LNG ship pipe fitting fatigue life modeling optimization method and system

Abstract

The invention provides an LNG ship pipe fitting fatigue life modeling optimization method and system. The method comprises the steps that a fatigue life simulation model of a low-temperature pipe fitting is established; fatigue state constraint is applied to a fatigue damage structure in the fatigue life simulation model based on the load spectrum and the temperature spectrum at the monitoring node, and a damage constraint condition of the target low-temperature pipe fitting in a vibration state is obtained; determining the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads according to the thermal fatigue damage and load spectrum characteristics of each connection part under different low-temperature thermal cycle loads; cost optimization is carried out on fatigue life control parameters of the fatigue life simulation model according to the damage constraint conditions and all the thermal fatigue losses, optimal cost parameters for fatigue life simulation under the thermal-vibration load working condition are obtained, and the local grid density of the fatigue life simulation model is adjusted based on the optimal cost parameters. By the adoption of the scheme, parameter cost optimization of the fatigue life simulation model of the LNG ship pipe fitting under the thermal-vibration load working condition can be achieved.

Description

Technical Field

[0001] The present application relates to the field of dynamic modeling technology, and more specifically, to a method and system for optimizing fatigue life modeling of LNG ship pipe fittings. Background Art

[0002] In LNG ships, cryogenic pipes are prone to fatigue damage due to the extreme temperature differences and alternating loads under complex sea conditions, which affects the safe operation of the entire ship. In order to accurately predict their service life, it is necessary to construct a dynamic fatigue life simulation model that can simultaneously characterize the thermal and mechanical coupling effects. Compared with traditional static evaluation methods, dynamic modeling can more realistically simulate the stress state of pipes under actual wave-induced vibrations and low-temperature thermal cycle load conditions, capture the influence of multi-source variables on fatigue response over time, and thus improve the prediction accuracy and modeling adaptability of the fatigue life of pipes.

[0003] Existing static modeling technology mainly relies on fixed operating parameters to predict the fatigue life of pipe fittings, ignoring the multivariable linkage fatigue effect caused by the thermal-vibration coupled loads that the pipe fittings are subjected to during long-term service. The modeling process cannot respond to the time-varying characteristics of vibration frequency, thermal cycle changes and local response of the connection structure in real time, resulting in inaccurate simulation of fatigue damage distribution. In addition, static modeling often lacks a dynamic feedback mechanism for monitoring data and cannot effectively reflect the fatigue response evolution path of the structure under actual wave excitation, resulting in deviations in fatigue life prediction and making it difficult to meet the engineering requirements of LNG ships for high-precision fatigue life management. By introducing a dynamic response modeling method based on coupled load monitoring data, adaptive regulation of the local characteristics of the model can be achieved. Therefore, how to achieve parameter cost optimization of the fatigue life simulation model of LNG ship pipe fittings under thermal-vibration load conditions has become a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides a fatigue life modeling optimization method and system for LNG ship pipe fittings, which can realize parameter cost optimization of the fatigue life simulation model of LNG ship pipe fittings under thermal-vibration load conditions.

[0005] In a first aspect, the present application provides a fatigue life modeling and optimization method for LNG ship pipe fittings, comprising the following steps: Monitoring nodes are deployed at each connection point of the target cryogenic pipe fittings, and a pipe-temperature-load coupled fatigue life simulation model is established based on the material parameters, connection structure, and operating parameters of the target cryogenic pipe fittings. When the target cryogenic pipe is in a vibration state induced by wave loads, the cross-correlation coefficients of the fatigue response characteristics of the pipe between different monitoring nodes are determined based on the load spectrum and temperature spectrum at each monitoring node. Then, fatigue state constraints are applied to the fatigue damage structure in the fatigue life simulation model based on all the cross-correlation coefficients to obtain the damage constraint conditions of the target cryogenic pipe under the vibration state. Apply different low-temperature thermal cycle loads to the fatigue life simulation model, extract the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads, and combine all thermal fatigue damage and the load spectrum characteristics of different low-temperature thermal cycle loads to determine the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads; According to the damage constraint conditions and all thermal fatigue losses, the fatigue life control parameters of the fatigue life simulation model are optimized, and the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions are obtained. Then, the local grid density of the fatigue life simulation model is adjusted based on the optimal cost parameters.

[0006] Preferably, determining the correlation coefficients of fatigue response characteristics of pipe fittings between different monitoring nodes based on the load spectrum and temperature spectrum at each monitoring node specifically includes: Extracting the stress response sequence and temperature response sequence of each monitoring node under the wave load-induced vibration, and then constructing the load spectrum and temperature spectrum at each monitoring node; The cross-correlation analysis of the load spectrum and temperature spectrum between different monitoring nodes is performed to obtain the cross-correlation coefficients of the fatigue response characteristics of the pipe fittings between different monitoring nodes.

[0007] Preferably, fatigue state constraints are applied to the fatigue damage structure in the fatigue life simulation model based on all the mutual correlation coefficients to obtain damage constraint conditions of the target cryogenic pipe under the vibration state, specifically including: Construct a cross-correlation coefficient matrix based on all cross-correlation coefficients; Perform correlation partitioning on all monitoring nodes according to the mutual correlation coefficient matrix to obtain multiple monitoring node groups with different correlation characteristics; Perform graded fatigue constraints on the connection parts of monitoring nodes in different monitoring node groups to obtain a set of damage distribution conditions for the connection parts of each monitoring node; All damage distribution condition sets are integrated to obtain the damage constraint conditions of the target cryogenic pipe under the vibration state.

