Bolt hydrogen-induced fatigue life evaluation method and system based on Bayesian method

Abstract

The invention discloses a bolt hydrogen-induced fatigue life evaluation method and system based on a Bayesian method, and relates to the field of fatigue life evaluation, and the method comprises the steps: obtaining a fatigue constitutive model of a bolt based on a material level; determining a basic fatigue characteristic curve according to the material-level-based fatigue constitutive model of the bolt and material experimental data, and performing stress analysis on a real structure of the bolt by adopting a finite element analysis method to determine a stress concentration coefficient; the fatigue constitutive model based on the material level is corrected according to the stress concentration coefficient and the hydrogen embrittlement correction function, and a corrected fatigue constitutive model is obtained; according to the corrected fatigue constitutive model, determining a nonlinear cumulative damage model by adopting a nonlinear cumulative damage theory; and optimizing the nonlinear cumulative damage model by adopting a Bayesian statistical method according to material experiment data. The method can improve the accuracy and reliability of bolt fatigue life prediction.

Description

Technical Field

[0001] The present application relates to the field of fatigue life assessment, and in particular to a method and system for assessing hydrogen-induced fatigue life of bolts based on a Bayesian approach. Background Art

[0002] High-strength bolts used in marine engineering and other extreme environments are often subject to the combined effects of corrosion, mechanical loads and environmental hydrogen. These factors can easily cause hydrogen embrittlement, which in turn significantly reduces the fatigue life of the bolts. Hydrogen embrittlement (HE) refers to the phenomenon that hydrogen atoms penetrate into the interior of metal materials, causing the plasticity and toughness of the materials to decrease sharply, especially having a profound impact on the fatigue behavior of high-strength bolts. Hydrogen embrittlement has a significant effect on the fatigue properties of materials, mainly manifested as: 1. Reducing fatigue strength and fatigue life: The presence of hydrogen will reduce the fatigue limit and fatigue strength coefficient of the material; 2. Accelerating the initiation and propagation of fatigue cracks: Hydrogen atoms gather in stress-concentrated areas (such as crack tips) to promote the formation and propagation of cracks. The mechanism of hydrogen embrittlement mainly includes the following aspects: The aggregation of hydrogen atoms inside the material will weaken the bonding force between atoms, reduce the bonding strength of the fracture surface, and lead to cleavage-type fracture. This mechanism is particularly significant in high-strength steel. The presence of hydrogen reduces the movement resistance of dislocations, promotes the slip of dislocations, leads to the concentration of local plastic deformation, and accelerates the accumulation of fatigue damage. Under the combined effects of tensile stress and corrosive environment, hydrogen penetrates into the material, triggering stress corrosion cracking and accelerating the expansion of fatigue cracks.

[0003] At present, the evaluation methods of bolt fatigue life in marine environment mainly include experimental methods based on traditional SN curves and numerical simulation methods. However, traditional fatigue life prediction methods often ignore the complex mechanism of hydrogen embrittlement effect, especially when the stress concentration effect is coupled with the hydrogen content, which leads to a large deviation between the evaluation results and the actual service conditions; when the experimental data is limited or the test cost is high, the traditional method cannot make full use of the limited information to statistically correct the model parameters. This makes the model respond poorly to the complex coupling effect, and the prediction results lack sufficient robustness and credibility; in addition, traditional fatigue life prediction methods often default to material parameters, environmental factors and load characteristics as definite values, lacking an effective description of parameter uncertainty and randomness. When there is insufficient data or large fluctuations in engineering practice, it is difficult for existing methods to make reasonable statistical inferences on model parameters, thus affecting the reliability of fatigue life prediction.

[0004] In the prior art, fatigue damage models usually adopt linear cumulative damage theory, such as Miner's linear cumulative damage rule. Although this method is easy to apply in practical engineering, it is difficult to effectively reflect the nonlinear fatigue damage accumulation process of materials under complex stress levels. Under the service conditions of high-strength bolts, the interaction between stress concentration effect and hydrogen embrittlement effect has an important influence on fatigue life. Stress concentration areas are potential starting points for fatigue failure, and the penetration of hydrogen atoms further exacerbates the initiation and propagation of cracks in these areas.

[0005] Existing technologies often rely on existing experimental data or empirical formulas, but have limited ability to dynamically incorporate new experimental data and field measurement results. It is difficult to effectively integrate existing experience (prior knowledge) and new observation data, resulting in poor adaptability of the model under complex working conditions and difficulty in updating prediction results in a timely manner.

[0006] Therefore, there is an urgent need for an advanced evaluation method that can simultaneously consider the stress concentration effect, the influence of hydrogen content, and the nonlinear fatigue damage accumulation characteristics. Summary of the invention

[0007] The purpose of this application is to provide a method and system for assessing hydrogen-induced fatigue life of bolts based on a Bayesian approach, which can improve the accuracy and reliability of bolt fatigue life prediction.

[0008] To achieve the above objectives, this application provides the following solutions: The present application provides a method for assessing the hydrogen-induced fatigue life of a bolt based on a Bayesian method, and the method for assessing the hydrogen-induced fatigue life of a bolt based on a Bayesian method comprises: Obtaining a material-level fatigue constitutive model of the bolt; the material-level fatigue constitutive model is used to characterize the relationship between stress, strain and fatigue life of the bolt under cyclic load; According to the fatigue constitutive model of the bolt based on the material level and the material experimental data, the basic fatigue characteristic curve is determined, and the stress concentration factor is determined by performing stress analysis on the real structure of the bolt using the finite element analysis method; the basic fatigue characteristic curve includes: a stress-life curve and a strain-life curve; The fatigue constitutive model based on the material level is corrected according to the stress concentration factor and the hydrogen embrittlement correction function to obtain a corrected fatigue constitutive model; According to the modified fatigue constitutive model, the nonlinear cumulative damage model is determined by using the nonlinear cumulative damage theory; According to the material experimental data, the Bayesian statistical method is used to optimize the nonlinear cumulative damage model.

[0009] Optionally, obtaining a fatigue constitutive model of the bolt based on material level specifically includes: Using the formula Determine the material level based fatigue constitutive model for the bolt; in, is the fatigue strength coefficient, which is used to reflect the fatigue strength of the bolt; is the fatigue life; is the fatigue strength index; is the fatigue ductility coefficient, which is used to reflect the anti-plastic fatigue performance; is the fatigue ductility index; is the total strain amplitude, including elastic strain amplitude and plastic strain amplitude; is the elastic modulus; is the local maximum stress.

[0010] Optionally, the method of determining a basic fatigue characteristic curve based on a fatigue constitutive model of the bolt based on material level and material experimental data, and performing stress analysis on the real structure of the bolt using a finite element analysis method to determine a stress concentration factor specifically includes: Using the formula Determining Stress Concentration Factors ; in, is the theoretical stress concentration factor, is the plasticity influence coefficient.

[0011] Alternatively, using the formula Determining the local maximum stress ; in, is the nominal stress, , is the external load, is the load-bearing cross-sectional area of ​​the bolt.

