Method for evaluating strength of titanium alloy hollow fan blade of aero-engine

Abstract

The invention belongs to the technical field of aero-engines, and provides an aero-engine titanium alloy hollow fan blade strength evaluation method, which comprises the following steps: constructing a finite element analysis model of an aero-engine titanium alloy hollow fan blade, and analyzing the static stress distribution condition of the aero-engine titanium alloy hollow fan blade to obtain the strength of the aero-engine titanium alloy hollow fan blade. Identifying a stress concentration area in the titanium alloy hollow fan blade of the aero-engine; a simulation program is started, the crack initiation risk of the stress concentration area is evaluated, and the strength of the titanium alloy hollow fan blade of the aero-engine is further evaluated; according to the method, the thickness of the titanium alloy hollow fan blade of the aero-engine is optimized, so that the possibility of failure occurrence of the engine is reduced, the overall service life of the aero-engine is prolonged, and the service life of the aero-engine is prolonged. And flight safety is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of aero-engines, and specifically relates to a method for evaluating the strength of a titanium alloy hollow fan blade of an aero-engine. Background Technique

[0002] As a core component of an aircraft, the performance and reliability of an aero-engine directly affect flight safety. Titanium alloy hollow fan blades are widely used in modern aero-engines due to their light weight and high strength. However, during actual service, the blades are subjected to complex loads, such as centrifugal force, aerodynamic force, vibration, etc., resulting in stress concentration areas inside the blades, which in turn lead to crack initiation and propagation, seriously threatening the safe operation of the engine.

[0003] Currently, traditional blade strength evaluation methods are mainly based on static stress analysis, ignoring the influence of the stress change gradient under dynamic conditions on crack initiation, resulting in a deviation between the evaluation results and the actual service environment. In addition, when optimizing the blade structure with existing methods, the high-risk areas are not specifically optimized in combination with the dynamic risk assessment results, lacking a quantitative analysis of the relationship between thickness parameters and stress response, resulting in insufficient pertinence and low efficiency of the optimization measures.

[0004] Therefore, the present invention provides a method for evaluating the strength of a titanium alloy hollow fan blade of an aero-engine. Summary of the Invention

[0005] In order to make up for the deficiencies of the existing technology and solve at least one technical problem proposed in the background technique.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows: Construct a finite element analysis model of the titanium alloy hollow fan blade of the aero-engine, analyze the static stress distribution of the titanium alloy hollow fan blade of the aero-engine, and identify the stress concentration areas in the titanium alloy hollow fan blade of the aero-engine; Start the simulation program, dynamically analyze the stress change gradient in the stress concentration areas under different working conditions, evaluate the crack initiation risk in the stress concentration areas, and determine the high-risk areas in the stress concentration areas; Evaluate the strength of the titanium alloy hollow fan blade of the aero-engine by analyzing the risk degree of the high-risk areas in the stress concentration areas; If the overall strength of the blade is weak, in combination with the blade thickness parameters in the high-risk areas of the titanium alloy hollow fan of the aero-engine, use the finite difference method to determine the sensitivity of each thickness parameter to the maximum stress in the high-risk areas, establish a double-objective optimization model for the high-risk areas, generate a trade-off curve between stress reduction and mass increase through the genetic algorithm, and determine the thickness adjustment amount in the high-risk areas.

[0007] As a further aspect of the present invention: The method for obtaining the stress concentration region in the titanium alloy hollow fan blade of the aeroengine is as follows: Perform layered scanning to ensure the grid resolution in the thickness direction and output the node and element topology information; Extract the integration point stresses of all elements on the solid wall surface of the titanium alloy hollow fan blade through Abaqus Python script, and obtain the nodal stresses by using the superconvergent patch recovery method; Calculate the coefficient of variation of all nodal stresses within the element through the coefficient of variation calculation formula to obtain the stress concentration coefficient; if the stress concentration coefficient is less than or equal to the stress concentration coefficient threshold, mark the region corresponding to the element as the stress concentration region.

