A rapid calculation method for stress, strain, and temperature of turbine blades.

Abstract

This invention discloses a rapid calculation method for stress, strain, and temperature of turbine blades, relating to the field of turbine blade analysis technology. The method includes analyzing the coupling relationship between gas temperature and rotational speed, constructing a coupled model of gas temperature and rotational speed using an RBP neural network, and simultaneously conducting fluid-thermal-structure interaction (FTE) simulations of the turbine blades within the range of gas temperature and rotational speed values ​​to determine the temperature and stress distribution of the turbine blades under different operating conditions. A mathematical model for calculating turbine blade temperature and stress based on gas temperature and rotational speed is constructed, and a method for rapidly calculating the temperature and stress at critical points of the turbine blades using rotational speed is established. Thus, the rapid calculation method for turbine blade stress, strain, and temperature can be determined using RBP neural networks and FTE-S coupled finite element simulations, improving the accuracy of turbine blade damage analysis and load spectrum compilation, and shortening data processing time.

Description

Technical Field

[0001] This invention relates to the field of turbine blade analysis technology, specifically a method for rapid calculation of stress, strain, and temperature of turbine blades under the coupled action of random thermomechanical loads. Background Technology

[0002] High-pressure turbine blades, as critical components of aero-engines, are susceptible to the coupling effects of random thermomechanical loads during operation, potentially leading to severe damage. Since the service life of high-pressure turbine blades significantly impacts the safety and reliability of the engine, it is necessary to conduct turbine blade life verification to ensure the engine operates safely and reliably within its design lifespan.

[0003] In existing technologies, finite element simulation studies of turbine blades are typically used for the design of blade cooling structures and the design and evaluation of blade structures based on static strength. These studies often lack research on the relationship between stress, strain, and temperature and service loads. Because turbine blades are subjected to the mutual coupling of various loads under service conditions, the loading situation is extremely complex. Furthermore, the internal stress, strain, and temperature of turbine blades do not increase linearly with increasing speed and load. Therefore, simply conducting finite element simulations under the maximum operating condition and using the ratio of applied load to the maximum operating condition to determine the stress, strain, and temperature of turbine blades under different operating conditions is unreasonable. When performing finite element simulations of turbine blades, the simulation convergence is extremely difficult due to factors such as material nonlinearity and geometric nonlinearity, and the simulation calculation time is excessively long. Methods that directly determine the stress, strain, and temperature distribution of turbine blades under different operating conditions through simulation are not applicable. Therefore, to determine the variation law of internal stress, strain, and temperature of turbine blades under service conditions with loads, it is necessary to establish a rapid calculation method for the stress, strain, and temperature of turbine blades under random thermomechanical load coupling. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a rapid calculation method for stress, strain, and temperature of turbine blades under random thermomechanical load coupling. This method can quickly determine the stress history of critical points on turbine blades under service conditions by using the measured rotational speed spectrum of the turbine blades, thereby improving the accuracy of turbine blade damage analysis and load spectrum compilation, and shortening data processing time.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0006] This invention provides a rapid calculation method for stress, strain, and temperature of turbine blades under random thermomechanical load coupling, comprising the following operations:

[0007] Statistical analysis was performed on the gas temperature and speed during engine operation in the measured load spectrum to determine the joint distribution of gas temperature and speed under service conditions.

[0008] Using 1% of the rotational speed as the rotational speed interval length, the gas temperature in each rotational speed interval was statistically analyzed, and the conditional probability distribution of gas temperature at each rotational speed was determined based on the Akaike information criterion and the Bayesian information criterion.

[0009] A neural network model for the coupling relationship between gas temperature and engine speed was constructed based on the RBP neural network.

[0010] Based on the maximum gas temperature in the measured load spectrum, a transient flow-thermal coupling simulation of the turbine blade is carried out to determine the time required for the temperature field inside the turbine blade to reach steady state.

[0011] Based on the range of gas temperature and speed values ​​in the measured load spectrum, unidirectional steady-state flow-thermal-structure coupling simulations of turbine blades under multiple operating conditions were carried out.