[0012] Optionally, the step of correcting the fatigue constitutive model based on the material level according to the stress concentration factor and the hydrogen embrittlement correction function to obtain the corrected fatigue constitutive model specifically includes: Using the formula Determine the modified fatigue constitutive model; in, is the hydrogen embrittlement correction function of the fatigue strength coefficient, is the hydrogen content in the bolt, is the hydrogen embrittlement correction function of the fatigue ductility coefficient, is the hydrogen embrittlement correction function of the fatigue strength index, is the hydrogen embrittlement correction function of the fatigue ductility index, is the hydrogen embrittlement correction function of the cross-sectional shrinkage rate, is the hydrogen embrittlement correction function of the elongation after fracture.

[0013] Optionally, the hydrogen embrittlement correction function specifically includes: Using the formula Determine the hydrogen embrittlement correction function for the fatigue strength coefficient; Using the formula Determine the hydrogen embrittlement correction function for fatigue ductility coefficient; Using the formula and formula Determine the hydrogen embrittlement correction function of the fatigue strength index and the hydrogen embrittlement correction function of the fatigue ductility index; Using the formula Determine the hydrogen embrittlement correction function for the reduction of area; Using the formula Determine the hydrogen embrittlement correction function for elongation after fracture; in, is the fatigue strength coefficient under the condition of no hydrogen charging, , is the correction parameter of fatigue strength coefficient, is the fatigue ductility coefficient under the condition of no hydrogen charging, , is the correction parameter of fatigue ductility coefficient, is the fatigue strength index under the condition of no hydrogen charging, is the fatigue ductility index under non-hydrogen charging conditions, , is the correction parameter of fatigue strength index, , is the correction parameter of fatigue ductility index, is the cross-sectional shrinkage under the condition of no hydrogen filling, is the elongation after fracture under the condition of no hydrogen charging, , is the correction parameter of the section shrinkage rate, , It is the correction parameter of elongation after fracture.

[0014] Optionally, according to the modified fatigue constitutive model, the nonlinear cumulative damage theory is adopted to determine the nonlinear cumulative damage model, which specifically includes: According to the modified fatigue constitutive model, the fatigue life is determined by using the Newton-Raphson numerical iteration method. According to fatigue life, a nonlinear cumulative damage model is constructed by adopting nonlinear cumulative damage theory; the nonlinear cumulative damage model is used to characterize the cumulative damage value of the bolt; The fatigue life of the bolt is evaluated based on the cumulative damage value of the bolt.

[0015] Optionally, according to the fatigue life, a nonlinear cumulative damage model is constructed using the nonlinear cumulative damage theory, specifically including: Using the formula Determine the nonlinear cumulative damage model; in, is the cumulative damage value, is the total number of different stress levels, For the The number of cycles at each stress level, For the Fatigue life at each stress level, is the hydrogen embrittlement correction function of the cross-sectional shrinkage rate, is the hydrogen embrittlement correction function of elongation after fracture, is the cross-sectional shrinkage under the condition of no hydrogen filling, is the elongation after fracture under the condition of no hydrogen charging, , , To correct the parameters.

[0016] Optionally, the fatigue life of the bolt is evaluated according to the cumulative damage value of the bolt, specifically including: When the cumulative damage value When the bolts have not reached fatigue failure conditions, they continue to serve; When the cumulative damage value When the bolt reaches fatigue failure condition, its service life is exhausted; When the cumulative damage value When the bolt exceeds the fatigue failure condition, take measures.

[0017] In a second aspect, the present application provides a bolt hydrogen-induced fatigue life assessment system based on a Bayesian method, and the bolt hydrogen-induced fatigue life assessment system based on a Bayesian method comprises: A fatigue constitutive model acquisition module is used to acquire a fatigue constitutive model of the bolt based on the material level; the fatigue constitutive model based on the material level is used to characterize the relationship between stress, strain and fatigue life of the bolt under cyclic load; A stress concentration factor determination module is used to determine a basic fatigue characteristic curve according to a fatigue constitutive model of the bolt based on material level and material experimental data, and to perform stress analysis on the real structure of the bolt using a finite element analysis method to determine the stress concentration factor; the basic fatigue characteristic curve includes: a stress-life curve and a strain-life curve; A fatigue constitutive model correction module is used to correct the fatigue constitutive model based on the material level according to the stress concentration factor and the hydrogen embrittlement correction function to obtain a corrected fatigue constitutive model; A nonlinear cumulative damage model determination module is used to determine the nonlinear cumulative damage model based on the modified fatigue constitutive model and the nonlinear cumulative damage theory; The nonlinear cumulative damage model optimization module is used to optimize the nonlinear cumulative damage model based on material experimental data using the Bayesian statistical method.

[0018] According to the specific embodiments provided in this application, this application has the following technical effects: The present application provides a method and system for assessing hydrogen-induced fatigue life of bolts based on Bayesian method. The stress concentration factor is determined based on the fatigue constitutive model of the bolt at the material level combined with the geometric characteristics of the bolt; the fatigue constitutive model based on the material level is corrected according to the stress concentration factor and the hydrogen embrittlement correction function to correct the stress state of the bolt under different service conditions. The nonlinear cumulative damage theory is adopted to systematically describe the accumulation process of fatigue damage, especially the influence of the gradual increase of hydrogen content on the fatigue life of the bolt, and determine the nonlinear cumulative damage model. In combination with the stress concentration effect and the nonlinear cumulative damage theory, the fatigue life of the bolt in a complex service environment can be more accurately predicted; through the Bayesian statistical method, the experimental data is combined with the nonlinear cumulative damage model, and the randomness and uncertainty of the material parameters are quantified, thereby achieving a more accurate fatigue life prediction. The present application effectively combines the hydrogen embrittlement effect, the stress concentration effect and the nonlinear cumulative damage theory, and can make a more accurate prediction of the fatigue life of the bolt in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is a schematic flow chart of a method for assessing hydrogen-induced fatigue life of bolts based on a Bayesian method in one embodiment of the present application; Figure 2 Schematic diagram of the relationship between fatigue life and local maximum stress of high-strength bolts under different hydrogen content conditions; Figure 3 This is the relationship diagram between the correction parameters and the hydrogen content; Figure 4 is the relationship between the section shrinkage and elongation after fracture and the hydrogen content; Figure 5 Graphs showing the prior and posterior distributions of parameters in Bayesian statistical methods. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] In order to make the above-mentioned objects, features and advantages of the present application more obvious and understandable, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0023] In an exemplary embodiment, Figure 1 As shown, a method for evaluating hydrogen-induced fatigue life of bolts based on a Bayesian method is provided, and the method includes the following steps 201 to 208. Among them, S101, obtaining a material-level fatigue constitutive model of the bolt; the material-level fatigue constitutive model is used to characterize the relationship between stress, strain and fatigue life of the bolt under cyclic load; The stress-life model is mainly used to describe high-cycle fatigue (usually life greater than Basquin proposed the classic stress-life (SN) relationship, which is expressed as: ; in, is the stress amplitude, i.e. the amplitude of the cyclic stress; is the fatigue strength coefficient, reflecting the fatigue resistance of the material; is the fatigue life, i.e. the number of cycles it takes for a material to fail under a given stress amplitude; It is the fatigue strength index, reflecting the slope of the SN curve, and is usually a negative value.

