Undercarriage system mechanical structure reliability simulation analysis method and electronic equipment

Abstract

The invention discloses an undercarriage system mechanical structure reliability simulation analysis method and electronic equipment, and the method comprises the steps: determining a simulation target of reliability analysis based on fault mechanism analysis, and further constructing a reliability analysis model; according to the method, based on failure mechanisms and structural characteristics of different components of an undercarriage system, a high-precision CAE simulation model is selected for complex structural parts and key parts to be combined with an agent model technology for reliability index solving, and the number of times of large-scale CAE solving of the complex structure is reduced; rapidly solving a reliability index by adopting a mathematical model aiming at the simple structural component; according to the method, on the basis of ensuring the reliability simulation analysis precision, the reliability simulation analysis efficiency of the mechanical structure of the undercarriage system is effectively improved; according to the method, the loss type mechanism of the mechanical structure of the undercarriage system is considered, the structural failure rate and the MTTF are solved in combination with the product time-varying reliability curve, and the reliability of the mechanical structure of the undercarriage system in the whole life process can be comprehensively reflected.

Description

Technical Field

[0001] The present invention relates to a method for simulating and analyzing the reliability of the mechanical structure of a landing gear system and an electronic device, belonging to the field of system reliability engineering. Background Art

[0002] With the progress of aircraft landing gear design concepts and materials science, and the increasingly complex operating conditions of landing gears, there are a large number of uncertainties in factors such as the loads, material parameters, and structural dimensions of landing gears. It is necessary to accurately evaluate the reliability of the mechanical structure of landing gears to provide a reference for the design optimization of aircraft landing gears.

[0003] Currently, in engineering practice, the reliability assessment of the mechanical structure of landing gear systems mostly relies on foreign standards and empirical formulas, and fails to fully consider the actual load conditions, structural characteristics, and material parameters, making it impossible to accurately evaluate the reliability of the mechanical structure of landing gears. At present, the reliability analysis of mechanical products only focuses on the failure probability. For the complex structural system of landing gears, its structure may experience varying degrees of wear during use. Simply relying on the failure probability cannot comprehensively reflect the reliability of the mechanical structure of the landing gear system throughout its entire life cycle. Therefore, there is an urgent need for a new method for simulating and analyzing the reliability of the mechanical structure of landing gear systems to effectively solve the above problems. Summary of the Invention

[0004] The present invention aims to provide a method for simulating and analyzing the reliability of the mechanical structure of a landing gear system and an electronic device, which can effectively reflect the time-varying characteristics of structural reliability and comprehensively reflect the reliability of the mechanical structure of the landing gear system throughout its entire life cycle.

[0005] To achieve the above object, the technical solution adopted by the present invention is: A method for simulating and analyzing the reliability of the mechanical structure of a landing gear system, comprising:

[0006] S1. Conduct a mechanism analysis on the mechanical structure of the landing gear system to determine the simulation objective; the mechanism analysis includes: failure mode analysis, mechanism and load analysis;

[0007] S2. Based on the mechanism analysis, identify the failure mechanism of the simulation objective and construct the probability distribution of random variables that affect the reliability of the failure mechanism; the failure mechanisms include: fatigue, wear, aging; the random variables include: loads, material parameters, structural dimensions;

[0008] S3. Based on the failure mechanism of the simulation objective, obtain the limit state function g(n) for reliability analysis and construct a reliability analysis model, that is, the functional relationship between the reliability of the simulation objective and the number of working cycles n: P r (n) = P[g(n) > 0]; where P[g(n) > 0] is the probability that the limit state function g(n) is greater than 0;

[0009] S4. Determine whether CAE simulation is required according to the complexity of the simulation objective. If yes, go to S5; if no, go to S6.

[0010] S5. Based on the probability distributions of the random variables in S2, sample the random variables as the input parameters for the CAE simulation to obtain the CAE simulation results; train a surrogate model based on the CAE simulation results to obtain the non - linear mapping relationship between the random variables and the simulation results; based on the non - linear mapping relationship, update the limit state function of the simulation objective to obtain an explicit expression; go to S6.

[0011] S6. Based on the probability distributions of the random variables in S2, sample the random variables, calculate the values of the limit state function corresponding to the sample points, fit the probability distribution f G [g(n)] of the limit state function, and integrate the probability distribution of the limit state function to obtain the reliability value of the simulation objective corresponding to the number of working cycles n. Obtain the reliability curve.