[0008] As a further aspect of the present invention: The process for obtaining the high-risk region is as follows: Use the script to extract the nodal stresses of each element under each working condition, construct a structured database, and record the maximum stress and stress mean value of the stress concentration region under each working condition; Based on any working condition, preset the simulation period, divide the simulation period into several time points at equal time intervals, calculate the stress change gradient of each time point in any stress concentration region, analyze the stress change gradients of all time points to obtain the stable concentration region, analyze the stability degree of the stable concentration region, determine the proportion of the number of abnormal stress time points and the stress gradient deviation degree, and perform multiplication processing to obtain the risk coefficient; If the risk coefficient of the current stable concentration region is greater than or equal to the risk coefficient threshold, mark the corresponding stable concentration region as the high-risk region.

[0009] As a further aspect of the present invention: The process for obtaining the stable concentration region is as follows: Integrate the stress change gradients corresponding to all time points within the simulation period into a gradient change data group, calculate the variance value of all stress change gradients in the gradient change data group through the variance formula to obtain the stress change coefficient corresponding to the current stress concentration region; If the stress change coefficient corresponding to the current stress concentration region is less than the preset stress change coefficient standard value, mark the corresponding stress concentration region as the stable concentration region.

[0010] As a further aspect of the present invention: The process for obtaining the proportion of the number of abnormal stress time points is as follows: Extract the stress change gradient corresponding to the stable concentration region. If the stress change gradient corresponding to the stable concentration region is greater than or equal to the stress change gradient standard value, mark the corresponding time point as an abnormal stress time point; Count the number of abnormal stress time points among all time points and calculate the proportion of the number of abnormal stress time points among all time points.

[0011] As a further solution of the present invention: The process for obtaining the stress gradient deviation degree is as follows: Take the absolute value of the difference between the stress change gradient corresponding to the abnormal stress time point and the standard value of the stress change gradient to obtain the stress gradient deviation value corresponding to the abnormal stress time point. Sum and average the stress gradient deviation values corresponding to all abnormal stress time points, and then perform a ratio process with the standard value of the stress change gradient to obtain the stress gradient deviation degree.

[0012] As a further solution of the present invention: The specific process for evaluating the strength of the titanium alloy hollow fan blade of an aeroengine is as follows: Analyze the risk degree of the high-risk area to obtain the proportion of the number of high-risk areas and the risk degree value. Multiply the proportion of the number of high-risk areas by the risk degree value to obtain the overall strength evaluation value of the titanium alloy hollow fan blade of the aeroengine; If the overall strength evaluation value is greater than or equal to the overall strength evaluation threshold, it indicates that the overall strength of the titanium alloy hollow fan blade of the aeroengine is weak.

[0013] As a further solution of the present invention: The process for obtaining the proportion of the number of high-risk areas is as follows: Count the number of high-risk areas in the stress concentration area, and calculate the proportion of the number of high-risk areas in the stress concentration area.

[0014] As a further solution of the present invention: The process for obtaining the risk degree value is as follows: Extract the risk coefficient corresponding to the high-risk area, take the difference between the risk coefficient corresponding to the high-risk area and the risk coefficient threshold to obtain the risk coefficient deviation value. Sum and average the risk coefficient deviation values corresponding to all high-risk areas, and then perform a ratio process with the risk coefficient threshold to obtain the risk degree value.

[0015] As a further solution of the present invention: The process for obtaining the thickness adjustment amount of the high-risk area is as follows: Extract all high-risk areas, define independent initial thickness parameters for each high-risk area, use the finite difference method to determine the sensitivity of each initial thickness parameter to the maximum stress in the high-risk area, and establish a two-objective optimization model for the high-risk area; Generate a trade-off curve between stress reduction and mass increase through the genetic algorithm, select the optimal solution in combination with engineering requirements, and allocate increments according to the sensitivity priority; The optimal solution is the thickness adjustment amount. Sum the thickness adjustment amount and the initial thickness parameter to obtain the optimized thickness parameter; Import the optimized thickness parameter into the finite element model, recalculate the risk coefficient, and verify whether the constraint conditions are met; If the risk of crack initiation is still high, readjust the thickness parameter and repeat the above process until convergence.

