Method and equipment for determining the failure probability of energy pipelines with axial cracks under fatigue loading

Abstract

This invention provides a method and apparatus for determining the failure probability of an energy pipeline with axial cracks under fatigue loading. The method includes: determining the rupture failure pressure of the energy pipeline based on current monitoring data; determining the total growth rate of the axial crack based on a first growth rate caused by environmental factors and a second growth rate caused by cyclic fatigue loading; constructing a limit state model for pipeline rupture or leakage based on the rupture failure pressure and the total growth rate of the axial crack; and solving the limit state model using a predefined algorithm to determine the failure probability of the energy pipeline. This method, based on current monitoring data of the energy pipeline and considering the influence of both environmental factors and fatigue loads within the pipeline on crack growth, enables the assessment of the pipeline's failure probability over a future operating period, saving time and economic costs and ensuring the safety of the energy pipeline.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy pipeline, and in particular to a method and device for determining failure probability of an energy pipeline with axial cracks under fatigue load. BACKGROUND

[0002] When pipelines transport new energy media such as oil and gas, supercritical carbon dioxide, etc., pressure fluctuations will occur due to factors such as the opening and closing of valves, the start and stop of pumps or compressors, and phase changes. If there are cracks inside or outside the pipeline, the cracks in the pipeline will easily expand when the pressure fluctuations in the pipeline form cyclic fatigue loads. At the same time, there are corrosive substances inside and outside the pipeline, and the crack defects in the pipeline will continue to expand under the action of corrosive substances. The above two factors pose a great danger to the safe operation of oil and gas and new energy pipelines. For example, once a supercritical carbon dioxide pipeline cracks, it will rapidly expand along the pipeline. Therefore, there is an urgent need for a pipeline failure probability calculation method that takes into account cyclic loads and crack growth to meet production needs.

[0003] Currently, there are some studies on the failure probability of pipelines with cracks, but they do not consider crack growth and load fluctuations, and cannot evaluate the failure probability of the pipeline in the future operating period. In production and operation, pipelines with crack defects are sometimes distributed in scenes such as rivers, highways, and mountains, which brings great difficulty to pipeline excavation. If the failure probability of the pipeline in the future period of time can be determined, unnecessary excavation or delayed excavation can be avoided, which can greatly reduce economic costs. At the same time, there is no method for batch calculation of the failure probability of pipelines with cracks based on internal detection data in current research, which has low calculation efficiency and is not convenient for frontline personnel to use. SUMMARY

[0004] The embodiments of the present application provide a method and device for determining the failure probability of an energy pipeline with axial cracks under fatigue load, which realizes the evaluation of the failure probability of the pipeline in the future operating period, saves time and economic costs, and ensures the safety of the energy pipeline.

[0005] In a first aspect, the embodiments of the present application provide a method for determining the failure probability of an energy pipeline with axial cracks under fatigue load, which comprises:

[0006] determining the rupture failure pressure of the energy pipeline according to the current detection data of the energy pipeline with axial cracks;

[0007] determining the total growth rate of the axial cracks according to a first growth rate of the axial cracks caused by the environment and a second growth rate of the axial cracks caused by the cyclic fatigue load;

[0008] Based on the rupture failure pressure of the energy pipeline and the total growth rate of the axial crack, a limit state model for the pipeline to burst or leak is constructed.

[0009] The failure probability of the energy pipeline is determined by solving the limit state model using a set algorithm.

[0010] Secondly, this embodiment provides an electronic device, including:

[0011] At least one processor; and

[0012] A memory communicatively connected to the at least one processor; wherein,

[0013] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for determining the failure probability of an energy pipeline with axial cracks under fatigue load, as described in any embodiment of the present invention.

[0014] This invention provides a method and apparatus for determining the failure probability of an energy pipeline with axial cracks under fatigue loading. The method includes: determining the rupture failure pressure of the energy pipeline based on current detection data; determining the total growth rate of the axial crack based on a first growth rate caused by environmental factors and a second growth rate caused by cyclic fatigue loading; constructing a limit state model for the pipeline to rupture or leak based on the rupture failure pressure and the total growth rate of the axial crack; and solving the limit state model using a predefined algorithm to determine the failure probability of the energy pipeline. The above technical solution, based on the current detection data of axial cracks in energy pipelines, predicts the rupture failure pressure and the total growth rate of axial cracks in the energy pipelines. When determining the total growth rate of axial cracks, it considers the influence of both the environment and fatigue loads inside the pipeline on crack growth. Then, based on the rupture failure pressure and the total growth rate of axial cracks in the energy pipelines, it constructs a limit state model for pipeline rupture or leakage. Subsequently, it uses a set algorithm to solve the limit state model, realizing the assessment of the failure probability of the pipeline in the future operating period. This can save time and economic costs and ensure the safety of energy pipelines.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for determining the failure probability of an energy pipeline with axial cracks under fatigue load, as provided in Embodiment 1 of the present invention.

[0018] Figure 2 This is a flowchart illustrating another method for determining the failure probability of an energy pipeline with axial cracks under fatigue load, provided in Embodiment 2 of the present invention.

[0019] Figure 3 This is an excerpted example diagram of the internal load cycle of an energy pipeline in an application scenario provided by Embodiment 2 of the present invention;

[0020] Figure 4 This is a minute-by-minute internal pressure recording diagram of an energy pipeline in an application scenario provided by Embodiment 2 of the present invention;

[0021] Figure 5 This is an example diagram illustrating the number of cycles under different cyclic ranges of internal pressure in an energy pipeline in an application scenario provided by Embodiment 2 of the present invention.

[0022] Figure 6 This is an example diagram illustrating the changes in internal pressure of a pipeline over a historical period, provided in Embodiment 2 of the present invention.

[0023] Figure 7 Example diagram of the Excel pipeline reliability variable input interface;

[0024] Figure 8 This is an example diagram illustrating the probability of burst failure of a cracked pipeline in an application scenario provided by Embodiment 2 of the present invention;

[0025] Figure 9 This is an example diagram illustrating the probability of leakage failure in a cracked pipeline in an application scenario provided by Embodiment 2 of the present invention.

