution of the material is related to the performance of the material itself, and can be defined using the variation rate γ, that is, the material strength follows N(1,γ 2 ) of the normal distribution f(S r );

[0040] In addition, reliability pre-allocation and load spectrum generation optimization are used to optimize the vulnerable areas based on the calculated composite damage distribution and expected reliability, and to modify the load spectrum generation method in real time according to the actual operating cycle during the commissioning phase to predict the remaining life.

[0041] The beneficial effects of the present invention are as follows:

[0042] The present invention has a compact and reasonable structure and is easy to operate. By comprehensively considering multiple factors, such as operating condition division, multi-dimensional damage prediction, and accurate life prediction, the accuracy of turbine blade life and reliability assessment is significantly improved. This method not only helps to optimize the design and improve the performance of gas turbines, but also provides a strong guarantee for the safe and stable operation of gas turbines. Through the implementation of the present invention, we can better understand and grasp the performance changes of turbines in actual operation, and contribute to the sustainable development and progress of the gas turbine industry.

[0043] At the same time, the present invention also has the following advantages:

[0044] The present invention provides a solid foundation for subsequent temperature field and pressure field calculations and fatigue and creep damage analysis by carefully dividing typical operating conditions and determining the import and export boundary conditions and load statistics under each condition. This refined method of dividing operating conditions enables us to more accurately simulate the behavior of the turbine under different operating conditions, thereby more accurately evaluating its life and reliability. This helps us to promptly discover potential problems in the design or operation of the turbine, providing strong support for optimizing the design and improving performance.

[0045] The present invention adopts a multi-dimensional damage prediction and joint damage analysis method, combining multiple damage mechanisms such as creep damage and fatigue damage, and taking into account the influence of random loads. Compared with the traditional single fatigue analysis method, this joint analysis method can more comprehensively evaluate the damage of turbine blades and more accurately reflect their actual working status and life. By counting the number of load cycles using the rain flow counting method and analyzing fatigue damage using the Coffin-Manson equation, the present invention can analyze in detail the fatigue failure laws of turbine blades under different loads, providing scientific data support for design and maintenance.​

[0046] The present invention combines the stress-strain field, temperature field and multi-condition load spectrum of the blade to accurately calculate the strain spectrum and damage distribution of the turbine blade under actual operating conditions. This accurate life prediction method helps to identify potential failures and fatigue damage points in advance, reduce the risk of turbine blade failure, and thus improve the overall reliability of the gas turbine. At the same time, the present invention also takes into account the variability and probability distribution of the material itself, providing a more accurate and personalized calculation model for the life prediction of turbine blades. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is the work flow chart of the present invention.

[0048] Figure 2 It is the smallest basic unit of the turbine in the present invention.

[0049] Figure 3 It is the turbine temperature field of rated operating condition in the present invention.

[0050] Figure 4 It is the total strain field of the turbine in the present invention.

[0051] Figure 5 It is the random load spectrum of the turbine in the present invention during one operation cycle.

[0052] Figure 6 This is the total strain cycle diagram of a single turbine blade in the present invention.

[0053] Figure 7 This is the composite damage distribution diagram of the turbine in the present invention.

[0054] Figure 8 This is a diagram showing the damage and material strength distribution of the turbine after 180 weeks of operation in the present invention. DETAILED DESCRIPTION

[0055] The following is a detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. Multi-dimensional damage prediction and joint damage analysis

[0056] like Figure 1 As shown, this embodiment discloses a method for calculating the reliability of a small gas turbine, which specifically includes the following steps:

[0057] S1: Classification of typical operating conditions

[0058] According to the actual operating conditions of the gas turbine, the operating conditions are divided into several typical conditions, such as:

[0059] Assembly condition: the state maintained for a long time during shutdown;

[0060] Low load conditions: low speed, small temperature and pressure loads;

[0061] Medium load condition: stable operation for a long time;

[0062] Rated load conditions: operating at rated load or close to rated load, with high temperature and pressure;

[0063] By carefully dividing the working conditions, we can more accurately simulate the behavior of the turbine under different working conditions, providing a solid foundation for subsequent temperature field and pressure field calculations as well as fatigue and creep damage analysis. In addition, this division also helps us better understand the performance changes of the turbine under different working conditions, providing strong support for optimizing design and improving reliability.

[0064] Determine the inlet and outlet boundary conditions for each typical operating condition of the turbine, such as

[0065] Turbine inlet boundary conditions: such as temperature, pressure, speed, etc.

