An RFID-based blowout preventer stack full life cycle evaluation method

Abstract

The application discloses a blowout preventer set full life cycle evaluation method based on RFID, belongs to the field of oil well safety monitoring, and comprises the following steps: installing an RFID tag on a blowout preventer, monitoring function data such as pressure, flow, temperature and sulfur content parameters in the blowout preventer set cavity, and establishing a blowout preventer set simulation model; further establishing a blowout preventer set full information storage database; correcting and predicting the simulation model; monitoring the blowout preventer set according to the RFID tag; performing safety prediction on the blowout preventer set components, such as the blowout preventer set ram, the blowout preventer set rubber core and the blowout preventer set flange connection seal, according to the simulation model and the prediction model; and S6, performing maintenance on the blowout preventer set components according to the safety prediction result. Based on the RFID technology, the blowout preventer cavity pressure, temperature, sulfur content, flow and other environmental parameters under the blowout working condition can be more truly perceived, and more reliable data is provided for safety prediction of the blowout preventer components.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil well safety monitoring, in particular to a blowout preventer set full life cycle evaluation method based on RFID. BACKGROUND

[0002] In the process of oil exploration and oilfield development, the blowout preventer set is the last barrier to ensure the safety of the oil well. Under the extreme well blowout working conditions of high temperature, high pressure, high sulfur content, etc., the sealing components of the blowout preventer set may be eroded by the high-speed fluid in the well and fail, causing deformation or leakage of the key components of the blowout preventer set. If a component of the blowout preventer set fails, it will result in unsuccessful well shut-in, and further cause blowout out of control, causing environmental pollution, personnel injury and other serious consequences.

[0003] The blowout preventer set is a fully sealed device, and it is impossible to directly observe and judge whether the key components in its cavity have deformed or failed. If the blowout preventer is disassembled for comprehensive inspection, the cost will be high. The current flaw detection equipment has poor detection effect on the cavity of the blowout preventer set. It is crucial to obtain more accurate information of the blowout preventer set under the well blowout working condition, more accurately evaluate the safety performance of each component of the blowout preventer set, and solve the technical problem of difficulty in maintenance for the on-site maintenance personnel under the condition of ensuring safety. SUMMARY

[0004] In view of the above problems, the present application provides a blowout preventer set full life cycle evaluation method based on RFID, which can accurately evaluate the safety performance of each component of the blowout preventer set and provide accurate guidance for on-site maintenance.

[0005] The technical scheme of the present application is:

[0006] A blowout preventer set full life cycle evaluation method based on RFID, comprising the following steps:

[0007] S1, installing an RFID tag on the blowout preventer, monitoring the function data of the cavity pressure, flow, temperature, sulfur content and other parameters of the blowout preventer set, and establishing a blowout preventer set simulation model;

[0008] S2, establishing a blowout preventer set full information storage database according to the obtained function data;

[0009] S3, correcting and predicting the simulation model;

[0010] S4, monitoring the blowout preventer set for the full life cycle according to the RFID tag;

[0011] S5, bringing the RFID tag data obtained in step S4 into the simulation model and the prediction model to predict the safety of the blowout preventer set components, including the blowout preventer set ram, the blowout preventer set rubber core and the blowout preventer set flange connection seal;

[0012] S6. Maintain the blowout preventer assembly based on the safety prediction results.

[0013] In step S1, a simulation model of the blowout preventer assembly is established using the geometric and physical parameters of the blowout preventer assembly to determine the location where the RFID tag to be installed on the blowout preventer is located.

[0014] The functional parameters in step S2 include: basic information of the blowout preventer assembly and simulation data of tag installation, various test data of blowout preventer assembly materials, monitoring data of special RFID safety monitoring tags, maintenance and operation knowledge of the blowout preventer assembly, and safety prediction data of the blowout preventer assembly. The safety prediction data of the blowout preventer assembly includes: operational mechanics simulation data of the blowout preventer assembly, erosion prediction data of sealing components, remaining life prediction data, and fatigue prediction data.

[0015] In step S3, fatigue, corrosion resistance, and erosion resistance tests are conducted on the materials used to manufacture the blowout preventer assembly, and the simulation model is corrected and predicted based on the test results.

[0016] In step S4, the data recorded by the RFID tag is transmitted to the full information storage database.