[0026] Figure 10 This is a schematic diagram of a device for determining the failure probability of an energy pipeline with axial cracks under fatigue load, provided in Embodiment 3 of the present invention.

[0027] Figure 11 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] It is understandable that the existing technology has the following shortcomings: (1) The current technology cannot consider the growth effect of cracks under environmental influences and the fluctuation of cyclic fatigue loads. (2) It cannot perform batch calculations of the failure probability of pipeline systems with axial crack defects. (3) It lacks a method for calculating the failure probability of cracked pipelines under pipeline leakage and burst modes.

[0031] Example 1

[0032] Figure 1 This is a flowchart illustrating a method for determining the failure probability of an energy pipeline with axial cracks under fatigue load, as provided in Embodiment 1 of the present invention. This method is applicable to determining the failure probability of an energy pipeline with axial cracks under fatigue load. This method can be executed by a device for determining the failure probability of an energy pipeline with axial cracks under fatigue load. This device can be implemented in hardware and / or software and is generally integrated into electronic equipment.

[0033] like Figure 1 As shown in the figure, the method for determining the failure probability of an energy pipeline with axial cracks under fatigue load provided in this embodiment 1 may specifically include the following steps:

[0034] S101. Based on the current inspection data of the energy pipeline containing axial cracks, determine the failure pressure of the energy pipeline.

[0035] In this embodiment, the energy pipeline can be specifically understood as a pipeline transporting oil, gas, new energy sources, etc. The current detection data specifically refers to parameters obtained from the current detection of an energy pipeline containing axial cracks, including the pipeline's physical properties and the size parameters of the axial cracks. The physical properties of the energy pipeline include the pipeline's diameter, yield strength, tensile strength, wall thickness, maximum operating pressure, environmental growth rate, Charpy impact value, regression parameters, and elastic modulus. The size parameters of the axial cracks include the length and depth of the axial cracks. The rupture failure pressure can be specifically understood as the maximum pressure that the pipeline material can withstand; exceeding this pressure will cause the pipeline to fail.

[0036] Specifically, relevant parameters such as the outer diameter, wall thickness, crack half-length, crack depth, material yield strength, material tensile strength, and fracture stress in the brittle failure mode of the energy pipeline can be extracted from the current testing data. Then, based on the outer diameter, wall thickness, and crack half-length, the coefficient of thermal expansion of the energy pipeline is determined. Based on the material yield strength and tensile strength, the fracture stress in the plastic failure mode is determined. Finally, based on the coefficient of thermal expansion, fracture stress in the plastic failure mode, fracture stress in the brittle failure mode, crack depth, and wall thickness, combined with the established pipeline failure pressure, a model is established to determine the rupture failure pressure of the energy pipeline.

[0037] S102. Determine the total growth rate of the axial crack based on the first growth rate caused by the environment and the second growth rate caused by cyclic fatigue load.

[0038] Understandably, internal inspection reports or non-destructive testing reports for energy pipelines can only provide information such as the current depth and length of axial cracks, thus allowing only deterministic evaluation. Crack growth is mainly caused by the influence of the external environment and fluctuations in internal pressure. In this embodiment, the axial crack growth rate is predicted from two aspects based on the influence of the external environment and internal pressure fluctuations. The crack growth rate caused by the external environment, i.e., the crack growth depth per unit time, is denoted as the first growth rate. The growth rate caused by fatigue cyclic loading, i.e., the crack growth depth per unit time, is denoted as the second growth rate. The first growth rate and the second growth rate are added together to obtain the total axial crack growth rate.

[0039] As described above, considering that the crack growth rate varies in different regions and pipelines due to environmental factors, the crack depth obtained from two internal inspections is generally used to determine the crack growth caused by the environment. The axial crack growth depth caused by the environment in the energy pipeline is obtained at set intervals, and the crack growth depth is divided by the set time to obtain the first growth rate of the axial crack caused by the environment.

[0040] In this embodiment, due to factors such as valve wall defects and water hammer, the internal pressure of the pipeline typically fluctuates cyclically, leading to cyclic fatigue damage and crack growth caused by cyclic fatigue loads. A three-point rainflow counting method is used to count the number of internal pressure fluctuation cycles within a set time period to determine the number of pressure fluctuation cycles. The steps to determine the growth rate of axial crack growth caused by a single pressure fluctuation can be described as follows: First, determine the free surface factor of the energy pipeline based on the crack depth, crack half-length, and wall thickness; second, determine the defect shape factor of the energy pipeline based on the crack depth and crack half-length; then, determine the stress intensity factor range of the energy pipeline under pressure fluctuation cycles based on the internal pressure change, free surface factor, defect shape factor, and crack depth; finally, determine the growth rate of axial crack growth caused by a single pressure fluctuation based on the stress intensity factor range and regression parameters. Multiplying the number of pressure fluctuation cycles by the growth rate of axial crack growth caused by a single pressure fluctuation yields the second growth rate of axial crack growth caused by cyclic fatigue loads.

[0041] S103. Based on the rupture failure pressure and the total growth rate of axial cracks in the energy pipeline, construct a limit state model for pipeline rupture or leakage.

[0042] In this embodiment, since cracks lead to pipeline rupture or leakage failure, limit state equations are constructed under leakage and rupture conditions, denoted as the limit state model. Regarding the aspect of cracks causing pipeline rupture: based on the rupture failure pressure of the energy pipeline and the maximum operating pressure of the energy pipeline, a limit state equation for the energy pipeline rupture is constructed.

[0043] Following the above description, regarding pipeline leakage caused by cracks: Based on the total growth rate of axial cracks, the growth depth of axial cracks during future operation can be predicted. Based on the pipeline wall thickness and the predicted crack growth depth, a limit state equation for pipeline leakage is constructed. The limit state equations for pipeline leakage and pipeline rupture are combined to form a limit state model for pipeline rupture or leakage.

[0044] S104. Use the set algorithm to solve the limit state model and determine the failure probability of the energy pipeline.

[0045] In this embodiment, the failure probability can be specifically understood as the probability of an energy pipeline bursting or leaking. When calculating the failure probability, if the energy pipeline bursts or leaks, it is considered that the energy pipeline has failed.

