A strength assessment method for titanium alloy hollow fan blades of aircraft engines

Abstract

The present invention belongs to the technical field of aircraft engines. The present invention provides a strength assessment method for titanium alloy hollow fan blades of aircraft engines, comprising: constructing a finite element analysis model of the titanium alloy hollow fan blades of the aircraft engine, analyzing the static stress distribution of the titanium alloy hollow fan blades of the aircraft engine, and identifying stress concentration areas in the titanium alloy hollow fan blades of the aircraft engine; starting a simulation program, assessing the crack initiation risk in the stress concentration area, and further assessing the strength of the titanium alloy hollow fan blades of the aircraft engine; generating a stress reduction and mass increase trade-off curve through a genetic algorithm, and determining the thickness adjustment amount of the high-risk area. The present invention optimizes the thickness of the titanium alloy hollow fan blades of the aircraft engine, thereby reducing the possibility of engine failure, thereby extending the overall service life of the aircraft engine and ensuring flight safety.

Description

Technical Field

[0001] The invention belongs to the technical field of aero-engines, and in particular relates to a strength evaluation method for titanium alloy hollow fan blades of an aero-engine. Background Art

[0002] As a core component of aircraft, the performance and reliability of aircraft engines directly impact flight safety. Titanium alloy hollow fan blades are widely used in modern aircraft engines due to their lightweight and high strength. However, in actual service, blades are subject to complex loads such as centrifugal force, aerodynamic forces, and vibration, which can lead to stress concentration areas within the blades, triggering crack initiation and propagation, seriously threatening the safe operation of the engine.

[0003] Currently, traditional blade strength assessment methods are primarily based on static stress analysis, ignoring the impact of stress gradients under dynamic operating conditions on crack initiation. This leads to discrepancies between assessment results and actual service environments. Furthermore, existing methods fail to incorporate dynamic risk assessment results into blade structural optimization to target high-risk areas. Furthermore, they lack quantitative analysis of the relationship between thickness parameters and stress response, resulting in ineffective and inefficient optimization measures.

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

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] Construct a finite element analysis model of aero-engine titanium alloy hollow fan blades, analyze the static stress distribution of aero-engine titanium alloy hollow fan blades, and identify stress concentration areas in aero-engine titanium alloy hollow fan blades;

[0008] Start the simulation program and dynamically analyze the stress gradient of the stress concentration area under different working conditions to assess the crack initiation risk in the stress concentration area and identify the high-risk areas in the stress concentration area.

[0009] By analyzing the risk level of high-risk areas in stress concentration areas, the strength of titanium alloy hollow fan blades of aircraft engines is evaluated;

[0010] If the overall strength of the blade is weak, combined with the blade thickness parameters in the high-risk area of the titanium alloy hollow fan of the aircraft engine, the finite difference method is used to determine the sensitivity of each thickness parameter in the high-risk area to the maximum stress, and a dual-objective optimization model for the high-risk area is established. The trade-off curve between stress reduction and mass increase is generated by the genetic algorithm to determine the thickness adjustment amount in the high-risk area.

[0011] As a further solution of the present invention, the stress concentration area in the titanium alloy hollow fan blade of the aircraft engine is obtained by:

[0012] Layered scanning is used to ensure mesh resolution in the thickness direction and output node and element topology information;

[0013] The integration point stresses of all elements on the solid wall of a titanium alloy hollow fan blade were extracted using an Abaqus Python script, and the nodal stresses were obtained using the superconvergent patch recovery method.

[0014] The coefficient of variation of stress at all nodes in the unit is calculated using 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, the area corresponding to the unit is recorded as the stress concentration area.

[0015] As a further solution of the present invention: the process of obtaining the high-risk area is:

[0016] Use scripts to extract the nodal stress of each unit under each working condition, build a structured database, and record the maximum stress and mean stress of the stress concentration area under each working condition;

[0017] Based on any working condition, a simulation period is preset and divided into several time points at equal time intervals. The stress change gradient at each time point in any stress concentration area is calculated. The stress change gradient at all time points is analyzed to obtain a stable concentration area. The stability of the stable concentration area is analyzed to determine the proportion of abnormal stress time points and the degree of stress gradient deviation, and the product is processed to obtain the risk coefficient.

[0018] If the risk coefficient of the current stable concentrated area is greater than or equal to the risk coefficient threshold, the corresponding stable concentrated area will be recorded as a high-risk area.