[0012] Based on the unidirectional steady-state fluid-thermal-structure interaction simulation results of the turbine blade, the critical points inside the turbine blade are determined, and the mapping relationship between the gas temperature, rotational speed and the stress, strain and temperature at the critical points of the turbine blade is constructed.

[0013] Based on a neural network model of the coupling relationship between gas temperature and rotational speed, the input variables in the mapping relationship between gas temperature, rotational speed and stress, strain and temperature at critical points of turbine blades are reduced in dimensionality. The input variables are gas temperature and rotational speed. A fast calculation method for stress, strain and temperature of turbine blades under random thermomechanical load coupling is established.

[0014] A further improvement of the present invention is as follows: after grouping the rotational speeds by using 1% of the rotational speed as the rotational speed interval length, the normal distribution, log-normal distribution, Weibull distribution, gamma distribution, and logistic distribution function are selected to describe the gas temperature distribution of each group respectively. Based on the Akaike information criterion and the Bayesian information criterion, it is determined that the logistic distribution has the best descriptive effect on the gas temperature distribution. Based on the logistic distribution, the conditional probability distribution of gas temperature at different rotational speeds is determined.

[0015] A further improvement of the present invention is that a neural network model for the coupling relationship between gas temperature and rotation speed is trained, with rotational speed as the input value and the expectation and divergence in the logistic distribution describing gas temperature as the output values.

[0016] A further improvement of the present invention is that: transient flow-thermal coupling simulation of turbine blades is carried out to determine the temperature change of turbine blades over time, and it is found that the temperature field inside the turbine blades reaches stability in a short time and the blades have low thermal inertia. The temperature field of turbine blades under different operating conditions is determined by steady-state flow-thermal coupling simulation.

[0017] A further improvement of the present invention is that the range of values ​​for gas temperature and speed is discretized, and the values ​​of gas temperature and speed are combined to form 24 operating conditions. Under the gas temperature and speed corresponding to each operating condition, a unidirectional steady-state flow-thermal-solid coupling simulation of the turbine blade is carried out.

[0018] A further improvement of the present invention is as follows: based on the simulation results of the unidirectional steady-state flow-thermal-solid coupling of the turbine blade, the stress maximum point and the stress maximum point in the high-temperature region are selected as the critical points. With the gas temperature and speed as independent variables, a two-variable quadratic equation is constructed to fit the stress, strain, and temperature of 24 working conditions, and a mapping relationship between the gas temperature, speed and the stress, strain, and temperature at the critical point of the turbine blade is constructed.

[0019] A further improvement of this invention is that, based on a neural network model of the coupling relationship between gas temperature and rotational speed, the input variables in the mapping relationship between gas temperature, rotational speed and stress, strain, and temperature at critical points of turbine blades are reduced in dimensionality. The input variables are gas temperature and rotational speed. The model that quickly calculates stress, strain, and temperature using gas temperature and rotational speed is transformed into a model that quickly calculates stress, strain, and temperature using rotational speed. This establishes a rapid calculation method for stress, strain, and temperature of turbine blades under the coupling of random thermomechanical loads.

[0020] The beneficial effects of this invention are as follows: This invention proposes a rapid calculation method for the stress, strain, and temperature of turbine blades under random thermomechanical load coupling. By analyzing the coupling relationship between gas temperature and rotational speed, a coupled model of gas temperature and rotational speed is constructed using an RBP neural network. Simultaneously, fluid-thermal-structure interaction (FTE) simulation of the turbine blade is conducted within the range of gas temperature and rotational speed values ​​to determine the temperature and stress distribution of the turbine blade under different operating conditions. A mathematical model for calculating turbine blade temperature and stress based on gas temperature and rotational speed is constructed, ultimately establishing a method for rapidly calculating the temperature and stress at critical points of the turbine blade using rotational speed. This invention can quickly determine the stress history of critical points of the turbine blade under service conditions using the measured rotational speed spectrum. By employing an RBP neural network and fluid-thermal-structure interaction finite element simulation, a rapid calculation method for turbine blade stress, strain, and temperature is determined, thereby improving the accuracy of turbine blade damage analysis and load spectrum compilation, and shortening data processing time. Attached Figure Description

[0021] Figure 1 A flowchart of a method for rapid calculation of stress, strain, and temperature of turbine blades under random thermomechanical load coupling provided in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the frequency distribution of temperature and rotational speed in 100 flight missions provided in an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram showing the gas temperature distribution at 90% rotation speed, provided in an embodiment of the present invention.