[0024] Through linear regression in logarithmic coordinates, it can be determined and The value of: ; The strain-life model is able to better describe the performance of materials in the low-cycle fatigue region, especially when plastic deformation is significant. The strain-life model is mainly used to describe low-cycle fatigue (usually life less than Coffin-Manson proposed the strain-life relationship, which is expressed as: ; in, Including elastic strain amplitude and plastic strain amplitude ; ; The energy-life model is based on the energy dissipation of the material during cyclic loading and is a commonly used model for low-cycle and high-cycle fatigue. That is: Using the formula Determine the material-level fatigue constitutive model of the bolt; the fatigue constitutive model is the Smith-Watson-Topper parameter model (Smith-Watson-Topper, SWT); the SWT model combines the contributions of elastic and plastic strain energy, and can better describe medium and high cycle fatigue behavior, especially suitable for situations with average stress or stress asymmetry.

[0025] in, is the fatigue ductility index, usually a negative value; is the total strain amplitude; is the elastic modulus; is the local maximum stress.

[0026] S102, determining a basic fatigue characteristic curve according to a fatigue constitutive model of the bolt based on material level and material experimental data, and performing stress analysis on the real structure of the bolt using a finite element analysis method to determine a stress concentration factor; the basic fatigue characteristic curve includes: a stress-life curve and a strain-life curve; S102 specifically includes: Using the formula Determining Stress Concentration Factors ; in, is the theoretical stress concentration factor, obtained through thread geometry parameters or finite element analysis, is the plasticity influence coefficient. For bolts made of high-strength steel, .

[0027] in, is the nominal stress, , is the external load, i.e. the axial force acting on the bolt, It is the force-bearing cross-sectional area of ​​the bolt, usually the cross-sectional area of ​​the bolt shank.

[0028] Assume that the geometric parameters of a bolt are the nominal diameter of the thread and thread bottom diameter ; Among them, using the formula Determine the theoretical stress concentration factor; Calculate the actual stress concentration factor : ;at this time, is 0.95. Using the formula Determining the local maximum stress ; S103, correcting the fatigue constitutive model based on the material level according to the stress concentration factor and the hydrogen embrittlement correction function to obtain a corrected fatigue constitutive model; The increase of hydrogen content will lead to the fatigue strength coefficient of the material , fatigue ductility coefficient , fatigue strength index and fatigue ductility index These changes reflect the negative impact of hydrogen embrittlement on the fatigue properties of materials. Specifically, the fatigue strength coefficient :Increase in hydrogen content leads to Decreases, indicating that the material's fatigue resistance under high cycle fatigue is weakened; fatigue ductility coefficient :Increase in hydrogen content leads to Decreases, reflecting the decrease in the material's ability to resist plastic deformation; fatigue strength index and fatigue ductility index :Increase in hydrogen content will lead to and The increase in the absolute value of indicates that the sensitivity of stress and strain to fatigue life increases.

[0029] like Figure 2 As shown in the figure, the fatigue life curves were obtained by numerically calculating the fatigue life of bolts with hydrogen contents of 4 ppm, 8 ppm and 12 ppm under different external loads. The results show that the fatigue life of the bolts decreases significantly with the increase of hydrogen content. For example, when the hydrogen content increases from 4 ppm to 12 ppm, the fatigue life decreases by more than an order of magnitude, which indicates that the hydrogen embrittlement effect has a serious impact on the fatigue properties of the bolts, especially at high hydrogen content, the attenuation of fatigue life is particularly obvious. Figure 2 The significant effect of hydrogen on the fatigue behavior of materials is intuitively demonstrated.

[0030] In order to introduce the hydrogen embrittlement effect into the fatigue constitutive model, it is necessary to establish a hydrogen embrittlement correction function affected by hydrogen: Using the formula Determine the hydrogen embrittlement correction function of the fatigue strength coefficient; the hydrogen embrittlement correction function of the fatigue strength coefficient reflects the saturation effect of hydrogen content on the fatigue strength coefficient. Figure 3 As shown, with the increase of hydrogen content, It gradually approaches 1, indicating that the decrease of fatigue strength coefficient approaches saturation.

[0031] Using the formula Determine the hydrogen embrittlement correction function for the fatigue ductility factor; e.g. Figure 3As shown in Figure 3, the hydrogen embrittlement correction function of the fatigue ductility coefficient describes the exponential decay effect of hydrogen content on the fatigue ductility coefficient, reflecting the influence of hydrogen on the material's ability to resist plastic fatigue deformation.

[0032] use and formula Determine the hydrogen embrittlement correction function of the fatigue strength index and the hydrogen embrittlement correction function of the fatigue ductility index; such as Figure 3 As shown in the figure, the hydrogen embrittlement correction function of the fatigue strength index and the hydrogen embrittlement correction function of the fatigue ductility index are logarithmic correction functions. The logarithmic correction function can describe the nonlinear effect of hydrogen content on the fatigue strength index and fatigue ductility index, especially in the low hydrogen content range. Approximately linear, the growth rate slows down as the hydrogen content increases; Based on the existing correction parameter fitting of fatigue strength coefficient, fatigue ductility coefficient, fatigue strength index and fatigue ductility index, a new correction parameter for section shrinkage is added. and elongation after fracture Correction parameters for fitting, such as Figure 4 Part (a) and Figure 4 As shown in part (b); Use nonlinear least squares methods, such as the Newton-Raphson method or the Levenberg-Marquardt algorithm, to minimize the error between the theoretical and experimental values ​​and solve for the correction parameters ; By calculating the coefficient of determination , mean square error (MSE) and other statistical indicators are used to evaluate the fitting accuracy of the correction function. An independent validation data set is used to ensure the applicability and accuracy of the correction parameters.

[0033] Take the correction of fatigue strength coefficient as an example: For each hydrogen content ,have: ; make: ; Will and Substituting the data into the nonlinear regression method, we can get and The fitting process of other correction parameters is similar, and they are determined by independent equation sets. , , , , , .

[0034] To ensure that the corrected fatigue constants accurately reflect the hydrogen embrittlement effect, additional experimental data are needed for verification. The specific method includes: selecting hydrogen content and fatigue life data that are not involved in the fitting, substituting them into the correction function to calculate the theoretical value, comparing them with the experimental value, and evaluating the prediction ability of the model. Calculate the deviation between the predicted value and the experimental value, and evaluate the prediction accuracy of the model (such as mean square error, mean absolute error, etc.). Judge the reliability and scope of application of the correction function through statistical indicators, and adjust the form of the correction function or refit the parameters if necessary.