[0017] The safety prediction method for blowout preventer gates is as follows:

[0018] Importing the blowout preventer operation data monitored by the tag into the constructed 3D gate simulation model, and based on the three-dimensional transient temperature field control equation, a set of linear differential equations with time t as the independent variable is obtained:

[0019]

[0020] In the formula, C is the heat capacity matrix; C is an array of derivatives of nodal temperatures with respect to time t; K is the heat conduction matrix; φ is the array of nodal temperatures; P is the array of temperature loads; the matrices C, K, and P are integrals of their respective matrix elements as follows:

[0021]

[0022]

[0023]

[0024] Using the above equations, the partial differential equation problem in the time and space domains is transformed into a temperature differential equation φ with N nodes in the space domain. i The initial value problem of (t) is solved; the equation calculated using thermal structural analysis is as follows:

[0025]

[0026] where M is the mass matrix; u and T are displacement and temperature loads, respectively; C is the structural damping matrix; C t is the specific heat matrix; K is the structural stiffness matrix; K t is the thermal conduction matrix; F is the total equivalent nodal force array; Q is the total equivalent nodal heat flux vector.

[0027] The safety prediction of the BOP rubber core is as follows:

[0028] The job information monitored by the label and the rubber core data stored in the full information storage database are imported into the 3D rubber core simulation model constructed, and the rubber core constitutive model is constructed based on the Mooney-Rivlin model, and the original model is expanded to obtain the following strain energy function model:

[0029]

[0030] In the formula, d k is the compressibility coefficient of the material, J is the ratio of the volume after deformation to the volume before deformation, and N is the polynomial order;

[0031] Based on the Miner damage mechanism, the rubber core fatigue is calculated by using Fe-safe, the maximum fatigue damage value is set as x (x < 1), and the calculation formula is:

[0032]

[0033] In the formula, Ni is the fatigue life of the rubber core when the stress is cyclically loaded; ni is the number of times when the stress is cyclically loaded; i Δσ i ;

[0034] Based on the variable conversion algorithm, the aging performance change and the service life of the rubber core are predicted, and the relationship between the corrected rubber core performance P, time t and temperature T is:

[0035]

[0036] P(T0,t0)=P(T,t)

[0037]

[0038] ρ1 and ρ2 are the densities of the rubber core at T1 and T2 temperatures; T0 is the reference temperature; α T is only related to T0; b is the point-slope slope after improvement of the Dakin life calculation method. The relationship diagram of performance and time at different temperatures is obtained, so that the aging performance change and the service life of the rubber core at different temperatures are predicted;

[0039] Based on the Oka model, the erosion rate of the rubber core is predicted, and the relationship is as follows:

[0040] ε(θ)=f(θ)ε(r)

[0041]

[0042] wherein ε(θ) and ε(r) are the erosion rates at and angle r, in mg / kg; n1 and n2 are coefficients related to the Vickers hardness (Hv) of the material; and f(θ) is the erosion angle function.

[0043] The sealing safety prediction method for the flange connection of the blowout preventer is as follows:

[0044] The tag monitoring data and the full information storage database data are imported into the constructed 3D model of the flange connection and the metal sealing ring, mechanical property analysis is performed, the maximum stress area and the flange sealing connection strength are determined;

[0045] The erosion prediction is performed based on the recognized semi-empirical erosion model, the continuous phase adopts the DES model, the boundary calculation uses the k-ε model, the discrete phase adopts the random walking model to calculate the influence of turbulent diffusion on particles, and the erosion model relationship is as follows:

[0046]

[0047]

[0048] wherein ER is the erosion rate, when the solid particles are spherical, the sharpness factor of the Fs particles is 0.2, when the solid particles are sharp, the sharpness factor of the Fs particles is 1; u p is the particle impact velocity (m / s); na is the velocity index;

[0049] Meanwhile, the compressed metal sealing ring model is obtained by simulating the installation process of the metal sealing ring, the thermal stress simulation is performed on the metal sealing ring respectively, the thermal stress distribution of the metal sealing ring is determined, finally, the fatigue life simulation prediction is performed based on the Conffin-Masson fatigue model, and the relationship between the fatigue model and the life prediction is as follows:

[0050]

[0051]

[0052] wherein ε a is the total deformation; ε ea is the elastic deformation component; ε pa is the plastic deformation component; E is the elastic modulus; σ' f is the fatigue strength coefficient; b is the fatigue strength index; ε' f is the fatigue ductility coefficient; C is the fatigue ductility index; N is the life cycle number; ΔT is the temperature difference; K is the Boltzmann constant; f is the cycle frequency; T maxTmax is the highest temperature; δ, β1, β2, E a are undetermined coefficients.