[0008] Preferably, extracting thermal fatigue damage of each connection part under different low-temperature thermal cycle loads specifically includes: Obtain stress-strain hysteresis curves of each connection under different low-temperature thermal cycle loads from the fatigue life simulation model; The thermal fatigue damage of each connection part under different low-temperature thermal cycle loads is determined based on all stress-strain hysteresis curves.

[0009] Preferably, determining the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads by combining all thermal fatigue damage and load spectrum characteristics of different low-temperature thermal cycle loads specifically includes: Determine the difference in thermal fatigue response between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the theoretical thermal fatigue damage; Constructing a mapping relationship vector between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the load spectrum characteristics of the low-temperature thermal cycle load; Extracting a weighting factor of the thermal fatigue response difference corresponding to each low-temperature thermal cycle load according to the mapping relationship vector; Based on all weighting factors, the differences in thermal fatigue responses under various low-temperature thermal cycle loads are weighted and fused to obtain the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads.

[0010] Preferably, the fatigue life control parameters of the fatigue life simulation model are optimized according to the damage constraint conditions and all thermal fatigue losses, and the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions are obtained, specifically including: The fatigue life control parameters in the fatigue life simulation model are used as optimization variables, and a cost function model is constructed with the goal of minimizing thermal fatigue loss and satisfying damage constraint conditions; Inputting the damage constraint condition and the thermal fatigue loss under each thermal cycle load into the cost function model as penalty terms; The cost function in the cost function model is iteratively solved by a preset optimization algorithm to obtain the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions.

[0011] Preferably, the material parameters specifically include elastic modulus, Poisson's ratio, yield strength, tensile strength, thermal expansion coefficient, fatigue limit and material fatigue curve.

[0012] In a second aspect, the present application provides a fatigue life modeling and optimization system for LNG ship pipe fittings, comprising: Model building module, used to establish a pipe-temperature-load coupled fatigue life simulation model based on the material parameters, connection structure and working condition parameters of the target low-temperature pipe; a processing module for determining, when the target cryogenic pipe is in a vibration state induced by wave loads, the cross-correlation coefficients of the fatigue response characteristics of the pipe between different monitoring nodes based on the load spectrum and the temperature spectrum at each monitoring node, and then applying fatigue state constraints to the fatigue damage structure in the fatigue life simulation model based on all the cross-correlation coefficients to obtain damage constraint conditions for the target cryogenic pipe under the vibration state; The processing module is further configured to load different low-temperature thermal cycle loads on the fatigue life simulation model, extract thermal fatigue damage of each connection part under different low-temperature thermal cycle loads, and determine thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads by combining all thermal fatigue damage and load spectrum characteristics of different low-temperature thermal cycle loads; An execution module is used to perform cost optimization on fatigue life control parameters of the fatigue life simulation model according to the damage constraint conditions and all thermal fatigue losses, obtain optimal cost parameters for fatigue life simulation under thermal-vibration load conditions, and then adjust the local grid density of the fatigue life simulation model based on the optimal cost parameters.

[0013] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned LNG ship pipe fatigue life modeling optimization method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned LNG ship pipe fatigue life modeling optimization method.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In an embodiment of the present application, monitoring nodes are first arranged at each connection part of the target cryogenic pipe, and a pipe-temperature-load coupled fatigue life simulation model is established based on the material parameters, connection structure and operating condition parameters of the target cryogenic pipe; when the target cryogenic pipe is in a vibration state induced by a wave load, the mutual correlation coefficients of the fatigue response characteristics of the pipe between different monitoring nodes are determined based on the load spectrum and temperature spectrum at each monitoring node, and then fatigue state constraints are imposed on the fatigue damage structure in the fatigue life simulation model based on all the mutual correlation coefficients to obtain damage constraint conditions for the target cryogenic pipe under the vibration state; different low-temperature thermal cycle loads are loaded on the fatigue life simulation model, and the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads is extracted, and the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads is determined by combining all thermal fatigue damage and load spectrum characteristics of different low-temperature thermal cycle loads; the fatigue life control parameters of the fatigue life simulation model are cost-optimized according to the damage constraint conditions and all thermal fatigue losses to obtain the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions, and then the local grid density of the fatigue life simulation model is adjusted based on the optimal cost parameters.