[0016] The beneficial effects of the present invention are as follows: By constructing a finite element analysis model of a titanium alloy hollow fan blade of an aeroengine, the present invention analyzes the static stress distribution of the titanium alloy hollow fan blade of the aeroengine, and identifies the stress concentration areas in the titanium alloy hollow fan blade of the aeroengine; starts the simulation program, and dynamically analyzes the stress change gradient in the stress concentration areas under different working conditions, evaluates the crack initiation risk in the stress concentration areas, and determines the high-risk areas in the stress concentration areas; through the construction of the finite element model and the static stress analysis, the present invention accurately identifies the stress concentration areas of the titanium alloy hollow fan blade, provides a key target area for subsequent dynamic risk assessment, combines the stress change gradient analysis under dynamic working conditions, realizes the chronological and quantitative assessment of the crack initiation risk, avoids the limitations of single static analysis, makes the evaluation results conform to the actual service environment, improves the reliability of prediction, and the identified high-risk areas are beneficial to reducing the fault risk caused by blade cracks in the aeroengine, and improving safety and reliability.

[0017] By analyzing the risk level of the high-risk areas in the stress concentration areas, the present invention evaluates the strength of the titanium alloy hollow fan blade of the aeroengine; if the overall strength of the blade is weak, combined with the blade thickness parameters in the high-risk areas of the titanium alloy hollow fan of the aeroengine, the finite difference method is used to determine the sensitivity of each thickness parameter in the high-risk areas to the maximum stress, establish a bi-objective optimization model for the high-risk areas, generate a trade-off curve of stress reduction and mass increase through the genetic algorithm (NSGA-II), and determine the thickness adjustment amount in the high-risk areas; by optimizing the thickness of the titanium alloy hollow fan blade of the aeroengine, the present invention reduces the stress concentration areas during the operation of the titanium alloy hollow fan blade of the aeroengine, and thus can effectively avoid the problem of blade fracture caused by the high stress concentration risk of the titanium alloy hollow fan blade of the aeroengine, which is beneficial to reducing the possibility of engine failure, thereby prolonging the overall service life of the aeroengine and ensuring flight safety. Description of the Drawings

[0018] The present invention will be further described below with reference to the accompanying drawings.

[0019] Figure 1 is a flowchart of the steps of a method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to an embodiment of the present invention; Figure 2 is a system block diagram of a system for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to an embodiment of the present invention. Detailed Embodiments

[0020] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0021] Example 1:

[0022] Please refer to Figure 1 As shown, a method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to an embodiment of the present invention includes the following steps: Step 1: Build a finite element analysis model of the titanium alloy hollow fan blade of the aeroengine, analyze the static stress distribution of the titanium alloy hollow fan blade of the aeroengine, and identify the stress concentration areas in the titanium alloy hollow fan blade of the aeroengine; Obtain the geometric data of the titanium alloy hollow fan blade of the aeroengine through various detection instruments. According to the geometric data of the titanium alloy hollow fan blade of the aeroengine, draw the geometric model of the titanium alloy hollow fan blade of the aeroengine through 3D modeling software, and import the created geometric model into the finite element analysis software ABAQUS to build a finite element analysis model of the titanium alloy hollow fan blade of the aeroengine. Among them, the geometric data includes blade thickness, blade height, aspect ratio, and twist angle; Input the mechanical property parameters of the blade material into the finite element analysis software ABAQUS. Among them, the mechanical property parameters include elastic modulus, Poisson's ratio, density, and thermal expansion coefficient; In the geometric model of the titanium alloy hollow fan blade of the aeroengine, decompose the geometry of the titanium alloy hollow fan blade into subdomains that can be hexahedralized by the multi-block method. For the straight section of the blade body, generate structured hexahedral meshes by mapped meshing, and supplement the high-curvature subdomains such as the leading edge / trailing edge with sweep meshing or transition elements. Use layer-by-layer sweeping to ensure the grid resolution in the thickness direction, and output the node and element topology information; Extract the integration point stresses of all elements on the solid wall surface of the titanium alloy hollow fan blade through the Abaqus Python script, and obtain the nodal stresses by using the superconvergent patch recovery (SPR) method; Analyze all the nodal stresses in the element through the standard deviation calculation formula to obtain the standard deviation of the nodal stresses in the element; Sum and average all the nodal stresses in the element through the mean calculation formula to obtain the mean value of the nodal stresses in the element. Calculate the ratio of the standard deviation of the nodal stresses in the element to the mean value of the nodal stresses in the element through the coefficient of variation calculation formula to obtain the stress concentration coefficient; In some embodiments, compare the stress concentration coefficient with the stress concentration coefficient threshold. The specific comparison process is as follows: If the stress concentration coefficient is less than or equal to the stress concentration coefficient threshold, mark the area corresponding to the element as the stress concentration area; If the stress concentration factor is greater than the stress concentration factor threshold, the area corresponding to the element is recorded as a non-stress concentration area; Step 2: Start the simulation program. By dynamically analyzing the stress change gradient in the stress concentration area under different working conditions, evaluate the crack initiation risk in the stress concentration area and determine the high-risk areas in the stress concentration area; Use a script to extract the nodal stresses of each element under each working condition, construct a structured database, and record the maximum stress and stress mean value in the stress concentration area under each working condition; Based on any working condition, preset the simulation period. Divide the simulation period into several time points at equal time intervals, and calculate the stress change gradient at the t-th time point in any stress concentration area , and the specific calculation formula is:

[0023] where represents the stress mean value of the stress concentration area at the t-th time point, represents the stress mean value of the stress concentration area at the (t - 1)-th time point, represents the time interval between adjacent time points; Integrate the stress change gradients corresponding to all time points within the simulation period into a gradient change data group, and calculate the variance value of all stress change gradients in the gradient change data group through the variance formula to obtain the stress change coefficient corresponding to the current stress concentration area; Compare the stress change coefficient corresponding to the current stress concentration area with the stress change coefficient standard value. The specific comparison process is as follows: If the stress change coefficient corresponding to the current stress concentration area is greater than or equal to the preset stress change coefficient standard value, the corresponding stress concentration area is recorded as an unstable concentration area; If the stress change coefficient corresponding to the current stress concentration area is less than the preset stress change coefficient standard value, the corresponding stress concentration area is recorded as a stable concentration area; It can be understood that the stress change coefficient standard value is set by the staff in this field according to the historical situation of the current working condition; Extract the stress change gradient corresponding to the stable concentration area, and compare the stress change gradient corresponding to any time point in the stable concentration area with the stress change gradient standard value: If the stress change gradient corresponding to the stable concentration area is greater than or equal to the stress change gradient standard value, the corresponding time point is recorded as an abnormal stress time point; If the stress change gradient corresponding to the stable concentration area is less than the stress change gradient standard value, the corresponding time point is recorded as a normal stress time point; It is understandable that the standard value of the stress change gradient is set by the staff in the field according to the historical conditions of the current working conditions; Count the number of abnormal stress time points among all time points, and calculate the proportion of the number of abnormal stress time points among all time points; Subtract the stress change gradient corresponding to the abnormal stress time point from the standard value of the stress change gradient, take the absolute value of the difference to obtain the stress gradient deviation value corresponding to the abnormal stress time point, sum and average all the stress gradient deviation values corresponding to the abnormal stress time points to obtain the average stress gradient deviation, and process the ratio of the average stress gradient deviation to the standard value of the stress change gradient to obtain the degree of stress gradient deviation; Multiply the proportion of the number of abnormal stress time points by the degree of stress gradient deviation to obtain the risk coefficient of the current stable concentration area; In some embodiments, compare the risk coefficient of the current stable concentration area with the risk coefficient threshold. The specific comparison process is as follows: If the risk coefficient of the current stable concentration area is greater than or equal to the risk coefficient threshold, it indicates that the crack initiation risk in the current area is relatively high, and the corresponding stable concentration area is recorded as a high-risk area; If the risk coefficient of the current stable concentration area is less than the risk coefficient threshold, it indicates that the crack initiation risk in the current area is relatively low, and the corresponding stable concentration area is recorded as a low-risk area; The setting of the risk coefficient has the following functions: Function 1: The risk coefficient combines the proportion of the number of abnormal stress time points and the degree of stress gradient deviation. The proportion of the number of abnormal stress time points reflects the frequency of stress abnormality, and the degree of stress gradient deviation avoids the one-sidedness of a single index (such as only looking at the maximum stress or gradient change), and more comprehensively depicts the dynamic stress characteristics of the stress concentration area; Function 2: The risk coefficient implies the "frequency × amplitude" effect of stress fluctuation, reflects the cumulative mechanism of fatigue damage, conforms to the Miner linear cumulative damage theory of fatigue failure, quantifies the risk through multi-factor coupling, and provides support for the reliability design of aero-engine blades; The technical solution of this embodiment is as follows: construct a finite element analysis model of the titanium alloy hollow fan blade of an aeroengine, analyze the static stress distribution of the titanium alloy hollow fan blade of the aeroengine, and identify the stress concentration areas in the titanium alloy hollow fan blade of the aeroengine; start the simulation program, and through dynamic analysis of the stress change gradient in the stress concentration areas under different working conditions, evaluate the crack initiation risk in the stress concentration areas, and determine the high-risk areas in the stress concentration areas; through the construction of the finite element model and static stress analysis, the present invention accurately identifies the stress concentration areas of the titanium alloy hollow fan blade, provides a key target area for subsequent dynamic risk assessment, and combines the stress change gradient analysis under dynamic working conditions to realize the chronological and quantitative assessment of the crack initiation risk, avoid the limitations of single static analysis, make the assessment results conform to the actual service environment, improve the reliability of prediction, and the identified high-risk areas are conducive to reducing the fault risk caused by blade cracks in the aeroengine and enhancing safety and reliability.

[0024] Embodiment 2:

[0025] Please refer to Figure 1 As shown, a method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to an embodiment of the present invention further includes the following steps: Step 3: Evaluate the strength of the titanium alloy hollow fan blade of the aeroengine by analyzing the risk level of the high-risk areas in the stress concentration areas; Count the number of high-risk areas in the stress concentration areas, and calculate the proportion of the number of high-risk areas in the stress concentration areas; Extract the risk coefficients corresponding to the high-risk areas, subtract the risk coefficients corresponding to the high-risk areas from the risk coefficient threshold to obtain a risk coefficient deviation value, sum and average the risk coefficient deviation values corresponding to all high-risk areas to obtain a risk coefficient deviation average value, and perform a ratio process on the risk coefficient deviation average value and the risk coefficient threshold to obtain a risk level value; Multiply the proportion of the number of high-risk areas by the risk level value to obtain an overall strength evaluation value of the titanium alloy hollow fan blade of the aeroengine; In some embodiments, compare the overall strength evaluation value with the overall strength evaluation threshold. The specific comparison process is as follows: If the overall strength evaluation value is greater than or equal to the overall strength evaluation threshold, it means that the overall strength of the titanium alloy hollow fan blade of the aeroengine is weak; If the overall strength evaluation value is less than the overall strength evaluation threshold, it means that the overall strength of the titanium alloy hollow fan blade of the aeroengine is strong, indicating that the overall strength of the blade is good and no adjustment is required; Step 4: If the overall strength of the blade is weak, combined with the blade thickness parameters in the high-risk areas of the titanium alloy hollow fan of the aero-engine, use the finite difference method to determine the sensitivity of each thickness parameter in the high-risk area to the maximum stress, establish a two-objective optimization model for the high-risk area, generate a trade-off curve between stress reduction and mass increase through the genetic algorithm (NSGA-II), and determine the thickness adjustment amount in the high-risk area; S41: Use the finite difference method to determine the sensitivity of each initial thickness parameter in the high-risk area to the maximum stress; Extract all high-risk areas and define independent initial thickness parameters for each high-risk area , where i = 1, 2, …, n, and n represents the total number of high-risk areas; Use the finite difference method to calculate the sensitivity of each thickness parameter to the maximum stress , and the specific calculation formula is:

[0026] where represents the maximum stress in the high-risk area; S42: Establish a two-objective optimization model for the high-risk area; Objective 1: Minimize the maximum stress in the high-risk area ; Objective 2: Minimize the mass increase , where is the material density, is the cross-sectional area of each high-risk area, is the thickness adjustment amount for the i-th high-risk area during the optimization process; Constraint conditions: The thickness parameter is greater than or equal to the lower limit of the thickness parameter, and the total mass increment does not exceed the mass threshold; The transition curvature is greater than or equal to the minimum transition curvature; S43: Generate a trade-off curve between stress reduction and mass increase through the genetic algorithm (NSGA-II), and determine the thickness adjustment amount in the high-risk area; Coding and population initialization: Perform real-number coding on each thickness parameter , initialize the population size, and randomly generate a thickness combination that satisfies the constraint conditions; Fitness function: Directly use the two objectives and , and calculate the individual fitness through non-dominated sorting and crowding degree; Genetic operations: Crossover: Arithmetic crossover (maintaining the constraint boundary); Mutation: Gaussian mutation (step size 0.1 mm); Termination condition: There is no significant change in the Pareto front for 10 consecutive generations, or the maximum number of iterations is reached; Trade-off Curve Analysis and Decision-making: Generate the Pareto front curve and select the optimal solution in combination with engineering requirements: Thickness Allocation for the Selected Solution: Allocate increments according to the sensitivity priority (for example, the high-risk areas with high sensitivity are preferentially increased in thickness, and the increment amplitude is positively correlated with the sensitivity); The optimal solution is the thickness adjustment amount. Sum the thickness adjustment amount with the initial thickness parameters to obtain the optimized thickness parameters; Verification and Iteration: Import the optimized thickness parameters into the finite element model, recalculate the risk coefficient, and verify whether the constraint conditions are met; if the crack initiation risk is still high, readjust the thickness parameters and repeat the above process until convergence; By preferentially adjusting the blade thickness in the high-risk areas with high sensitivity, it is beneficial to reduce the maximum stress in the high-risk areas, weaken the stress amplitude and gradient required for crack initiation, and inhibit the initial crack initiation from the mechanical mechanism; The technical solution of this embodiment is as follows: By analyzing the risk level of the high-risk areas in the stress concentration area, evaluate the strength of the titanium alloy hollow fan blade of the aeroengine; if the overall strength of the blade is weak, combine the blade thickness parameters in the high-risk areas of the titanium alloy hollow fan of the aeroengine, use the finite difference method to determine the sensitivity of each thickness parameter to the maximum stress in the high-risk areas, establish a bi-objective optimization model for the high-risk areas, generate a trade-off curve of stress reduction and mass increase through the genetic algorithm (NSGA-II), and determine the thickness adjustment amount in the high-risk areas; The present invention optimizes the thickness of the titanium alloy hollow fan blade of the aeroengine to reduce the stress concentration area during the operation of the titanium alloy hollow fan blade of the aeroengine, thereby effectively avoiding the problem of blade fracture caused by the high stress concentration risk of the titanium alloy hollow fan blade of the aeroengine, which is beneficial to reducing the possibility of engine failure, thereby extending the overall service life of the aeroengine and ensuring flight safety.