[0024] Figure 4 A schematic diagram showing the expected comparison between grouped temperature and full-profile temperature logistic distribution provided in an embodiment of the present invention;

[0025] Figure 5 A schematic diagram comparing the divergence of grouped temperature and full-section temperature logistic distribution provided in an embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram illustrating the comparison between the expected prediction result of the neural network and the expected result of the measured data provided in an embodiment of the present invention.

[0027] Figure 7 A schematic diagram showing the divergence between the expected prediction result of the neural network and the measured data provided in an embodiment of the present invention;

[0028] Figure 8 A schematic diagram of the transient heat transfer temperature field of a blade provided in an embodiment of the present invention;

[0029] Figure 9 A schematic diagram of the steady-state temperature field of a turbine blade provided in an embodiment of the present invention;

[0030] Figure 10 A schematic diagram of the stress distribution of turbine blades when the turbine inlet temperature is 1100℃ and the rotational speed is 100%, provided for an embodiment of the present invention;

[0031] Figure 11 A schematic diagram of the strain distribution of turbine blades when the turbine inlet temperature is 1100℃ and the rotational speed is 100%, provided for an embodiment of the present invention;

[0032] Figure 12 This is a schematic diagram illustrating the mapping relationship between gas temperature, rotation speed, and stress, strain, and temperature at critical points, as provided in an embodiment of the present invention.

[0033] Figure 13 This is a schematic diagram illustrating the relationship between stress, strain, temperature, and rotational speed at a critical point, as provided in an embodiment of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0035] See Figure 1This embodiment of a method for rapid calculation of stress, strain, and temperature of turbine blades under random thermomechanical load coupling includes the following steps:

[0036] Statistical analysis was performed on the gas temperature and speed during engine operation in the measured load spectrum to determine the joint distribution of gas temperature and speed under service conditions.

[0037] Using 1% of the rotational speed as the rotational speed interval length, the gas temperature in each rotational speed interval was statistically analyzed, and the conditional probability distribution of gas temperature at each rotational speed was determined based on the Akaike information criterion and the Bayesian information criterion.

[0038] A neural network model for the coupling relationship between gas temperature and engine speed was constructed based on the RBP neural network.

[0039] Based on the maximum gas temperature in the measured load spectrum, a transient flow-thermal coupling simulation of the turbine blade is carried out to determine the time required for the temperature field inside the turbine blade to reach steady state.

[0040] Based on the range of gas temperature and speed values ​​in the measured load spectrum, unidirectional steady-state flow-thermal-structure coupling simulations of turbine blades under multiple operating conditions were carried out.

[0041] Based on the unidirectional steady-state fluid-thermal-structure interaction simulation results of the turbine blade, the critical points inside the turbine blade are determined, and the mapping relationship between the gas temperature, rotational speed and the stress, strain and temperature at the critical points of the turbine blade is constructed.

[0042] Based on a neural network model of the coupling relationship between gas temperature and rotational speed, the input variables in the mapping relationship between gas temperature, rotational speed and stress, strain and temperature at critical points of turbine blades are reduced in dimensionality. The input variables are gas temperature and rotational speed. A fast calculation method for stress, strain and temperature of turbine blades under random thermomechanical load coupling is established.

[0043] Using 1% of the rotational speed as the speed interval length, the rotational speeds were grouped, and the normal distribution, log-normal distribution, Weibull distribution, gamma distribution, and logistic distribution function were selected to describe the gas temperature distribution of each group. Based on the Akaike information criterion and the Bayesian information criterion, it was determined that the logistic distribution has the best descriptive effect on the gas temperature distribution. Based on the logistic distribution, the conditional probability distribution of gas temperature at different rotational speeds was determined.

[0044] This invention analyzes the gas temperature and rotational speed data of 100 measured load spectra. The frequency distribution of temperature and rotational speed in 100 flight missions is as follows: Figure 2 As shown, due to the influence of various random conditions such as inlet flow rate and inlet temperature, the gas temperature at different speeds is not a fixed value.

