Heat exchanger system management method based on digital twinning technology

Abstract

According to the heat exchanger system management method based on the digital twinborn body, operation parameters of the heat exchanger are collected in real time, and a multi-physical field simulation model including a thermodynamic system simulation model and a vibration stress simulation model is built in combination with a heat transfer mechanism and material characteristics of the heat exchanger; a digital twinborn body capable of accurately reflecting the actual state of the heat exchanger is constructed; the operation state of the heat exchanger is monitored and diagnosed in real time through the digital twinborn body, and accurate recognition of faults, prediction of the service life of key components and optimization of the operation working condition are achieved. The management efficiency and reliability of the heat exchanger system are remarkably improved, the equipment maintenance cost and downtime are reduced, and powerful technical support is provided for efficient and stable operation of the heat exchanger in industrial production.

Description

Technical Field

[0001] The present invention relates to the technical field of heat exchanger operation monitoring and management, and particularly to a management method for a heat exchanger system based on digital twin technology. Background Art

[0002] Heat exchangers are widely used in industrial production and are key equipment for realizing heat exchange between media. Their operating performance directly affects the energy efficiency and reliability of the system. However, traditional management methods mainly rely on static monitoring and empirical judgment, and have limited ability to identify the dynamic operating state and potential faults under complex working conditions. This method often makes it difficult to detect problems in a timely manner, which may lead to a decrease in equipment operating efficiency or the risk of failure, increasing maintenance costs and downtime losses.

[0003] With the in-depth development of industrial digitization, digital twin technology provides a new solution for equipment management. By constructing a virtual model synchronized with the physical device, the operating state of the device can be reflected in real time, and potential problems can be predicted and operating parameters optimized by means of multi-physical field simulation and data analysis. However, in existing heat exchanger management systems, the research and application of digital twin still have limitations. Most research focuses on single thermodynamic modeling, with less coverage of multi-field coupling analysis such as vibration and stress, and lacks a complete multi-physical field simulation system. In addition, the monitoring and prediction functions of the existing technology for the operating state are not comprehensive enough, and there is still much room for improvement in aspects such as fault location, life assessment, and operation optimization.

[0004] Therefore, aiming at the complex working conditions and dynamic characteristics of the heat exchanger system, developing a management method based on digital twin technology, through multi-physical field modeling and real-time data drive, to improve the accuracy of equipment monitoring, the comprehensiveness of fault prediction, and the intelligence of operation management, has important technical value and practical significance. Summary of the Invention

[0005] In the first aspect of the present disclosure, a management method for a heat exchanger system based on digital twins is provided, including the following steps:

[0006] S1. Construct a digital twin of the heat exchanger:

[0007] Collect the operating parameters of the heat exchanger in real time, including the inlet and outlet temperatures, pressures, flows of the hot side and the cold side, and the vibration and deformation parameters of the tube bundle,

[0008] Based on the heat transfer mechanism and the operating parameters, establish a thermal system simulation model, and simulate the dynamic heat exchange process of the fluid by the mass, energy, and momentum conservation equations to calculate the temperature field, pressure field, and heat transfer coefficient distribution,

[0009] Based on the vibration deformation parameters and the material properties of the heat exchanger, a vibration and thermal stress simulation model is established to calculate the stress distribution and fatigue life of the tube bundle.

[0010] Integrate the thermal system simulation model and the vibration stress simulation model through multi-physics coupling, and generate a digital twin after dynamic calibration.

[0011] S2. Use the digital twin to realize the management of the heat exchanger system:

[0012] Monitor the operating state of the heat exchanger in real time, compare the deviation between the simulation data and the measured data, and trigger an abnormal alarm.

[0013] Predict the life of key components based on the stress distribution, identify high-stress areas and failure risks.

[0014] Optimize the heat transfer efficiency and pressure drop loss, and output the adjusted operating parameters.

[0015] Combined with the first aspect, the vibration deformation parameters include amplitude, frequency and deformation amount, and the material properties include elastic modulus, Poisson's ratio and thermal expansion coefficient. In the thermal system simulation model:

[0016] The thermal mass conservation equation is: where ρ is the density, is the fluid velocity vector, and t is the time;

[0017] The energy conservation equation is: where V p is the specific heat capacity, T is the temperature, k is the thermal conductivity, is the heat source term per unit volume;

[0018] The momentum conservation equation is: where μ is the dynamic viscosity, is the body force;

[0019] The pressure drop ΔP is calculated by the friction loss formula: where f is the friction coefficient, D h is the pipe diameter, L h is the flow channel length.