[0035] In order to further accurately describe the influence of hydrogen embrittlement on the fatigue properties of materials, the section shrinkage rate ( ) and elongation after fracture ( ) is used as a direct correction factor, and the specific correction function is as follows: ; ; in, and Respectively, the cross-sectional shrinkage and elongation after fracture under the condition of no hydrogen filling These correction functions reflect the direct effect of hydrogen content on the plastic deformation capacity and cross-sectional shrinkage of the material. By establishing the hydrogen effect correction function, the model can quantitatively describe the effect of hydrogen content on the fatigue performance of the material. in, is the fatigue strength coefficient under the condition of no hydrogen charging, , is the correction parameter of fatigue strength coefficient, is the fatigue ductility coefficient under the condition of no hydrogen charging, , is the correction parameter of fatigue ductility coefficient, is the fatigue strength index under the condition of no hydrogen charging, is the fatigue ductility index under non-hydrogen charging conditions, , is the correction parameter of fatigue strength index, , is the correction parameter of fatigue ductility index, is the cross-sectional shrinkage under the condition of no hydrogen filling, is the elongation after fracture under the condition of no hydrogen charging, , is the correction parameter of the section shrinkage rate, , It is the correction parameter of elongation after fracture.

[0036] in, is the hydrogen embrittlement correction function of the fatigue strength coefficient, is the hydrogen content in the bolt, is the hydrogen embrittlement correction function of the fatigue ductility coefficient, is the hydrogen embrittlement correction function of the fatigue strength index, is the hydrogen embrittlement correction function of the fatigue ductility index, is the hydrogen embrittlement correction function of the cross-sectional shrinkage rate, is the hydrogen embrittlement correction function of the elongation after fracture.

[0037] Furthermore, using the formula Determine the modified fatigue constitutive model; The modified fatigue constitutive model is about The nonlinear equation can be solved by numerical method. The specific steps are as follows: Initial guess: Setting As an estimate of the initial fatigue life.

[0038] Iterative calculation: Use the Newton-Raphson method or other appropriate numerical iteration method to update Values: ; in, This is the difference between the left side and the right side of the equation: ; for right The derivative of : ; Convergence judgment: When (set tolerance), stop the iteration and take as the ultimate fatigue life.

[0039] Assuming all parameters are known, solve according to the above method : Setting parameters , , , ; Correction parameters , ; Hydrogen content ; External load , cross-sectional area ; Elastic modulus .

[0040] Calculate the corrected fatigue constant ; ; calculate , Similarly. Finally, calculate the stress concentration factor And obtain and strain amplitude Finally, the nonlinear equation is established and solved, and the iterative solution is obtained .

[0041] S104, according to the modified fatigue constitutive model, using the nonlinear cumulative damage theory, determine the nonlinear cumulative damage model; Fatigue damage is the process in which the internal structure of a material gradually deteriorates under cyclic loads, eventually leading to fracture. The cumulative damage model is used to quantify this degradation process to predict the fatigue life of a material or component. The classic Miner rule assumes that damage accumulates linearly at each stress level, while the nonlinear cumulative damage model takes into account the nonlinear characteristics of damage accumulation at different stress levels.

[0042] The expression of linear Miner rule is: ; in, is the total cumulative damage; For the The actual number of cycles at each stress level; For the Fatigue life at a given stress level.

[0043] The expression of the nonlinear cumulative damage model is: ; in, is the nonlinear cumulative damage index, usually ; Other symbols are the same as Miner rules.

[0044] The nonlinear cumulative damage model is based on the following assumptions and theoretical foundations: The nonlinearity of damage accumulation, that is, the damage accumulation rate is different at different stress levels, and the damage accumulation rate is faster at high stress levels. Consideration of interactive effects, that is, cycles at different stress levels may affect each other, leading to the complexity of damage accumulation. The influence of material and environmental factors, such as the acceleration of hydrogen embrittlement on damage accumulation.

[0045] The key to the nonlinear cumulative damage model is to determine the nonlinear exponent And the correction parameters and correction parameters These parameters are usually obtained by fitting experimental data.

[0046] in, The value of reflects the nonlinear degree of damage accumulation. It is determined by the following steps: fatigue tests are carried out at different stress levels to obtain and . Use the least squares method or other optimization algorithms to fit the experimental data to determine . Correction parameters and correction parameters It is used to reflect the influence of hydrogen embrittlement on cumulative damage. The corresponding determination steps include: obtaining different hydrogen contents Sectional shrinkage and elongation after fracture By fitting the experimental data through nonlinear regression method, we can determine and An independent validation dataset was used to evaluate the accuracy and applicability of the modified parameters.

[0047] The application of nonlinear cumulative damage model for fatigue life prediction usually includes the following steps: The rain flow counting method is used to convert the actual bolt loading load time series into a stress-time curve, identify closed stress cycles, and count the stress amplitudes. and the corresponding number of cycles For each stress amplitude , perform the following calculations: Calculate strain amplitude : ; Apply the modified SWT model to build The equation is: ; Numerical solution of fatigue life : The Newton-Raphson method is used to numerically iterate the above nonlinear equations and we get Using the nonlinear cumulative damage model, the total cumulative damage is calculated : ; in, is the total number of different stress levels; For the Number of cycles at each stress level; For the Fatigue life at each stress level; , Hydrogen content The section shrinkage and elongation after fracture under , It is the section shrinkage and elongation under the condition of no hydrogen charging; , , To correct the parameters.

[0048] Assume that there is stress levels. The local maximum stress at each stress level is: , , , . The corresponding number of cycles: , , , . Material parameters: , , , , . Hydrogen embrittlement correction parameters: , , , , , . Nonlinear index: .

[0049] For each stress level , calculate the strain amplitude: ; Apply the modified SWT model to build The equation is: ; Numerical solution of fatigue life By solving the above equations using numerical methods such as the Newton-Raphson method, we can obtain .

[0050] Calculate the total cumulative damage: ; According to the cumulative damage value Determine whether the component has reached or exceeded fatigue failure conditions.

[0051] When the cumulative damage value When the bolts have not reached fatigue failure conditions, they continue to serve; When the cumulative damage value When the bolt reaches fatigue failure condition, its service life is exhausted; When the cumulative damage value When the bolt exceeds the fatigue failure condition, take measures.

[0052] S105, based on material experimental data, the Bayesian statistical method is used to optimize the nonlinear cumulative damage model.

[0053] The Bayesian statistical method is based on Bayes' theorem and updates the understanding of parameters by combining prior information and observed data. Its core idea is to use known information (prior distribution) and newly acquired data (likelihood function) to infer the posterior distribution of parameters.

[0054] like Figure 5 As shown in the figure, the Bayesian method shows high flexibility in dealing with uncertainty and small amounts of data, and is particularly effective in complex material systems. This figure compares the range of parameter uncertainty before and after Bayesian inference by visualizing the sampling trajectory and posterior probability density function of model parameters (such as α1, β1, γ1). The results show that the model significantly reduces the estimated uncertainty of parameters by combining observed data, thereby improving the reliability of fatigue life prediction. Figure 5 The comparison between the prior and the posterior clearly demonstrates the impact of data on the estimation of model parameters in the Bayesian framework, making the prediction results more robust and accurate, reflecting the advantages of the Bayesian method in dealing with complex material fatigue problems.