[0045] The engine speeds were grouped with a 1% speed interval as the length, and the gas temperature data for each group were statistically analyzed. Various distribution functions, including normal distribution, log-normal distribution, Weibull distribution, gamma distribution, and logistic distribution, were used to fit the temperature distribution at different speeds. The gas temperature distribution at 90% speed is shown below. Figure 3 As shown. Using the Akaike Information Criterion and the Bayesian Information Criterion, it can be determined that the logistic distribution best describes the gas temperature distribution, and the mathematical model to which the conditional probability density of the gas temperature follows is determined. The mathematical formulas for the corresponding distribution function and probability density function are as follows:

[0046]

[0047] In the formula, F(T) air f(T) represents the distribution function of the gas temperature distribution; air T represents the probability density function of the gas temperature distribution; air τ represents the gas temperature; μ represents the expected value of the gas temperature; τ represents the divergence of the gas temperature; e represents the natural constant.

[0048] Using engine speed as input and the expectation and divergence of the logistic distribution describing gas temperature as output, a neural network model coupling the relationship between gas temperature and engine speed is trained.

[0049] In this process, after determining the logistic distribution parameters of temperature at different rotational speeds, the RBP neural network is trained using rotational speed as the input variable and the expected value and divergence of the gas temperature as the output variables. To generate sufficient training data and improve training accuracy, the flight mission profiles can be grouped, and the combination of the expected value and divergence of the temperature distribution in each group can be used as the dataset. The 100 flight mission profiles are randomly divided into 10 groups, with each group containing 10 flight mission profiles. For example... Figure 4-5 As shown, the temperature distribution characteristics under this grouping method are consistent with the temperature distribution characteristics obtained after statistical analysis of the entire flight mission profile. By statistically analyzing the temperature distribution through this grouping method, the obtained temperature distribution can reflect the temperature distribution of this type of engine.

[0050] Understandably, multiple neural networks with different numbers of hidden layers and neurons can be constructed, and the performance of different neural networks can be evaluated using the mean squared error (MSE). A smaller MSE value indicates better predictive performance. Ultimately, the constructed neural network has 6 hidden layers, with 40 neurons in each hidden layer. Based on this neural network, temperature distribution can be predicted, such as... Figure 6-7As shown, the neural network has a good effect on temperature distribution prediction.

[0051] Transient flow-thermal coupling simulation of turbine blades was conducted to determine the temperature change of turbine blades over time. It was found that the temperature field inside the turbine blades reaches stability in a short time and the blades have low thermal inertia. The temperature field of the turbine blades under different operating conditions was determined by steady-state flow-thermal coupling simulation.

[0052] In practical applications, in order to accurately reproduce the distribution of stress, strain, and temperature within turbine blades under different operating conditions, this embodiment of the invention uses ANSYS to perform finite element simulation for fluid-thermal-structure interaction (FTE) simulation calculations. The simulations can be divided into transient FTE simulations that determine the time required for the turbine blade temperature field to reach a steady state and steady-state FTE simulations that determine the distribution of stress, strain, and temperature within turbine blades under different operating conditions.

[0053] In this invention, DD6 was selected as the turbine blade material. To determine the time required for the temperature field inside the turbine blade to reach stability, a transient heat transfer simulation was performed using the Fluent simulation module with a time step of 0.01 s. The calculation results are as follows: Figure 8 As shown. Therefore, the turbine blade temperature field can reach a stable state in a very short time, and the turbine blade temperature field under different gas temperature can be determined through steady-state simulation.

[0054] The range of gas temperature and speed is discretized, and the gas temperature and speed values ​​are combined to form 24 operating conditions. Under the gas temperature and speed corresponding to each operating condition, a unidirectional steady-state flow-thermal-structure coupling simulation of the turbine blade is carried out.

[0055] Based on the range of gas temperature and rotational speed, six gas temperature levels and four rotational speed levels were determined and combined into 24 operating conditions. Loads corresponding to these 24 operating conditions were applied to the turbine blades, and steady-state fluid-thermal-structure interaction (FHTS) simulations were performed. The steady-state temperature field of the turbine blades obtained after performing steady-state FHTS simulations with six gas temperature levels is shown below. Figure 9 As shown.