[0066] Turbine outlet boundary conditions: such as flow velocity, pressure distribution, etc.

[0067] Based on actual operating data or design experience, a load statistics table is given for the expected frequency of occurrence and operating time of each typical operating condition of the turbine. The load spectrum of different time periods can be given as a time constraint, which is crucial for the subsequent generation of random load spectra and calculation of damage. The formulation of the load statistics table needs to be based on a large amount of actual operating data or design experience to ensure that it can truly reflect the operating conditions of the turbine. Through this step, we can more accurately evaluate the damage of the turbine in long-term use and provide a strong basis for predicting its life and reliability.

[0068] S2: Define the smallest basic unit

[0069] According to the number of blades n, the turbine is evenly divided into n parts along the circumference, which are defined as the minimum basic unit body. A single turbine can be obtained by rotating the array through the minimum basic unit body. Subsequent aerodynamic and structural strength analysis of the minimum basic unit body can reduce the amount of calculation. The definition of the minimum basic unit body not only reduces the amount of calculation, but also allows us to focus more on analyzing the key parts of the turbine, such as the stress and strain distribution of the blades. In addition, obtaining the model of the entire turbine by rotating the array also facilitates subsequent aerodynamic and structural strength analysis.

[0070] S3: Calculate the blade temperature field

[0071] Select the pressure surface or suction surface of the blade, rotate 360° / n along the central axis of the blade, and extend it forward and backward for a certain distance to form a fluid domain. Use Boolean operations to subtract the solid blade part to obtain the blade fluid domain. Divide the blade fluid domain into high-quality grids (such as structured grids or hybrid grids) to ensure the accuracy of the boundary layer grid to capture the flow characteristics.

[0072] Using the inlet and outlet boundary conditions of each typical operating condition in S1, the computational fluid dynamics (CFD) tool is used to calculate the flow field under each operating condition, and the temperature field T(x, y, z) and pressure field P(x, y, z) of the turbine blade under each operating condition are output.

[0073] The calculation of temperature and pressure fields is the basis for subsequent structural static analysis and damage calculation. Through CFD simulation, we can capture the temperature distribution and pressure changes on the blade surface, providing key data for evaluating the thermal stress and fatigue damage of the blade. In addition, high-quality meshing ensures the accuracy and stability of the calculation results.

[0074] S4: Structural Static Analysis

[0075] The solid domain of the turbine is divided into structural grids for finite element calculation, the axial and circumferential degrees of freedom of the turbine are restricted, and rotational symmetry conditions are imposed. The temperature field and pressure field obtained by CFD simulation are imported into the finite element model of the turbine as boundary conditions to solve the total strain field ε(x, y, z) and equivalent stress field σ(x, y, z) of the blades.

[0076] Structural static analysis is an important step in evaluating the structural strength of blades. Through finite element calculation, we can understand the stress and strain distribution of blades under complex loads, which provides an important basis for judging whether the blades will break or yield. In addition, this step also provides the necessary input data for subsequent fatigue and creep damage calculations.

[0077] S5: Random load spectrum generation

[0078] Using a self-written Python program, input the load statistics table and use the rejection sampling method: using the frequency and time of the working conditions in S1, randomly generate the working load spectrum for a certain time period (such as one day or one week).

[0079] S6: High-low cycle fatigue damage calculation

[0080] Perform rain flow counting on the generated random load spectrum and count the number of load cycles N and the total strain amplitude Δε corresponding to each working condition. a . The linear damage accumulation method is used for fatigue damage calculation:

[0081]

[0082] Where: D H-L is high-low cycle combined fatigue damage, N i is the number of cycles under fatigue load i, N f, is the total number of cycles under fatigue load i;

[0083] S7: Creep damage calculation

[0084] The creep life is calculated using the Larson-Miller parameter equation:

[0085]

[0086] Where T is the blade temperature, A, B, C are material constants.

[0087] The creep damage accumulation also uses the linear damage accumulation law:

[0088]

[0089] Where: D creep Creep damage, t i is the running time under creep load i, t c, is the total operating time under creep load i;

[0090] S8: Composite damage calculation

[0091] By adding up the fatigue and creep damage, we can get the composite damage of a single blade under the random load spectrum:

[0092] D=D creep +D H-L

[0093] S9: Probability distribution of composite damage to the turbine as a whole

[0094] Repeat steps 6-8 to obtain the frequency histogram of composite damage of turbine blades during a certain period of operation (e.g., one week). Use the kernel density estimation method (KDE) to estimate its distribution and obtain its distribution f(D). According to the theory of order statistics, run the composite damage D for n time periods. n The probability distribution is calculated as follows:

[0095] f(D n )=n[F(D)] n-1 f(D)

[0096] Where F(D) is the cumulative distribution function of the composite damage of a single blade.