[0055] The energy distribution and parameter sampling autocorrelation of the Bayesian sampler (NUTS algorithm) show that the Markov chain mixing effect is good. When the results show a low lag correlation, it means that there is a high independence between the samples. These results show that the Bayesian sampling process is stable and reliable, and the sampling effect is good, thus ensuring the effective estimation of model parameters and the reliability of model results.

[0056] The expression of Bayes' theorem is as follows: ; in, is the posterior distribution, indicating the parameter under the observed data data distribution. is the likelihood function, which means that The probability of the next observed data data occurring. is the prior distribution, which means the parameters are calculated before observing the data. cognition. is the marginal likelihood and serves as a normalization constant to ensure that the probability density of the posterior distribution integrates to 1.

[0057] The prior distribution reflects the assumption about the parameter before any observations are made. The prior distribution can be set based on historical data, theoretical analysis, or engineering experience. The likelihood function is based on given parameters The probability of the observed data occurring when . It reflects the degree of match between the model and the observed data. The posterior distribution combines the prior information and the observed data to provide a parameter under the condition of the observed data. In the fatigue life prediction of high-strength bolts, accurate estimation of model parameters and uncertainty quantification are crucial to the accuracy and reliability of prediction. Bayesian statistical methods provide a systematic theoretical framework for this process.

[0058] Fatigue strength factor Prior distribution of : ; in, is the mean value of fatigue strength coefficient; is the variance of the fatigue strength coefficient. Fatigue ductility coefficient Prior distribution of : ; in, is the mean value of fatigue ductility coefficient; is the variance of the fatigue ductility coefficient. Correction parameters , The prior distribution of ; ; in, represents uniform distribution; and It is the value range of the parameter, which is set based on physical meaning and engineering experience.

[0059] Nonlinear cumulative damage index Prior distribution of : ; in, is the mean value of the nonlinear cumulative damage index; is the variance of the nonlinear cumulative damage index.

[0060] The rationality test of the prior distribution needs to ensure that the prior distribution reflects reasonable physical meaning and the parameter value range is within the practical possible range. The rationality of the prior distribution can be verified through sensitivity analysis and expert evaluation. Likelihood function Reflects the given parameters In this study, it is assumed that there is an error between the observed value of fatigue life and the model predicted value. , and the error follows a normal distribution.

[0061] Error model: ; Take the logarithm: ; in, ; The expression of the likelihood function is: ; in, is the number of observations; is the variance of the observation error, which is usually estimated through experimental data or included in the model as a parameter to be estimated.

[0062] Error variance The determination of can be estimated based on the accuracy of experimental measurement, or As a parameter to be estimated, it is included in the Bayesian analysis. Usually it is impossible to express analytically, so the Markov Chain Monte Carlo (MCMC) method is used for numerical solution. Basic steps for solving using No-U-Turn Sampler Initialization parameters : Choose an initial parameter value that is as close as possible to the mean of the prior distribution.

[0063] The iterative sampling process of the NUTS method is: Prior distribution setting: ; Likelihood function: Assuming that the observation error follows a log-normal distribution, the likelihood function is: ; in, The fatigue life predicted by the model depends on the parameters .

[0064] NUTS combines the advantages of Hamiltonian Monte Carlo (HMC) by introducing potential energy and kinetic energy , construct the Hamiltonian system: ; in, is the model parameter. is an auxiliary momentum variable, usually following a normal distribution , is the mass matrix, which is often set to the identity matrix.

[0065] HMC simulates trajectories in parameter space through Hamiltonian dynamics, achieving efficient parameter exploration. However, traditional HMC requires manual adjustment of step size. and jump steps , which is more difficult in high-dimensional and complex models. NUTS automatically avoids U-turns and improves sampling efficiency by adaptively adjusting the step size and number of jumps.

[0066] For each parameter Introducing Momentum , the momentum follows a normal distribution: ; Constructing a Hamiltonian system specifically involves: 1. Define potential energy and kinetic energy: ; ; 2. Hamiltonian is: ; 3. Dynamic path construction: Using gradient information, Leapfrog method is used to simulate Hamiltonian dynamics and generate candidate parameter paths. NUTS automatically determines the path length , avoiding U-turns in the path.

[0067] 4. Sampling candidate parameters: Randomly select a new parameter sample in the constructed path .

[0068] 5. Accept or reject; 6. Calculate the change in Hamiltonian: ; 7. The probability of acceptance is: ; Generate uniform random numbers ,like ,accept As a new sample, otherwise keep the current sample .

[0069] 8. Iterative update: Repeat the above steps to generate samples of the posterior distribution of the parameters.

[0070] The core of NUTS is to effectively explore the parameter space through Hamiltonian dynamics and avoid inefficient random walks. The specific formulas include: : ; ; ; ; ; ; Through the above steps, NUTS can efficiently sample from complex posterior distributions and generate high-quality parameter samples.

[0071] Ensuring that the sampling process has fully explored the posterior distribution and reached convergence is a key step in MCMC sampling. Common methods for judging convergence include monitoring the autocorrelation of samples and calculating the Gelman-Rubin diagnostic index. .

[0072] Autocorrelation measures the degree of dependence between samples. Low autocorrelation indicates high sample independence. High autocorrelation will reduce the effective sample size and affect the accuracy of parameter estimation.

[0073] For parameters , the autocorrelation function is defined as: ; in, is the lag order. By drawing the ACF graph, the autocorrelation of the sample can be intuitively judged.

[0074] Effective sample size The calculation formula is: ; in, is the total sample size. The larger the ESS is, the higher the sample independence is.

[0075] The Gelman-Rubin diagnostic assesses convergence between and within chains by comparing the variances of multiple independent chains. Multiple independent MCMC chains are run, each generating Calculate the sample mean for each chain and the sample variance . Calculate the overall mean of all chains and variance : ; Estimating population variance : ; Calculate the Gelman-Rubin index: ; like , the chain is considered to have converged to the target posterior distribution.

[0076] After obtaining a sufficient number of posterior samples, the parameters can be statistically analyzed to quantify the uncertainty.

[0077] Posterior mean and variance: ; ; in, Indicates samples, is the total number of samples.

[0078] Using the percentile method, calculate the 95% confidence interval for the parameter: ; in, Representation parameters In the The value at the quantile.

[0079] Calculate the correlation coefficient matrix between parameters and identify highly correlated parameters, which may require model simplification or parameter reconstruction.

[0080] For each parameter sample , calculate the corresponding fatigue life prediction value Through statistical analysis, we obtain The posterior distribution of , and then calculate its mean, variance and confidence interval. Calculate the posterior distribution of each parameter and analyze its central tendency and dispersion. The uncertainty distribution can provide an intuitive understanding of the failure probability at different life levels.

[0081] Known observation data: fatigue test samples, and the observed fatigue life of each sample is ,in . Model parameters: Fatigue strength coefficient , fatigue ductility coefficient , correction parameters , , nonlinear cumulative damage index , correction parameters , .