[0019] As a further solution of the present invention: the process of obtaining the stable concentrated area is:

[0020] The stress change gradients corresponding to all time points in the simulation period are integrated into a gradient change data group. The variance values of all stress change gradients in the gradient change data group are calculated using the variance formula to obtain the stress change coefficient corresponding to the current stress concentration area.

[0021] If the stress variation coefficient corresponding to the current stress concentration area is less than the preset stress variation coefficient standard value, the corresponding stress concentration area is recorded as a stable concentration area.

[0022] As a further solution of the present invention: the process of obtaining the ratio of the number of abnormal stress time points is as follows:

[0023] Extract the stress change gradient corresponding to the stable concentrated area. If the stress change gradient corresponding to the stable concentrated area is greater than or equal to the stress change gradient standard value, the corresponding time point is recorded as the abnormal stress time point.

[0024] Count the number of abnormal stress time points among all time points, and calculate the proportion of abnormal stress time points among all time points.

[0025] As a further solution of the present invention: the process of obtaining the degree of stress gradient deviation is:

[0026] The stress gradient deviation value corresponding to the abnormal stress time point is obtained by subtracting the stress change gradient corresponding to the abnormal stress time point from the standard value of the stress change gradient and taking the absolute value. The stress gradient deviation values corresponding to all abnormal stress time points are summed and averaged, and then the sum is compared with the standard value of the stress change gradient to obtain the degree of stress gradient deviation.

[0027] As a further solution of the present invention: the specific process of evaluating the strength of the titanium alloy hollow fan blade of an aero-engine is as follows:

[0028] Analyze the risk level of high-risk areas to obtain the proportion of high-risk areas and the risk level value. Multiply the proportion of high-risk areas and the risk level value to obtain the overall strength assessment value of the aircraft engine titanium alloy hollow fan blade;

[0029] If the overall strength assessment value is greater than or equal to the overall strength assessment threshold, it means that the overall strength of the aircraft engine titanium alloy hollow fan blade is weak.

[0030] As a further solution of the present invention: the process of obtaining the proportion of the number of high-risk areas is:

[0031] Count the number of high-risk areas in the stress concentration area and calculate the proportion of high-risk areas in the stress concentration area.

[0032] As a further solution of the present invention: the process of obtaining the risk level value is as follows:

[0033] Extract the risk coefficient corresponding to the high-risk area, subtract the risk coefficient corresponding to the high-risk area from 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 compare them with the risk coefficient threshold to obtain the risk degree value.

[0034] As a further solution of the present invention: the process of obtaining the thickness adjustment amount of the high-risk area is:

[0035] All high-risk areas were extracted, and independent initial thickness parameters were defined for each high-risk area. The sensitivity of each initial thickness parameter to the maximum stress in the high-risk area was determined using the finite difference method, and a dual-objective optimization model for the high-risk area was established. A trade-off curve between stress reduction and mass increase was generated using a genetic algorithm. The optimal solution was selected based on engineering requirements, and increments were allocated according to sensitivity priority.

[0036] The optimal solution is the thickness adjustment amount. The thickness adjustment amount is summed with the initial thickness parameter to obtain the optimized thickness parameter. The optimized thickness parameter is imported into the finite element model, and the risk coefficient is recalculated to verify whether the constraint conditions are met. If the crack initiation risk is still high, the thickness parameter is readjusted and the above process is repeated until convergence.

[0037] The beneficial effects of the present invention are as follows:

[0038] The present invention constructs a finite element analysis model of titanium alloy hollow fan blades of aircraft engines, analyzes the static stress distribution of titanium alloy hollow fan blades of aircraft engines, and identifies stress concentration areas in titanium alloy hollow fan blades of aircraft engines; starts a simulation program, and dynamically analyzes the stress change gradient of the stress concentration area under different working conditions to evaluate the crack initiation risk in the stress concentration area, and determines the high-risk area in the stress concentration area; the present invention accurately identifies the stress concentration area of titanium alloy hollow fan blades through finite element model construction and static stress analysis, provides key target areas for subsequent dynamic risk assessment, and combines the stress change gradient analysis under dynamic working conditions to achieve a time-series and quantitative assessment of the crack initiation risk, avoids the limitations of a single static analysis, makes the assessment results fit the actual service environment, improves the reliability of the prediction, and identifies high-risk areas, which is beneficial to reducing the failure risk of aircraft engines caused by blade cracks and improving safety and reliability.