[0012] S7. Based on the reliability curve, obtain the failure rate and MTTF.

[0013] S8. Based on the probability distribution parameters of the random variables and the reliability function, calculate the reliability sensitivity of the simulation objective and analyze the influence degree of the random variables on the reliability of the simulation objective.

[0014] Based on the mechanical structure reliability analysis, the above - mentioned scheme also evaluates the structure failure rate, MTTF (Mean Time To Failure), and reliability sensitivity. The above - mentioned reliability indicators can effectively reflect the time - varying characteristics of the structure reliability and meet the actual needs of aerospace engineering. At the same time, since the reliability simulation of the mechanical structure of the landing gear system requires a large number of high - precision CAE (Computer - Aided Engineering Simulation) analyses, and to accurately solve its reliability indicators, a huge computational cost is required. The above - mentioned scheme can, based on the failure mechanisms and structural characteristics of different components of the landing gear system, select high - precision CAE simulation models for complex structural parts and key parts in combination with the surrogate model technology to solve the reliability indicators. The surrogate model reduces the order of the high - precision CAE simulation model, reducing the number of large - scale CAE solutions for complex structures. For simple structural parts, the reliability indicators are directly solved based on the explicit limit state function. The above - mentioned scheme effectively improves the efficiency of the reliability simulation analysis of the mechanical structure of the landing gear system while ensuring the accuracy of the reliability simulation analysis. The above - mentioned scheme also determines the simulation objective based on the mechanism analysis process in the existing durability analysis, providing a positive design idea for determining the reliability simulation objective. In addition, the failure probability and reliability are complementary events, and the sum of the two is always 1.

[0015] According to an embodiment of the present invention, the present invention can be further optimized, and the following is the technical solution formed after optimization:

[0016] In one preferred embodiment, in S4, it is judged whether CAE simulation is required according to the complexity of the simulation target, including: when the complexity of the simulation target meets the following conditions: high geometric complexity, and / or complex loading conditions, and / or having material behavior, and / or having contact and assembly effects, then CAE simulation is required; the complex loading conditions include dynamic loads, asymmetric / nonlinear loading, multi-physics field coupling, and it is difficult to decompose the load to the part level; the material behavior includes the plastic stage of the material and material anisotropy; the contact and assembly effects include non-bonded contact and tooth surface meshing.

[0017] In one preferred embodiment, in S5, the CAE simulation uses the finite element method to simulate the simulation target. S5 specifically includes:

[0018] S5.1: Sampling random variables, and the sampling data is used as the input parameters for the finite element simulation;

[0019] S5.2: Construct the geometric model of the simulation target and complete the mesh division;

[0020] S5.3: Based on the input parameters in S5.1, set the material parameters of the simulation target, and convert the geometric model into a finite element model;

[0021] S5.4: Based on the finite element model, obtain the simulation results of the failure mechanism;

[0022] S5.5: Use all the sampling data in S5.1 as the input and the corresponding simulation results as the output, set the training set and the test set, train the surrogate model through the training set, and construct the non-linear mapping relationship between the random variables and the simulation results;

[0023] S5.6: Judge whether the surrogate model meets the accuracy requirements. If so, based on the non-linear mapping relationship, update the limit state function of the simulation target to obtain an explicit expression, and enter S6. If not, enter S5.1.

[0024] In one preferred embodiment, in S5.6, the accuracy requirement is that the relative error between the simulation results in the test set and the calculation results of the corresponding surrogate model is not greater than the set threshold, that is,

[0025] The calculation result of the surrogate model is the failure mechanism result calculated through the non-linear mapping relationship in S5.5 based on the sampling data in the test set;

[0026] Preferably, the set threshold is less than 5%.

[0027] In one of the preferred embodiments, in S5, Latin Hypercube Sampling is used for the sampling. Latin Hypercube Sampling can reflect the probability characteristics of the distribution of random variables and has good convergence.

[0028] In one of the preferred embodiments, in S6, the Monte Carlo method is used for the sampling.

[0029] In one of the preferred embodiments, in S7, based on the reliability curve, the failure rate and MTTF are obtained, including:

[0030]

[0031] where h(n) is the initial failure rate of the mechanical structure, Δn is the change in the number of working cycles n, P r (n - Δn) is the reliability value at the corresponding number of working cycles n - Δn, P r (n + Δn) is the reliability value at the corresponding number of working cycles n + Δn.