[0082] According to the mechanical properties of materials and engineering experience, the prior distribution of each parameter is set: ; ; ; ; ; Assuming that the observation error follows a normal distribution, the likelihood function is: ; The Metropolis-Hastings algorithm is used for iterative sampling to generate samples of the posterior distribution of the parameters. .

[0083] Compute the posterior mean: ; Compute the posterior variance: ; Calculate the 95% confidence interval: ; For each parameter sample , calculate the corresponding fatigue life prediction value Through statistical analysis, we obtain The posterior distribution of is then used to calculate its mean, variance, and confidence interval.

[0084] The posterior distribution of each parameter is displayed through the parameter posterior distribution histogram to analyze its central tendency and dispersion. The probability density function (PDF) and cumulative distribution function (CDF) of the fatigue life are calculated and visualized. The uncertainty distribution of the failure probability at different life levels can be intuitively understood.

[0085] Uncertainty quantification in probabilistic prediction of fatigue life is crucial in fatigue life prediction. Through the posterior distribution, the probability of component failure at different numbers of cycles can be evaluated.

[0086] ; in, Indicates the target life of the component reliability.

[0087] In reliability assessment, a target life can be set , calculate the reliability of the bolt under this life: ; in, ; ; By setting different reliability levels (such as 90%, 95%), the design life or replacement cycle of the bolt is determined to ensure its safety during service.

[0088] This application is not only applicable to the fatigue life prediction of high-strength bolts, but also applicable to fatigue assessment of other hydrogen-affected structural parts (such as offshore platform bolts, pipeline joints, wind power tower connection bolts, etc.) by adjusting geometric parameters, material properties and hydrogen content distribution, thereby providing a theoretical basis for the design, maintenance and safety management of related structures. Through the comprehensive application of Bayesian statistics, nonlinear cumulative damage theory and stress concentration analysis, this application can dynamically correct the local stress level and damage rate function. At the same time, the experimental data is used to update the parameters a posteriori, and the hydrogen embrittlement effect and stress concentration effect are coupled to consider, ultimately providing a reliable technical means for the accurate prediction and uncertainty quantification of the remaining fatigue life of bolts (or other hydrogen-sensitive structural parts) in a complex environment.

[0089] It can be seen that the technical effects of this application are: 1. Comprehensive uncertainty quantification and parameter updating: By using Bayesian statistical methods, we can combine prior information with limited and imperfect experimental data to quantitatively characterize the uncertainty of parameters with posterior distribution. By sampling the posterior distribution with the Markov Chain Monte Carlo (MCMC) method, we can more effectively explore the true distribution range of material properties, hydrogen content, and load conditions in the parameter space. This not only improves the robustness of the model for parameter estimation under limited data conditions, but also makes the predicted fatigue life results have a credibility interval, thus providing a more reliable reference for engineering decision-making.

[0090] 2. Dynamically integrate prior and observed data to achieve model adaptability: When new experimental data or field monitoring data (such as bolt strain, stress and hydrogen content measurement results) appear, the Bayesian update mechanism can timely correct the posterior distribution and dynamically update the model parameters. In this way, the model can be continuously optimized as the environment and working conditions change, thereby maintaining high prediction accuracy and adaptability under complex and changeable marine service conditions.

[0091] 3. More comprehensive characterization of multi-factor coupling effects: Combining the Bayesian statistical method with the nonlinear cumulative damage theory and stress concentration analysis, the hydrogen embrittlement effect, stress concentration and the nonlinear accumulation process of fatigue damage can be comprehensively modeled. Compared with the deterministic model that only relies on the traditional SN curve, the method proposed in this application can better handle multiple uncertainties and complex coupling effects, and improve the accuracy and robustness of fatigue life prediction.

[0092] This application establishes a high-cycle fatigue life assessment model for high-strength bolts that comprehensively considers the effects of hydrogen embrittlement, stress concentration effects, nonlinear cumulative damage, and fatigue performance conversion from material level to component level. It requires in-depth understanding and application of multiple fatigue theories and related mechanical principles, including fatigue constitutive relations, hydrogen embrittlement effects, stress concentration effects, nonlinear cumulative damage theory, and fatigue performance conversion methods from material level to component level. Through systematic theoretical analysis and formula derivation, the theoretical framework of the model is constructed, laying a solid theoretical foundation for the establishment of subsequent models and parameter solution.

[0093] The following specific embodiments illustrate that the present application combines stress concentration effect, nonlinear cumulative damage theory and Bayesian statistical method to construct a comprehensive model to systematically predict the fatigue life of bolts in complex service environments, which has high applicability and reliability; Example 1: Calculation of stress concentration factor In this embodiment, the geometric characteristics of the bolt are firstly analyzed by finite element analysis (FEA) to calculate the stress concentration factor ( The geometric parameters of the bolt include the nominal thread diameter ( ) and thread bottom diameter ( ). The theoretical stress concentration factor is obtained through finite element simulation , and its calculation formula is: ; Actual stress concentration factor The correction factor Make adjustments, The value of : ; Through accurate geometric modeling and load application, the local stress distribution of the bolt under complex loading conditions was simulated, thus providing the necessary input parameters for subsequent fatigue life assessment.

[0094] Example 2: Application of nonlinear cumulative damage theory Based on the stress concentration analysis, this embodiment uses nonlinear cumulative damage theory to describe the fatigue damage accumulation process of the bolt. The fatigue life of the high-strength bolt is quantified using a modified fatigue constant for the damage evolution at different hydrogen contents.

[0095] Fatigue strength coefficient ( ) and fatigue ductility coefficient ( ) are hydrogen contents The nonlinear function is corrected by the following formulas: ; ; In addition, the fatigue strength index ( ) and fatigue ductility index ( ) is also affected by the hydrogen content, and its correction formula is as follows: ; ; By combining the above modified fatigue constant with the local maximum stress , the fatigue life performance of bolts under different hydrogen contents and stress levels can be obtained.

[0096] Example 3: Calculation of fatigue life In this embodiment, based on the corrected fatigue constant and stress level, the Brentq root-finding method is used to numerically solve the fatigue life ( ) is solved. The cumulative damage function of the cumulative damage theory is defined as follows: ; in, is the damage rate function, represents the time variation of stress level, is the hydrogen content. Fatigue life Meet the cumulative damage The conditions are obtained by numerical solution method.

[0097] In the specific calculation, the local maximum stress is first calculated by the stress concentration factor. , and then combined with the modified fatigue strength coefficient and fatigue ductility coefficient, the fatigue life is solved using the following equation: ; Fatigue life The solution equation is: ; The above equations are numerically solved by the Brentq root-finding method to obtain the fatigue life of the bolt under different conditions.

[0098] Example 4: Application of Bayesian statistical methods In order to improve the accuracy of fatigue life prediction, this embodiment introduces the Bayesian statistical method to optimize the model parameters. By measuring the fatigue life experimental data of the bolts and combining prior information, a Bayesian statistical model is constructed to quantify the uncertainty of material parameters.