[0056] Based on the unidirectional steady-state flow-thermal-structure interaction simulation results of the turbine blade, the stress maximum point and the stress maximum point in the high-temperature region were selected as the critical points. With the gas temperature and speed as independent variables, a two-variable quadratic equation was constructed to fit the stress, strain, and temperature of 24 operating conditions, and the mapping relationship between gas temperature, speed and stress, strain, and temperature at the critical points of the turbine blade was established.

[0057] Understandably, after importing the steady-state temperature field data into the steady-state structural simulation module, four different speed levels can be applied to the turbine blades to complete fluid-thermal-structure interaction simulations under 24 operating conditions. The stress and strain distribution of the turbine blades under the maximum operating condition is shown below. Figure 10-11 As shown in the figure, the maximum stress and strain points of the turbine blade occur at the blade root fillet, but the temperature in the region of maximum stress is relatively low. Although the blade body temperature is high, the temperatures of stress and strain are both relatively low. To obtain the damage status of the turbine blade, while selecting the point of maximum stress as the critical point, a point with relatively high stress in the higher temperature region can also be selected as a critical point, and damage analysis will be performed on the two points separately in the subsequent process. The stress, strain, and temperature conditions at the two critical points under different gas temperatures and speeds are determined by simulation, as shown in Table 1-6:

[0058] Table 1. Danger Point 1: Temperature

[0059]

[0060] Table 2 Critical Points - Stress

[0061]

[0062] Table 3. Critical Points and Responses

[0063]

[0064] Table 4. Danger Point Two: Temperature

[0065]

[0066] Table 5. Critical Points and Stress

[0067]

[0068] Table 6. Critical Point Two Strain

[0069]

[0070] The relationship between stress, strain, and temperature at the critical point and the gas temperature and rotational speed is fitted. The fitting results are as follows: Figure 12 As shown, the correlation coefficients of the fitted surfaces are all greater than 0.99.

[0071] By combining the RBP neural network model of the relationship between gas temperature and rotational speed, the mapping relationship between gas temperature, rotational speed, and stress, strain, and temperature at critical points can be simplified, and the correlation between rotational speed and turbine blade stress, strain, and temperature can be determined. Figure 13 As shown, the correlation between the rotational speed and the stress, strain, and temperature of the turbine blades is fitted, thus completing the construction of a rapid calculation model for stress, strain, and temperature under the coupled action of random thermomechanical loads.

[0072] in:

[0073] The rapid calculation model for one danger point is as follows:

[0074]

[0075] The rapid calculation model for danger point two is as follows:

[0076]

[0077] In the above formula, σ represents the stress of the turbine blade under random thermomechanical load coupling; γ represents the strain of the turbine blade under random thermomechanical load coupling; T represents the temperature of the turbine blade under random thermomechanical load coupling; ω represents the percentage of rotational speed, which ranges from 0 to 100.

[0078] Based on a neural network model of the coupling relationship between gas temperature and rotational speed, the input variables in the mapping relationship between gas temperature, rotational speed and stress, strain and temperature at critical points of turbine blades are reduced in dimensionality. The input variables are gas temperature and rotational speed. The model that quickly calculates stress, strain and temperature based on gas temperature and rotational speed is transformed into a model that quickly calculates stress, strain and temperature based on rotational speed. A rapid calculation method for stress, strain and temperature of turbine blades under the coupling of random thermomechanical loads is established.

[0079] Through the above embodiments, this invention provides a rapid calculation method for stress, strain, and temperature of turbine blades under random thermomechanical load coupling. It analyzes the coupling relationship between gas temperature and rotational speed, constructs a coupling model of gas temperature and rotational speed using an RBP neural network, and conducts fluid-thermal-structure interaction (FTE) simulations of the turbine blades within the range of gas temperature and rotational speed values ​​to determine the temperature and stress distribution of the turbine blades under different operating conditions. A mathematical model for calculating turbine blade temperature and stress based on gas temperature and rotational speed is then established, ultimately providing a method for rapidly calculating the temperature and stress at critical points of the turbine blade using rotational speed. This invention can quickly determine the stress history of critical points of the turbine blade under service conditions using the measured rotational speed spectrum. By employing an RBP neural network and fluid-thermal-structure interaction finite element simulation, it establishes a rapid calculation method for turbine blade stress, strain, and temperature, thereby improving the accuracy of turbine blade damage analysis and load spectrum compilation, and shortening data processing time.