[0046] Preferably, the algorithm can be the Monte Carlo algorithm, which is a computational method based on random sampling. Its basic idea is to generate random samples and use statistical principles to estimate the solution to a mathematical problem.

[0047] First, the current inspection data of the energy pipeline is used as a random variable. The random variables mainly involve parameters such as diameter, yield strength, tensile strength, pipe wall thickness, maximum working pressure, maximum crack depth error, maximum crack length error, elastic modulus, environmental growth rate, and equivalent cycle number. Based on the corresponding distribution of the random variables, multiple sets of random samples are generated.

[0048] Then, the random samples from each group are substituted into the limit state model to calculate the value of its function. The number of groups with function values ​​less than zero is counted, and this number is divided by a predetermined number to obtain the failure probability of the energy pipeline.

[0049] The above technical solution, based on the current detection data of axial cracks in energy pipelines, predicts the rupture failure pressure and the total growth rate of axial cracks in the energy pipelines. When determining the total growth rate of axial cracks, it considers the influence of both the environment and fatigue loads inside the pipeline on crack growth. Then, based on the rupture failure pressure and the total growth rate of axial cracks in the energy pipelines, it constructs a limit state model for pipeline rupture or leakage. Subsequently, it uses a set algorithm to solve the limit state model, realizing the assessment of the failure probability of the pipeline in the future operating period. This can save time and economic costs and ensure the safety of energy pipelines.

[0050] Example 2

[0051] Figure 2 This is a flowchart illustrating another method for determining the failure probability of an energy pipeline with axial cracks under fatigue load, provided in Embodiment 2 of the present invention. This embodiment is a further optimization of the above embodiment. In this embodiment, the following steps are further optimized: "determining the rupture failure pressure of the pipeline based on the current detection data of the energy pipeline with axial cracks"; "determining the first growth rate of the axial crack caused by the environment"; "determining the second growth rate of the axial crack caused by cyclic fatigue load"; "determining the total growth rate of the axial crack based on the first growth rate of the axial crack caused by the environment and the second growth rate of the axial crack caused by cyclic fatigue load"; "constructing a limit state model for the pipeline to burst or leak based on the rupture failure pressure of the energy pipeline and the total growth rate of the axial crack"; and "solving the limit state model using a set algorithm to determine the failure probability of the energy pipeline".

[0052] like Figure 2As shown in the figure, this embodiment 2 provides a method for determining the failure probability of an energy pipeline with axial cracks under fatigue loading, which specifically includes the following steps:

[0053] S201. Determine the expansion coefficient of the energy pipeline based on its outer diameter, wall thickness, and crack half-length.

[0054] In this embodiment, the coefficient of thermal expansion refers to the degree to which the length or volume of the pipeline changes when the temperature changes. The coefficient of thermal expansion of energy pipelines. Specifically, it can be expressed as:

[0055]

[0056] In the formula, Indicates the outer diameter of the energy pipeline. This indicates the wall thickness of the energy pipeline. This represents the crack half-length of an axial crack in an energy pipeline. This represents the total length of an axial crack in an energy pipeline.

[0057] Specifically, based on the current inspection data of the energy pipeline, the radius, wall thickness, and crack half-length of the energy pipeline are extracted from the current inspection data and substituted into the above formula to obtain the expansion coefficient of the energy pipeline.

[0058] S202. Determine the fracture stress of the plastic failure mode of the energy pipeline based on the material yield strength and tensile strength of the material.

[0059] In this embodiment, the fracture stress in the plastic failure mode refers to the ultimate stress, i.e., the ultimate strength, at which the energy pipeline fractures. The fracture stress in the plastic failure mode of the energy pipeline... Specifically, it can be expressed as: In the formula, Indicates the yield strength of the material used in energy pipelines. This indicates the true tensile strength of the material used in energy pipelines.

[0060] Specifically, based on the current testing data of the energy pipeline, the material yield strength and tensile strength of the energy pipeline are extracted from the current testing data and substituted into the above formula to obtain the fracture stress of the plastic failure mode of the energy pipeline.

[0061] S203. Based on the expansion coefficient, fracture stress of plastic failure mode, fracture stress of brittle failure mode, crack depth and wall thickness of the energy pipeline, and combined with the set pipeline failure pressure, determine the model to determine the rupture failure pressure of the energy pipeline.

[0062] In this embodiment, the pipeline failure pressure determination model employs a model for predicting pipeline failure pressures containing axial cracks (Corrosion-induced Leakage Assessment System, CorLAS). For energy pipelines containing axial cracks, the CorLAS model is used to determine the pipeline's rupture failure pressure. The rupture failure pressure P of the energy pipeline... b It can be represented as:

[0063]

[0064] In the formula, min represents taking the minimum value. and These represent the outer diameter and wall thickness of the energy pipeline, respectively. Indicates the crack depth; Indicates half the length of the crack. The crack is the full length; Indicates the crack area; Indicates the reference area of ​​the crack; Indicates the coefficient of thermal expansion; The fracture stress, or flow stress, represents the plastic failure mode. The fracture stress represents the brittle fracture mode.

[0065] Specifically, the expansion coefficient of the energy pipeline, the fracture stress of the plastic failure mode, and some current test data of the energy pipeline, such as the fracture stress, crack depth, and wall thickness of the brittle failure mode, obtained in the above steps, are all substituted into the above formula to obtain the rupture failure pressure of the energy pipeline.

[0066] The above describes the steps for determining the crack failure pressure of an energy pipeline.

[0067] S204. Obtain the crack growth depth of the axial crack in the energy pipeline due to environmental factors at a set interval.

[0068] Specifically, the crack growth depth can be understood as how much the axial crack in the energy pipeline has grown within a set time. It is understood that internal inspection reports or non-destructive testing reports for energy pipelines, i.e., reports containing inspection data of the energy pipeline, can only provide information such as the current depth and length of the axial crack, and therefore can only provide a deterministic evaluation. Crack growth is mainly caused by the influence of the external environment and fluctuations in internal pressure. In this embodiment, the axial crack growth rate is predicted from two aspects based on the influence of the external environment and the influence of internal pressure fluctuations. Steps S204 and S205 are used to determine the crack growth caused by the external environment. The total crack growth rate of the energy pipeline can be expressed as:

[0069] In the formula, Indicates the crack depth. Indicates time, This indicates the rate of crack growth caused by environmental factors. This represents the crack growth rate caused by cyclic fatigue loading.