[0016] It can be seen that this application optimizes the fatigue life control parameters of the fatigue life simulation model through damage constraint conditions and thermal fatigue losses of fatigue life simulation under different low-temperature thermal cycle loads, and obtains the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions, and then adjusts the local grid density of the fatigue life simulation model based on the optimal cost parameters; first, fatigue state constraints are imposed on the fatigue damage structure in the fatigue life simulation model according to all the mutual correlation coefficients, and damage constraint conditions of the target low-temperature pipe under the said vibration state are obtained. The damage constraint conditions can give the simulation model the ability to express the correlation relationship between structures, so that the model no longer processes single-point responses in isolation, but can understand the fatigue evolution characteristics of the pipe structure from a global coupling perspective, thereby effectively improving the modeling accuracy of damage distribution and evolution path; then, by loading multiple sets of low-temperature thermal cycle loads and extracting the thermal fatigue damage of each connection part, different working conditions can be achieved. The fatigue response under different conditions is differentiated and identified, and the thermal fatigue loss law is further extracted in combination with the load spectrum characteristics, so that the fatigue life simulation model can accurately capture the damage growth mechanism and evolution trend under thermal cycle loads; finally, based on the damage constraint conditions and the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads, the fatigue life control parameters are cost-optimized, so that the modeling process can dynamically match the optimal control parameters, and the resolution of the fatigue life simulation model for high damage areas is improved by adjusting the local grid density, thereby achieving the coordinated optimization of global modeling efficiency and local accuracy; in summary, this scheme can realize the parameter cost optimization of the fatigue life simulation model of LNG ship pipe fittings under thermal-vibration load conditions, and can effectively make up for the technical shortcomings of static modeling, such as poor adaptability to complex thermal-vibration coupling conditions and insufficient damage prediction ability, thereby providing a more engineering-applicable dynamic modeling scheme for fatigue life prediction of LNG ship low-temperature pipe fittings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a fatigue life modeling and optimization method for LNG ship pipe fittings according to some embodiments of the present application; Figure 2 This is an application workflow of the ship pipe fatigue life modeling and optimization system shown in some embodiments of the present application; Figure 3 is a schematic diagram of a process for determining thermal fatigue loss according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a fatigue life modeling and optimization system for LNG ship pipe fittings according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing a fatigue life modeling optimization method for LNG ship pipe fittings according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a fatigue life modeling optimization method for LNG ship pipe fittings according to some embodiments of the present application. The fatigue life modeling optimization method 100 for LNG ship pipe fittings mainly includes the following steps: In step 101, monitoring nodes are arranged at various connection locations of the target cryogenic pipe, and a pipe-temperature-load coupled fatigue life simulation model is established based on the material parameters, connection structure and operating parameters of the target cryogenic pipe.

[0020] It should be noted that in this application, LNG ship refers to a ship that transports liquefied natural gas (LNG); the connection part in this application refers to the area in the cryogenic pipe where there are changes in the geometric structure of welding, bolting or transition joints, which is prone to fatigue damage due to structural discontinuity; the material parameters in this application refer to the physical properties that describe the mechanical, thermal and fatigue properties of the material under different working conditions, including but not limited to elastic modulus, Poisson's ratio, yield strength, tensile strength, thermal expansion coefficient, fatigue limit and material fatigue curve; the connection structure in this application refers to the connection method and geometric characteristics of various parts in the cryogenic pipe, including welded joints, bolted joints, flange connections and transition joints. These connection forms will affect the force distribution, stress concentration and fatigue damage characteristics of the pipe; the working condition parameters in this application refer to the conditions that affect the performance of cryogenic pipes in the actual working environment, including temperature changes (such as low temperature environment), load conditions (such as wave loads), vibration characteristics (such as frequency, amplitude) and thermal cycling characteristics.

[0021] In some embodiments, reference Figure 2 As shown, this figure is an application workflow of the ship pipe fatigue life modeling optimization system in some embodiments of the present application. At the monitoring site, monitoring nodes are arranged at various connection parts of the cryogenic pipes to collect stress and temperature data of the cryogenic pipes in the actual working environment. The monitoring equipment 110, including sensors and cameras, is installed on the monitoring nodes to monitor and record the stress and temperature changes of the cryogenic pipes in real time, and transmit the monitored data to the data processor 120 for processing and fatigue life simulation analysis. The processed data is sent to the simulation visualization module 130 to display the simulation results in a graphical manner, so that engineers can intuitively understand the fatigue life status of the pipes.

[0022] In specific implementation, first, the following method can be used to arrange monitoring nodes at various connection parts of the target cryogenic pipe fittings, namely: at various connection parts of the target cryogenic pipe fittings, the monitoring nodes are accurately arranged through finite element mesh division to ensure that the stress, temperature and load data of each key part can be accurately collected; then, based on the material parameters, connection structure and operating parameters of the target cryogenic pipe fittings, a pipe-temperature-load coupled fatigue life simulation model can be established in the following method, namely: according to the material properties, connection structure and operating parameters of the target cryogenic pipe fittings, a multi-physics field coupling analysis method is used to establish a fatigue life simulation model of the interaction between the temperature field and the load field in the simulation software (such as COMSOL).

[0023] In step 102, when the target cryogenic pipe is in a vibration state induced by wave loads, the cross-correlation coefficients of the fatigue response characteristics of the pipe between different monitoring nodes are determined based on the load spectrum and temperature spectrum at each monitoring node, and then fatigue state constraints are applied to the fatigue damage structure in the fatigue life simulation model based on all the cross-correlation coefficients to obtain the damage constraint conditions of the target cryogenic pipe in the vibration state.

[0024] It should be noted that the wave load in this application refers to the periodically changing external force generated by the action of waves on the LNG hull structure; in addition, when the target cryogenic pipe is in a wave load-induced vibration state in this application refers to the random vibration condition caused by the periodic action of waves on the target cryogenic pipe during the navigation of the LNG ship. Under this condition, the connection parts of the pipes are prone to periodic stress concentration, enhanced local vibration of the structure, and fatigue damage accumulation caused by thermal-mechanical coupling in a low-temperature environment, thereby becoming high-risk areas for fatigue cracks.

