Composite tube heat exchanger anti-flow corrosion optimization method based on heat-fluid-solid coupling

Abstract

The invention relates to the technical field of corrosion resistance optimization of heat exchanger equipment, and discloses a heat-fluid-solid coupling-based flow corrosion resistance optimization method for a composite tube heat exchanger, which comprises the following steps of: firstly, constructing a heat exchanger flow-heat transfer-mass transfer multi-field coupling mathematical model; cFD numerical simulation under various working conditions is carried out on the heat exchanger with different set structural parameters and composite pipe coating thicknesses on the basis of the model; flow corrosion of the heat exchanger is quantitatively predicted and the corrosion risk is quantitatively evaluated in combination with a simulation result; carrying out an adhesive force mechanical property test on the heat exchange tube coating sample to obtain coating adhesive stress data; checking the strength and the stability of key components of the heat exchanger by adopting a fluid thermosetting indirect coupling method; and finally, multiple targets are designed, and an optimal design scheme meeting constraint conditions is obtained based on the data. The problem of complex flow corrosion which is difficult to process in a traditional design method is solved, and the overall performance and safety of the heat exchanger are improved.

Description

Technical Field

[0001] This invention relates to the field of corrosion resistance optimization technology for heat exchanger equipment, specifically a method for optimizing the flow corrosion resistance of composite tube heat exchangers based on thermal-fluid-structure interaction. Background Technology

[0002] Atmospheric and vacuum distillation PPS composite tube heat exchangers are widely used in chemical, energy, and other fields due to their excellent heat transfer performance and strong corrosion resistance. However, in practical applications, heat exchangers may face the problem of flow corrosion under atmospheric and vacuum distillation conditions. Flow corrosion is a localized material loss caused by the interaction between the fluid and the surface of the heat exchanger material. Its impact is particularly significant under high temperature and high pressure environments, and in severe cases, it can lead to equipment failure, affecting the safety and economy of the system.

[0003] Traditional heat exchanger corrosion resistance design methods rely on experience and material selection. While these methods can alleviate corrosion problems to some extent, they cannot provide sufficient support for optimized design under complex operating conditions and urgently need to be addressed. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides an optimization method for the anti-flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction, which effectively solves the complex flow corrosion problem that is difficult to handle in traditional design methods, and improves the overall performance and safety of the heat exchanger.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention discloses an optimization method for the flow corrosion resistance of composite tube heat exchangers based on thermal-fluid-structure interaction, comprising the following steps: S1. Based on the mass, momentum, energy conservation equations and composition equations containing source terms in vector form, a Mixture multiphase flow model for the tube side and shell side is constructed. The RNG k-ε turbulence model is used to describe the turbulence characteristics. The heat conduction equation of the solid tube wall is established, forming a multi-field coupled mathematical model of heat exchanger flow-heat transfer-mass transfer. S2. Based on the multi-field coupled mathematical model, CFD numerical simulations were performed on heat exchangers with different structural parameters and composite tube coating thicknesses under various operating conditions. The CFD numerical simulation results include temperature field, velocity field, phase fraction, pressure drop and heat transfer efficiency data. S3. Combining CFD numerical simulation results, field failure case analysis, and microscopic analysis of scale and corroded pipe fittings, the corrosion risks that heat exchangers may face in atmospheric and vacuum distillation units are analyzed according to different flow corrosion types. An RBI integrated database and a heat exchanger failure model are constructed to quantitatively predict the flow corrosion of heat exchangers and quantitatively assess the corrosion risk. S4. Perform adhesion and mechanical property tests on the heat exchanger tube coating samples to obtain coating adhesion stress data; S5. Using the fluid-thermal-structure indirect coupling method, the gravity load, as well as the temperature load, wall pressure and shear stress calculated from the CFD numerical simulation results, are applied to the solid structure finite element element to check the strength and stability of the key components of the heat exchanger and obtain finite element calculation data. S6. With corrosion resistance, high efficiency, and high reliability as objectives, and with relevant heat exchanger design standards and operating conditions as constraints, based on the data obtained from S2 to S5, a nonlinear function mapping relationship between optimization parameters and heat exchanger performance, corrosion, strength, and stability indicators is constructed using machine learning algorithms. The optimal design scheme that meets the constraints is selected from the solution set.