[0070] Considering that crack growth rates vary across different regions and pipelines due to environmental factors, the crack depth obtained from two internal inspections is generally used to determine the environmentally induced crack growth. The set time can be adjusted according to actual conditions and is not specifically limited here. The method involves obtaining the environmentally induced crack growth depth of axial cracks in energy pipelines at set intervals.

[0071] S205. Divide the crack growth depth by the set time to obtain the first growth rate of axial crack growth caused by the environment.

[0072] In this embodiment, the rate of crack growth caused by the external environment is denoted as the first growth rate. Specifically, the crack growth depth at a set interval determined in step S204 is divided by the set time to obtain the growth rate of axial crack growth caused by the environment, which is denoted as the first growth rate.

[0073] The above describes the steps for determining crack growth caused by environmental factors. The following describes the steps for determining crack growth caused by internal pressure fluctuations (i.e., cyclic fatigue loads).

[0074] S206. Using the three-point rainflow counting method, the number of internal pressure fluctuation cycles in the energy pipeline within a set time is counted to determine the number of pressure fluctuation cycles in the energy pipeline.

[0075] The three-point rainflow counting method is a variant of the rainflow counting method, mainly used in programming and real-time calculations. It determines the range of a cycle by judging whether a hysteresis loop is formed by using three consecutive points A, B, and C. Specifically, the three-point rainflow counting method uses three consecutive points A, B, and C, and determines whether a hysteresis loop is formed by judging the length relationship between line segments AB and BC.

[0076] The above steps determine the crack growth rate caused by environmental factors within the pipeline, but cannot obtain the crack growth rate caused by pressure fluctuations within the pipeline. Due to factors such as valve walls and water hammer, the pressure inside the pipeline typically fluctuates cyclically, leading to cyclic fatigue damage. Cyclic fatigue loads induce crack growth. The steps involved in inducing crack growth under cyclic fatigue loads will be described below.

[0077] This step is used to determine the number of pressure cycles in the pipeline. The effective pressure of the energy pipeline in its working environment is a known parameter, which can be obtained, for example, through monitoring and control systems (SCADA). Therefore, the pressure cycles within a set time period (e.g., one year) can be represented by rainflow counting. Knowing only the internal pressure is insufficient; for fatigue reliability analysis, it is more important to know how many cycles the pipeline has undergone under such internal pressure, and the magnitude of pressure change in each cycle. Clearly, the actual operating state of a pipeline is not stable. Therefore, the cycles it experiences are not all the same. The duration of each cycle varies, and the range of cycle changes differs. It is even possible that many cycles with very small ranges are interspersed within two cycles with large ranges. Thus, it is necessary to accurately calculate the number of cycles within these complex internal pressures. In this embodiment, a three-point rainflow counting method is used to count the number of internal pressure cycles in the pipeline and the number of internal pressure fluctuation cycles within a set time period to determine the number of pressure fluctuation cycles in the energy pipeline.

[0078] For example, Figure 3 This is a partial example diagram illustrating the internal load cycle of an energy pipeline in an application scenario provided by Embodiment 2 of the present invention, as shown below. Figure 3 As shown in the diagram, the internal load cycle of the pipeline is illustrated. The horizontal axis represents time, and the vertical axis represents load. Points A, B, C, and D represent points where the load direction changes. For example, DE represents the segment from point D to point E, and LP represents the segment from point L to point P. The pink and blue lines are merely used as distinctions, marking DE and LP in the diagram, and have no specific meaning. The diagram shows that three consecutive points (such as A, B, and C) can form two line segments. The length of these line segments determines whether a hysteresis loop is formed. If it is, it is counted as one cycle; otherwise, it is counted as 0.5 cycles. Figure 3 In the example of A, B, and C, the line segments formed in ABC are AB and BC. Assuming AB is not the initial segment, if AB > BC, a hysteresis loop cannot be formed. The mean and amplitude of A and B are extracted and recorded as 0.5 cycles. If AB ≤ BC, a hysteresis loop can be formed. The amplitudes of A and B are read and recorded as 1 cycle. Following the above steps, for... Figure 3 The internal cyclic load of the pipeline was counted, and the results are shown in Table 1. Table 1 shows the actual operation steps of the cyclic technology. In the table, Z refers to the line segment formed by the first two points of the load history, X is the current line segment, Y is the previous line segment adjacent to X, and r(X) and r(Y) refer to the lengths of line segments X and Y, respectively. The contents of the table will not be listed one by one.

[0079] Table 1

[0080]

[0081] For example, Figure 4 This is a minute-by-minute internal pressure recording graph of an energy pipeline in an application scenario provided by Embodiment 2 of the present invention. Figure 5 This is an example diagram illustrating the statistical analysis of the number of cycles under different pressure ranges within an energy pipeline in an application scenario provided by Embodiment 2 of the present invention. (See diagram for example.) Figure 4 As shown, the horizontal axis represents time, and the vertical axis represents internal pressure. Assuming that the above method is used to further investigate... Figure 4 Perform the calculations, and see the results. Figure 5 , Figure 5 In the graph, the horizontal axis represents the cyclic pressure range (MPa), and the vertical axis represents the cumulative number of cycles. It can be seen that the results accurately determine the number of cycles for different pressure ranges in the pipeline. Therefore, the rainflow counting method can rigorously determine how many cycles the pressure fluctuations underwent over a period of time.

[0082] S207. Determine the growth rate of axial crack growth caused by a single pressure fluctuation.

[0083] In this embodiment, the steps for determining the growth rate of an axial crack caused by a single pressure fluctuation can be described as follows: First, the free surface factor of the energy pipeline is determined based on the crack depth, crack half-length, and wall thickness of the axial crack; second, the defect shape factor of the energy pipeline is determined based on the crack depth and crack half-length of the axial crack; then, the stress intensity factor range of the energy pipeline under pressure fluctuation cycles is determined based on the internal pressure change, free surface factor, defect shape factor, and crack depth of the energy pipeline; finally, the growth rate of the axial crack caused by a single pressure fluctuation is determined based on the stress intensity factor range and regression parameters.