[0053] The beneficial effects of the present application are:

[0054] 1. Based on the technology of RFID, the environmental parameters such as the pressure, temperature, sulfur content and flow in the blowout preventer cavity under the blowout condition can be more truly perceived, and more reliable data for the safety prediction of the blowout preventer assembly is provided.

[0055] 2. The safety prediction calculation is respectively performed on the ram of the blowout preventer group, the flange connection seal and the rubber core, various factors affecting the sealing performance of the blowout preventer group are fully considered, and the reliability and accuracy of the safety monitoring of the blowout preventer group in the whole life cycle are improved. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 is a method flow chart of the blowout preventer group whole life cycle evaluation method based on RFID according to the embodiments of the present application;

[0057] Figure 2 is a schematic diagram of a blowout preventer group simulation model of the blowout preventer group whole life cycle evaluation method based on RFID according to the embodiments of the present application;

[0058] Figure 3 is an RFID tag schematic diagram of the blowout preventer group whole life cycle evaluation method based on RFID according to the embodiments of the present application;

[0059] Figure 4 is a full information storage database schematic diagram of the blowout preventer group whole life cycle evaluation method based on RFID according to the embodiments of the present application;

[0060] Figure 5 is a blowout preventer group material performance test schematic diagram of the blowout preventer group whole life cycle evaluation method based on RFID according to the embodiments of the present application;

[0061] Figure 6 is a blowout preventer group component safety prediction model schematic diagram of the blowout preventer group whole life cycle evaluation method based on RFID according to the embodiments of the present application;

[0062] Figure 7 is a blowout preventer group guidance maintenance replacement model schematic diagram of the blowout preventer group whole life cycle evaluation method based on RFID according to the embodiments of the present application;

[0063] Figure 8 is a history maintenance information and virtual guidance scheme fusion schematic diagram of the blowout preventer group whole life cycle evaluation method based on RFID according to the embodiments of the present application;

[0064] Figure 9It is the information interaction schematic diagram of the whole life cycle evaluation method of the blowout preventer group based on RFID. DETAILED DESCRIPTION

[0065] The embodiments of the present application are further described below with reference to the drawings.

[0066] Embodiment:

[0067] As shown in the figure, a blowout preventer group whole life cycle evaluation method based on RFID comprises the following steps: Figures 1-5

[0068] S1, install RFID tags on the blowout preventer, monitor the function data such as pressure, flow, temperature, and sulfur content in the blowout preventer group cavity, and establish a blowout preventer group simulation model;

[0069] The blowout preventer group simulation model is established through the geometric parameters and physical parameters of the blowout preventer group, and the position of the blowout preventer to be installed RFID tag is determined.

[0070] S2, according to the obtained function data, establish a blowout preventer group whole information storage database;

[0071] The function parameters include: blowout preventer group basic information and tag installation simulation data, blowout preventer group material test data of various types, special RFID safety monitoring tag monitoring data, blowout preventer group maintenance operation knowledge, and blowout preventer group safety prediction data, wherein the blowout preventer group safety prediction data includes: blowout preventer group operation mechanics simulation data, sealing component erosion prediction data, residual life prediction data, and fatigue prediction data.

[0072] S3, correct and predict the simulation model;

[0073] The blowout preventer group manufacturing materials are subjected to fatigue, corrosion resistance, and erosion resistance tests, and the simulation model is corrected and predicted through the test results.

[0074] S4, according to the RFID tag, the whole life cycle of the blowout preventer group is monitored;

[0075] The data recorded by the RFID tag is transmitted to the whole information storage database.

[0076] S5, according to the RFID tag data obtained in step S4, the simulation model and the prediction model are brought into the blowout preventer group, and the safety of the blowout preventer group valve, the blowout preventer group rubber core, and the blowout preventer group flange connection sealing is predicted;

[0077] The blowout preventer valve safety prediction method is as follows:

[0078] ​The tag-monitored blowout preventer operation data is imported into the constructed 3D ram simulation model, and based on the three-position transient temperature field control equation, a linear differential equation group with time t as the independent variable is obtained:

[0079]

[0080] In the formula, C is the heat capacity matrix; is the derivative array of node temperature with respect to time t; K is the heat conduction matrix; φ is the node temperature array; P is the temperature load array; the matrices C, K and P are integrated by corresponding matrix elements of elements as follows:

[0081]

[0082]

[0083]

[0084] Using the above equation, the partial differential equation problem in the time domain and the spatial domain is converted into an initial value problem of the temperature differential equation φ i (t) in the spatial domain with N nodes; the heat structure analysis calculation equation is as follows:

[0085]

[0086] Where M is the mass matrix; u and T are displacement and temperature load respectively; C is the structure damping matrix; C t is the specific heat matrix; K is the structure stiffness matrix; K t is the heat conduction matrix; F is the total equivalent node force array; Q is the total equivalent node heat flux vector.