[0080] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0081] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rapid calculation method for stress, strain, and temperature of turbine blades, characterized in that, It includes the following steps: Statistical analysis was performed on the gas temperature and speed during engine operation in the measured load spectrum to determine the joint distribution of gas temperature and speed under service conditions. Using 1% of the rotational speed as the rotational speed interval length, the gas temperature in each rotational speed interval was statistically analyzed, and the conditional probability distribution of gas temperature at each rotational speed was determined based on the Akaike information criterion and the Bayesian information criterion. A neural network model for the coupling relationship between gas temperature and engine speed was constructed based on the RBP neural network. Based on the maximum gas temperature in the measured load spectrum, a transient flow-thermal coupling simulation of the turbine blade is carried out to determine the time required for the temperature field inside the turbine blade to reach steady state. Based on the range of gas temperature and speed values ​​in the measured load spectrum, unidirectional steady-state flow-thermal-structure coupling simulations of turbine blades under multiple operating conditions were carried out. Based on the unidirectional steady-state fluid-thermal-structure interaction simulation results of the turbine blade, the critical points inside the turbine blade are determined, and the mapping relationship between the gas temperature, rotational speed and the stress, strain and temperature at the critical points of the turbine blade is constructed. Based on a neural network model of the coupling relationship between gas temperature and rotational speed, the input variables in the mapping relationship between gas temperature, rotational speed and stress, strain and temperature at critical points of turbine blades are reduced in dimensionality. The input variables are gas temperature and rotational speed. The model that quickly calculates stress, strain and temperature based on gas temperature and rotational speed is transformed into a model that quickly calculates stress, strain and temperature based on rotational speed. A rapid calculation method for stress, strain and temperature of turbine blades under the coupling of random thermomechanical loads is established.

2. The method for rapid calculation of stress, strain, and temperature of turbine blades according to claim 1, characterized in that, Using 1% of the rotational speed as the speed interval length, the rotational speeds were grouped, and the normal distribution, log-normal distribution, Weibull distribution, gamma distribution, and logistic distribution function were selected to describe the gas temperature distribution of each group. Based on the Akaike information criterion and the Bayesian information criterion, it was determined that the logistic distribution has the best descriptive effect on the gas temperature distribution. Based on the logistic distribution, the conditional probability distribution of gas temperature at different rotational speeds was determined.

3. The method for rapid calculation of stress, strain, and temperature of turbine blades according to claim 1, characterized in that, Using engine speed as input and the expectation and divergence of the logistic distribution describing gas temperature as output, a neural network model coupling the relationship between gas temperature and engine speed is trained.

4. The method for rapid calculation of stress, strain, and temperature of turbine blades according to claim 1, characterized in that, Transient flow-thermal coupling simulation of turbine blades was conducted to determine the temperature change of turbine blades over time, and steady-state flow-thermal coupling simulation was used to determine the temperature field of turbine blades under different operating conditions.

5. The method for rapid calculation of stress, strain, and temperature of turbine blades according to claim 1, characterized in that, The range of gas temperature and speed is discretized, and the gas temperature and speed values ​​are combined to form 24 operating conditions. Under the gas temperature and speed corresponding to each operating condition, a unidirectional steady-state flow-thermal-structure coupling simulation of the turbine blade is carried out.

6. The method for rapid calculation of stress, strain, and temperature of turbine blades according to claim 1, characterized in that, Based on the unidirectional steady-state flow-thermal-structure interaction simulation results of the turbine blade, the stress maximum point and the stress maximum point in the high-temperature region were selected as the critical points. With the gas temperature and speed as independent variables, a two-variable quadratic equation was constructed to fit the stress, strain, and temperature of 24 operating conditions, and the mapping relationship between gas temperature, speed and stress, strain, and temperature at the critical points of the turbine blade was established.

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