[0027] Example 3:

[0028] Please refer to Figure 2 As shown, a strength evaluation system for a titanium alloy hollow fan blade of an aeroengine according to an embodiment of the present invention includes the following modules: Static Stress Analysis Module: Construct a finite element analysis model of the titanium alloy hollow fan blade of the aeroengine, analyze the static stress distribution of the titanium alloy hollow fan blade of the aeroengine, and identify the stress concentration areas in the titanium alloy hollow fan blade of the aeroengine; Dynamic Simulation Module: Start the simulation program, dynamically analyze the stress change gradient in the stress concentration area under different working conditions, evaluate the crack initiation risk in the stress concentration area, and determine the high-risk areas in the stress concentration area; Strength assessment module: Analyze the risk level of high-risk areas in the stress concentration area to evaluate the strength of the titanium alloy hollow fan blade of the aeroengine; Optimization and adjustment module: If the overall strength of the blade is weak, combine the blade thickness parameters in the high-risk area of the titanium alloy hollow fan of the aeroengine, use the finite difference method to determine the sensitivity of each thickness parameter in the high-risk area to the maximum stress, establish a bi-objective optimization model for the high-risk area, generate a trade-off curve between stress reduction and mass increase through the genetic algorithm (NSGA-II), and determine the thickness adjustment amount in the high-risk area; The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine, characterized in that: Including: Construct a finite element analysis model of a titanium alloy hollow fan blade for an aeroengine, analyze the static stress distribution of the titanium alloy hollow fan blade for the aeroengine, and identify the stress concentration areas in the titanium alloy hollow fan blade for the aeroengine; Start the simulation program, dynamically analyze the stress change gradient in the stress concentration area under different working conditions, evaluate the crack initiation risk in the stress concentration area, and determine the high-risk areas in the stress concentration area; Evaluate the strength of the titanium alloy hollow fan blade for the aeroengine by analyzing the risk level in the high-risk area of the stress concentration area; If the overall strength of the blade is weak, combine the blade thickness parameters in the high-risk area of the titanium alloy hollow fan for the aeroengine, use the finite difference method to determine the sensitivity of each thickness parameter in the high-risk area to the maximum stress, establish a bi-objective optimization model for the high-risk area, generate a trade-off curve of stress reduction and mass increase through the genetic algorithm, and determine the thickness adjustment amount in the high-risk area.

2. The strength evaluation method of a titanium alloy hollow fan blade for an aeroengine according to claim 1, characterized in that: The method for obtaining the stress concentration area in the titanium alloy hollow fan blade for the aeroengine is as follows: Adopt layer-by-layer scanning to ensure the grid resolution in the thickness direction, and output the node and element topology information; Extract the integration point stresses of all elements on the solid wall surface of the titanium alloy hollow fan blade through the Abaqus Python script, and obtain the node stresses by using the superconvergent patch recovery method; Calculate the coefficient of variation of all node stresses in the element through the coefficient of variation calculation formula to obtain the stress concentration coefficient; If the stress concentration coefficient is less than or equal to the stress concentration coefficient threshold, mark the area corresponding to the element as the stress concentration area.

3. A method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to claim 2, characterized in that: The process for obtaining the high-risk area is as follows: Use the script to extract the node stresses of each element under each working condition, construct a structured database, and record the maximum stress and stress mean value of the stress concentration area under each working condition; Based on any working condition, preset the simulation period, divide the simulation period into several time points at equal time intervals, calculate the stress change gradient of each time point in any stress concentration area, analyze the stress change gradients of all time points, obtain the stable concentration area, analyze the stability degree of the stable concentration area, determine the proportion of the number of abnormal stress time points and the stress gradient deviation degree, and perform a product process to obtain the risk coefficient; If the risk coefficient of the current stable concentration area is greater than or equal to the risk coefficient threshold, mark the corresponding stable concentration area as the high-risk area.