[0084] As a specific implementation, the step of determining the growth rate of the axial crack caused by a single pressure fluctuation can be optimized, including:

[0085] a1) Determine the free surface factor of the energy pipeline based on the crack depth, crack half-length, and wall thickness of the axial crack.

[0086] In this embodiment, the free surface factor is a key parameter used to correct the influence of crack free surface effects on fracture mechanics parameters. The free surface factor of the energy pipeline... , can be represented as:

[0087]

[0088] In the formula, This indicates the wall thickness of the energy pipeline. Indicates the crack depth; This indicates half the length of the crack.

[0089] Specifically, by substituting the crack venom, crack half-length, and wall thickness of the axial crack into the above formula, the free surface factor of the energy pipeline is obtained.

[0090] b1) Determine the defect shape factor of the energy pipeline based on the crack depth and crack half-length of the axial crack.

[0091] In this embodiment, the free surface factor refers to the influence of crack shape on failure, and the defect shape factor of the energy pipeline. It can be represented as:

[0092]

[0093] In the formula, Indicates the crack depth; This indicates half the length of the crack.

[0094] Specifically, by substituting the crack depth and crack growth of the axial crack into the above formula, the defect shape factor of the energy pipeline is obtained.

[0095] c1) Determine the range of stress intensity factors of the energy pipeline under pressure fluctuation cycles based on the internal pressure changes, free surface factor, defect shape factor, and crack depth of the energy pipeline.

[0096] In this embodiment, the stress intensity factor range refers to the range of variation of the stress intensity factor at the crack tip, reflecting the driving force for crack propagation under cyclic loading. The stress intensity factor range of the energy pipeline under pressure fluctuation cycles... , can be represented as:

[0097] , ,

[0098] In the formula, This indicates the use of maximum operating pressure, etc. The specific details included in the diagram will not be elaborated here; it represents the pressure changes inside the pipeline over a historical period calculated using damage criteria. Indicates the outer diameter of the energy pipeline. This indicates the wall thickness of the energy pipeline. Indicates the crack depth. The defect shape factor represents the energy pipeline. This represents the free surface factor of the energy pipeline.

[0099] It should be noted that the internal pressure change of the energy pipeline refers to the internal pressure change of the pipeline over a historical period determined using the linear fatigue accumulation (Miner) damage criterion. For example, Figure 6 This is an example diagram illustrating the historical pressure changes within a pipeline, as provided in Embodiment 2 of the present invention.Figure 6 As shown in the figure, the horizontal axis represents pressure change. The vertical axis represents the number of pressure fluctuation cycles, as shown in the figure. Using Miner's fatigue damage criterion and considering the fluctuations in cyclic pressure, the pressure fluctuations inside the pipeline over a historical period can be transformed into a series of constant pressure changes using the three-point rainflow counting method. .

[0100] Fatigue cyclic load can be expressed as: In the formula, This indicates the number of times the internal pressure of a pipeline changes within a historical period. This represents the equivalent number of iterations determined using the Miner criterion. This represents the equivalent variable load. Indicates all The fatigue damage effect is normalized to an equivalent pressure range to simplify calculations. For example, the equivalent pressure cycle number inside the pipeline is 50 times per year, and the equivalent variable load is 2 MPa.

[0101] For example, suppose a pipeline pressure history contains two cycles:

[0102] ΔP1 = 1000 kPa, N1 = 200 occurrences;

[0103] ΔP2 = 2000 kPa, N2 = 50 occurrences;

[0104] Material constant m=3.

[0105] Equivalent calculation:

[0106] NeqΔP 3 =(200×1000 3 )+(50×2000 3 )

[0107] Assuming ΔP = 2000 kPa is chosen as the standard range, then:

[0108] Neq = 75 times / year

[0109] Here, Neq represents the equivalent cycle number, thus simplifying the complex variable amplitude load into 75 equivalent 2000 kPa pressure fluctuations per year.

[0110] d1) Determine the growth rate of axial crack growth caused by a single pressure fluctuation based on the stress intensity factor range and regression parameters.

[0111] In this embodiment, the growth rate of an axial crack caused by a single pressure fluctuation can be expressed as: In the formula, and The regression parameters representing Paris's Law are obtained experimentally. This indicates the range of stress intensity factors under a specific pressure cycle.

[0112] Specifically, by substituting the stress intensity factor range and regression parameters into the above formula, the growth rate of axial crack growth caused by a single pressure fluctuation can be obtained.

[0113] S208. Multiply the number of pressure fluctuation cycles by the growth rate of axial crack growth caused by one pressure fluctuation to obtain the second growth rate of axial crack growth caused by cyclic fatigue load.

[0114] In this embodiment, the growth rate of axial crack growth caused by fatigue cyclic loading is denoted as the second growth rate. , can be represented as:

[0115] In the formula, This indicates the number of pressure fluctuation cycles inside the pipeline. The meanings of the other letters can be found in the description above and will not be repeated here.

[0116] S209. Add the first growth rate to the second growth rate and obtain the sum as the total growth rate of the axial crack.

[0117] In this embodiment, the total growth rate of axial cracks , can be represented as: In the formula, Indicates the first growth rate. This indicates the second growth rate.

[0118] Specifically, the first growth rate and the second growth rate are added together, and the sum is taken as the total growth rate of the axial crack.

[0119] S210. Based on the rupture failure pressure of the energy pipeline and the maximum operating pressure of the energy pipeline, construct the first limit state equation for the rupture of the energy pipeline.

[0120] In this embodiment, the maximum operating pressure can be specifically understood as the highest internal pressure that the pipeline system can withstand under normal operating conditions. This step is used to construct the limit state equation for the energy pipeline rupture, denoted as the first limit state equation. The first limit state equation can be expressed as:

[0121] In the formula, the subscript These respectively indicate pipe rupture. This indicates the failure pressure of an energy pipeline due to rupture. This indicates the maximum operating pressure of the pipeline.

[0122] S211. Determine the predicted crack growth depth of the axial crack based on the total growth rate of the axial crack.

[0123] In this embodiment, the growth depth of the axial crack during future operation can be predicted based on the total growth rate of the axial crack, denoted as the predicted crack growth depth.