[0087] The safety prediction of the blowout preventer group rubber core is as follows:

[0088] The tag-monitored operation information and the rubber core data stored in the full information storage database are imported into the constructed 3D rubber core simulation model, and based on the Mooney-Rivlin model, the rubber core constitutive model is constructed, and the original model is expanded to obtain the following strain energy function model:

[0089]

[0090] In the formula, d k is the compressibility coefficient of the material, J is the ratio of the volume after deformation to the volume before deformation, and N is the polynomial order;

[0091] Based on the Miner damage mechanism, the rubber core fatigue is calculated by using Fe-safe, and the maximum fatigue damage value is set as x (x < 1), and the calculation formula is:

[0092]

[0093] In the formula, Ni is Δσ i Fatigue life when stress is cyclically loaded; ni is the number of times i Stress is cyclically loaded;

[0094] Based on the variable conversion algorithm to predict the aging performance changes and life of the rubber core, the corrected rubber core performance P, time t and temperature T relationship is:

[0095]

[0096] P(T0,t0)=P(T,t)

[0097]

[0098] ρ1 and ρ2 are the rubber core densities at T1 and T2 temperatures; T0 is the reference temperature; α T Only related to T0; b is the improved point-slope slope of the Dakin life prediction algorithm. The relationship between performance and time at different temperatures is obtained, so as to predict the aging performance changes and life of the rubber core at different temperatures;

[0099] Based on the Oka model to predict the erosion rate of the rubber core, the relationship is as follows:

[0100] ε(θ)=f(θ)ε(r)

[0101]

[0102] In the formula, ε(θ) and ε(r) are the erosion rates at and angles r, respectively, in mg / kg; n1 and n2 are coefficients related to the Vickers hardness (Hv) of the material. f(θ) is the erosion angle function.

[0103] The sealing safety prediction method of the blowout preventers flange connection is as follows:

[0104] The tag monitoring data and full information storage database data are imported into the constructed flange connection and metal sealing ring 3D model, and the mechanical property analysis is carried out to determine the maximum stress area and the flange sealing connection strength;

[0105] Based on the recognized semi-empirical erosion model, the erosion prediction is carried out, the continuous phase adopts the DES model, the boundary calculation uses the k-ε model, the discrete phase adopts the random walking model to calculate the influence of turbulent diffusion on particles, and the erosion model relationship is as follows:

[0106]

[0107]

[0108] Wherein, ER is the erosion rate, when the solid particles are spherical, the sharpness factor of Fs particles is 0.2, when the solid particles are sharp, the sharpness factor of Fs particles is 1; u p is the particle impact velocity (m / s); na is the velocity index;

[0109] At the same time, the metal seal ring installation process simulation obtains the compressed metal seal ring model, and the thermal stress simulation is carried out respectively to determine the thermal stress distribution of the metal seal ring. Finally, based on the Conffin-Masson fatigue model, the fatigue life simulation prediction is carried out, and the relationship between the fatigue model and the life prediction is as follows:

[0110]

[0111]

[0112] Wherein, ε a is the total deformation; ε ea is the elastic deformation component; ε pa is the plastic deformation component; E is the elastic modulus; σ' f is the fatigue strength coefficient; b is the fatigue strength index; ε' f is the fatigue ductility coefficient; C is the fatigue ductility index; N is the life cycle number; ΔT is the temperature difference; K is the Boltzmann constant; f is the cycle frequency; T max is the highest temperature; δ, β1, β2, E a are undetermined coefficients.

[0113] S6, according to the safety prediction result, the blowout preventer assembly is maintained.

[0114] The data after arrangement is transmitted to the full information storage database and the information interaction module;

[0115] When the data arrangement comparison unit does not satisfy the safety boundary condition set in advance, the fatigue, erosion, life prediction alarm unit of the information interaction module sends an alarm.