4. A method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to claim 3, characterized in that: The process for obtaining the stable concentration area is as follows: Integrate the stress change gradients corresponding to all time points within the simulation period into a gradient change data group, calculate the variance value of all stress change gradients in the gradient change data group through the variance formula, and obtain the stress change coefficient corresponding to the current stress concentration area; If the stress change coefficient corresponding to the current stress concentration area is less than the preset stress change coefficient standard value, mark the corresponding stress concentration area as the stable concentration area.

5. A method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to claim 4, characterized in that: The process for obtaining the proportion of the number of abnormal stress time points is as follows: Extract the stress change gradient corresponding to the stable concentration region. If the stress change gradient corresponding to the stable concentration region is greater than or equal to the stress change gradient standard value, record the corresponding time point as the abnormal stress time point; Count the number of abnormal stress time points among all time points, and calculate the proportion of the number of abnormal stress time points among all time points.

6. The strength evaluation method of a titanium alloy hollow fan blade for an aeroengine according to claim 5, wherein: The process of obtaining the stress gradient deviation degree is as follows: Take the absolute value after subtracting the stress change gradient corresponding to the abnormal stress time point from the stress change gradient standard value to obtain the stress gradient deviation value corresponding to the abnormal stress time point. Sum and average all the stress gradient deviation values corresponding to the abnormal stress time points, and then perform a ratio process with the stress change gradient standard value to obtain the stress gradient deviation degree.

7. A method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to claim 6, characterized in that: The specific process of evaluating the strength of the titanium alloy hollow fan blade of an aeroengine is as follows: Analyze the risk degree of the high-risk regions to obtain the proportion of the number of high-risk regions and the risk degree value. Multiply the proportion of the number of high-risk regions by the risk degree value to obtain the overall strength evaluation value of the titanium alloy hollow fan blade of the aeroengine; If the overall strength evaluation value is greater than or equal to the overall strength evaluation threshold, it indicates that the overall strength of the titanium alloy hollow fan blade of the aeroengine is weak.

8. A method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to claim 7, characterized in that: The process of obtaining the proportion of the number of high-risk regions is as follows: Count the number of high-risk regions in the stress concentration region, and calculate the proportion of the number of high-risk regions in the stress concentration region.

9. A method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to claim 7, characterized in that: The process of obtaining the risk degree value is as follows: Extract the risk coefficient corresponding to the high-risk region. Subtract the risk coefficient threshold from the risk coefficient corresponding to the high-risk region to obtain the risk coefficient deviation value. Sum and average all the risk coefficient deviation values corresponding to the high-risk regions, and then perform a ratio process with the risk coefficient threshold to obtain the risk degree value.

10. A method for evaluating the strength of a titanium alloy hollow fan blade of an aeroengine according to claim 1, characterized in that: The process of obtaining the thickness adjustment amount of the high-risk region is as follows: Extract all high-risk regions. Define independent initial thickness parameters for each high-risk region. Use the finite difference method to determine the sensitivity of each initial thickness parameter in the high-risk region to the maximum stress, and establish a two-objective optimization model for the high-risk region; Generate a trade-off curve of stress reduction and mass increase through the genetic algorithm, select the optimal solution in combination with engineering requirements, and allocate the increment according to the sensitivity priority; The optimal solution is the thickness adjustment amount. Add the thickness adjustment amount to the initial thickness parameter to obtain the optimized thickness parameter; Import the optimized thickness parameter into the finite element model, recalculate the risk coefficient, and verify whether the constraint conditions are met; If the crack initiation risk is still high, readjust the thickness parameter and repeat the above process until convergence.

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