[0006] As a further improvement to the above scheme, in step S1, the expression for the Mixture multiphase flow model of the tube side and shell side is as follows: ; ; ; ; In the formula, Pressure; The density of the mixed phase; It is a velocity vector; For time; Gradient operator; For quality source items; The dynamic viscosity of the mixed phase; It is the vector of gravitational acceleration; The total number of categories of components; Components The drift velocity vector; superscript It is the transpose of the vector; Components Volume fraction; The mixed phase temperature; Components The ratio of the total energy; Effective thermal conductivity; Components Explicit enthalpy; Components The diffusion flux; The effective stress tensor is calculated as the sum of the molecular stress tensor and the turbulent stress tensor. Components The mass fraction; Components Mass transfer source term; For energy source terms; It is turbulent viscosity; For an effective Schmidt number; Components The effective diffusion coefficient is expressed as follows: ; In the formula, Components and components The binary diffusion coefficient; ; and Components and components molar mass; and Components and components The diffusion volume; Components The concentration.

[0007] As a further improvement to the above scheme, the expression for the heat conduction equation of the solid tube wall is as follows: ; In the formula, , , , and These represent the solid tube wall density, specific heat capacity, temperature, thermal conductivity, and heat source, respectively, with subscripts. Indicates the number of pipe wall layers.

[0008] As a further improvement to the above scheme, in step S2, the different structural parameters refer to: different spatial arrangements of heat exchange tubes, different baffle structures, and / or different tube sheet structures; wherein, the spatial arrangement of heat exchange tubes includes triangular, quadrilateral, and concentric circular arrangements; the boundary conditions of the CFD numerical simulation adopt velocity inlet and pressure outlet, and the inlet and outlet thermodynamic states and material parameters are determined by the process simulation results of atmospheric and vacuum distillation towers; the outer wall of the heat exchanger is treated under constant wall temperature or adiabatic conditions; the physical properties of water, water vapor, and metal materials are obtained from the database provided by the CFD software; the physical properties of oil, gas, and coating materials are imported through UDF custom editing.

[0009] As a further improvement to the above scheme, in step S3, the types of flow corrosion include aqueous corrosion, dew point corrosion, and wear corrosion. The quantitative assessment methods for corrosion risk include: determining the severe locations of flow corrosion and the distribution characteristics of localized corrosion flow through numerical simulation, and verifying the accuracy of corrosion rate prediction by comparing the locations of field failure cases; obtaining the flow corrosion mechanism and failure mode through microscopic morphology, elemental composition, and metallographic analysis of scale and corroded pipe fittings; collecting multi-source heterogeneous data such as integrated heat exchanger equipment parameters, operating parameters, and environmental parameters, identifying risks based on the RBI integrated database and failure model, calculating the failure probability and consequences, and determining the risk level through a risk assessment model.

[0010] As a further improvement to the above scheme, in step S3, the corrosion risk is quantified using the following formula: ; in, This represents the total risk value of corrosion; It is the probability of failure. Both the severity of the failure consequences and the actual failure are estimated based on numerical simulations, field failure cases, and experimental data. The failure probability of the flow corrosion zone was calculated by comparing numerical simulations with field data. The formula is: ; In the formula, This represents the number of times corrosion failure occurs in simulations or tests. This represents the total number of experiments or simulations. The consequences assessment after a failure considers factors including, but not limited to: economic losses, downtime, and safety risks; among these, the severity of the consequences. The formula for expressing it is: ; In the formula, For the first q The weight of each factor considered in the consequence assessment; For the first q The severity score of each consequence assessment factor is considered.

[0011] As a further improvement to the above scheme, based on the calculated total risk value... The risk level is determined using the following grading criteria: when At that time, the risk level was low. when At that time, the risk level was medium risk; when At that time, the risk level was high.

[0012] As a further improvement to the above scheme, in step S4, the adhesion mechanical performance test includes: using the pull-out method and the cross-cutting method to conduct tensile adhesion test and shear adhesion test of the composite pipe coating respectively, according to the preset standard.

[0013] As a further improvement to the above scheme, in step S5, the fluid-thermal-structure indirect coupling method includes: The fluid and solid flow-heat transfer coupled physical fields were solved to obtain the velocity, pressure, and temperature fields. The solid mesh generation was consistent with the CFD analysis, and the temperature field, wall pressure, shear stress, and gravity load were applied to the finite element elements of the solid structure. The Von-Mises equivalent stress analysis method was used, combined with the distortion energy density intensity theory, for strength verification, considering primary and secondary stresses. The critical buckling load was obtained through elastic system stability analysis, and the flexural stability was verified by comparing it with the actual load, using the tube sheet center deflection and the maximum deflection of the tube bundle as stiffness indicators. The strength and stability verification met the preset standards.