[0020] Combined with the first aspect, the calculation of the heat transfer coefficient includes:

[0021] Reynolds number Prandtl number Nusselt number Nu = C·Re m ·Pr n , convective heat transfer coefficient where C, m, and n are empirical coefficients.

[0022] Combined with the first aspect, in the vibration and thermal stress simulation model:

[0023] The strain ε is:

[0024] The stress of the tube bundle: σ = E·ε, where E is the elastic modulus;

[0025] Thermal stress: where α is the coefficient of thermal expansion of the material, v is the Poisson's ratio, and ΔT is the temperature difference between the hot and cold sides;

[0026] Generate a stress distribution contour map based on finite element analysis, and calculate the fatigue cumulative damage value in combination with the stress-life curve.

[0027] Combined with the first aspect, the dynamic calibration of the digital twin includes:

[0028] Correct the boundary conditions and initial parameters of the simulation model through real-time data;

[0029] Verify the simulation results of the temperature field, pressure field and stress field, and adjust the model output error;

[0030] Integrate the calibrated model into a digitally twinned body with real-time updates, and synchronously output the operating state data.

[0031] Combined with the first aspect, the system architecture of the digital twin includes:

[0032] Real-time database: Store the operating parameters collected by the sensors;

[0033] Model-driven layer: Drive the thermal and stress simulation models to generate multi-physical field data;

[0034] Application development layer: Provide function modules for status monitoring, life prediction and operation optimization;

[0035] Communication interface: Realize the bidirectional transmission of real-time data and simulation results.

[0036] Combined with the first aspect, the operation optimization includes:

[0037] Adjust the set value of the hot side flow rate or the cold side outlet temperature according to the real-time heat transfer efficiency and pressure drop loss;

[0038] Combine the high stress area distribution, and dynamically limit the flow rate or temperature threshold to reduce the fatigue risk.

[0039] In the second aspect of the present disclosure, an electronic device is provided, including:

[0040] One or more processors;

[0041] A storage unit for storing one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the method for managing a heat exchanger system based on a digital twin.

[0042] In a third aspect of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, it can implement the method for managing a heat exchanger system based on a digital twin.

[0043] Advantageous effects: The method for managing a heat exchanger system based on a digital twin provided by the present invention collects the operating parameters of the heat exchanger in real time (such as temperature, pressure, flow rate, vibration deformation, etc.), and establishes a multi-physical field simulation model including a thermal system simulation model and a vibration stress simulation model in combination with the heat transfer mechanism and material characteristics of the heat exchanger, and constructs a digital twin that accurately reflects the actual state of the heat exchanger; uses this digital twin to monitor and diagnose the operating state of the heat exchanger in real time, realizing accurate identification of faults, prediction of the service life of key components, and optimization of operating conditions. It significantly improves the management efficiency and reliability of the heat exchanger system, reduces equipment maintenance costs and downtime, and provides strong technical support for the efficient and stable operation of heat exchangers in industrial production. Description of the Drawings

[0044] Figure 1 It is a schematic flowchart of a method for managing a heat exchanger system of a digital twin according to an embodiment of the present disclosure;

[0045] Figure 2 It is a schematic flowchart of step S1 of the present disclosure;

[0046] Figure 3 It is a schematic flowchart of step S2 of the present disclosure;

[0047] Figure 4 It is an electronic device according to an embodiment of the present disclosure. Detailed Description of the Embodiments

[0048] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present disclosure.

[0049] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present disclosure. The singular forms "a", "the", and "said" used in the embodiments of the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0050] As Figure 1 shown, it is a schematic flowchart of a method for managing a heat exchanger system of a digital twin body according to an embodiment of the present disclosure, including:

[0051] S1: Construct a digital twin body of the heat exchanger.

[0052] S2: Use the digital twin body to achieve heat exchanger system management.

[0053] Combined with Figure 2 , wherein, constructing a digital twin body of the heat exchanger includes:

[0054] S201: Real-time collect the operating parameters of the heat exchanger, including the inlet and outlet temperatures, pressures, flow rates of the hot side and the cold side, and the vibration deformation parameters of the tube bundle.