[0025] In some embodiments, determining the correlation coefficients of fatigue response characteristics of pipe fittings between different monitoring nodes based on the load spectrum and temperature spectrum at each monitoring node can be achieved by the following steps: Extracting the stress response sequence and temperature response sequence of each monitoring node under the wave load-induced vibration, and then constructing the load spectrum and temperature spectrum at each monitoring node; The cross-correlation analysis of the load spectrum and temperature spectrum between different monitoring nodes is performed to obtain the cross-correlation coefficients of the fatigue response characteristics of the pipe fittings between different monitoring nodes.

[0026] It should be noted that the load spectrum in this application is a spectral characteristic that describes the stress response intensity distribution of cryogenic pipes under wave load-induced vibration; the temperature spectrum in this application is a spectral characteristic that describes the temperature response intensity distribution of cryogenic pipes under wave load; the cross-correlation coefficient in this application is a statistical indicator that describes the temporal correlation of the fatigue response characteristics of pipes between monitoring nodes.

[0027] In specific implementation, first, the stress response sequence and temperature response sequence of each monitoring node under the vibration induced by wave load are extracted, and then the load spectrum and temperature spectrum at each monitoring node are constructed. This can be achieved in the following way: based on the fatigue life simulation model, the monitoring nodes arranged at each connection position are dynamically simulated and solved under the vibration working condition induced by wave load, and the stress response sequence and temperature response sequence of each monitoring node in the time domain are extracted. The stress response sequence and temperature response sequence extracted from each monitoring node are de-averaged and windowed to reduce spectrum leakage and DC component interference, and then the time domain signal (i.e., stress response sequence and temperature response sequence) is converted into frequency domain signal by fast Fourier transform. The signal is obtained by calculating the modulus of the Fourier coefficient, and the power spectrum density is further extracted to characterize the frequency energy distribution. The main period information is determined by identifying the main frequency component in the amplitude spectrum. Finally, the frequency vector is combined with its corresponding amplitude spectrum, power spectrum density and main period information. The combination corresponding to the stress response sequence is used as the load spectrum at the monitoring node, and the combination corresponding to the temperature response sequence is used as the temperature spectrum at the monitoring node. Then, the load spectrum and temperature spectrum between different monitoring nodes are cross-correlated and analyzed to obtain the cross-correlation coefficient of the fatigue response characteristics of the pipe fittings between different monitoring nodes. This can be achieved in the following way: using the cross-correlation function (CCF) to perform joint statistical analysis on the load spectrum and temperature spectrum of any two monitoring nodes, calculating the correlation strength under different time delays through a sliding window, and extracting the maximum cross-correlation coefficient as the fatigue response correlation index between the monitoring node pair. The maximum cross-correlation coefficient is further normalized, and the coefficient obtained by the normalization is used as the cross-correlation coefficient of the fatigue response characteristics of the pipe fittings between the any two monitoring nodes.

[0028] In some embodiments, fatigue state constraints are applied to the fatigue damage structure in the fatigue life simulation model based on all the mutual correlation coefficients to obtain damage constraint conditions for the target cryogenic pipe under the vibration state. The following steps can be used to achieve this: Construct a cross-correlation coefficient matrix based on all cross-correlation coefficients; Perform correlation partitioning on all monitoring nodes according to the mutual correlation coefficient matrix to obtain multiple monitoring node groups with different correlation characteristics; Perform graded fatigue constraints on the connection parts of monitoring nodes in different monitoring node groups to obtain a set of damage distribution conditions for the connection parts of each monitoring node; All damage distribution condition sets are integrated to obtain the damage constraint conditions of the target cryogenic pipe under the vibration state.

[0029] It should be noted that the hierarchical fatigue constraint in this application refers to the application of hierarchical fatigue constraint control strategies to the connection parts of different areas based on the correlation characteristics between monitoring nodes; the damage constraint conditions in this application refer to a set of judgment conditions used to limit the spatial distribution and degree range of structural damage in fatigue life simulation.