[0014] As a further improvement to the above scheme, in step S6, the machine learning algorithm includes the XGBoost algorithm, the RF algorithm, and the MLP algorithm. The optimal model is selected by comparing the prediction accuracy to construct the strength prediction model of the heat exchanger. The optimization objectives specifically include: maximizing heat exchange efficiency, maximizing critical instability load, minimizing pressure drop, minimizing corrosion risk level, and minimizing maximum effective stress. The constraints specifically include: the optimization parameters are within the set upper and lower limits, the working compressive stress of the tube sheet and tube bundle meets the safety factor requirements, and meets the operating conditions specified in the relevant design standards. The optimization parameters include: composite tube coating thickness, tube bundle arrangement, tube sheet thickness, baffle structure, baffle thickness, and tube joint connection method. The Pareto solution set is obtained by using the NSGA-II algorithm, and the optimal design scheme is selected based on the principle of minimum distance in multidimensional space using the ideal point method.

[0015] Compared with the prior art, the beneficial effects of the present invention are: The present invention discloses an optimization method for the anti-flow corrosion of composite tube heat exchangers. Through thermo-fluid-structure interaction and multi-field coupling numerical simulation, it provides accurate corrosion prediction and optimized design scheme for heat exchangers. By using a multi-objective optimization method combined with machine learning, it maximizes the corrosion resistance and working efficiency of the heat exchanger. It effectively solves the complex flow corrosion problem that is difficult to handle in traditional design methods, and improves the overall performance and safety of the heat exchanger. Attached Figure Description

[0016] Figure 1 This is a basic flowchart of the optimization method for anti-flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction in Embodiment 1 of the present invention.

[0017] Figure 2 This is a flowchart of the optimization method for the flow corrosion resistance of composite tube heat exchangers based on thermal-fluid-structure interaction in Embodiment 1 of the present invention.

[0018] Figure 3 This is a schematic diagram of the structure of the computer terminal in Embodiment 2 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0020] Example 1 This embodiment provides an optimization method for flow corrosion resistance of composite tube heat exchangers based on thermal-fluid-structure interaction. This method is applicable to flue gas heat exchangers using atmospheric and vacuum distillation PPS (polyphenylene sulfide) composite tubes. The heat exchanger comprises a basic structure consisting of triangularly arranged metal tube bundles, tube sheets, baffles, a shell, and tube boxes. The tube side and shell side undergo convective heat exchange through a multi-component medium, involving the coupling of heat conduction, flow, heat transfer, and mass transfer mechanisms at the tube wall. The prototype flue gas heat exchangers for atmospheric and vacuum distillation furnaces are both fixed tube sheet heat exchangers. Except for some differences in temperature, pressure, and material flow, the main body of the heat exchanger consists of triangularly arranged metal tube bundles, tube sheets, baffles, a shell, tube boxes, end caps, tube-side and shell-side inlet and outlet pipes, and support structures. The tube side and shell side undergo convective heat exchange of a multi-component medium, while the tube wall is conductive. The flow, heat transfer, and mass transfer mechanisms are completely identical. If water flows on one side, it can be considered a special case of a multi-component medium, with only differences in boundary conditions; therefore, the same mathematical model can be used to describe it. Since water or other media may undergo phase change in the tube side and shell side, the gas-liquid phase transfer of the medium needs to be considered.

[0021] Please see Figure 1 The optimization method includes the following steps, namely S1-S6.

[0022] S1. Based on the mass, momentum, energy conservation equations and composition equations containing source terms in vector form, a Mixture multiphase flow model for the tube side and shell side is constructed. The RNG k-ε turbulence model is used to describe the turbulence characteristics. The heat conduction equation of the solid tube wall is established, forming a multi-field coupled mathematical model of heat exchanger flow-heat transfer-mass transfer.

[0023] In step S1, the expressions for the Mixture multiphase flow model of the tube side and shell side are as follows: ; ; ; ; In the formula, Pressure; The density of the mixed phase; It is a velocity vector; For time; Gradient operator; For quality source items; The dynamic viscosity of the mixed phase; It is the vector of gravitational acceleration; The total number of categories of components; Components The drift velocity vector; superscript It is the transpose of the vector; Components Volume fraction; The mixed phase temperature; Components The ratio of the total energy; Effective thermal conductivity; Components Explicit enthalpy; Components The diffusion flux; The effective stress tensor is calculated as the sum of the molecular stress tensor and the turbulent stress tensor. Components The mass fraction; Components Mass transfer source term; For energy source terms; It is turbulent viscosity; For an effective Schmidt number, this embodiment uses 0.7; Components The effective diffusion coefficient is expressed as follows: ; In the formula, Components and components The binary diffusion coefficient; ; and Components and components molar mass; and Components and components The diffusion volume; Components The concentration is calculated using the following formula: ; ; ; In addition, energy source items The calculation formula is: ; In the formula, For the condensation interface mesh area, denoted as ρ, where ρ is the grid volume near the condensation surface in the gas phase, and r is the latent heat of the component.