[0055] Specifically, arrange Internet of Things sensors on the heat exchanger system to collect the following real-time operating parameters:

[0056] Temperature: hot side inlet temperature T hi , hot side outlet temperature T ho and cold side inlet temperature T ci , cold side outlet temperature T co ;

[0057] Pressure: hot side inlet pressure P hi , hot side outlet pressure P ho and cold side inlet pressure P ci , cold side outlet pressure P co ;

[0058] Flow rate: hot side flow rate m h and cold side flow rate m c ;

[0059] Vibration: amplitude A, frequency f r and deformation amount δ of the heat exchanger tube bundle.

[0060] The sensor data is uploaded to the enterprise-level data platform (such as the PI system) through the OPC-UA protocol and stored in the real-time database for subsequent simulation and analysis.

[0061] Further, in S202: Based on the heat transfer mechanism and the operating parameters, a thermal system simulation model is established, and the dynamic heat transfer process of the fluid is simulated through the mass, energy, and momentum conservation equations to calculate the temperature field, pressure field, and heat transfer coefficient distribution.

[0062] Specifically, based on the heat transfer mechanism of the heat exchanger, the heat transfer process between the hot-side and cold-side fluids is simulated through the mass conservation, energy conservation, and momentum conservation equations to obtain the distributions of heat transfer efficiency, temperature field, and pressure field. Combining the geometric parameters (pipe diameter D h , flow channel length L h , heat transfer area A h ) and fluid physical property parameters (density ρ, viscosity μ, specific heat capacity C p , thermal conductivity k), a thermal system simulation model of the heat exchanger is established using CFD simulation software. Specific calculations include:

[0063] The thermal mass conservation equation is: where ρ is the density, is the fluid velocity vector, and t is time;

[0064] The energy conservation equation is: where C p is the specific heat capacity, T is the temperature, k is the thermal conductivity, is the heat source term per unit volume;

[0065] The momentum conservation equation is: where μ is the dynamic viscosity, is the body force;

[0066] The pressure drop ΔP is calculated through the friction loss formula: where f is the friction coefficient, D h is the pipe diameter, and L h is the flow channel length.

[0067] Further, in S203: Based on the vibration deformation parameters and the heat exchanger material characteristics, a vibration and thermal stress simulation model is established to calculate the stress distribution and fatigue life of the tube bundle.

[0068] Specifically, the calculation of the heat transfer coefficient includes:

[0069] Reynolds number Prandtl number Nusselt number Nu = C·Re m ·Pr n , convective heat transfer coefficient where C, m, and n are empirical coefficients.

[0070] Next, the strain ε is calculated as:

[0071] Calculate the stress of the tube bundle: σ = E·ε, where E is the elastic modulus;

[0072] Thermal stress: where α is the coefficient of thermal expansion of the material, ν is the Poisson's ratio, and ΔT is the temperature difference between the hot and cold sides;

[0073] Generate a stress distribution contour map based on finite element analysis, and calculate the fatigue cumulative damage value in combination with the stress-life curve.

[0074] Combined with the geometric parameters (wall thickness, length) and material properties (elastic modulus E, Poisson's ratio) of the heat exchanger tube bundle, use the finite element simulation platform to establish a vibration and stress simulation model.

[0075] Further, S204: Integrate the thermal system simulation model and the vibration stress simulation model through multi-physics field coupling, and generate a digital twin after dynamic calibration.

[0076] Specifically, multi-physics field coupling refers to the correlation calculation of different physical fields (such as temperature field, pressure field, and stress field) to simulate the comprehensive operating state of the heat exchanger:

[0077] Thermal system simulation model: Based on the mass, energy, and momentum conservation equations, calculate the temperature field, pressure field, and fluid flow characteristics.

[0078] Vibration and thermal stress simulation model: Based on the heat exchanger structure parameters, material properties, and vibration deformation data, calculate the stress distribution, fatigue life, and structural stability.

[0079] Coupling method:

[0080] Use the temperature field and pressure field output by the thermal system model as the input of the vibration and stress simulation model, and calculate the stress distribution caused by temperature difference and pressure change.

[0081] Use the high stress area and fatigue damage risk feedback by the vibration stress model as the constraint conditions for the optimization of the thermal system, and adjust the operating parameters, such as flow rate, temperature setting, etc.

[0082] The purpose of dynamic calibration is to correct the initial parameters of the simulation model to make it consistent with the actual working conditions:

[0083] Real-time data correction: Correct the boundary conditions of the simulation model according to the operating parameters (temperature, pressure, flow rate, vibration deformation amount, etc.) collected by the sensors.