[0030] In the specific implementation, first, the process of constructing a mutual correlation coefficient matrix based on all the mutual correlation coefficients is to arrange the mutual correlation coefficients between each monitoring node according to the spatial position information of the monitoring node to form a symmetrical matrix (i.e., the mutual correlation coefficient matrix). Specifically, each matrix element represents the mutual correlation coefficient between two monitoring nodes, and the rows and columns of the mutual correlation coefficient matrix correspond to the spatial position of each monitoring node respectively. The mutual correlation coefficient matrix obtained by such arrangement can reflect the degree of correlation between each monitoring node; secondly, all monitoring nodes are partitioned according to the mutual correlation coefficient matrix to obtain multiple monitoring node groups with different correlation characteristics. This can be achieved in the following way, namely: using the K-means clustering algorithm (i.e., K-means clustering algorithm). Means algorithm) divides the monitoring nodes into several groups (i.e., monitoring node groups) according to the similarity measurement of the mutual correlation coefficient matrix. The response characteristics of the monitoring nodes in each group under the load and temperature spectrum are similar, which can effectively reflect the fatigue damage correlation between the monitoring nodes. It should be noted that the K-means algorithm can automatically consider the positional relationship between the monitoring nodes. By optimizing the distance measurement function, it can ensure that the monitoring nodes that are close in space and have similar fatigue responses are classified into the same group, thereby improving the accuracy of the analysis and the rationality of the node grouping; then, the connection parts of the monitoring nodes in different monitoring node groups are subjected to graded fatigue constraints to obtain the damage distribution condition set of the connection parts of each monitoring node. The following method can be used to implement Now, that is, you can first set multiple fatigue constraint levels, and combine the classic fatigue damage model (such as the thermal-mechanical coupling fatigue model based on the stress-strain hysteresis curve) to predict the fatigue damage of the connection parts at each node. Specifically, according to the grading standards such as stress amplitude, temperature amplitude or fatigue damage history, fatigue constraints of different strengths are imposed. Among them, the stress-strain model integrates the stress response and temperature response of the node, adopts the thermal-mechanical coupling fatigue life prediction method, and quantitatively analyzes the fatigue damage of the connection parts. Based on the damage accumulation effect, the Miner linear fatigue cumulative damage theory (i.e., Miner's law) is used to accumulate the fatigue damage of the connection parts of each node to obtain the corresponding damage distribution data. Finally, the fatigue damage results of all monitoring node groups are sorted and integrated to obtain a complete set of damage distribution conditions. It should be further explained that the setting of different fatigue constraint levels can be achieved based on the fatigue response characteristics of the nodes in each monitoring node group, especially the main frequency band and frequency range can be identified based on their frequency response. The load and temperature changes in the main frequency band are more likely to induce local fatigue, so a higher level of fatigue constraint should be imposed on such nodes; finally, all damage distribution condition sets are integrated to obtain the damage constraint conditions of the target cryogenic pipe under the vibration state. This can be achieved in the following way, namely: the set composed of the damage distribution condition sets of the connection parts at all monitoring nodes can be used as the damage constraint conditions of the target cryogenic pipe under the vibration state.

[0031] In step 103, different low-temperature thermal cycle loads are loaded on the fatigue life simulation model, and the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads is extracted. The thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads is determined by combining all thermal fatigue damage and load spectrum characteristics of different low-temperature thermal cycle loads.

[0032] It should be noted that the low-temperature thermal cycle load in this application refers to the periodic thermal stress caused by the repeated temperature rise and fall changes that the low-temperature pipe fittings experience during use; it should also be noted that loading different low-temperature thermal cycle loads in the fatigue life simulation model is intended to simulate the service response behavior of low-temperature pipe fittings under various temperature change conditions under actual working conditions, and comprehensively evaluate the degree of thermal fatigue damage of the connection parts under different thermal cycle conditions. By loading different low-temperature thermal cycle loads, the influence of thermal load changes on the local structural stress state and fatigue life can be revealed, and the evolution of material properties and damage accumulation mechanism under thermal-mechanical coupling can be captured, thereby improving the accuracy and adaptability of fatigue life prediction, and providing real boundary conditions and data basis for life assessment in complex service environments.

[0033] In some embodiments, extracting thermal fatigue damage of each connection part under different low-temperature thermal cycle loads can be achieved by using the following steps: Obtain stress-strain hysteresis curves of each connection under different low-temperature thermal cycle loads from the fatigue life simulation model; The thermal fatigue damage of each connection part under different low-temperature thermal cycle loads is determined based on all stress-strain hysteresis curves.

[0034] It should be noted that the stress-strain hysteresis curve in this application is a curve that describes the change of stress and strain over time at the connection part under different low-temperature thermal cycle loads; the thermal fatigue damage in this application is a characteristic indicator to measure the degree of cumulative plastic deformation caused by thermal stress at the connection part under different low-temperature thermal cycle loads.

[0035] Specifically, first, the stress-strain hysteresis curves of each connection under different low-temperature thermal cycle loads are obtained from the fatigue life simulation model. This can be achieved by the following method: setting boundary conditions in the fatigue life simulation model to simulate the thermal stress evolution process of each connection under different low-temperature thermal cycle loads, and extracting the stress-strain relationship curves in each loading cycle during the simulation process from the fatigue life simulation model. The stress-strain relationship curves are used as stress-strain hysteresis curves. The stress-strain hysteresis curves can reflect the local plastic deformation and energy dissipation characteristics of the material under thermal cycling. Then, the thermal fatigue damage of each connection under different low-temperature thermal cycle loads is determined based on all stress-strain hysteresis curves. This can be achieved by the following method: all stress-strain hysteresis curves can be input into the Coffin-Manson model (i.e., the Coffin-Manson model). The plastic strain energy dissipation of each connection under different low-temperature thermal cycle loads is integrated through the Coffin-Manson model to obtain the cumulative damage of each connection under different low-temperature thermal cycle loads, and the obtained cumulative damage is used as the thermal fatigue damage of the corresponding connection under different low-temperature thermal cycle loads.