[0024] Given RNG k - ε The turbulence model performs well for swirling and separated flows and is economical for large-scale simulations, so it is proposed to select it. Its turbulent kinetic energy and turbulent dissipation rate equations are as follows: ; ; In the formula, The effective dynamic viscosity of the mixed phase, and These are the average velocity gradient and the turbulent kinetic energy generation rate caused by buoyancy, respectively. The contribution of wave expansion to the total dissipation rate in compressible turbulence. Additional terms for turbulent dissipation rate, turbulent viscosity Model constants .

[0025] Furthermore, the heat conduction through the solid tube wall is expressed by the following equation: ; In the formula, , , , and These represent the solid tube wall density, specific heat capacity, temperature, thermal conductivity, and heat source, respectively, with subscripts. This indicates the number of tube wall layers. When the tube wall consists of a base material and a coating, the physical properties and thermal conductivity calculations are defined separately. Both the tube side and shell side use velocity inlet and pressure outlet boundary conditions. The inlet and outlet thermodynamic states and material flow parameters are determined by process simulations of atmospheric and vacuum distillation columns. The outer wall surface of the heat exchanger is treated as a constant wall temperature or adiabatic conditions depending on the insulation method. The physical properties of water (water vapor) and metallic materials are provided by the CFD software's built-in database. The physical properties of oil, gas, and coating materials are based on publicly available databases from authoritative institutions and databases formed through actual experimental tests, obtained through custom UDF editing.

[0026] S2. Based on the multi-field coupled mathematical model, CFD numerical simulations were performed on heat exchangers with different structural parameters and composite tube coating thicknesses under various operating conditions. The CFD numerical simulation results include temperature field, velocity field, phase fraction, pressure drop, and heat transfer efficiency data.

[0027] In step S2, the different structural parameters refer to: different spatial arrangements of heat exchange tubes, different baffle structures, and / or different tube sheet structures; wherein, the spatial arrangement of heat exchange tubes includes triangular, quadrilateral, and concentric circular arrangements; the boundary conditions of the CFD numerical simulation adopt velocity inlet and pressure outlet, and the inlet and outlet thermodynamic states and material parameters are determined by the process simulation results of atmospheric and vacuum distillation towers; the outer wall of the heat exchanger is treated under constant wall temperature or adiabatic conditions; the physical properties of water, water vapor, and metal materials are obtained from the database built into the CFD software; the physical properties of oil, gas, and coating materials are imported through UDF custom editing, and the custom physical properties are based on publicly available databases from authoritative institutions and actual experimental test data.

[0028] Through the multiphase, multi-field CFD numerical simulation of the heat exchanger with tube-shell-solid-wall coupling, the quantitative influence relationships of temperature field, velocity field, concentration field, phase fraction, pressure drop, and heat transfer efficiency under different heat exchanger tube spatial arrangements, baffle and tube sheet structures under design and variable load conditions (triangular, quadrilateral and concentric circular arrangement) are obtained. This provides a multi-field coupling simulation method and dataset foundation for the optimal design of heat exchanger structure.

[0029] S3. Combining CFD numerical simulation results, field failure case analysis, and microscopic analysis of scale and corroded pipe fittings, this study analyzes the potential corrosion risks of heat exchangers in atmospheric and vacuum distillation units based on different flow corrosion types. It constructs an RBI integrated database and a heat exchanger failure model to quantitatively predict flow corrosion of heat exchangers and quantitatively assess corrosion risks.

[0030] In step S3, the types of flow corrosion may include aqueous corrosion, dew point corrosion, and wear corrosion.

[0031] The quantitative assessment methods for corrosion risk include: determining the severe locations of flow corrosion and the distribution characteristics of localized corrosion flow through numerical simulation, and verifying the accuracy of corrosion rate prediction by comparing the locations of field failure cases; obtaining the flow corrosion mechanism and failure mode through microscopic morphology, elemental composition, and metallographic analysis of scale and corroded pipe fittings; collecting multi-source heterogeneous data such as integrated heat exchanger equipment parameters, operating parameters, and environmental parameters, identifying risks based on the RBI integrated database and failure model, calculating the failure probability and consequences, and determining the risk level through a risk assessment model.