[0084] Error analysis and verification:

[0085] Calculate the deviation between the simulation output data and the measured data, such as the temperature distribution error ΔT, pressure deviation ΔP, and stress deviation Δσ.

[0086] Adopt algorithms such as the least squares method or Kalman filtering to dynamically adjust the parameters of the simulation model, so that the simulation results gradually converge to the real working conditions.

[0087] Calibration result integration: Recombine the calibrated thermal system model and stress model to generate a digital twin that can be updated in real time.

[0088] Generate digital twin:

[0089] Simulation result fusion: Integrate the calculation results of the thermal system model and the stress model to form complete digital twin data, including:

[0090] Distribution of temperature field, pressure field, and flow velocity field;

[0091] Structural stress field and fatigue life assessment;

[0092] Fault risk prediction.

[0093] Real-time monitoring and optimization:

[0094] Use the digital twin to perform predictive maintenance on the health status of the heat exchanger and detect potential faults in advance;

[0095] Combine with the distribution of high stress areas to optimize operating parameters, such as reducing the flow rate or adjusting the inlet and outlet temperatures, to extend the life of the heat exchanger.

[0096] Beneficial effects: Closely combine multiple physical fields, enabling the digital twin to more accurately reflect the real operating state of the heat exchanger, and continuously optimize the simulation accuracy through dynamic calibration, providing data support for intelligent monitoring and optimization management.

[0097] Combine Figure 3 , wherein, using the digital twin to implement heat exchanger system management includes:

[0098] S301: Real-time monitor the operating status of the heat exchanger, compare the deviation between the simulation data and the measured data, and trigger an abnormal alarm.

[0099] Specifically, the operating status of the heat exchanger is mainly monitored through multiple key parameters, including: Thermal system parameters:

[0100] Inlet and outlet temperatures (Th i, Tho, Tc i, Tco), inlet and outlet pressures (Ph i, Pho, Pc i, Pco), mass flow rates (mh, mc)

[0101] Structural vibration parameters:

[0102] Amplitude A, frequency fr, deformation δ,

[0103] Stress and fatigue characteristics: Stress σ, strain ε, structural fatigue cumulative damage.

[0104] These parameters are collected in real time by sensors (such as temperature sensors, pressure sensors, flow meters, vibration accelerometers, etc.) arranged on the heat exchanger and uploaded to the data processing system.

[0105] The digital twin generates theoretical calculated values through the thermal system simulation model and the vibration stress simulation model, and compares them with the measured data, mainly including:

[0106] Temperature deviation ΔT = |T_sim - T_meas|,

[0107] Pressure deviation ΔP = |P_sim - P_meas|,

[0108] Flow deviation Δm = |m_sim - m_meas|,

[0109] Stress deviation Δσ = |σ_sim - σ_meas|,

[0110] Among them, T_sim, P_sim, m_sim and σ_sim represent the simulation calculated values, while T_meas, P_meas, m_meas and σ_meas represent the sensor measured values.

[0111] The deviation calculation can be smoothed by methods such as the moving average method or Kalman filtering to reduce the influence of instantaneous errors.

[0112] When the deviation ΔT, ΔP, Δm or Δσ exceeds the set threshold, the system triggers an abnormal alarm. The specific trigger mechanisms of the alarm can be divided into the following categories:

[0113] Overlimit alarm: When the deviation of a certain parameter exceeds the set threshold (such as ΔT > ΔT_thres).

[0114] Trend anomaly: Based on historical data analysis, if the parameter change trend is abnormal (such as the temperature fluctuation frequency increases abnormally).

[0115] Multi-parameter coupling anomaly: If multiple parameters are abnormal at the same time (such as large temperature deviation and large flow deviation), a linkage alarm is triggered.

[0116] After triggering the alarm, the system can automatically perform the following countermeasures:

[0117] Record and analyze the abnormal data, generate an abnormal report; adjust the operating parameters of the heat exchanger, such as increasing / decreasing the cooling water flow rate and optimizing the temperature setting; if the vibration stress is too large, the flow rate can be adjusted or the support structure of the heat exchanger can be optimized to reduce fatigue damage; if signs of blockage on the hot side or cold side are found, maintenance or cleaning of the heat exchange tubes can be arranged in advance.

[0118] Optionally, combined with machine learning algorithms, the system can optimize the operation mode of the heat exchanger according to historical data and current anomalies. For example:

[0119] Predict the future temperature or pressure change trend through a neural network model and give early warnings of possible anomalies; combine reinforcement learning algorithms to optimize the operation parameters of the heat exchanger, improve the heat transfer efficiency and reduce the failure rate.