[0116] Figure 7The maintenance guidance replacement of damaged or high-position components is shown, wherein: a maintenance guidance platform is established according to a blowout preventer set failure mode maintenance guidance technical manual, a 3D model of the blowout preventer set is established, then a maintenance standard schematic diagram is established, finally a blowout preventer set maintenance guidance animation is formed, the blowout preventer set failure analysis and information matching unit firstly identifies and classifies the prediction results, classifies the failure type identification, then establishes a blowout preventer set failure form database, then establishes a blowout preventer set failure ID identification number, finally matches the best maintenance solution of the blowout preventer set failure;

[0117] Figure 8 The best maintenance solution matched in S6 and the historical maintenance information are virtually and virtually fused by combining the failure feature registration and the artificial identification registration, a blowout preventer set failure part maintenance guidance solution is generated and transmitted to a visual portable device to guide maintenance personnel to maintain the blowout preventer set failure;

[0118] Figure 9 The historical maintenance information includes an RFID tag positioning coordinate, a blowout preventer component failure identification, a maintenance 3D scene reconstruction, a blowout preventer component failure part life and a failure form.

[0119] The technology can sense the environment data information in the blowout preventer set cavity under the blowout working condition through the special RFID safety monitoring tag, monitor the blowout preventer operation, provide accurate data support for safety prediction, consider the working condition change in real time, and ensure the safety performance of the blowout preventer set in the whole life cycle.

[0120] The above-described embodiments only express the specific implementation of the present application, and the description is more specific and detailed, but it cannot be understood as the limitation of the scope of the present application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which belong to the protection scope of the present application.

Claims

1. An RFID-based blowout preventer stack full life cycle assessment method, characterized in that, Comprise the following steps: S1, install RFID tag on blowout preventer, monitor blowout preventer group cavity pressure, flow, temperature, sulfur content parameter function data, establish blowout preventer group simulation model; S2, according to the function data, establish the full information storage database of blowout preventer group; S3, correct and predict the simulation model; S4, according to the RFID tag, the whole life cycle of blowout preventer group is monitored; S5, according to the RFID tag data obtained in step S4, the simulation model and the prediction model are substituted, and the safety of the blowout preventer group, the blowout preventer group and the blowout preventer group flange connection sealing is predicted; S6, according to the safety prediction result, the blowout preventer assembly is maintained; Wherein, the safety prediction of blowout preventer group gate is as follows: The operation data monitored by the tag is imported into the 3D gate simulation model, the linear differential equation group with time as the independent variable is obtained based on the three-dimensional transient temperature field control equation, the corresponding matrix element of the element is integrated into the temperature differential equation initial value problem, and the temperature field and stress characteristics of the gate are determined by the thermal structure analysis calculation equation; The safety prediction of blowout preventer group rubber core is as follows: The operation information monitored by the tag and the rubber core data stored in the full information storage database are imported into the 3D rubber core simulation model, and the rubber core constitutive model is constructed based on the Mooney-Rivlin model, the rubber core fatigue is calculated based on the Miner damage mechanism, the aging performance change and life of the rubber core are predicted based on the variable conversion algorithm, and the corrosion rate of the rubber core is predicted based on the Oka model; The safety prediction of blowout preventer group flange connection sealing is as follows: The tag monitoring data and the full information storage database data are imported into the 3D model of flange connection and metal sealing ring, the mechanical property analysis is carried out to determine the maximum stress area and the flange sealing connection strength, the erosion prediction is carried out based on the semi-empirical erosion model, and the fatigue life simulation prediction is carried out based on the Coffin-Manson fatigue model.

2. The method of claim 1, wherein, The step S1, the blowout preventer group simulation model is established by the blowout preventer group geometric parameters and physical parameters, and the position of the blowout preventer to be installed RFID tag is determined.

3. The method of claim 1, wherein, The step S2, the function parameters include: blowout preventer group basic information and tag installation simulation data, blowout preventer group material various test data, special RFID safety monitoring tag monitoring data, blowout preventer group maintenance operation knowledge and blowout preventer group safety prediction data, wherein the blowout preventer group safety prediction data includes: blowout preventer group operation mechanics simulation data, sealing component erosion prediction data, residual life prediction data and fatigue prediction data.

4. The method of claim 1, wherein, The step S3, the fatigue, corrosion resistance and impact resistance test of the blowout preventer group manufacturing material is carried out, and the simulation model is corrected and predicted through the test result.

5. The RFID-based BOP stack life cycle assessment method of claim 1, wherein, The step S4, the data recorded by the RFID tag is transmitted to the full information storage database.