[0032] In one of the preferred embodiments, in S8, the calculation formula for the reliability sensitivity of the simulation target is:

[0033]

[0034] where is the reliability sensitivity, is the partial derivative of the reliability function P r (n), and θ is the probability distribution parameter of the random variable affecting the reliability.

[0035] Based on the same concept, the present invention also provides an electronic device, which includes a memory and one or more processors; one or more programs are stored on the memory, and when the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the above method.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a method for simulating and analyzing the mechanical structure reliability of a landing gear system. This method determines the simulation objectives of reliability analysis based on failure mechanism analysis and further constructs a reliability analysis model. The method provided by the present invention can, based on the failure mechanisms and structural characteristics of different components of the landing gear system, select high-precision CAE simulation models combined with surrogate model technology for solving reliability indicators for complex structural components and key components, reducing the number of large-scale CAE solutions for complex structures; for simple structural components, a mathematical model is used to quickly solve reliability indicators. On the basis of ensuring the accuracy of reliability simulation analysis, this method effectively improves the efficiency of simulating and analyzing the mechanical structure reliability of the landing gear system and is applicable to engineering practice; the present invention considers the wear-out type mechanisms existing in the mechanical structure of the landing gear system, combines the time-varying reliability curve of the product to solve the structure failure rate and MTTF, and can comprehensively reflect the reliability of the mechanical structure of the landing gear system during the entire life process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flowchart of simulating and analyzing the mechanical structure reliability of a landing gear system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The present invention will be described in detail below with reference to the drawings and in conjunction with embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0039] Embodiment 1

[0040] This Embodiment 1 proposes a method for simulating and analyzing the mechanical structure reliability of a landing gear system, including constructing a reliability analysis model, CAE simulation and surrogate model construction, and reliability index calculation and analysis. Figure 1 is the specific flowchart of this method.

[0041] Construction of the reliability analysis model: Decompose the structure of the landing gear system and conduct failure mode, mechanism and load analysis. Screen and combine failure mechanisms for the wear-out type mechanisms (including fatigue, wear, aging, etc.) existing in the landing gear system to determine the simulation analysis objectives. Based on data such as the structural design parameters of the landing gear, construct the probability distributions of random variables (such as loads, material parameters, structural dimensions) that affect the structural reliability of the landing gear. Based on the failure mechanisms of the simulation objectives and the probability distributions of random variables, construct the corresponding reliability analysis model. Specifically, it includes the probability distribution model of random variable parameters that affect the mechanical structure reliability of the landing gear system, the failure mechanism model (including fatigue, wear, aging and other failure mechanism models) that reflects the wear-out type failure of the mechanical structure of the landing gear system, and the limit state function that identifies the failure state of the mechanical structure of the landing gear system.

[0042] CAE Simulation and Surrogate Model Construction: Determine whether CAE simulation is required based on the complexity of the simulation target. If the simulation target has one of the following characteristics: 1. High geometric complexity (irregular shapes, etc.); 2. Complex loading conditions (dynamic loads, asymmetric / nonlinear loading, multi-physics coupling, loads difficult to decompose to the part level, etc.); 3. Material behavior (plastic stage of materials, material anisotropy, etc.); 4. Contact and assembly effects (non-bonded contacts, such as tooth surface meshing, etc.), then CAE simulation is required: sample simulation parameters according to the probability distribution of random variables, and train a surrogate model based on the simulation results. Solve the structural failure probability, failure rate, MTTF, and reliability sensitivity index on the basis of the trained surrogate model; if it does not have the above characteristics, CAE simulation is not required, and the reliability index can be directly solved based on the explicit limit state function.

[0043] Reliability Index Calculation and Analysis: Solve the time-varying reliability curve at different times or number of working cycles based on the reliability analysis model, and solve the failure rate and MTTF of the mechanical structure of the landing gear system based on the reliability curve. By calculating the reliability sensitivity index, clarify the influence degree of each random variable parameter on the structural reliability, and based on the calculation results of the foregoing reliability indexes (i.e., reliability curve, mechanical structure failure rate, MTTF, and reliability sensitivity), put forward design improvement suggestions for the mechanical structure of the landing gear system. Embodiment 1 of the present invention provides a method for reliability simulation analysis of the mechanical structure of a landing gear system. Select an analysis method according to the complexity of the mechanical structure. For complex structures, use CAE simulation analysis combined with surrogate model technology to quickly and quantitatively evaluate the reliability indexes of the mechanical structure of the landing gear system, providing a reference basis for guiding the optimization and improvement of the mechanical structure of the landing gear system in engineering development.