[0120] Optionally, the abnormal alarm information can be displayed through a remote monitoring platform. Combined with a visual interface, the operation status of the heat exchanger is presented, including:

[0121] Temperature, pressure, and flow distribution nephograms; vibration stress distribution nephograms; early warning information and trend prediction diagrams.

[0122] Beneficial effects: Through real-time monitoring, data comparison, abnormal alarm, and intelligent optimization, the efficient operation and maintenance of the heat exchanger system are realized, ensuring the safety and stability of the system.

[0123] Furthermore, S302 predicts the lifespan of key components based on stress distribution and identifies high-stress areas and failure risks.

[0124] Specifically, through the stress nephogram, high-stress concentration areas can be visually identified. For example:

[0125] Tube bundle welding points: Due to the change in material structure at the welding site, they may become stress concentration points.

[0126] Heat exchanger elbows and support structures: These positions are subject to greater fluid impact forces and may cause fatigue failure.

[0127] Hot side and cold side junction area: Areas with large temperature gradients may cause stress concentration due to thermal expansion and contraction.

[0128] When the maximum principal stress exceeds the material yield limit, this area is determined as a high-stress risk area.

[0129] Based on the identification results of high-stress areas, further evaluate potential failure risks:

[0130] Short-term failure (such as stress overload failure): If the maximum stress in a certain area exceeds the yield strength of the material, plastic deformation or fracture may occur.

[0131] Long-term failure (such as fatigue crack growth): Predict the crack growth rate through a fatigue crack growth model.

[0132] Based on the prediction results of high-stress areas and failure risks, the following measures can be taken to optimize the operation of the heat exchanger:

[0133] Optimize operation parameters: Adjust the flow rate and temperature gradient to reduce stress concentration.

[0134] Structural enhancement: Add supports in high-stress areas to improve structural stiffness.

[0135] Material optimization: Select materials more resistant to fatigue, such as high-strength alloys or composite materials.

[0136] Intelligent early warning: Based on AI, predict the future stress evolution trend to achieve preventive maintenance

[0137] Furthermore, S303 optimizes the heat transfer efficiency and pressure drop loss, and outputs the adjusted operating parameters.

[0138] Specifically, for heat transfer efficiency optimization, the core function of a heat exchanger is to transfer heat from the hot side to the cold side. Improving the heat transfer efficiency can reduce energy consumption and improve system performance. Methods for optimizing heat transfer efficiency include increasing the convective heat transfer coefficient. Methods for improving heat transfer efficiency:

[0139] Increase the degree of turbulence (increase the Reynolds number): Optimize the flow channel structure of the heat exchanger, adopt heat transfer enhancement elements (such as fins, turbulators), increase the fluid turbulence degree, and make the heat transfer more sufficient.

[0140] Optimize fluid parameters: Select a heat transfer medium with a higher thermal conductivity to improve the heat transfer capacity.

[0141] Adjust operating parameters: Increase the flow rate (appropriately increase the flow rate on the hot side or cold side), but the impact of pressure drop needs to be weighed.

[0142] Increase the heat transfer area, adopt high-efficiency heat transfer tubes (such as spiral groove tubes, microchannel structures), and increase the heat transfer area per unit volume. Optimize the arrangement method of heat transfer tubes, such as increasing the number of tube bundles, adopting staggered arrangement, and improving the heat exchange efficiency.

[0143] Optimize the temperature gradient. By adjusting the hot side / cold side flow rate, optimize the inlet and outlet temperature difference to increase the heat transfer driving force. Adopt countercurrent heat transfer mode to reduce the temperature difference loss and improve the utilization rate of the temperature gradient.

[0144] The pressure drop affects the energy consumption of fluid transportation. Excessive pressure drop will reduce the system efficiency. Methods for reducing pressure drop include:

[0145] Reduce flow resistance: Optimize the streamline design of the heat exchanger pipes, reduce elbows and expansion / contraction components, and reduce turbulence loss.

[0146] Select an appropriate pipe diameter: Too small a pipe diameter will increase the flow rate and cause greater frictional loss. Selecting an appropriate pipe diameter can reduce the pressure drop.

[0147] Optimize the heat exchanger layout: Reduce the path length of the fluid flow and reduce the system pressure loss.

[0148] Uniformly distribute the flow rate: Non-uniform flow can lead to excessively high or low local flow velocities, increasing the local pressure drop. Optimizing the distributor structure can reduce flow deviation.