6. The RFID-based BOP stack life cycle assessment method of claim 1, wherein, The step S5, the blowout preventer gate safety prediction method is as follows: The blowout preventer operation data monitored by the tag is imported into the 3D gate simulation model, the linear differential equation group with time t as the independent variable is obtained based on the three-dimensional transient temperature field control equation: ， where C is a heat capacity matrix; is an array of derivatives of the node temperatures with respect to time t; K is the heat conduction matrix; is the array of node temperatures; P is the array of temperature loads; The matrix C, K and P are integrated by corresponding matrix elements of elements as follows: ， ， ， Using the above equation, the partial differential equation problem in the time domain and the spatial domain is converted into the initial value problem of the temperature differential equation with N nodes in the spatial domain ; the heat structure analysis calculation equation is as follows: ， where M is the mass matrix; u and T are the displacement and temperature loads, respectively; C is the structural damping matrix; C t is the specific heat matrix; K is the structural stiffness matrix; K t is the thermal conduction matrix; F is the total equivalent nodal force array; Q is the total equivalent nodal heat flux vector.

7. The RFID-based BOP stack life cycle assessment method of claim 1, wherein, The step S5, the blowout preventer group rubber core safety prediction is as follows: The job information and the rubber core data stored in the full information storage database are imported into the 3D rubber core simulation model, a rubber core constitutive model is established based on the Mooney-Rivlin model, and the original model is unfolded to obtain the following strain energy function model: ， where d k is the compressibility of the material, J is the ratio of the volume after deformation to the volume before deformation, and N is the order of the polynomial. Based on the Miner damage mechanism, the rubber core fatigue is calculated by using Fe-safe, the maximum fatigue damage value is set as x, x < 1, and the calculation formula is as follows: ， In the formula, Ni is the fatigue life when stress is cyclically loaded; ni is the number of times stress is cyclically loaded. In the formula, Ni is the fatigue life when stress is cyclically loaded; ni is the number of times stress is cyclically loaded. Based on the variable conversion algorithm, the aging performance change and the service life of the rubber core are predicted, and the relationship between the corrected rubber core performance P, time t and temperature T is as follows: ， ， ， and T1 and T2 are the densities of the rubber core at temperatures T1 and T2; T0 is the reference temperature; only related to T0; b is the modified point-slope slope of the Dakin lifetime calculation; a graph of performance versus time at different temperatures is obtained, thereby predicting the change in aging performance and lifetime of the rubber core at different temperatures; Based on the Oka model, the rubber core erosion rate is predicted, and the relationship is as follows: ， ， wherein and respectively the erosion rate at an angle of θ and at an angle of r in mg / kg; n1 and n2 are coefficients related to the Vickers hardness of the material; is the erosion angle function.

8. The RFID-based BOP stack life cycle assessment method of claim 1, wherein, The step S5, the blowout preventer group flange connection sealing safety prediction method is as follows: The tag monitoring data and the full information storage database data are imported into the flange connection and metal sealing ring 3D model, mechanical property analysis is carried out, the maximum stress area and the flange sealing connection strength are determined; Based on the well-accepted semi-empirical erosion model, the erosion prediction is carried out. The continuous phase is calculated by the DES model, and the boundary is calculated by the model. The discrete phase is calculated by the random walk model to calculate the influence of turbulent diffusion on particles. The erosion model relationship is as follows: ， ， wherein ER is the erosion rate, Fsparticle is the sharpness factor of the solid particle, which is 0.2 when the solid particle is spherical and 1 when the solid particle is sharp; is the particle impact velocity in m / s; na is the velocity exponent; At the same time, the metal sealing ring model after compression is obtained by simulating the installation process of the metal sealing ring, thermal stress simulation is carried out on the metal sealing ring respectively, the thermal stress distribution of the metal sealing ring is determined, finally, fatigue life simulation prediction is carried out based on the Conffin-Masson fatigue model, and the relationship between the fatigue model and the life prediction is as follows: ， ， wherein is the total deformation; is the elastic deformation component; is the plastic deformation component; E is the modulus of elasticity; is the fatigue strength coefficient; b is the fatigue strength exponent; is the fatigue ductility coefficient; C is the fatigue ductility exponent; N is the number of life cycles; is the temperature difference; K is the Boltzmann constant; is the cycle frequency; T max is the maximum temperature; , β1, β2, E a is the undetermined coefficient.

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