[0032] In this embodiment, the corrosion risk is quantified using the following formula: ; in, This represents the total risk value of corrosion; It is the probability of failure. Both the severity of the failure consequences and the actual failure are estimated based on numerical simulations, field failure cases, and experimental data. The failure probability of the flow corrosion zone was calculated by comparing numerical simulations with field data. The formula is: ; In the formula, This represents the number of times corrosion failure occurs in simulations or tests. This represents the total number of experiments or simulations. The consequences assessment after a failure considers factors including, but not limited to: economic losses, downtime, and safety risks; among these, the severity of the consequences. The formula for expressing it is: ; In the formula, For the first q The weight of each factor considered in the consequence assessment; For the first q The severity score of each consequence assessment factor is considered.

[0033] Based on the calculated total risk value The risk level is determined using the following grading criteria: when At that time, the risk level was low. when At that time, the risk level was medium risk; when At that time, the risk level was high.

[0034] Once the risk level is determined, different maintenance and prevention measures can be taken according to different risk levels.

[0035] The above formulas clearly quantify corrosion risks and define the criteria for classifying each risk level, providing a basis for subsequent risk management and prevention.

[0036] S4. Perform adhesion and mechanical property tests on the heat exchanger tube coating samples to obtain coating adhesion stress data.

[0037] In step S4, the adhesion mechanical performance test includes: performing tensile adhesion test and shear adhesion test on the composite pipe coating using the pull-out method and cross-cutting method respectively, according to preset standards.

[0038] The tensile adhesion test can be performed according to the national standard GB / T5210-2006. The test column diameter is 20 mm and the thickness is ≥10 mm. The substrate test piece diameter is ≥30 mm. The PPS coating thickness is a variable parameter. Samples with different spray thicknesses are selected during the test. When the thickness is approximately 250 μm, the test result is represented by the symbol h. pps =250 μm is used as a constraint in the multi-objective optimization process. The gelling agent selected is cyanoacrylate or two-component fast-drying epoxy gelling agent with higher cohesiveness and adhesion than PPS coating. The test conditions are (23±2)℃, relative humidity (50±5)%, loading direction is perpendicular to the plane of the coating substrate, stress increase rate is ≤1MPa / s, the test is completed within 90s after the load is applied, and the test is repeated ≥3 times and the average value is taken.

[0039] Shear adhesion testing can be performed according to national standard GB / T30790.6. The substrate test panel should be ≥150mm×70mm in size and ≥2mm in thickness. The surface should be spray-cleaned to Sa2.5 grade with a roughness of "Medium (G)". The dry coating thickness deviation should be ≤20% of 250μm. The loading direction should be parallel to the plane of the test piece. The coating thickness is a variable parameter; samples with different spray thicknesses should be selected during testing. The test results are represented by the symbol h. PPS This indicates that it is used as a constraint in the multi-objective optimization process. Other test conditions and procedures are the same as those for tensile adhesion testing. The test is repeated ≥3 times and the average value is taken.

[0040] In the subsequent multi-objective optimization process, the test results for different coating thicknesses can be mapped to different performance parameters (e.g., corrosion resistance, heat transfer efficiency, etc.), and the optimal coating thickness can be selected based on experimental data. For example, suppose the test data are: This indicates performance data (such as corrosion resistance, heat exchange efficiency, etc.) when the coating thickness is h1. This represents the performance data when the coating thickness is h2.

[0041] In optimizing the objective function, the impact of different coating thicknesses on performance can be introduced by establishing a functional relationship, for example: ; in, and These are weighting coefficients. The heat exchanger performance is obtained based on test data, taking into account the impact of coating thickness on performance.

[0042] S5. Using the fluid-thermal-structure indirect coupling method, the temperature load, wall pressure, shear stress and gravity load obtained from the CFD numerical simulation results are applied to the solid structure finite element element to check the strength and stability of the key components of the heat exchanger and obtain finite element calculation data.

[0043] The fluid-thermal-solid indirect coupling method includes: First, the fluid and solid flow-heat transfer coupled physical fields are solved to obtain the velocity field, pressure field, and temperature field results. The solid mesh generation is consistent with the CFD analysis, and the temperature field, wall pressure, shear stress, and gravity load are applied to the solid structure finite element elements. The Von-Mises equivalent stress analysis method is used, combined with the distortion energy density intensity theory, to check the strength, considering primary and secondary stresses. The critical buckling load is obtained through elastic system stability analysis, and the flexural stability is checked by comparing it with the actual load, using the tube sheet center deflection and the maximum deflection of the tube bundle as stiffness indicators. The strength and stability checks are based on the national standards GB150-2011, GB / T151-2014, and JB / T3024-2014.