[0039] The present invention evaluates the strength of titanium alloy hollow fan blades of aircraft engines by analyzing the risk degree of high-risk areas in stress concentration areas; if the overall strength of the blade is weak, the sensitivity of each thickness parameter in the high-risk area of the titanium alloy hollow fan of the aircraft engine to the maximum stress is determined by the finite difference method in combination with the blade thickness parameters in the high-risk area of the titanium alloy hollow fan of the aircraft engine, a dual-objective optimization model for the high-risk area is established, and a trade-off curve between stress reduction and mass increase is generated by a genetic algorithm (NSGA-II) to determine the thickness adjustment amount of the high-risk area; the present invention reduces the stress concentration area of the titanium alloy hollow fan blade of the aircraft engine during operation by optimizing the thickness of the titanium alloy hollow fan blade of the aircraft engine, thereby effectively avoiding the problem of blade breakage caused by the high stress concentration risk of the titanium alloy hollow fan blade of the aircraft engine, which is beneficial to reducing the possibility of engine failure, thereby extending the overall service life of the aircraft engine and ensuring flight safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below with reference to the accompanying drawings.

[0041] Figure 1 This is a flowchart of the steps of a method for evaluating the strength of a titanium alloy hollow fan blade for an aircraft engine according to an embodiment of the present invention;

[0042] Figure 2 This is a system block diagram of a strength assessment system for titanium alloy hollow fan blades of an aero-engine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0044] Example 1:

[0045] See also Figure 1 As shown, a method for evaluating the strength of a titanium alloy hollow fan blade of an aircraft engine according to an embodiment of the present invention includes the following steps:

[0046] Step 1: Construct a finite element analysis model of the titanium alloy hollow fan blade of an aero-engine, analyze the static stress distribution of the titanium alloy hollow fan blade of an aero-engine, and identify the stress concentration area in the titanium alloy hollow fan blade of an aero-engine;

[0047] The geometric data of the titanium alloy hollow fan blade of the aircraft engine is obtained through various testing instruments. Based on the geometric data of the titanium alloy hollow fan blade of the aircraft engine, a geometric model of the titanium alloy hollow fan blade of the aircraft engine is drawn through three-dimensional modeling software. The created geometric model is imported into the finite element analysis software ABAQUS to construct a finite element analysis model of the titanium alloy hollow fan blade of the aircraft engine, wherein the geometric data includes blade thickness, blade height, aspect ratio, and torsion angle;

[0048] Input the mechanical properties parameters of the blade material into the finite element analysis software ABAQUS, where the mechanical properties parameters include elastic modulus, Poisson's ratio, density, and thermal expansion coefficient;

[0049] In the geometric model of titanium alloy hollow fan blades for aircraft engines, the multi-block method is used to decompose the titanium alloy hollow fan blade geometry into hexahedral subdomains. Mapped meshing is used to generate structured hexahedral meshes for straight sections such as the blade body. Sweep meshing or transition elements are used for high curvature subdomains such as the leading and trailing edges. Layered sweeping is used to ensure mesh resolution in the thickness direction, and node and element topology information is output.

[0050] The integration point stresses of all elements on the solid wall of a titanium alloy hollow fan blade were extracted using an Abaqus Python script, and the nodal stresses were obtained using the superconvergent patch recovery (SPR) method.

[0051] Analyze all node stresses in the unit through the standard deviation calculation formula to obtain the standard deviation of node stress in the unit;

[0052] The mean value calculation formula is used to sum up the stresses of all nodes in the unit and take the mean value to obtain the mean value of the node stress in the unit. The coefficient of variation calculation formula is used to ratio the standard deviation of the node stress in the unit to the mean value of the node stress in the unit to obtain the stress concentration factor.

[0053] In some embodiments, the stress concentration factor is compared with a stress concentration factor threshold. The specific comparison process is:

[0054] If the stress concentration factor is less than or equal to the stress concentration factor threshold, the area corresponding to the element is recorded as the stress concentration area;

[0055] 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;

[0056] Step 2: Start the simulation program and dynamically analyze the stress gradient of the stress concentration area under different working conditions to evaluate the crack initiation risk in the stress concentration area and identify the high-risk areas in the stress concentration area.