[0097] S10: Reliability Calculation

[0098] Reliability is defined as the total material damage D n Less than material strength S r Probability:

[0099] R=P(D n r )

[0100] ​The residual strength distribution of the material is related to the performance of the material itself, and can be defined using the variation rate γ, that is, the material strength follows N(1,γ 2 ) of the normal distribution f(S r ).

[0101] The reliability of the turbine can be obtained by Monte Carlo method according to the definition, or by analytical solution through function calculation:

[0102]

[0103] S11: Reliability pre-assignment and load spectrum generation optimization

[0104] The turbine reliability in this model is calculated based on the design operating frequency and time, which can obtain the composite damage distribution and expected reliability of different locations, making it convenient to optimize the vulnerable areas; during the commissioning phase, the load spectrum generation method can be modified in real time according to the actual operating cycle, and the remaining life can be predicted based on reliability.

[0105] The generation of random load spectra takes into account the randomness and uncertainty of the loads during actual operation of the turbine, making the evaluation results closer to the actual situation. The calculation of fatigue and creep damage is based on advanced damage theories and models to ensure the accuracy of the evaluation. The calculation and probability distribution evaluation of composite damage provide us with the damage status and reliability level of the turbine in different time periods. Finally, through reliability pre-allocation and load spectrum generation optimization, we can optimize the design of vulnerable areas and improve the overall reliability of the turbine.

[0106] 1. Multi-dimensional damage prediction and joint damage analysis

[0107] The present invention uses creep damage, fatigue damage and other damage mechanisms combined with random loads for joint analysis, which can more comprehensively evaluate the damage of turbine blades. Compared with the traditional single fatigue analysis method, this joint analysis method can more accurately reflect the actual working state and life of turbine blades.

[0108] By counting the number of load cycles using the rain flow counting method and analyzing fatigue damage using the Coffin-Manson equation, the present invention can analyze in detail the fatigue failure laws of turbine blades under different loads, providing more scientific data support for design and maintenance.

[0109] This joint analysis method not only improves the accuracy of the assessment, but also provides us with more information about the damage mechanism and life prediction of turbine blades. By comparing the impact of different damage mechanisms on blade life, we can better understand the failure mode of the blade and provide a scientific basis for optimizing design and maintenance strategies.

[0110] 2. Improve the accuracy of blade reliability calculation

[0111] By combining the stress-strain field, temperature field and multi-condition load spectrum of the blade, the strain spectrum and damage distribution of the turbine blade under actual operating conditions can be accurately calculated, so as to more accurately predict the fatigue life and reliability of the blade. This accurate life prediction helps to identify potential failures and fatigue damage points in advance, reduce the risk of turbine blade failure, and thus improve the overall reliability of the gas turbine.

[0112] Spiral analysis combines the variability and probability distribution of the material itself to provide a more accurate and personalized calculation model for the life prediction of turbine blades.

[0113] This accurate life prediction is crucial for identifying potential failure and fatigue damage points in advance. By detecting and addressing these problems in a timely manner, we can effectively reduce the risk of turbine blade failure and improve the overall reliability of the gas turbine. In addition, this accurate prediction also provides strong support for the formulation of reasonable maintenance plans and replacement strategies.

[0114] 3. Adapt to complex practical application scenarios

[0115] The present invention can adapt to various working conditions and complex operating environments by monitoring and predicting the entire life cycle of the gas turbine. In particular, under conditions of different load frequencies and complex thermal loads, the damage and failure mechanisms of the turbine blades are more comprehensively considered.

[0116] This adaptability enables the invention to be widely used in gas turbines, aircraft engines and other fields, providing reliable life prediction and reliability assessment for different types of turbine blades. At the same time, by continuously optimizing and improving the calculation method, we can further improve the accuracy and efficiency of the assessment and provide more powerful support for practical applications in the industry.

[0117] This embodiment can be widely used in gas turbines, aircraft engines and other fields, and has strong versatility and adaptability.