[0044] S6. With corrosion resistance, high efficiency, and high reliability as objectives, and with relevant heat exchanger design standards and operating conditions as constraints, based on the data obtained from S2 to S5, a nonlinear function mapping relationship between optimization parameters and heat exchanger performance, corrosion, strength, and stability indicators is constructed using machine learning algorithms. The optimal design scheme that meets the constraints is selected from the solution set.

[0045] In this embodiment, three machine learning algorithms—XGBoost (an ensemble learning algorithm based on a gradient boosting framework), RF (random forest), and MLP (multilayer perceptron)—are used to construct nonlinear function mapping relationships between optimization parameters such as PPS coating thickness, tube bundle arrangement, tube sheet thickness, baffle structure, and tube joint connection method of the heat exchanger and heat exchanger performance indicators such as pressure drop and heat transfer efficiency, corrosion evaluation indicators such as corrosion risk level, strength indicators such as maximum effective stress, and stability indicators such as critical instability load. The prediction accuracy of the three models is compared, and the model with higher prediction accuracy is selected to construct the heat exchanger strength prediction model. Then, with the optimization objectives of minimizing heat exchanger pressure drop, maximizing heat transfer efficiency, minimizing corrosion risk level, minimizing maximum effective stress, and maximizing critical instability load, and with constraints including meeting strength and stability requirements, operating conditions specified in relevant national design standards, and the range of geometric parameter variations, a multi-objective optimization mathematical model for the heat exchanger is constructed. Finally, the NSGA-II algorithm is used to solve the model, obtaining the Pareto solution set. Finally, the ideal point method is used to select a solution from the Pareto front based on the principle of minimum distance in multidimensional space as the optimal design scheme for the heat exchanger structure. Note: The ideal point is the extreme point of a multivariate function, while the design parameter points are usually discrete points, so the two generally do not coincide. The objective function and constraints to be constructed are as follows: The optimization objective function can be expressed as: ; in, Indicates the thickness of the PPS coating. and This indicates the heat transfer efficiency and the critical instability load. , and Indicates pressure drop, corrosion risk level, and maximum effective value. These represent the PPS coating thickness, tube bundle arrangement, tube sheet thickness, baffle structure, baffle thickness, and pipe fitting connection method, respectively. The constraints can be expressed as: ; In the formula, and Indicates the first i The lower and upper limits of the optimization parameters, For PPS coating thickness, For the working compressive stress of the tube sheet or tube bundle, The critical instability stress, n For safety factors, the heat exchanger structure optimization design process is as follows: Figure 2 As shown, the optimal combination of geometric parameters for high-performance corrosion resistance, stability, and safe design of the heat exchanger is obtained.

[0046] It should be noted that, Figure 2In the equation, the objective function for coating peeling risk corresponds to minimizing corrosion risk, because reducing the risk of coating peeling will also reduce the probability of corrosion; the objective function for structural stability corresponds to maximizing the critical instability load; and the objective function for fatigue life corresponds to minimizing the maximum effective stress, because the fatigue life function is usually optimized by optimizing the magnitude of stress to extend the service life of the material, and reducing the maximum effective stress directly affects the extension of fatigue life.

[0047] Example 2

[0048] This embodiment provides a computer terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for optimizing the anti-flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction as described in Embodiment 1.

[0049] like Figure 3 As shown, the computer terminal provided in this embodiment includes: at least one processor 101, and a memory 102 connected to at least one processor 101. This embodiment does not limit the specific connection medium between the processor 101 and the memory 102. Figure 3 The example shown is the connection between processor 101 and memory 102 via bus 100. Bus 100 is... Figure 3 The connections between other components are shown in bold lines and are for illustrative purposes only, not as limiting information. Bus 100 can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 3 The bus is represented by a single thick line, but this does not indicate that there is only one bus or one type of bus. Alternatively, the processor 101 may also be called a controller; there is no restriction on the name.

[0050] In this embodiment, the memory 102 stores instructions that can be executed by at least one processor 101. The at least one processor 101 can execute the aforementioned method by executing the instructions stored in the memory 102.

[0051] The processor 101 is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory 102 and calling data stored in memory 102, the processor can perform various functions and process data, thereby monitoring the device as a whole.

[0052] In one possible design, processor 101 may include one or more processing units. Processor 101 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 101. In some embodiments, processor 101 and memory 102 may be implemented on the same chip; in some embodiments, they may also be implemented on separate chips.