[0057] Use scripts to extract the nodal stress of each unit under each working condition, build a structured database, and record the maximum stress and mean stress of the stress concentration area under each working condition;

[0058] Based on any working condition, the simulation period is preset and divided into several time points at equal time intervals. The stress change gradient at time point t in any stress concentration area is calculated. , the specific calculation formula is:

[0059]

[0060] in, represents the mean stress value of the stress concentration area at time t, represents the mean stress value of the stress concentration area at time t-1, Indicates the duration between adjacent time points;

[0061] The stress change gradients corresponding to all time points in the simulation period are integrated into a gradient change data group. The variance values of all stress change gradients in the gradient change data group are calculated using the variance formula to obtain the stress change coefficient corresponding to the current stress concentration area.

[0062] Compare the stress variation coefficient corresponding to the current stress concentration area with the standard value of the stress variation coefficient. The specific comparison process is as follows:

[0063] If the stress variation coefficient corresponding to the current stress concentration area is greater than or equal to the preset stress variation coefficient standard value, the corresponding stress concentration area is recorded as an unstable concentration area;

[0064] If the stress variation coefficient corresponding to the current stress concentration area is less than the preset stress variation coefficient standard value, the corresponding stress concentration area is recorded as a stable concentration area;

[0065] It is understood that the standard value of the stress variation coefficient is set by those skilled in the art based on historical circumstances of the current working conditions;

[0066] Extract the stress change gradient corresponding to the stable concentrated area, and compare the stress change gradient corresponding to any time point in the stable concentrated area with the standard value of the stress change gradient:

[0067] If the stress change gradient corresponding to the stable concentrated area is greater than or equal to the standard value of the stress change gradient, the corresponding time point is recorded as the abnormal stress time point;

[0068] If the stress change gradient corresponding to the stable concentrated area is less than the standard value of the stress change gradient, the corresponding time point is recorded as the normal stress time point;

[0069] It is understood that the standard value of the stress change gradient is set by those skilled in the art based on the historical circumstances of the current working conditions;

[0070] Count the number of abnormal stress time points among all time points, and calculate the proportion of abnormal stress time points among all time points;

[0071] The stress gradient corresponding to the abnormal stress time point is subtracted from the standard value of the stress gradient, and the absolute value of the difference is taken to obtain the stress gradient deviation value corresponding to the abnormal stress time point. The stress gradient deviation values corresponding to all abnormal stress time points are summed and averaged to obtain the stress gradient deviation mean. The stress gradient deviation mean is ratioed with the standard value of the stress gradient to obtain the stress gradient deviation degree.

[0072] The risk coefficient of the current stable concentrated area is obtained by multiplying the proportion of abnormal stress time points by the degree of stress gradient deviation.

[0073] In some embodiments, the risk factor of the current stable concentrated area is compared with the risk factor threshold. The specific comparison process is:

[0074] If the risk coefficient of the current stable concentrated area is greater than or equal to the risk coefficient threshold, it means that the crack initiation risk of the current area is high, and the corresponding stable concentrated area is recorded as a high-risk area;

[0075] If the risk coefficient of the current stable concentrated area is less than the risk coefficient threshold, it means that the crack initiation risk of the current area is low, and the corresponding stable concentrated area is recorded as a low-risk area;

[0076] The risk factor setting has the following effects:

[0077] Function 1: The risk coefficient combines the proportion of abnormal stress time points and the degree of stress gradient deviation. The proportion of abnormal stress time points reflects the frequency of stress anomalies, while the degree of stress gradient deviation avoids the one-sidedness of a single indicator (such as only looking at maximum stress or gradient changes) and more comprehensively depicts the dynamic stress characteristics of stress concentration areas.

[0078] Function 2: The risk factor implicitly reflects the "frequency × amplitude" effect of stress fluctuations, reflecting the cumulative mechanism of fatigue damage. It conforms to Miner's linear cumulative damage theory of fatigue failure and quantifies risk through multi-factor coupling, providing support for the reliability design of aircraft engine blades.

[0079] The technical solution of this embodiment is: constructing a finite element analysis model of the titanium alloy hollow fan blade of an aero-engine, analyzing the static stress distribution of the titanium alloy hollow fan blade of an aero-engine, and identifying the stress concentration area in the titanium alloy hollow fan blade of an aero-engine; starting the simulation program, and dynamically analyzing the stress change gradient of the stress concentration area under different working conditions, evaluating the crack initiation risk of the stress concentration area, and determining the high-risk area in the stress concentration area; the present invention accurately identifies the stress concentration area of the titanium alloy hollow fan blade through finite element model construction and static stress analysis, provides key target areas for subsequent dynamic risk assessment, and combines the stress change gradient analysis under dynamic working conditions to realize the time-series and quantitative assessment of the crack initiation risk, avoids the limitations of single static analysis, makes the assessment results fit the actual service environment, improves the reliability of the prediction, and identifies the high-risk areas, which is conducive to reducing the failure risk of the aero-engine caused by blade cracks and improving safety and reliability.