[0044] Embodiment 2

[0045] Taking a certain mechanical structure component of the landing gear system as an example, the following is the process of a reliability simulation method for the mechanical structure of the landing gear system provided by Embodiment 2:

[0046] Step 1: Mechanism analysis and simulation target determination. Determine the simulation target based on the mechanism analysis process in the durability analysis process, providing a positive design idea for determining the reliability simulation target. Taking a certain mechanical structure component of the landing gear system as an example, decompose the structure component to the lowest agreed hierarchical unit and conduct failure mode, mechanism, and load analysis. For the wear-out mechanisms existing in the analysis results, conduct failure mechanism screening and merging, and finally determine that the simulation targets of the structure component include structural parts such as earring joints and tension springs; the wear-out mechanisms include fatigue, wear, and aging.

[0047] Step 2: Modeling the probability distribution of random variables [Reference: Lv Zhenzhou, Song Shufang, Li Luyi, et al. Fundamentals of Structural / Institutional Reliability Design [M]. Xi'an: Northwestern Polytechnical University Press, 2019.]. For the earring joint, its main failure mechanism is identified as fatigue. The random variables affecting its fatigue reliability include the elastic modulus and the end load, and the probability distributions of the two random variables are modeled (see Table 1); for the extension spring, its main failure mechanism is identified as fatigue. The random variables affecting its fatigue reliability include the mean coil diameter, wire diameter, maximum working load, and minimum working load, and the probability distributions of the four random variables are modeled (see Table 2).

[0048] Step 3: Constructing the reliability analysis model. Based on the failure mechanism of the simulation target and the probability distribution of the random variables, the corresponding reliability analysis model is constructed.

[0049] For the earring joint, based on the structural fatigue Miner linear cumulative damage theory, the limit state function g1(n) for its structural fatigue reliability analysis is constructed as follows:

[0050]

[0051] Table 1 Probability distribution parameters of random variables for the earring joint

[0052] Random variable Distribution type Mean Standard deviation Elastic modulus (GPa) Normal distribution 195 3.9 Force (N) Normal distribution 281503 5630

[0053] Table 2 Probability distribution parameters of random variables for the extension spring

[0054] Random variable Distribution Mean Standard deviation Mean coil diameter (mm) Normal distribution 39.5 0.395 Wire diameter of spring (mm) Normal distribution 6.5 0.065 Maximum working load (N) Normal distribution 1696 90.9 Minimum working load (N) Normal distribution 1250 84.2

[0055] where D c is the critical damage of the material. Considering the scatter of the fatigue life, take D c = 0.2; D(n) is the total damage at the corresponding number of working cycles n. The damage amount is affected by random variables such as the elastic modulus and the end load, and its definition is:

[0056]

[0057] where N is the fatigue life corresponding to the stress level S, and the corresponding relationship can be obtained from the S-N curve. The reliability P r1 of the earring joint as a function of the number of working cycles n is expressed as follows:

[0058] P r1 (n) = P[g1(n) > 0] = P[lgD c - lgD(n) > 0] Equation (3)

[0059] where P is the probability, and P[g1(n) > 0] is the probability that the limit state function g1(n) is greater than 0.

[0060] For a tension spring, based on the spring shear fatigue strength failure theory, the limit state function g2(n) for its spring shear fatigue reliability analysis is constructed as follows:

[0061]

[0062] where τ0 is the spring pulsating cyclic fatigue limit. By referring to the "Spring Handbook", the functional relationship τ0(n) between the spring pulsating cyclic fatigue limit τ0 and the spring working cycle number can be obtained. τ max and τ min are the maximum and minimum shear stresses of the spring respectively. The limit state function values at different spring working cycle numbers are calculated through formula (4). Finally, the expression for the reliability P r2 of the tension spring varying with the working cycle number n is as follows:

[0063] P r2 (n) = P[g2(n) > 0] = P[τ0(n) + 0.75τ min -τ max > 0] Formula (5)

[0064] Those skilled in the art can determine the corresponding limit state function according to different mechanical structures and their failure mechanisms.