[0124] S212. Based on the wall thickness of the energy pipeline and the predicted crack growth depth of the axial crack, construct the second limit state equation for the leakage of the energy pipeline.

[0125] In this embodiment, since cracks lead to pipe rupture or leakage failure, limit state equations are constructed under leakage and rupture conditions. This step is used to construct the limit state equation for energy pipeline leakage, denoted as the second limit state equation. The second limit state equation can be expressed as:

[0126] In the formula, the subscript This indicates a pipe leak. This indicates the wall thickness of the energy pipeline. This indicates the predicted crack growth depth in an energy pipeline.

[0127] S213. The first and second limit state equations are used as the limit state models for the bursting or leakage of energy pipelines.

[0128] In this embodiment, the limit state model for an energy pipeline rupture or leak can be expressed as: The meanings of each letter in this model can be found in the explanations above, and will not be repeated here.

[0129] S214. Generate a set number of random samples based on the corresponding distribution of the current detection data of the energy pipeline.

[0130] In this embodiment, the current detection data of the energy pipeline is used as a random variable. The random variable mainly involves parameters such as diameter, yield strength, tensile strength, pipe wall thickness, maximum working pressure, maximum crack depth error, maximum crack length error, elastic modulus, environmental growth rate, and equivalent cycle count. Multiple sets of random samples are generated based on the corresponding distribution of the random variables. The number of samples can be set according to actual needs and is not specifically limited here. For example, based on the random variables... The corresponding distribution produces Different sets of random numbers ,in, .

[0131] S215. Substitute each group of random samples into the limit state model to obtain the function values ​​for each group.

[0132] Specifically, each group of random samples is substituted into the limit state model to calculate the value of its function. ,in, .

[0133] S216. Count the number of groups whose function values ​​are less than zero, and divide the number by a set number to obtain the failure probability of the energy pipeline.

[0134] In this embodiment, the number of groups with a function value less than zero is counted, and the quotient of this number with a set quantity is used to obtain the failure probability of the energy pipeline. Continuing with the above example, if there are... If the function value corresponding to the set of random numbers is less than 0, then the probability of failure of the energy pipeline is: It is understandable that, according to Bernoulli's law of large numbers and the properties of normally distributed random variables, when the generated random array... As the quantity approaches infinity, the probability of structural failure gets closer to its true value.

[0135] The above technical solution specifies the steps for determining the pipeline's rupture failure pressure, determining the environmental factors leading to axial crack growth, determining the axial crack growth caused by cyclic fatigue loads, determining the total axial crack growth rate, constructing a limit state model for pipeline rupture or leakage, and solving the limit state model using a defined algorithm to determine the failure probability of the energy pipeline. First, the rupture failure pressure of the energy pipeline is calculated. Then, the number of internal pressure fluctuation cycles is determined using the three-point rainflow counting method. Further, based on the calculated number of cycles, the crack growth rate is determined. Then, limit state equations for pipeline leakage and rupture failure are constructed using reliability methods, and the failure probability of the energy pipeline is solved using the Monte Carlo method. This technical solution provides a rapid calculation method for the failure probability of energy pipelines with axial cracks under cyclic fatigue loads based on internal detection data. It combines internal detection data to calculate the failure probability of pipelines with multiple cracks, facilitating use and operation by frontline production personnel and improving their work efficiency. Simultaneously, considering the cyclic effect of internal pressure loads and crack growth, the failure probability of energy pipelines with axial cracks can be calculated, providing guidance for the safe operation of energy pipelines and ensuring their safe operation. Furthermore, considering pipeline leakage and rupture failure modes, the time-varying failure probability of pipeline systems with a large number of axial cracks can be calculated in batches. This method can be used to develop cloud computing tools, which can greatly improve computational efficiency and save economic costs.

[0136] To more clearly illustrate the method for determining the failure probability of an energy pipeline with axial cracks under fatigue load provided in this embodiment of the invention, a practical application scenario of determining the failure probability of an energy pipeline with axial cracks under fatigue load is used as an example. In this embodiment, the proposed method is extended into a cloud-based pipeline reliability tool by using VBA (Visual Basic for Applications) and Matlab programming. Internal detection data and failure probabilities are integrated to form a cloud-based calculation tool, and the proposed method is used to create a tool for batch calculation of the failure probability of pipelines with crack defects. This tool is developed using the VBA environment command set "macro" built into the Microsoft Excel platform. VBA is the macro language version of Microsoft Visual Basic, which is based on VB (Visual Basic) and has a similar language structure to VB. The developed tool can handle a variety of widely used probability distribution functions, including normal, lognormal, exponential, Rayleigh, and uniform distributions. Figure 7 Example diagram of the Excel pipeline reliability variable input interface, such as... Figure 7 The image shows a partial screenshot of the parameter input interface, which consists of two parts: The first part, "Probability Distribution Parameter Definition," is used to select the type of probability distribution and define the mean and standard deviation of variables based on the nominal values ​​of the material and geometric characteristics of the pipe with crack defects. Specifically, this can include the number of simulations for the analog control system (MCS), sign, distribution type, mean / nominal value, compilation coefficient, standard deviation, model selection, calculation time, diameter, yield strength, tensile strength, etc. The second part, "Setting Nominal Parameter Values," is used to input the pipe material and geometric properties, as well as the defect size. These nominal values ​​can be provided by the internal inspection report. Specifically, this can include internal inspection and pipe information input, ID, sign, diameter, nominal minimum yield strength, nominal minimum tensile strength, pipe wall thickness, maximum operating pressure, etc. The specific details shown in the image will not be elaborated further here.