[0080] Example 2:

[0081] See also Figure 1 As shown, the strength assessment method for aero-engine titanium alloy hollow fan blade according to an embodiment of the present invention further includes the following steps:

[0082] Step 3: Evaluate the strength of the titanium alloy hollow fan blades of the aircraft engine by analyzing the risk level of high-risk areas in the stress concentration area;

[0083] Count the number of high-risk areas in the stress concentration area and calculate the proportion of high-risk areas in the stress concentration area;

[0084] Extract the risk coefficient corresponding to the high-risk area, perform subtraction processing on 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 to obtain the risk coefficient deviation mean, perform ratio processing on the risk coefficient deviation mean and the risk coefficient threshold to obtain the risk degree value;

[0085] The overall strength assessment value of the titanium alloy hollow fan blade of an aircraft engine is obtained by multiplying the proportion of high-risk areas by the risk level.

[0086] In some embodiments, the overall strength evaluation value is compared with the overall strength evaluation threshold. The specific comparison process is:

[0087] If the overall strength assessment value is greater than or equal to the overall strength assessment threshold, it means that the overall strength of the aircraft engine titanium alloy hollow fan blade is weak;

[0088] If the overall strength assessment value is less than the overall strength assessment threshold, it means that the overall strength of the aircraft engine titanium alloy hollow fan blade is relatively strong, indicating that the overall strength of the blade is good and no adjustment is required;

[0089] Step 4: If the overall blade strength is weak, the sensitivity of each thickness parameter to the maximum stress in the high-risk area of the titanium alloy hollow fan of the aircraft engine is determined using the finite difference method, combined with the blade thickness parameters in the high-risk area. A dual-objective optimization model for the high-risk area is established, and a stress reduction and mass increase trade-off curve is generated using a genetic algorithm (NSGA-II) to determine the thickness adjustment amount in the high-risk area.

[0090] S41: Determine the sensitivity of each initial thickness parameter to the maximum stress in the high-risk area using the finite difference method;

[0091] 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;

[0092] The sensitivity of each thickness parameter to the maximum stress is calculated using the finite difference method , the specific calculation formula is:

[0093]

[0094] in, Indicates the maximum stress in high-risk areas;

[0095] S42: Establish a dual-objective optimization model for high-risk areas;

[0096] Goal 1: Minimize the maximum stress in high-risk areas ;

[0097] Goal 2: Minimize mass gain ,in, 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;

[0098] Constraints: Thickness parameter is greater than or equal to the lower limit of thickness parameter, total mass increment Does not exceed the quality threshold; the transition curvature is greater than or equal to the minimum transition curvature;

[0099] S43: Generate a stress reduction vs. mass increase trade-off curve using a genetic algorithm (NSGA-II) to determine the thickness adjustment amount in high-risk areas;

[0100] Encoding and population initialization: For each thickness parameter Perform real number encoding, initialize the population size, and randomly generate thickness combinations that meet the constraints;

[0101] Fitness function: directly use dual objectives and , calculate individual fitness through non-dominated sorting and crowding;

[0102] Genetic operations: Crossover: arithmetic crossover (maintaining constraint boundaries); Mutation: Gaussian mutation (step size 0.1 mm);

[0103] Termination conditions: No significant change in the Pareto frontier for 10 consecutive generations, or the maximum number of iterations is reached;

[0104] Trade-off curve analysis and decision-making: Generate a Pareto frontier curve and select the optimal solution based on engineering requirements:

[0105] Allocate thickness for the selected solution: allocate increments based on sensitivity priority (e.g., high-risk areas with high sensitivity are given priority for increasing thickness, and the increment is positively correlated with sensitivity);

[0106] The optimal solution is the thickness adjustment amount. The thickness adjustment amount is summed with the initial thickness parameter to obtain the optimized thickness parameter.

[0107] Verification and iteration: Import the optimized thickness parameters into the finite element model, recalculate the risk coefficient, and verify whether the constraints are met. If the crack initiation risk is still high, readjust the thickness parameters and repeat the above process until convergence.