[0065] Step 4: Determine whether CAE simulation is required. Since the geometric structure of the earring joint is irregular and the input load is difficult to be decomposed onto the parts, the finite element method is used to perform CAE simulation on the earring joint to solve for the stress and damage (see Step 5 of this embodiment for details), which is used for subsequent reliability analysis. Since the geometric structure of the tension spring is simple, the input load is clear, and there are no complex contact and assembly effects, the maximum and minimum shear stresses τ max and τ min of the spring can be calculated through mature engineering algorithms, and the subsequent reliability curve can be solved (see Step 11 of this embodiment for details).

[0066] Step 5: Sampling of finite element model parameters. Based on the probability distributions of the random variables affecting the fatigue reliability of the earring joint, Latin hypercube sampling is performed on the random variables as the parameter input for the finite element simulation. For sampling of random variables, methods such as uniform sampling and direct random sampling can also be used. Latin hypercube sampling can better reflect the probability characteristics of the random variable distribution compared to uniform sampling, and has the advantage of better convergence compared to direct random sampling.

[0067] Step 6: Geometric model construction and mesh generation. According to the earring joint design scheme, use 3D modeling software to draw a digital model and complete mesh generation [Reference: Deng Gang, Qi Guangwei, Teng Yun, etc. Normative method for reliability analysis of mechanical structures based on stochastic finite element method [J]. China Standardization, 2022, (03): 206-211.].

[0068] Step 7: Setting of material parameters and boundary conditions. Based on the probability distribution sampling results of the random variables affecting the fatigue reliability of the earring joint, set material parameters such as the elastic modulus and Poisson's ratio of the earring joint, and define the S-N curve. Set the load boundary conditions according to the structural fatigue load spectrum, and set the constraints and contacts according to the actual structure installation form. Convert the 3D digital model into a finite element model [Reference: Deng Gang, Qi Guangwei, Teng Yun, etc. Normative method for reliability analysis of mechanical structures based on stochastic finite element method [J]. China Standardization, 2022, (03): 206-211.].

[0069] Step 8: Simulation calculation and result analysis. Solve the CAE model. For example, for fatigue simulation, obtain the maximum fatigue damage of the simulation target; for wear simulation, obtain its maximum wear amount; for aging simulation (mainly for rubber parts), obtain its maximum crack propagation amount.

[0070] Step 9: Training the surrogate model: Among the simulation fatigue damage calculation results obtained from all parameter sampling samples, take 90% of the simulation results to train the Kriging surrogate model, construct the non-linear mapping relationship between the input parameters in Step 5 and the simulation damage amount, and record the damage amount calculated by this surrogate model as D * (n).

[0071] A surrogate model is a simplified model used to replace a complex and computationally expensive original model (such as high-precision numerical simulation, experiments, etc.). While maintaining a certain accuracy, it significantly reduces the computational cost, thereby accelerating the optimization, design, or analysis process. In this embodiment, other applicable surrogate models include neural networks and support vector machines.

[0072] Step 10: Determine whether the surrogate model meets the accuracy. Verify the surrogate accuracy of the surrogate model for the fatigue damage of the earring joint through the remaining 10% of the simulation results until the surrogate model meets the accuracy requirements. Based on the constructed non-linear mapping relationship, update the limit state function of the simulation target to obtain an explicit expression; otherwise, repeat Steps 5 to 9. Considering the accuracy requirement that under the same number of working cycles, the maximum relative error ε between the actual simulation damage amount D(n) of the remaining 10% of the simulation results and the damage amount D * (n) calculated by the surrogate model is ≤ 5%, and its calculation is as follows:

[0073]

[0074] Step 11. Solve the reliability curve. Based on the aforementioned fatigue reliability analysis models of the earring joint and the spring shear fatigue reliability analysis model respectively, solve the reliability curve. When the earring joint and the tension spring correspond to different numbers of working cycles n, use the Monte Carlo method to sample the random variables affecting the reliability of the earring joint and the tension spring in Step 2 to obtain Monte Carlo sample points. Variance reduction techniques such as the importance sampling method and the subset simulation method can also be used for sampling.

[0075] Calculate the limit state function values g(n) corresponding to all Monte Carlo sample points, and fit the probability distribution f G [g(n)]. Integrate the probability distribution based on formula (7) to obtain the reliability value P r (n) of the structure under the corresponding number of working cycles. Finally, calculate the time-varying reliability curve P r ~n curve.