[0053] Processor 101 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the optimization method for anti-flow corrosion of composite tube heat exchangers based on thermo-fluid-structure interaction disclosed in Embodiment 1 can be directly manifested as execution by a hardware processor, or executed by a combination of hardware and software modules in processor 101.

[0054] Memory 102, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 102 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 102 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In this embodiment, memory 102 can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0055] By designing and programming the processor 101, the code corresponding to the optimization method for anti-flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction described in the foregoing embodiments can be embedded into the chip, thereby enabling the chip to execute the code during operation. Figure 1 The steps of the optimization method for flow corrosion resistance of composite tube heat exchangers based on thermal-fluid-structure interaction are shown. How to design and program the processor 101 is a technique well-known to those skilled in the art and will not be described further here.

[0056] Example 3 This embodiment provides a computer-readable storage medium storing a computer program thereon. When the program is executed by a processor, it implements the steps of the method for optimizing the anti-flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction as described in Embodiment 1.

[0057] The computer-readable storage medium may include flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage medium may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., provided on the computer device. Of course, the storage medium may include both internal storage units and external storage devices of the computer device. In this embodiment, the memory is typically used to store the operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or will be output.

[0058] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing the resistance to flow corrosion of composite tube heat exchangers based on thermo-fluid-structure interaction, characterized in that, Includes the following steps: S1. Based on the mass, momentum, energy conservation equations and composition equations containing source terms in vector form, a Mixture multiphase flow model for the tube side and shell side is constructed. The RNG k-ε turbulence model is used to describe the turbulence characteristics. The heat conduction equation of the solid tube wall is established, forming a multi-field coupled mathematical model of heat exchanger flow-heat transfer-mass transfer. S2. Based on the multi-field coupled mathematical model, CFD numerical simulations were performed on heat exchangers with different structural parameters and composite tube coating thicknesses under various operating conditions. The CFD numerical simulation results include temperature field, velocity field, phase fraction, pressure drop and heat transfer efficiency data. S3. Combining CFD numerical simulation results, field failure case analysis, and microscopic analysis of scale and corroded pipe fittings, the corrosion risks that heat exchangers may face in atmospheric and vacuum distillation units are analyzed according to different flow corrosion types. An RBI integrated database and a heat exchanger failure model are constructed to quantitatively predict the flow corrosion of heat exchangers and quantitatively assess the corrosion risk. S4. Perform adhesion and mechanical property tests on the heat exchanger tube coating samples to obtain coating adhesion stress data; S5. Using the fluid-thermal-structure indirect coupling method, the gravity load, as well as the temperature load, wall pressure and shear stress calculated from the CFD numerical simulation results, are applied to the solid structure finite element element to check the strength and stability of the key components of the heat exchanger and obtain finite element calculation data. S6. With corrosion resistance, high efficiency, and high reliability as objectives, and with relevant heat exchanger design standards and operating conditions as constraints, based on the data obtained from S2 to S5, a nonlinear function mapping relationship between optimization parameters and heat exchanger performance, corrosion, strength, and stability indicators is constructed using machine learning algorithms. The optimal design scheme that meets the constraints is selected from the solution set.

2. The method for optimizing the resistance to flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 1, characterized in that, In step S1, the expressions for the Mixture multiphase flow model of the tube side and shell side are as follows: In the formula, Pressure; The density of the mixed phase; It is a velocity vector; For time; Gradient operator; For quality source items; The dynamic viscosity of the mixed phase; It is the vector of gravitational acceleration; The total number of categories of components; Components The drift velocity vector; superscript It is the transpose of the vector; Components Volume fraction; The mixed phase temperature; Components The ratio of the total energy; Effective thermal conductivity; Components Explicit enthalpy; Components The diffusion flux; The effective stress tensor is calculated as the sum of the molecular stress tensor and the turbulent stress tensor. Components The mass fraction; Components Mass transfer source term; For energy source terms; It is turbulent viscosity; For an effective Schmidt number; Components The effective diffusion coefficient is expressed as follows: In the formula, Components and components The binary diffusion coefficient; ; and Components and components molar mass; and Components and components The diffusion volume; Components The concentration.

3. The method for optimizing the resistance to flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 2, characterized in that, The expression for the heat conduction equation of a solid tube wall is as follows: In the formula, , , , and These represent the solid tube wall density, specific heat capacity, temperature, thermal conductivity, and heat source, respectively, with subscripts. Indicates the number of pipe wall layers.