[0108] By prioritizing the adjustment of blade thickness in high-risk areas with high sensitivity, the maximum stress in high-risk areas is reduced, the stress amplitude and gradient required for crack initiation are weakened, and the initial crack initiation is inhibited from a mechanical mechanism perspective.

[0109] The technical solution of this embodiment is: by analyzing the risk degree of high-risk areas in stress concentration areas, the strength of the titanium alloy hollow fan blades of the aircraft engine is evaluated; 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 aircraft engine, the sensitivity of each thickness parameter in the high-risk area to the maximum stress is determined by the finite difference method, a dual-objective optimization model for the high-risk area is established, and a trade-off curve between stress reduction and mass increase is generated by the genetic algorithm (NSGA-II) to determine the thickness adjustment amount of the high-risk area; the present invention reduces the stress concentration area of the titanium alloy hollow fan blades of the aircraft engine during operation by optimizing the thickness of the titanium alloy hollow fan blades of the aircraft engine, thereby effectively avoiding the problem of blade breakage caused by the high stress concentration risk of the titanium alloy hollow fan blades of the aircraft engine, which is beneficial to reducing the possibility of engine failure, thereby extending the overall service life of the aircraft engine and ensuring flight safety.

[0110] Example 3:

[0111] See also Figure 2 As shown, an aircraft engine titanium alloy hollow fan blade strength assessment system according to an embodiment of the present invention includes the following modules:

[0112] Static Stress Analysis Module: Constructs a finite element analysis model of aero-engine titanium alloy hollow fan blades, analyzes the static stress distribution of aero-engine titanium alloy hollow fan blades, and identifies stress concentration areas in aero-engine titanium alloy hollow fan blades;

[0113] Dynamic simulation module: Start the simulation program to dynamically analyze the stress gradient of the stress concentration area under different working conditions, evaluate the crack initiation risk in the stress concentration area, and identify the high-risk areas in the stress concentration area;

[0114] Strength Assessment Module: Evaluates the strength of titanium alloy hollow fan blades for aircraft engines by analyzing the risk level of high-risk areas in stress concentration areas;

[0115] Optimization and Adjustment Module: If the overall blade strength is weak, the finite difference method is used to determine the sensitivity of each thickness parameter to the maximum stress in the high-risk area of the aircraft engine titanium alloy hollow fan, combined with the blade thickness parameters in the high-risk area. A dual-objective optimization model is established for the high-risk area, and a genetic algorithm (NSGA-II) is used to generate a stress reduction and mass increase trade-off curve to determine the thickness adjustment amount in the high-risk area.

[0116] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the strength of titanium alloy hollow fan blades for aircraft engines, characterized by: include: Construct a finite element analysis model of aero-engine titanium alloy hollow fan blades, analyze the static stress distribution of aero-engine titanium alloy hollow fan blades, and identify stress concentration areas in aero-engine titanium alloy hollow fan blades; The method for obtaining the stress concentration area in the titanium alloy hollow fan blade of the aircraft engine is: Layered scanning is used to ensure mesh resolution in the thickness direction and output node and element topology information; The integration point stresses of all elements on the solid wall of a titanium alloy hollow fan blade were extracted using an Abaqus Python script, and the nodal stresses were obtained using the superconvergent patch recovery method. The coefficient of variation of stress of all nodes in the unit is calculated by the coefficient of variation calculation formula to obtain the stress concentration factor; If the stress concentration factor is less than or equal to the stress concentration factor threshold, the area corresponding to the element is recorded as the stress concentration area; Start the simulation program and dynamically analyze the stress gradient of the stress concentration area under different working conditions to assess the crack initiation risk in the stress concentration area and identify the high-risk areas in the stress concentration area. The process of obtaining the high-risk area is as follows: Use scripts to extract the nodal stress of each unit under each working condition, build a structured database, and record the maximum stress and mean stress of the stress concentration area under each working condition; Based on any working condition, a simulation period is preset and divided into several time points at equal time intervals. The stress change gradient at each time point in any stress concentration area is calculated. The stress change gradient at all time points is analyzed to obtain a stable concentration area. The stability of the stable concentration area is analyzed to determine the proportion of abnormal stress time points and the degree of stress gradient deviation, and the product is processed to obtain the risk coefficient. If the risk coefficient of the current stable concentrated area is greater than or equal to the risk coefficient threshold, the corresponding stable concentrated area will be recorded as a high-risk area; By analyzing the risk level of high-risk areas in stress concentration areas, the strength of titanium alloy hollow fan blades of aircraft engines is evaluated; If the overall strength of the blade is weak, combined with the blade thickness parameters in the high-risk area of the titanium alloy hollow fan of the aircraft engine, the finite difference method is used to determine the sensitivity of each thickness parameter in the high-risk area to the maximum stress, and a dual-objective optimization model for the high-risk area is established. The trade-off curve between stress reduction and mass increase is generated by the genetic algorithm to determine the thickness adjustment amount in the high-risk area.