[0076]

[0077] Among them, for the earring joint, substitute the surrogate calculated damage amount D * (n) obtained in Step 10 for the implicit function D(n) in the limit state function g1(n) of the original fatigue reliability analysis of the earring joint structure; for the tension spring, directly calculate the limit state function value corresponding to the sample point based on the explicit limit state function g2(n) of formula (4). Formula (8) is the calculation formula of τ max and τ min , as follows:

[0078]

[0079] Among them, K is the spring curvature coefficient, K = (4C - 1) / (4C - 4) + 0.615 / C, C is the spring coil ratio, C = d0 / d, d0 is the mean diameter of the spring, d is the wire diameter of the spring, F max and F min are the maximum and minimum working loads.

[0080] Step 12. Solve the failure rate and MTTF. Based on the definition formula of the failure rate, and use the difference method to solve the failure rate of the first flip period of the simulation target. See formula (9) specifically. This method does not depend on the probability distribution assumption of the life model of the simulation target and can accurately solve the failure rate under any life distribution (such as exponential distribution, Weibull distribution, etc.):

[0081]

[0082] Solve the MTTF value based on formula (10):

[0083]

[0084] Among them, P r (n - Δn) is the reliability value at the number of working cycles of n - Δn, and P r (n + Δn) is the reliability value at the number of working cycles of n + Δn, and Δn is the change in the number of working cycles.

[0085] Step 13: Solve the reliability sensitivity. Based on formula (11), calculate the structural reliability sensitivity Quantitatively analyze the influence degree of each random variable parameter on the reliability of the earring joint and the tension spring.

[0086]

[0087] Among them, P r (n) is the reliability corresponding to the number of working cycles n, is the partial derivative of the reliability function P r (n), and θ is the probability distribution parameter of the random variable affecting the structural reliability. Taking the fatigue reliability analysis of the tension spring as an example, the random variables affecting the spring reliability are the maximum and minimum working loads of the spring, the wire diameter of the spring wire, and the mean diameter of the spring. Considering that the four random variables are normally distributed, θ is the mean μ and standard deviation σ of each random variable.

[0088] The sensitivity analysis results of the tension spring corresponding to the working cycles of the first turnover period are shown in Table 3. It can be seen that the wire diameter of the tension spring has the greatest influence on its reliability.

[0089] Step 14: Analyze the reliability index and put forward optimization suggestions. Based on the reliability curves, failure rates, and MTTF values of the earring joint and the tension spring, evaluate whether the structural reliability meets the quantitative indicators of the product design, and based on the analysis results of the structural reliability sensitivities of each structure, propose design improvement measures for each structure, such as increasing the wire diameter of the tension spring to improve the shear fatigue reliability of the tension spring.

[0090] Table 3 Sensitivity analysis results of the tension spring reliability

[0091] Parameter name Mean coil diameter Wire diameter of spring Maximum load Minimum load Mean sensitivity -3.50E-04 7.69E-03 -2.37E-05 1.78E-05 Standard deviation sensitivity -5.73E-05 -4.40E-03 -5.92E-05 -3.09E-05

[0092] The content clarified in the above embodiments should be understood that these embodiments are only used to illustrate the present invention more clearly, rather than to limit the scope of the present invention. After reading the present invention, various equivalent forms of modification of this embodiment by those skilled in the art all fall within the scope defined by the appended claims of the present invention.