4. The method for optimizing the resistance to flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 1, characterized in that, In step S2, the different structural parameters refer to: different spatial arrangements of heat exchange tubes, different baffle structures, and / or different tube sheet structures; wherein, the spatial arrangement of heat exchange tubes includes triangular, quadrilateral, and concentric circular arrangements; the boundary conditions of the CFD numerical simulation adopt velocity inlet and pressure outlet, and the inlet and outlet thermodynamic states and material parameters are determined by the process simulation results of atmospheric and vacuum distillation towers; the outer wall of the heat exchanger is treated as a constant wall temperature or adiabatic conditions; the physical properties of water, water vapor, and metal materials are obtained from the database provided by the CFD software; the physical properties of oil, gas, and coating materials are imported through UDF custom editing.

5. The method for optimizing the resistance to flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 1, characterized in that, In step S3, the types of flow corrosion include aqueous corrosion, dew point corrosion, and wear corrosion; The quantitative assessment methods for corrosion risk include: determining the severe locations of flow corrosion and the distribution characteristics of localized corrosion flow through numerical simulation, and verifying the accuracy of corrosion rate prediction by comparing the locations of field failure cases; obtaining the flow corrosion mechanism and failure mode through microscopic morphology, elemental composition, and metallographic analysis of scale and corroded pipe fittings; collecting multi-source heterogeneous data such as integrated heat exchanger equipment parameters, operating parameters, and environmental parameters, identifying risks based on the RBI integrated database and failure model, calculating the failure probability and consequences, and determining the risk level through a risk assessment model.

6. The method for optimizing the resistance to flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 5, characterized in that, In step S3, the corrosion risk is quantified using the following formula: in, This represents the total risk value of corrosion; It is the probability of failure. Both the severity of the failure consequences and the actual failure are estimated based on numerical simulations, field failure cases, and experimental data. The failure probability of the flow corrosion zone was calculated by comparing numerical simulations with field data. The formula is: In the formula, This represents the number of times corrosion failure occurs in simulations or tests. This represents the total number of experiments or simulations. The consequences assessment after a failure considers factors including, but not limited to: economic losses, downtime, and safety risks; among these, the severity of the consequences. The formula for expressing it is: In the formula, For the first q The weight of each factor considered in the consequence assessment; For the first q The severity score of each consequence assessment factor is considered.

7. The method for optimizing the flow corrosion resistance of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 6, characterized in that, Based on the calculated total risk value The risk level is determined using the following grading criteria: when At that time, the risk level was low. when At that time, the risk level was medium risk; when At that time, the risk level was high.

8. The method for optimizing the resistance to flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 1, characterized in that, In step S4, the adhesion mechanical performance test includes: performing tensile adhesion test and shear adhesion test on the composite pipe coating using the pull-out method and cross-cutting method respectively, according to preset standards.

9. The method for optimizing the flow corrosion resistance of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 1, characterized in that, In step S5, the fluid-thermal-solid indirect coupling method includes: The fluid and solid flow-heat transfer coupled physical fields were solved to obtain the velocity, pressure, and temperature fields. The solid mesh generation was consistent with the CFD analysis, and the temperature field, wall pressure, shear stress, and gravity load were applied to the finite element elements of the solid structure. The Von-Mises equivalent stress analysis method was used, combined with the distortion energy density intensity theory, for strength verification, considering primary and secondary stresses. The critical buckling load was obtained through elastic system stability analysis, and the flexural stability was verified by comparing it with the actual load, using the tube sheet center deflection and the maximum deflection of the tube bundle as stiffness indicators. The strength and stability verification met the preset standards.

10. The method for optimizing the resistance to flow corrosion of composite tube heat exchangers based on thermal-fluid-structure interaction according to claim 1, characterized in that, In step S6, the machine learning algorithms include XGBoost, RF, and MLP algorithms. The optimal model is selected by comparing prediction accuracy to construct the strength prediction model of the heat exchanger. The optimization objectives specifically include: maximizing heat exchange efficiency, maximizing critical instability load, minimizing pressure drop, minimizing corrosion risk level, and minimizing maximum effective stress. The constraints specifically include: the optimization parameters are within the set upper and lower limits, the working compressive stress of the tube sheet and tube bundle meets the safety factor requirements, and the operating conditions specified in the relevant design standards are met. The optimization parameters include: composite tube coating thickness, tube bundle arrangement, tube sheet thickness, baffle structure, baffle thickness, and tube joint connection method. The Pareto solution set is obtained by using the NSGA-II algorithm, and the optimal design scheme is selected based on the principle of minimum distance in multidimensional space using the ideal point method.

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