[0137] For example, in a certain application scenario, cloud computing tools are used to calculate the failure probability of a cracked energy pipeline. Table 2 shows the pipeline physical property parameters in an application scenario provided by Embodiment 2 of the present invention. As shown in Table 2, the pipeline physical property parameters include diameter, yield strength, tensile strength, pipe wall thickness, maximum operating pressure, environmental growth rate, Charpy impact value, and regression parameters. , And the elastic modulus. Table 3 is an example table of crack defect size parameters of energy pipeline in an application scenario provided by Embodiment 2 of the present invention. As shown in Table 3, the crack defect size parameters include crack length and depth. The failure probability of the cracked energy pipeline was calculated using cloud computing tools, and the calculation results are shown in Tables 4 and 5.Figure 8 and Figure 9 Table 4 is an example table of the failure probability of a pipe with crack defects bursting in an application scenario provided by Embodiment 2 of the present invention. As shown in Table 4, the failure probability of a pipe with crack defects bursting is calculated based on the accumulated years, and the failure probabilities of defects 1, 2, and 3 are calculated respectively. Table 5 is an example table of the failure probability of a pipe with crack defects leaking in an application scenario provided by Embodiment 2 of the present invention. As shown in Table 5, the failure probability of a pipe with crack defects leaking is calculated based on the accumulated years, and the failure probabilities of defects 1, 2, and 3 are calculated respectively.

[0138] Table 2

[0139]

[0140] Table 3

[0141]

[0142] Table 4

[0143]

[0144] Table 5

[0145]

[0146] Figure 8 This is an example diagram illustrating the probability of burst failure of a cracked pipeline in an application scenario provided in Embodiment 2 of the present invention, as shown in the figure. Figure 8 As shown, the horizontal axis represents time in years, and the vertical axis represents the probability of bursting. The figure shows the probability of the energy pipeline bursting and failing, and curves are plotted for defects 1, 2 and 3 respectively. Figure 9 This is an example diagram illustrating the probability of leakage failure in a cracked pipeline in an application scenario provided by Embodiment 2 of the present invention. Figure 9 As shown, the horizontal axis represents time in years, and the vertical axis represents the probability of leakage. The graph illustrates the probability of leakage failure of this energy pipeline, with curves plotted for defects 1, 2, and 3. According to relevant standards, defect 1 is considered acceptable within two years, defect 2 within four years, and defect 3 within three years. This example demonstrates that the method proposed in this embodiment can quickly calculate the failure probability of a large number of defective pipelines, thereby determining whether the failure risk is acceptable. Practical applications have been conducted using the failure probability calculation tool developed using the method provided in this embodiment, and the results show that it can accurately predict the probability of rupture and leakage failure within the next 20 years.

[0147] Example 3

[0148] Figure 10This is a schematic diagram of a device for determining the failure probability of an energy pipeline with axial cracks under fatigue load, provided in Embodiment 3 of the present invention. This device is applicable to determining the failure probability of an energy pipeline with axial cracks under fatigue load. This device can be implemented in hardware and / or software and is generally integrated into electronic equipment. Figure 10 As shown, the device includes: a pressure determination module 31, a rate determination module 32, a model building module 33, and a probability determination module 34, wherein,

[0149] The pressure determination module 31 is used to determine the rupture failure pressure of the energy pipeline based on the current detection data of the energy pipeline containing the axial crack;

[0150] The rate determination module 32 is used to determine the total growth rate of the axial crack based on the first growth rate caused by the environment and the second growth rate caused by cyclic fatigue load.

[0151] The model building module 33 is used to build a limit state model of the pipeline rupture or leakage based on the rupture failure pressure of the energy pipeline and the total growth rate of the axial crack.

[0152] The probability determination module 34 is used to solve the limit state model using a set algorithm to determine the failure probability of the energy pipeline.

[0153] The above technical solution, based on the current detection data of axial cracks in energy pipelines, predicts the rupture failure pressure and the total growth rate of axial cracks in the energy pipelines. When determining the total growth rate of axial cracks, it considers the influence of both the environment and fatigue loads inside the pipeline on crack growth. Then, based on the rupture failure pressure and the total growth rate of axial cracks in the energy pipelines, it constructs a limit state model for pipeline rupture or leakage. Subsequently, it uses a set algorithm to solve the limit state model, realizing the assessment of the failure probability of the pipeline in the future operating period. This can save time and economic costs and ensure the safety of energy pipelines.

[0154] Optionally, the pressure determination module 31 is specifically used for:

[0155] The expansion coefficient of the energy pipeline is determined based on its outer diameter, wall thickness, and crack half-length.

[0156] Based on the material yield strength and tensile strength of the energy pipeline, the fracture stress of the plastic failure mode of the energy pipeline is determined;

[0157] Based on the expansion coefficient, fracture stress of plastic failure mode, fracture stress of brittle failure mode, crack depth, and wall thickness of the energy pipeline, and in conjunction with the established pipeline failure pressure determination model, the rupture failure pressure of the energy pipeline is determined.

[0158] Optionally, the device further includes a first determining module for:

[0159] The depth of axial crack growth in the energy pipeline due to environmental factors is obtained at a set interval.

[0160] Divide the crack growth depth by the set time to obtain the first growth rate of the axial crack caused by the environment.

[0161] Optionally, the device further includes a second determining module, which includes:

[0162] The number of cycles determination unit is used to count the number of internal pressure fluctuation cycles of the energy pipeline within a set time using the three-point rainflow counting method, and to determine the number of pressure fluctuation cycles of the energy pipeline.

[0163] The first rate determination unit is used to determine the growth rate of the axial crack caused by a single pressure fluctuation.

[0164] The second rate determination unit is used to multiply the number of pressure fluctuation cycles by the growth rate of the axial crack caused by one pressure fluctuation to obtain the second growth rate of the axial crack caused by cyclic fatigue load.

[0165] Optionally, the first rate determining unit is specifically used for:

[0166] The free surface factor of the energy pipeline is determined based on the crack depth, crack half-length, and wall thickness of the axial crack.

[0167] The defect shape factor of the energy pipeline is determined based on the crack depth and half-length of the axial crack.

[0168] Based on the internal pressure change of the energy pipeline, the free surface factor, the defect shape factor, and the crack depth, the range of the stress intensity factor of the energy pipeline under pressure fluctuation cycle is determined.

[0169] Based on the stress intensity factor range and regression parameters, the growth rate of the axial crack caused by a single pressure fluctuation is determined.

[0170] Optionally, the internal pressure change of the energy pipeline is the internal pressure change of the pipeline over a historical period determined using a linear fatigue cumulative damage criterion.

[0171] Optionally, the rate determination module 32 is specifically used for:

[0172] The first growth rate is added to the second growth rate, and the sum is taken as the total growth rate of the axial crack.