Claims

1. A method for simulating and analyzing the mechanical structure reliability of a landing gear system, characterized in that Including: S1. Conduct a mechanism analysis on the mechanical structure of the landing gear system to determine the simulation objectives; The mechanism analysis includes: failure mode analysis, mechanism and load analysis; S2. Based on the mechanism analysis, identify the failure mechanisms of the simulation objectives, and construct the probability distributions of random variables that affect the reliability of the failure mechanisms; the failure mechanisms include: fatigue, wear, aging; the random variables include: loads, material parameters, structural dimensions; S3. Based on the failure mechanism of the simulation target, obtain the limit state function g(n) for reliability analysis, and construct a reliability analysis model, that is, the functional relationship between the reliability of the simulation target and the number of working cycles n: P r (n) = P[g(n) > 0]; Among them, P[g(n)>0] is the probability that the limit state function g(n) is greater than 0; S4. According to the complexity of the simulation objectives, determine whether CAE simulation is required. If so, enter S5. If not, enter S6; S5. Based on the probability distributions of the random variables in S2, sample the random variables as the input parameters of the CAE simulation to obtain the CAE simulation results; train the surrogate model based on the CAE simulation results to obtain the non-linear mapping relationship between the random variables and the simulation results; based on the non-linear mapping relationship, update the limit state function of the simulation objectives to obtain an explicit expression; enter S6; S6. Based on the probability distribution of the random variable in S2, sample the random variable, calculate the limit state function values corresponding to the sample points, fit the probability distribution f of the limit state function G [g(n)], integrate the probability distribution of the limit state function, and obtain the reliability value of the working cycle number n corresponding to the simulation target Obtain the reliability curve; S7. Based on the reliability curve, obtain the failure rate and MTTF; S8. Based on the probability distribution parameters of the random variables and the reliability function, calculate the reliability sensitivity of the simulation objectives, and analyze the influence degree of the random variables on the reliability of the simulation objectives.

2. The method for simulating and analyzing the mechanical structure reliability of the landing gear system according to claim 1, characterized in that In S4, according to the complexity of the simulation objectives, determine whether CAE simulation is required, including: When the complexity of the simulation objectives meets the following conditions: high geometric complexity, and / or complex loading conditions, and / or having material behavior, and / or having contact and assembly effects, then CAE simulation is required; The complex loading conditions include dynamic loads, asymmetric / non-linear loading, multi-physical field coupling, and loads that are difficult to decompose to the part level; the material behavior includes the plastic stage of the material, material anisotropy; the contact and assembly effects include non-bonded contact, tooth surface meshing.

3. The method for simulating and analyzing the mechanical structure reliability of the landing gear system according to claim 1, characterized in that In S5, the CAE simulation uses the finite element method to simulate the simulation objectives. S5 specifically includes: S5.

1. Sample the random variables, and the sampled data is used as the input parameters of the finite element simulation; S5.

2. Construct the geometric model of the simulation objectives and complete the mesh division; S5.

3. Based on the input parameters in S5.1, set the material parameters of the simulation objectives, and convert the geometric model into a finite element model; S5.

4. Based on the finite element model, obtain the simulation results of the failure mechanisms; S5.

5. Use all the sampled data in S5.1 as the input and the corresponding simulation results as the output, set the training set and the test set, and train the surrogate model through the training set to construct the non-linear mapping relationship between the random variables and the simulation results; S5.

6. Determine whether the surrogate model meets the accuracy requirements. If so, based on the non-linear mapping relationship, update the limit state function of the simulation objectives to obtain an explicit expression, and enter S6. If not, enter S5.1。 4. The method for simulating and analyzing the mechanical structure reliability of the landing gear system according to claim 3, characterized in that In S5.6, the accuracy requirement is that the relative error between the simulation results in the test set and the calculation results of the corresponding surrogate model is not greater than the set threshold, that is, The calculation result of the surrogate model is the result calculated through the non-linear mapping relationship in S5.5 based on the sampled data in the test set; Preferably, the set threshold is less than 5%.

5. The method for simulating and analyzing the mechanical structure reliability of the landing gear system according to claim 1, characterized in that, In S5, the sampling uses Latin hypercube sampling.

6. The method for simulating and analyzing the mechanical structure reliability of the landing gear system according to claim 1, wherein In S6, the sampling uses the Monte Carlo method for sampling.

7. The method for simulating and analyzing the mechanical structure reliability of the landing gear system according to claim 1, wherein In S7, based on the reliability curve, the failure rate and MTTF are obtained, including: Among them, h(n) is the initial failure rate of the mechanical structure, Δn is the change in the number of working cycles n, and P r (n - Δn) is the reliability value at the corresponding number of working cycles n - Δn, and P r (n + Δn) is the reliability value at the corresponding number of working cycles n + Δn.

8. The reliability simulation analysis method for the mechanical structure of the landing gear system according to claim 1, wherein, In S8, the calculation formula for the reliability sensitivity of the simulation target is: Among them, is the reliability sensitivity, is the partial derivative of the reliability function P r (n), and θ is the probability distribution parameter of the random variable affecting the reliability.

9. An electronic device, characterized in that, The electronic device includes a memory and one or more processors; one or more programs are stored on the memory, and when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the steps of the method according to any one of claims 1 to 8.

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