[0099] Specifically, the prior distribution is constructed based on previous experimental experience data, and the observed data comes from fatigue test results under different hydrogen contents and stress levels. The Bayesian update process is implemented through the Markov Chain Monte Carlo (MCMC) method, which samples the posterior distribution and obtains the posterior estimate of the model parameters. The mathematical expression of the Bayesian update process is: ; in, is the prior distribution, is the likelihood function. The posterior distribution is sampled by the MCMC method to achieve dynamic update of model parameters, thereby significantly improving the accuracy of fatigue life prediction.

[0100] Example 5: Experimental verification and result visualization In order to verify the effectiveness of the proposed method, a series of experiments were conducted. The fatigue life of the bolts was measured under different hydrogen contents (4 ppm, 8 ppm and 12 ppm) and different external loads (4000 N to 7000 N), and the experimental results were compared with the model predictions. The experimental results show that the proposed model can accurately capture the trend of the influence of hydrogen content on fatigue life, especially under high hydrogen content conditions, the model predictions are highly consistent with the experimental measurement results.

[0101] In order to more intuitively demonstrate the prediction ability of this application, the following Figure 2 The fatigue life curves under different hydrogen contents are shown in Figure 3 Plot of the relationship between the modified material parameters and the hydrogen content. Figure 2 The relationship between the local maximum stress and fatigue life is presented, and the results show that the fatigue life of the bolts decreases significantly with increasing hydrogen content. Figure 3 The influence of hydrogen embrittlement on key parameters such as fatigue strength coefficient and fatigue ductility coefficient is demonstrated.

[0102] The embodiment of the present application provides a method for dynamically evaluating the fatigue life of bolts, especially considering the coupling effect of hydrogen embrittlement effect and stress concentration effect in complex service environments. By optimizing the parameters of the model through Bayesian statistics, the present application achieves a high-precision evaluation of fatigue life prediction, which is particularly suitable for the design, maintenance and replacement of high-strength bolts in harsh environments such as marine engineering, and provides important technical support and scientific basis for the safety of engineering structures.

[0103] In this embodiment, the fatigue life of high-strength bolts under different hydrogen contents and local maximum stress conditions is calculated and analyzed. By applying the aforementioned modeling method (including stress concentration analysis, nonlinear cumulative damage theory, and Bayesian statistical parameter update) to actual bolt working conditions, fatigue life prediction results under different conditions are obtained. At the same time, the posterior distribution of model parameters is estimated by Bayesian statistical method to quantify uncertainty and improve prediction credibility. The specific calculation process and results are given below.

[0104] The fatigue life of bolts was predicted under the conditions of hydrogen content of 4.0ppm, 8.0ppm and 12.0ppm. Finite element analysis (FEA) and the aforementioned stress concentration factor The calculation method to obtain the local maximum stress On this basis, the fatigue life of the bolt is estimated by using the nonlinear cumulative damage model and the modified fatigue constitutive parameters (corrected according to the hydrogen content). Perform calculations.

[0105] When the hydrogen content H = 4.0 ppm, the bolt life is relatively high at a lower hydrogen content. The specific calculation results are shown in Table 1 (local maximum stress / MPa, fatigue life / number of loops); Table 1

[0106] When the hydrogen content H = 8.0 ppm, the fatigue life of the bolt decreases significantly as the content increases, especially at high stress levels, as shown in Table 2: Table 2

[0107] When the hydrogen content is H = 12.0 ppm, the hydrogen-induced embrittlement effect is more significant at a higher hydrogen content, resulting in a further shortening of the fatigue life, as shown in Table 3: Table 3

[0108] It can be seen from the above results that with the increase of hydrogen content, the fatigue life of the bolt is greatly reduced at the same stress level, which is consistent with the mechanism of hydrogen embrittlement effect, indicating that the method of the present application is highly effective in quantifying hydrogen-induced fatigue life loss.

[0109] The above fatigue life prediction results are combined with experimental data, and the key parameters (such as And the standard deviation of the observation error ) to estimate the posterior distribution. After MCMC sampling is completed, the following statistical results can be obtained, as shown in Table 4: Table 4

[0110] From the parameter estimation results, it can be seen that the posterior distributions of parameters alpha1, beta1 and gamma1 are relatively concentrated, and the Gelman-Rubin diagnostic index (r_hat) is close to 1, indicating that the MCMC chain has converged and the parameter estimation is stable and reliable. By extracting the posterior distribution, the model can provide parameter uncertainty quantification for future life prediction under different working conditions, so that the prediction results have a credible interval. This provides more robust and referenceable decision-making information for engineering practice.

[0111] In summary, this embodiment verifies the effectiveness and robustness of the method of this application for evaluating the fatigue life of high-strength bolts under the influence of hydrogen embrittlement and stress concentration through numerical calculation and Bayesian statistical analysis. The calculation results show that the increase in hydrogen content significantly reduces the fatigue life. Bayesian statistical analysis provides technical means for parameter estimation and quantification of prediction uncertainty, which helps to make more scientific and reliable evaluation and decision-making on the life of bolts in actual engineering.

[0112] Based on the same inventive concept, the embodiment of the present application also provides a bolt hydrogen-induced fatigue life assessment system based on the Bayesian method for implementing the above-mentioned bolt hydrogen-induced fatigue life assessment method based on the Bayesian method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the bolt hydrogen-induced fatigue life assessment system based on the Bayesian method provided below can refer to the limitations of the bolt hydrogen-induced fatigue life assessment method based on the Bayesian method above, and will not be repeated here.

[0113] In an exemplary embodiment, a bolt hydrogen-induced fatigue life assessment system based on a Bayesian method is provided, comprising: A fatigue constitutive model acquisition module is used to acquire a fatigue constitutive model of the bolt based on the material level; the fatigue constitutive model based on the material level is used to characterize the relationship between stress, strain and fatigue life of the bolt under cyclic load; A stress concentration factor determination module is used to determine a basic fatigue characteristic curve according to a fatigue constitutive model of the bolt based on material level and material experimental data, and to perform stress analysis on the real structure of the bolt using a finite element analysis method to determine the stress concentration factor; the basic fatigue characteristic curve includes: a stress-life curve and a strain-life curve; A fatigue constitutive model correction module is used to correct the fatigue constitutive model based on the material level according to the stress concentration factor and the hydrogen embrittlement correction function to obtain a corrected fatigue constitutive model; A nonlinear cumulative damage model determination module is used to determine the nonlinear cumulative damage model based on the modified fatigue constitutive model and the nonlinear cumulative damage theory; The nonlinear cumulative damage model optimization module is used to optimize the nonlinear cumulative damage model based on material experimental data using the Bayesian statistical method.