[0118] Example 1

[0119] Taking a certain type of gas turbine as an example, the above method is used to calculate its turbine and predict its reliability:

[0120] According to the working environment and purpose of the gas turbine, its load spectrum and boundary conditions are statistically analyzed, and a load reference table is made as shown in the following table:

[0121] Table 1 Turbine operating conditions reference table

[0122]

[0123] The gas turbine turbine is divided into Figure 2 The smallest basic unit shown

[0124] Through the temperature and pressure boundary conditions under different working conditions, the temperature distribution and pressure distribution of the turbine blades under different working conditions are calculated using fluid calculation software. The temperature distribution is as follows: Figure 3 shown.

[0125] The temperature field and pressure of the turbine are imported into the turbine finite element calculation as boundary conditions, and the rotational symmetry boundary and displacement constraints are set to calculate the total strain distribution and equivalent stress distribution of the turbine. The total strain distribution of the turbine is as follows: Figure 4 shown.

[0126] According to Table 1, a random load spectrum of a turbine running for one week can be generated using a self-written Python program. Figure 5 It is a random load spectrum automatically generated based on turbine operation statistics or design experience, which can better restore the randomness of loads in actual turbine operation.

[0127] In such Figure 5 Under the load spectrum shown, combined with the calculated strain field and stress field under different working conditions, the total strain change at a certain point on the turbine can be obtained as follows: Figure 6 As shown:

[0128] The above total strain cycles were statistically analyzed by rain flow counting method using a self-written python program, and the load amplitude ε was calculated respectively. a And the number of load cycles N i , high-low cycle fatigue damage can be calculated using the improved formula of Morrow.

[0129] The above formula includes terms related to both elastic strain and plastic strain, so it can be applied to high-cycle and low-cycle fatigue life calculations. The high-low cycle fatigue damage to turbine blades is determined by the number of load cycles N to which they are subjected. i Divide by the total number of cycles under this load N fi The high-low cycle fatigue damage is shown as follows:

[0130]

[0131] The creep life of the turbine is mainly calculated based on the temperature and stress distribution in the turbine under steady-state operating conditions, combined with the Larson-Miller curve of the material. The following formula is the Larson-Miller curve fitting formula for a new type of deformed high-temperature alloy of turbine material:

[0132] lgσ=0.00634P 3 -0.75085P 2 +29.54462P-383.38896

[0133] Among them, P = T (20 + lgt) × 10-3, lgt is the logarithm of the creep life of the material. Substituting the temperature field T (x, y, z) and stress field σ (x, y, z) obtained by the previous finite element calculation into the formula, the creep life of the turbine blade can be calculated. The creep damage of a single turbine blade under Si working conditions is determined by the operating time t under the working condition. i Divided by the creep life t ri The total creep damage is shown as follows:

[0134]

[0135] According to Miner's linear damage accumulation rule, the creep damage and high-low cycle fatigue damage are added together to obtain the total damage D of a single turbine blade:

[0136] D=D creep +D H-L

[0137] The calculated damage D of the turbine follows a certain probability distribution. Its distribution function f(D) can be obtained by using the kernel density estimation method through a self-written Python program. The following figure is the probability density distribution image of the damage when the turbine runs for one week:

[0138] Figure 7 Composite damage distribution of turbine

[0139] According to the theory of order statistics, the damage D distribution of a turbine running for one week is known, so the damage D after running for n weeks is n The distribution can be calculated by the following formula:

[0140] f(D n )=n[F(D)] n-1 f(D)

[0141] The above calculation of turbine damage takes into account the randomness of the load, but a new type of deformed high-temperature alloy of the turbine material also has a certain degree of randomness. Assuming that the variation rate γ is 0.07, its strength obeys the normal probability distribution of N(1,0.07).

[0142] According to the strength interference theory, reliability is defined as the total material damage D n Less than material strength S r The probability of a certain point on the turbine running for 180 weeks can be calculated, and the strength and damage distribution are shown in the following figure:

[0143] Figure 8 Turbine damage and material strength distribution after 180 weeks of operation

[0144] The reliability of the turbine is based on the intersection area in the figure. Through integration through a self-written Python program, it can be obtained that the reliability is 97.34%, that is, the reliability of the turbine running for 180 weeks without failure is 97.34%.

[0145] The above description is an explanation of the present invention, not a limitation of the present invention. The scope of the present invention is defined in the claims. Any form of modification may be made within the scope of protection of the present invention.