2. The strength assessment method for aero-engine titanium alloy hollow fan blade according to claim 1, characterized in that: The process of obtaining the stable concentrated area is as follows: The stress change gradients corresponding to all time points in the simulation period are integrated into a gradient change data group. The variance values of all stress change gradients in the gradient change data group are calculated using the variance formula to obtain the stress change coefficient corresponding to the current stress concentration area. If the stress variation coefficient corresponding to the current stress concentration area is less than the preset stress variation coefficient standard value, the corresponding stress concentration area is recorded as a stable concentration area.

3. The strength assessment method for aero-engine titanium alloy hollow fan blade according to claim 2, characterized in that: The process of obtaining the ratio of the number of abnormal stress time points is as follows: Extract the stress change gradient corresponding to the stable concentrated area. If the stress change gradient corresponding to the stable concentrated area is greater than or equal to the stress change gradient standard value, the corresponding time point is recorded as the abnormal stress time point. Count the number of abnormal stress time points among all time points, and calculate the proportion of abnormal stress time points among all time points.

4. The strength assessment method for aero-engine titanium alloy hollow fan blade according to claim 3, characterized in that: The process of obtaining the stress gradient deviation degree is as follows: The stress gradient deviation value corresponding to the abnormal stress time point is obtained by subtracting the stress change gradient corresponding to the abnormal stress time point from the standard value of the stress change gradient and taking the absolute value. The stress gradient deviation values corresponding to all abnormal stress time points are summed and averaged, and then the sum is compared with the standard value of the stress change gradient to obtain the degree of stress gradient deviation.

5. The strength assessment method for aero-engine titanium alloy hollow fan blade according to claim 4, characterized in that: The specific process of evaluating the strength of the titanium alloy hollow fan blades of an aero-engine is as follows: Analyze the risk level of high-risk areas to obtain the proportion of high-risk areas and the risk level value. Multiply the proportion of high-risk areas and the risk level value to obtain the overall strength assessment value of the aircraft engine titanium alloy hollow fan blade; If the overall strength assessment value is greater than or equal to the overall strength assessment threshold, it means that the overall strength of the aircraft engine titanium alloy hollow fan blade is weak.

6. The method for evaluating the strength of a titanium alloy hollow fan blade for an aero-engine according to claim 5, characterized in that: The process of obtaining the proportion of high-risk areas is as follows: Count the number of high-risk areas in the stress concentration area and calculate the proportion of high-risk areas in the stress concentration area.

7. The method for evaluating the strength of a titanium alloy hollow fan blade for an aircraft engine according to claim 5, characterized in that: The process of obtaining the risk level value is as follows: Extract the risk coefficient corresponding to the high-risk area, subtract the risk coefficient corresponding to the high-risk area from 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 compare them with the risk coefficient threshold to obtain the risk degree value.

8. The method for evaluating the strength of a titanium alloy hollow fan blade for an aircraft engine according to claim 1, wherein: The process of obtaining the thickness adjustment amount of the high-risk area is as follows: All high-risk areas were extracted, and independent initial thickness parameters were defined for each high-risk area. The sensitivity of each initial thickness parameter to the maximum stress in the high-risk area was determined using the finite difference method, and a dual-objective optimization model for the high-risk area was established. A trade-off curve between stress reduction and mass increase was generated using a genetic algorithm. The optimal solution was selected based on engineering requirements, and increments were allocated according to sensitivity priority. The optimal solution is the thickness adjustment amount. The thickness adjustment amount is summed with the initial thickness parameter to obtain the optimized thickness parameter. Import the optimized thickness parameters into the finite element model, recalculate the risk coefficient, and verify whether the constraints are met; If the crack initiation risk is still high, readjust the thickness parameters and repeat the above process until convergence.

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