[0114] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0115] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for evaluating hydrogen-induced fatigue life of bolts based on Bayesian method, characterized in that: The method for assessing hydrogen-induced fatigue life of bolts based on the Bayesian method includes: Obtaining a material-level fatigue constitutive model of the bolt; the material-level fatigue constitutive model is used to characterize the relationship between stress, strain and fatigue life of the bolt under cyclic load; According to the fatigue constitutive model of the bolt based on the material level and the material experimental data, the basic fatigue characteristic curve is determined, and the stress concentration factor is determined by performing stress analysis on the real structure of the bolt using the finite element analysis method; the basic fatigue characteristic curve includes: a stress-life curve and a strain-life curve; The fatigue constitutive model based on the material level is corrected according to the stress concentration factor and the hydrogen embrittlement correction function to obtain a corrected fatigue constitutive model; According to the modified fatigue constitutive model, the nonlinear cumulative damage model is determined by using the nonlinear cumulative damage theory; According to the material experimental data, the Bayesian statistical method is used to optimize the nonlinear cumulative damage model.

2. The method for evaluating hydrogen-induced fatigue life of bolts based on the Bayesian method according to claim 1, characterized in that: The method of obtaining the fatigue constitutive model of the bolt based on the material level specifically includes: Using the formula Determine the material level based fatigue constitutive model for the bolt; in, is the fatigue strength coefficient, which is used to reflect the fatigue strength of the bolt; is the fatigue life; is the fatigue strength index; is the fatigue ductility coefficient, which is used to reflect the anti-plastic fatigue performance; is the fatigue ductility index; is the total strain amplitude, including elastic strain amplitude and plastic strain amplitude; is the elastic modulus; is the local maximum stress.

3. The method for evaluating hydrogen-induced fatigue life of bolts based on the Bayesian method according to claim 2, characterized in that: The method of determining the basic fatigue characteristic curve based on the fatigue constitutive model of the bolt based on the material level and the material experimental data, and performing stress analysis on the real structure of the bolt using the finite element analysis method to determine the stress concentration factor specifically includes: Using the formula Determining Stress Concentration Factors ; in, is the theoretical stress concentration factor, is the plasticity influence coefficient.

4. The method for evaluating hydrogen-induced fatigue life of bolts based on the Bayesian method according to claim 3 is characterized in that: Using the formula Determining the local maximum stress ; in, is the nominal stress, , is the external load, is the load-bearing cross-sectional area of ​​the bolt.

5. The method for evaluating hydrogen-induced fatigue life of bolts based on the Bayesian method according to claim 4, characterized in that: The method of correcting the fatigue constitutive model based on the material level according to the stress concentration factor and the hydrogen embrittlement correction function to obtain the corrected fatigue constitutive model specifically includes: Using the formula Determine the modified fatigue constitutive model; in, is the hydrogen embrittlement correction function of the fatigue strength coefficient, is the hydrogen content in the bolt, is the hydrogen embrittlement correction function of the fatigue ductility coefficient, is the hydrogen embrittlement correction function of the fatigue strength index, is the hydrogen embrittlement correction function of the fatigue ductility index, is the hydrogen embrittlement correction function of the cross-sectional shrinkage rate, is the hydrogen embrittlement correction function of the elongation after fracture.

6. The method for assessing hydrogen-induced fatigue life of bolts based on the Bayesian method according to claim 5, characterized in that: The hydrogen embrittlement correction function specifically includes: Using the formula Determine the hydrogen embrittlement correction function for the fatigue strength coefficient; Using the formula Determine the hydrogen embrittlement correction function for fatigue ductility coefficient; use and formula Determine the hydrogen embrittlement correction function of the fatigue strength index and the hydrogen embrittlement correction function of the fatigue ductility index; Using the formula Determine the hydrogen embrittlement correction function for the reduction of area; Using the formula Determine the hydrogen embrittlement correction function for elongation after fracture; in, is the fatigue strength coefficient under the condition of no hydrogen charging, , is the correction parameter of fatigue strength coefficient, is the fatigue ductility coefficient under the condition of no hydrogen charging, , is the correction parameter of fatigue ductility coefficient, is the fatigue strength index under the condition of no hydrogen charging, is the fatigue ductility index under non-hydrogen charging conditions, , is the correction parameter of fatigue strength index, , is the correction parameter of fatigue ductility index, is the cross-sectional shrinkage under the condition of no hydrogen filling, is the elongation after fracture under the condition of no hydrogen charging, , is the correction parameter of the section shrinkage rate, , It is the correction parameter of elongation after fracture.

7. The method for evaluating hydrogen-induced fatigue life of bolts based on Bayesian method according to claim 1, characterized in that: According to the modified fatigue constitutive model, the nonlinear cumulative damage theory is used to determine the nonlinear cumulative damage model, which includes: According to the modified fatigue constitutive model, the fatigue life is determined by using the Newton-Raphson numerical iteration method. According to fatigue life, a nonlinear cumulative damage model is constructed by adopting nonlinear cumulative damage theory; the nonlinear cumulative damage model is used to characterize the cumulative damage value of the bolt; The fatigue life of the bolt is evaluated based on the cumulative damage value of the bolt.

8. The method for evaluating hydrogen-induced fatigue life of bolts based on the Bayesian method according to claim 7, characterized in that: According to the fatigue life, the nonlinear cumulative damage model is constructed using the nonlinear cumulative damage theory, including: Using the formula Determine the nonlinear cumulative damage model; in, is the cumulative damage value, is the total number of different stress levels, For the The number of cycles at each stress level, For the Fatigue life at each stress level, is the hydrogen embrittlement correction function of the cross-sectional shrinkage rate, is the hydrogen embrittlement correction function of elongation after fracture, is the cross-sectional shrinkage under the condition of no hydrogen filling, is the elongation after fracture under the condition of no hydrogen charging, , , To correct the parameters.

9. The method for assessing hydrogen-induced fatigue life of bolts based on the Bayesian method according to claim 7, characterized in that: The fatigue life of the bolt is evaluated based on the cumulative damage value of the bolt, including: When the cumulative damage value When the bolts have not reached fatigue failure conditions, they continue to serve; When the cumulative damage value When the bolt reaches fatigue failure condition, its service life is exhausted; When the cumulative damage value When the bolt exceeds the fatigue failure condition, take measures.

10. A bolt hydrogen-induced fatigue life assessment system based on Bayesian method, characterized in that: The bolt hydrogen-induced fatigue life assessment system based on the Bayesian method includes: A fatigue constitutive model acquisition module is used to acquire a fatigue constitutive model of the bolt based on the material level; the fatigue constitutive model based on the material level is used to characterize the relationship between stress, strain and fatigue life of the bolt under cyclic load; A stress concentration factor determination module is used to determine a basic fatigue characteristic curve according to a fatigue constitutive model of the bolt based on material level and material experimental data, and to perform stress analysis on the real structure of the bolt using a finite element analysis method to determine the stress concentration factor; the basic fatigue characteristic curve includes: a stress-life curve and a strain-life curve; A fatigue constitutive model correction module is used to correct the fatigue constitutive model based on the material level according to the stress concentration factor and the hydrogen embrittlement correction function to obtain a corrected fatigue constitutive model; A nonlinear cumulative damage model determination module is used to determine the nonlinear cumulative damage model based on the modified fatigue constitutive model and the nonlinear cumulative damage theory; The nonlinear cumulative damage model optimization module is used to optimize the nonlinear cumulative damage model based on material experimental data using the Bayesian statistical method.

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