[0036] In some embodiments, reference Figure 3 As shown in FIG. 1 , this figure is a schematic diagram of a process for determining thermal fatigue loss in some embodiments of the present application. In this embodiment, the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads is determined by combining all thermal fatigue damage and load spectrum characteristics of different low-temperature thermal cycle loads. The following steps can be used: In step 1031, the difference in thermal fatigue response between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the theoretical thermal fatigue damage is determined; In step 1032, a mapping relationship vector is constructed between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the load spectrum characteristics of the low-temperature thermal cycle load; In step 1033, a weighting factor of the thermal fatigue response difference corresponding to each low-temperature thermal cycle load is extracted according to the mapping relationship vector; In step 1034 , the thermal fatigue response differences under various low-temperature thermal cycle loads are weightedly integrated based on all weighting factors to obtain thermal fatigue losses of fatigue life simulations under different low-temperature thermal cycle loads.

[0037] It should be noted that the weight coefficient of the weighting factor in this application; the thermal fatigue loss in this application is an indicator to measure the fatigue life attenuation loss caused by the thermal fatigue effect of the connection part under different low-temperature thermal cycle load conditions.

[0038] In specific implementation, first, determining the difference in thermal fatigue response between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the theoretical thermal fatigue damage can be achieved in the following manner, namely: the absolute difference between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the theoretical thermal fatigue damage can be used as the thermal fatigue response difference between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the theoretical thermal fatigue damage; secondly, constructing a mapping relationship vector between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the load spectrum characteristics of the low-temperature thermal cycle load can be achieved in the following manner, namely: obtaining the load spectrum characteristics of different low-temperature thermal cycle loads, wherein the load spectrum characteristics refer to a set of statistical parameters that characterize the variation law of the thermal cycle load in the time domain, including temperature amplitude, cycle frequency, average temperature gradient and load duration, and then performing a mapping between the thermal fatigue damage of the connection part under different low-temperature thermal cycle loads and the load spectrum characteristics of the low-temperature thermal cycle load. Principal component analysis, the mapping features obtained by principal component analysis are used as the mapping relationship between the thermal fatigue damage of the connection parts under different low-temperature thermal cycle loads and the load spectrum characteristics of the low-temperature thermal cycle load, and then the vector composed of all mapping relationships is used as the mapping relationship vector; it should be further explained that the mapping feature refers to the set of main variables extracted by principal component analysis that can reflect the correlation between thermal fatigue damage and load spectrum characteristics to the greatest extent. Its essence is to extract the most representative low-dimensional linear combination from the high-dimensional input data (i.e., thermal fatigue damage and multiple load spectrum statistical parameters). In the principal component analysis process, the thermal fatigue damage and corresponding load spectrum characteristics of all connection parts are first standardized, and then the covariance matrix is calculated, and the eigenvector and eigenvalue of the covariance matrix are solved. The eigenvector corresponding to the maximum eigenvalue is used as the projection direction, and the original data is projected onto this direction. The obtained principal component is the mapping feature, which is used to describe the key change trends and correlation patterns between the two types of data.

[0039] In addition, in the specific implementation, the weighting factor of the thermal fatigue response difference corresponding to each low-temperature thermal cycle load is extracted according to the mapping relationship vector, which can be achieved in the following manner, namely: all elements in the mapping relationship vector are normalized, and the normalized elements are used as the weighting factor of the thermal fatigue response difference corresponding to each low-temperature thermal cycle load; based on all the weighting factors, the thermal fatigue response differences under various low-temperature thermal cycle loads are weighted and fused to obtain the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads, which can be achieved in the following manner, namely: the response differences under all thermal cycle load conditions are weighted according to the weighting factors, and each weighted value is used as the thermal fatigue loss of fatigue life simulation under the corresponding low-temperature thermal cycle load.

[0040] In step 104, the fatigue life control parameters of the fatigue life simulation model are cost-optimized according to the damage constraint conditions and all thermal fatigue losses to obtain the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions, and then the local grid density of the fatigue life simulation model is adjusted based on the optimal cost parameters.

[0041] In some embodiments, cost optimization of fatigue life control parameters of a fatigue life simulation model is performed based on the damage constraint conditions and all thermal fatigue losses to obtain optimal cost parameters for fatigue life simulation under thermal-vibration load conditions. The following steps can be used: The fatigue life control parameters in the fatigue life simulation model are used as optimization variables, and a cost function model is constructed with the goal of minimizing thermal fatigue loss and satisfying damage constraint conditions; Inputting the damage constraint condition and the thermal fatigue loss under each thermal cycle load into the cost function model as penalty terms; The cost function in the cost function model is iteratively solved by a preset optimization algorithm to obtain the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions.

[0042] It should be noted that the fatigue life control parameters in this application refer to adjustable input variables used to adjust the accuracy of fatigue behavior response in the fatigue life simulation model, specifically including local grid resolution, material degradation coefficient and fatigue limit coefficient; the optimal cost parameters in this application are the minimized fatigue life prediction cost obtained by optimizing the control parameters under the conditions of thermal fatigue loss and damage constraints.