Claims

1. A method for calculating the reliability of a small gas turbine turbine, characterized in that: include: The module for dividing the typical operating conditions is used to divide the operating conditions into several typical operating conditions according to the actual operating conditions of the gas turbine, and determine the inlet and outlet boundary conditions of each typical operating condition and the load statistics table of the expected occurrence frequency and operating time; Define the minimum basic unit module, which is used to evenly divide the turbine into several parts along the circumference according to the number of blades, define it as the minimum basic unit, and obtain the model of the entire turbine by rotating the array; The module for calculating blade temperature field is used to select the blade surface, form the fluid domain, divide the high-quality grid, and use computational fluid dynamics tools to calculate the flow field under each working condition, and output the temperature field and pressure field of the turbine blade; Structural static analysis module, used to perform finite element calculation on the solid domain of the turbine, taking the temperature field and pressure field as boundary conditions to solve the total strain field and equivalent stress field of the blade; A random load spectrum generation module is used to randomly generate a working load spectrum for a certain period of time according to a load statistics table; High-low cycle fatigue damage calculation module, used to count the rain flow of the generated random load spectrum and calculate fatigue damage using the linear damage accumulation method; Creep damage calculation module, used to calculate creep life using Larson-Miller parameter equation and creep damage using linear damage accumulation law; Composite damage calculation module, used to accumulate fatigue damage and creep damage to obtain composite damage of a single blade under random load spectrum; The turbine overall composite damage probability distribution module is used to obtain the composite damage frequency histogram of the turbine blades during a certain period of operation, and to perform distribution estimation and calculate the composite damage probability distribution of n time periods of operation; The reliability calculation module is used to define reliability according to the probability that the total material damage is less than the material strength, and obtain an analytical solution through the Monte Carlo method or function calculation.

2. The method for calculating the reliability of a small gas turbine according to claim 1, characterized in that: In the typical operating condition module, typical conditions include but are not limited to assembly conditions, low load conditions, medium load conditions and rated load conditions.

3. The method for calculating the reliability of a small gas turbine according to claim 1, characterized in that: In the module for defining the minimum basic unit body, the minimum basic unit body is obtained by evenly dividing the turbine into n parts along the circumferential direction, where n is the number of blades.

4. The method for calculating the reliability of a small gas turbine according to claim 1, characterized in that: In the module for calculating blade temperature field, the high-quality grid includes a structured grid or a hybrid grid, and the accuracy of the boundary layer grid is ensured to capture the flow characteristics.

5. The method for calculating the reliability of a small gas turbine according to claim 1, characterized in that: In the structural static analysis module, the boundary conditions imposed also include limiting the axial and circumferential degrees of freedom of the turbine, and imposing rotational symmetry conditions.

6. The method for calculating the reliability of a small gas turbine according to claim 1, characterized in that: In the random load spectrum generation module, a rejection sampling method is used to randomly generate a working load spectrum according to the frequency and time of occurrence of working conditions.

7. The method for calculating the reliability of a small gas turbine according to claim 1, characterized in that: In the high-low cycle fatigue damage calculation module, the linear damage accumulation method is used to calculate fatigue damage, and the elastic strain and plastic strain terms are considered, which is suitable for high-cycle and low-cycle fatigue life calculation; The fatigue damage calculation formula is: Total number of cycles under fatigue load i.

8. The method for calculating the reliability of a small gas turbine according to claim 7, characterized in that: In the creep damage calculation module, the Larson-Miller parameter equation is: Where T is the blade temperature, A, B, C are material constants; The creep damage accumulation also uses the linear damage accumulation law: The total time available for running.

9. The method for calculating the reliability of a small gas turbine according to claim 8, characterized in that: In the probability distribution of the overall composite damage of the turbine, the composite damage D n The probability distribution is calculated as follows: f(D n )=n[F(D)] n-1 f(D) Where F(D) is the cumulative distribution function of the composite damage of a single blade.

10. The method for calculating the reliability of a small gas turbine according to claim 1, characterized in that: The reliability calculation module comprises: Reliability calculation, reliability is defined as the total material damage D n Less than material strength S r Probability: R=P(D n <S r ) The residual strength distribution of the material is related to the performance of the material itself, and can be defined using the variation rate γ, that is, the material strength follows N(1,γ 2 ) of the normal distribution f(S r ); The reliability of the turbine can be obtained by Monte Carlo method according to the definition, or by analytical solution through function calculation: In addition, reliability pre-allocation and load spectrum generation optimization are used to optimize the vulnerable areas based on the calculated composite damage distribution and expected reliability, and to modify the load spectrum generation method in real time according to the actual operating cycle during the commissioning phase to predict the remaining life.

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