[0173] Optionally, model building module 33 is specifically used for:

[0174] Based on the rupture failure pressure of the energy pipeline and the maximum operating pressure of the energy pipeline, a second limit state equation for the rupture of the energy pipeline is constructed.

[0175] The predicted crack growth depth of the axial crack is determined based on the total growth rate of the axial crack.

[0176] Based on the wall thickness of the energy pipeline and the predicted crack growth depth of the axial crack, a second limit state equation for the leakage of the energy pipeline is constructed.

[0177] The first and second limit state equations are used as the limit state models for the energy pipeline to burst or leak.

[0178] Optionally, the probability determination module 34 is specifically used for:

[0179] Based on the corresponding distribution of the current detection data of the energy pipeline, a set number of random samples are generated;

[0180] Substitute each group of random samples into the limit state model to obtain the function values ​​for each group;

[0181] The number of groups with function values ​​less than zero is counted, and the number is divided by the set number to obtain the failure probability of the energy pipeline.

[0182] The device for determining the failure probability of an energy pipeline with axial cracks under fatigue load provided in this embodiment of the invention can execute the method for determining the failure probability of an energy pipeline with axial cracks under fatigue load provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0183] Example 4

[0184] Figure 11This is a schematic diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0185] like Figure 11 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0186] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0187] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the method for determining the failure probability of an energy pipeline with axial cracks under fatigue loading.

[0188] In some embodiments, the method for determining the failure probability of an energy pipeline with axial cracks under fatigue loading can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the method for determining the failure probability of an energy pipeline with axial cracks under fatigue loading described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the method for determining the failure probability of an energy pipeline with axial cracks under fatigue loading by any other suitable means (e.g., by means of firmware).

[0189] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0190] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0191] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0192] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0193] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0194] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0195] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for determining the failure probability of an energy pipeline with axial cracks under fatigue load as provided in any embodiment of this invention.

[0196] In implementing a computer program product, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0197] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0198] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining failure probability of an energy pipeline containing axial crack under fatigue loading, characterized in that, The method comprises the following steps: determining the burst failure pressure of the energy pipeline according to the current detection data of the energy pipeline containing an axial crack; determining the total growth rate of the axial crack according to a first growth rate of the axial crack caused by the environment and a second growth rate of the axial crack caused by the cyclic fatigue load; constructing a limit state model of the pipeline burst or leakage according to the burst failure pressure of the energy pipeline and the total growth rate of the axial crack; determining the failure probability of the energy pipeline by solving the limit state model with a set algorithm; wherein the step of determining the second growth rate of the axial crack caused by the cyclic fatigue load comprises: counting the internal pressure fluctuation cycle number of the energy pipeline within a set time by using a three-point rainflow counting method to determine the pressure fluctuation cycle number of the energy pipeline; determining the growth rate of the axial crack caused by one pressure fluctuation; multiplying the pressure fluctuation cycle number by the growth rate of the axial crack caused by one pressure fluctuation to obtain the second growth rate of the axial crack caused by the cyclic fatigue load; wherein the step of determining the growth rate of the axial crack caused by one pressure fluctuation comprises: determining a free surface factor of the energy pipeline according to the crack depth, crack half-length and wall thickness of the axial crack; determining a defect shape factor of the energy pipeline according to the crack depth and crack half-length of the axial crack; determining a stress intensity factor range of the energy pipeline under pressure fluctuation cycle according to the internal pressure change of the energy pipeline, the free surface factor, the defect shape factor and the crack depth; determining the growth rate of the axial crack caused by one pressure fluctuation according to the stress intensity factor range and regression parameters.

2. The method of claim 1, wherein, The step of determining the burst failure pressure of the pipeline according to the current detection data of the energy pipeline containing an axial crack comprises: determining an expansion coefficient of the energy pipeline according to the outer diameter, wall thickness and crack half-length of the energy pipeline; determining a burst stress of a plastic failure mode of the energy pipeline according to the material yield strength and material tensile ultimate strength of the energy pipeline; determining the burst failure pressure of the energy pipeline according to the expansion coefficient, burst stress of the plastic failure mode, burst stress of the brittle failure mode, crack depth and wall thickness of the energy pipeline, and combining a set pipeline failure pressure determination model.

3. The method of claim 1, wherein, The step of determining the first growth rate of the axial crack caused by the environment comprises: obtaining the crack growth depth of the axial crack of the energy pipeline caused by the environment at intervals of a set time; dividing the crack growth depth by the set time to obtain the first growth rate of the axial crack caused by the environment.

4. The method of claim 1, wherein, The internal pressure change of the energy pipeline is the internal pressure change of the pipeline within a historical time period determined by using a linear fatigue cumulative damage criterion.

5. The method of claim 1, wherein, The step of determining the total growth rate of the axial crack according to the first growth rate of the axial crack caused by the environment and the second growth rate of the axial crack caused by the cyclic fatigue load comprises: adding the first growth rate and the second growth rate to obtain an addition result as a total growth rate of the axial crack.

6. The method of claim 1, wherein, constructing a limit state model of burst or leakage of the pipeline according to the burst failure pressure of the energy pipeline and the total growth rate of the axial crack, including: constructing a first limit state equation of burst of the energy pipeline according to the burst failure pressure of the energy pipeline and the maximum operating pressure of the energy pipeline; determining a predicted crack growth depth of the axial crack according to the total growth rate of the axial crack; constructing a second limit state equation of leakage of the energy pipeline according to the wall thickness of the energy pipeline and the predicted crack growth depth of the axial crack; taking the first limit state equation and the second limit state equation as the limit state model of burst or leakage of the energy pipeline.

7. The method of claim 1, wherein, solving the limit state model by using a set algorithm to determine a failure probability of the energy pipeline, including: generating a set number of groups of random samples according to corresponding distributions of current detection data of the energy pipeline; substituting each group of the random samples into the limit state model to obtain a function value of each group; counting a number of groups of the function values less than zero and taking a quotient of the number and the set number to obtain the failure probability of the energy pipeline.

8. An electronic device, comprising: including: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the failure probability determination method of the energy pipeline with axial crack under fatigue load as claimed in any one of claims 1-7.

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