[0043] In the specific implementation, first, the fatigue life control parameters in the fatigue life simulation model are used as optimization variables, with the goal of minimizing thermal fatigue loss and satisfying damage constraints. The cost function model can be constructed in the following way, namely: the fatigue life control parameters (such as local grid resolution, material degradation coefficient and fatigue limit coefficient) in the fatigue life simulation model are set as optimization variables, and the objective function is defined as minimization of thermal fatigue loss. At the same time, damage constraints are introduced to form a constrained optimization problem; then, the damage constraints and the thermal fatigue loss under each thermal cycle load are input as penalty items into the cost function model. The following method can be used to implement it, namely: based on the thermal fatigue damage obtained by simulation under each low-temperature thermal cycle load condition, the thermal fatigue loss under each thermal cycle load condition is minimized. Data, define the objective function as the sum of thermal fatigue losses, and introduce the degree of deviation caused by violating the damage constraint conditions into the objective function in the form of a penalty function to form a constrained cost function; finally, the cost function in the cost function model is iteratively solved by a preset optimization algorithm to obtain the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions. This can be achieved in the following way, namely: particle swarm optimization can be used to initialize a set of random parameter solutions, iteratively simulate thermal fatigue responses in rounds, re-evaluate the cost function value based on the obtained damage and penalty terms, and screen for better solutions. This iteration is repeated until the cost function converges, and finally the control parameters corresponding to the minimum cost are determined, which are the optimal cost parameters for fatigue life simulation under the current thermal-vibration conditions.

[0044] It should be noted that adjusting the local grid density of the fatigue life simulation model based on the optimal cost parameters in this application refers to redividing the finite element grid of the damage-sensitive area in the fatigue life simulation model according to the fatigue life control parameters corresponding to the optimal simulation cost, and enhancing the simulation accuracy by improving the grid resolution of the local area to ensure that the fatigue life prediction results have higher numerical convergence in key connection parts.

[0045] On the other hand, in some embodiments, the present application provides a fatigue life modeling and optimization system for LNG ship pipe fittings, referring to Figure 4 This figure is a schematic diagram of the structure of a fatigue life modeling and optimization system for LNG ship pipe fittings according to some embodiments of the present application. The fatigue life modeling and optimization system 400 for LNG ship pipe fittings includes: a model building module 401, a processing module 402, and an execution module 403, which are described as follows: Model building module 401, in this application, the model building module 401 is mainly used to establish a pipe-temperature-load coupled fatigue life simulation model based on the material parameters, connection structure and working condition parameters of the target low-temperature pipe; Processing module 402, in the present application, is used to determine, when the target cryogenic pipe is in a vibration state induced by wave loads, the cross-correlation coefficients of the pipe fatigue response characteristics between different monitoring nodes based on the load spectrum and temperature spectrum at each monitoring node, and then apply fatigue state constraints to the fatigue damage structure in the fatigue life simulation model based on all the cross-correlation coefficients to obtain damage constraint conditions for the target cryogenic pipe in the vibration state; In the present application, the processing module 402 is further configured to load different low-temperature thermal cycle loads on the fatigue life simulation model, extract the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads, and determine the thermal fatigue loss of the fatigue life simulation under different low-temperature thermal cycle loads by combining all thermal fatigue damage and the load spectrum characteristics of different low-temperature thermal cycle loads; Execution module 403. In this application, execution module 403 is mainly used to perform cost optimization on the fatigue life control parameters of the fatigue life simulation model according to the damage constraint conditions and all thermal fatigue losses, obtain the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions, and then adjust the local grid density of the fatigue life simulation model based on the optimal cost parameters.

[0046] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned LNG ship pipe fatigue life modeling optimization method.

[0047] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing the fatigue life modeling optimization method of LNG ship pipe fittings according to some embodiments of the present application. The fatigue life modeling optimization method of LNG ship pipe fittings in the above embodiment can be achieved by Figure 5 The computer device 500 shown in FIG. 5 is implemented as shown in FIG. 5 . The computer device 500 includes at least one processor 501 , a communication bus 502 , a memory 503 , and at least one communication interface 504 .

[0048] The processor 501 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0049] The communication bus 502 may be used to transmit information between the aforementioned components.

[0050] The memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 503 may be independent and connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0051] Memory 503 is used to store program code for implementing the present invention, and processor 501 controls its execution. Processor 501 is configured to execute the program code stored in memory 503. The program code may include one or more software modules. The fatigue life modeling and optimization method for LNG ship pipe fittings in the above embodiment can be implemented by processor 501 and one or more software modules in the program code stored in memory 503.

[0052] The communication interface 504 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0053] In a specific implementation, as an example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0054] The aforementioned computer device can be a general-purpose computer device or a dedicated computer device. In a specific implementation, the computer device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of computer device.

[0055] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned LNG ship pipe fatigue life modeling optimization method.

[0056] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0057] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A fatigue life modeling and optimization method for LNG ship pipe fittings, characterized in that: The steps include: Monitoring nodes are deployed at each connection point of the target cryogenic pipe fittings, and a pipe-temperature-load coupled fatigue life simulation model is established based on the material parameters, connection structure, and operating parameters of the target cryogenic pipe fittings. When the target cryogenic pipe is in a vibration state induced by wave loads, the cross-correlation coefficients of the fatigue response characteristics of the pipe between different monitoring nodes are determined based on the load spectrum and temperature spectrum at each monitoring node. Then, fatigue state constraints are applied to the fatigue damage structure in the fatigue life simulation model based on all the cross-correlation coefficients to obtain the damage constraint conditions of the target cryogenic pipe under the vibration state. Apply different low-temperature thermal cycle loads to the fatigue life simulation model, extract the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads, and combine all thermal fatigue damage and the load spectrum characteristics of different low-temperature thermal cycle loads to determine the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads; According to the damage constraint conditions and all thermal fatigue losses, the fatigue life control parameters of the fatigue life simulation model are optimized, and the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions are obtained. Then, the local grid density of the fatigue life simulation model is adjusted based on the optimal cost parameters.

2. The method according to claim 1, wherein The mutual correlation coefficients of the fatigue response characteristics of the pipe fittings between different monitoring nodes are determined based on the load spectrum and temperature spectrum at each monitoring node. Specifically, the following are the correlation coefficients: Extracting the stress response sequence and temperature response sequence of each monitoring node under the wave load-induced vibration, and then constructing the load spectrum and temperature spectrum at each monitoring node; The cross-correlation analysis of the load spectrum and temperature spectrum between different monitoring nodes is performed to obtain the cross-correlation coefficients of the fatigue response characteristics of the pipe fittings between different monitoring nodes.

3. The method according to claim 1, wherein According to all the mutual correlation coefficients, fatigue state constraints are imposed on the fatigue damage structure in the fatigue life simulation model, and the damage constraint conditions of the target cryogenic pipe under the vibration state are obtained, which specifically include: Construct a cross-correlation coefficient matrix based on all cross-correlation coefficients; Perform correlation partitioning on all monitoring nodes according to the mutual correlation coefficient matrix to obtain multiple monitoring node groups with different correlation characteristics; Perform graded fatigue constraints on the connection parts of monitoring nodes in different monitoring node groups to obtain a set of damage distribution conditions for the connection parts of each monitoring node; All damage distribution condition sets are integrated to obtain the damage constraint conditions of the target cryogenic pipe under the vibration state.

4. The method according to claim 1, wherein Extracting thermal fatigue damage of each connection part under different low-temperature thermal cycle loads specifically includes: Obtain stress-strain hysteresis curves of each connection under different low-temperature thermal cycle loads from the fatigue life simulation model; The thermal fatigue damage of each connection part under different low-temperature thermal cycle loads is determined based on all stress-strain hysteresis curves.

5. The method according to claim 1, wherein Combining all thermal fatigue damage and the load spectrum characteristics of different low-temperature thermal cycle loads to determine the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads specifically includes: Determine the difference in thermal fatigue response between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the theoretical thermal fatigue damage; Constructing a mapping relationship vector between the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads and the load spectrum characteristics of the low-temperature thermal cycle load; Extracting a weighting factor of the thermal fatigue response difference corresponding to each low-temperature thermal cycle load according to the mapping relationship vector; Based on all weighting factors, the differences in thermal fatigue responses under various low-temperature thermal cycle loads are weighted and fused to obtain the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads.

6. The method according to claim 1, wherein According to the damage constraint conditions and all thermal fatigue losses, the fatigue life control parameters of the fatigue life simulation model are optimized, and the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions are obtained, which specifically include: The fatigue life control parameters in the fatigue life simulation model are used as optimization variables, and a cost function model is constructed with the goal of minimizing thermal fatigue loss and satisfying damage constraint conditions; Inputting the damage constraint condition and the thermal fatigue loss under each thermal cycle load into the cost function model as penalty terms; The cost function in the cost function model is iteratively solved by a preset optimization algorithm to obtain the optimal cost parameters for fatigue life simulation under thermal-vibration load conditions.

7. The method according to claim 1, wherein The material parameters specifically include elastic modulus, Poisson's ratio, yield strength, tensile strength, thermal expansion coefficient, fatigue limit and material fatigue curve.

8. A fatigue life modeling and optimization system for LNG ship pipe fittings, characterized by: include: Model building module, used to establish a pipe-temperature-load coupled fatigue life simulation model based on the material parameters, connection structure and working condition parameters of the target low-temperature pipe; a processing module for determining, when the target cryogenic pipe is in a vibration state induced by wave loads, the cross-correlation coefficients of the fatigue response characteristics of the pipe between different monitoring nodes based on the load spectrum and the temperature spectrum at each monitoring node, and then applying fatigue state constraints to the fatigue damage structure in the fatigue life simulation model based on all the cross-correlation coefficients to obtain damage constraint conditions for the target cryogenic pipe under the vibration state; The processing module is further used to load different low-temperature thermal cycle loads on the fatigue life simulation model, extract the thermal fatigue damage of each connection part under different low-temperature thermal cycle loads, and determine the thermal fatigue loss of fatigue life simulation under different low-temperature thermal cycle loads by combining all thermal fatigue damage and load spectrum characteristics of different low-temperature thermal cycle loads; An execution module is used to perform cost optimization on fatigue life control parameters of the fatigue life simulation model according to the damage constraint conditions and all thermal fatigue losses, obtain optimal cost parameters for fatigue life simulation under thermal-vibration load conditions, and then adjust the local grid density of the fatigue life simulation model based on the optimal cost parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a code, wherein: The processor is configured to obtain the code and execute the fatigue life modeling and optimization method for LNG ship pipe fittings according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the fatigue life modeling and optimization method for LNG ship pipe fittings according to any one of claims 1 to 7 is implemented.

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