[0149] Adopt heat exchange elements with low resistance: For example, reduce the fin pitch and the narrowness of the flow channels to lower the flow resistance.

[0150] Based on the optimization of heat transfer efficiency and the control of pressure drop loss, adjust the operating parameters of the heat exchanger to achieve optimal performance. The adjustment schemes include: optimizing the flow rate and adjusting the inlet and outlet temperatures to control the flow state.

[0151] Beneficial effects: The S303 heat exchanger can maintain efficient operation under different working conditions, while reducing energy consumption and improving the stability and reliability of the overall system.

[0152] The electronic device 400 can be a desktop computer, a notebook, a palm computer, a cloud server, and other electronic devices. The electronic device 400 may include but is not limited to a processor 401 and a memory 402. Those skilled in the art can understand that Figure 4 These are merely examples of the electronic device 400 and do not constitute a limitation on the electronic device 400. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0153] The processor 401 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0154] The memory 402 can be an internal storage unit of the electronic device 400. For example, it can be the hard disk or memory of the electronic device 400. The memory 402 can also be an external storage device of the electronic device 400. For example, it can be a plug-in hard disk equipped on the electronic device 400, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 402 can also include both the internal storage unit of the electronic device 400 and the external storage device. The memory 402 is used to store the computer program 403 and other programs and data required by the electronic device. The memory 402 can also be used to temporarily store the data that has been output or will be output.

[0155] In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus / electronic device and method can be implemented in other ways. For example, the apparatus / electronic device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. Multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0156] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above method embodiments of the present disclosure, it can also be completed by a computer program instructing the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program can include computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a Read Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0157] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should all be included within the protection scope of the present disclosure.

Claims

1. A heat exchanger system management method based on digital twin, characterized in that: The following steps are involved: S1. Build a digital twin of the heat exchanger: Real-time collection of heat exchanger operating parameters, including hot and cold side inlet and outlet temperatures, pressures, flow rates, and tube bundle vibration deformation parameters. Based on the heat transfer mechanism and the operating parameters, a thermal system simulation model is established to simulate the dynamic heat transfer process of the fluid through the mass, energy and momentum conservation equations, and to calculate the temperature field, pressure field and heat transfer coefficient distribution. Based on the vibration deformation parameters and the material characteristics of the heat exchanger, a vibration and thermal stress simulation model is established to calculate the stress distribution and fatigue life of the tube bundle. The thermal system simulation model is integrated with the vibration stress simulation model through multi-physics field coupling, and a digital twin is generated after dynamic calibration; S2. Using the digital twin to manage the heat exchanger system: Monitor the operating status of the heat exchanger in real time, compare the deviation between simulation data and measured data, and trigger abnormal alarms. Predict the life of key components based on stress distribution, identify high stress areas and failure risks, Optimize heat transfer efficiency and pressure drop loss, and output adjusted operating parameters.

2. The method according to claim 1, characterized in that: The vibration deformation parameters include amplitude, frequency and deformation, the material properties include elastic modulus, Poisson's ratio and thermal expansion coefficient, and in the thermal system simulation model: The thermal mass conservation equation is: where ρ is the density, is the fluid velocity vector, t is the time; The energy conservation equation is: Among them C p is the specific heat capacity, T is the temperature, k is the thermal conductivity, is the heat source term per unit volume; The momentum conservation equation is: where μ is the dynamic viscosity, It is the volume force; The pressure drop ΔP is calculated using the friction loss formula: Where f is the friction coefficient, D h is the pipe diameter, L h is the flow channel length.

3. The method according to claim 1, characterized in that The calculation of the heat transfer coefficient includes: Reynolds number Prandtl number Nusselt number Nu=C·Re m ·Pr n , convective heat transfer coefficient Where C, m, and n are empirical coefficients.

4. The method according to claim 1, characterized in that: In the vibration and thermal stress simulation model: The strain ε is: The stress of the tube bundle: σ = E·ε, where E is the elastic modulus; Heat stress: Where α is the thermal expansion coefficient of the material, ν is Poisson's ratio, and ΔT is the temperature difference between the hot and cold sides; The stress distribution cloud diagram is generated based on finite element analysis, and the fatigue cumulative damage value is calculated in combination with the stress-life curve.

5. The method according to claim 1, characterized in that The dynamic calibration of the digital twin includes: Correct the boundary conditions and initial parameters of the simulation model through real-time data; Verify the simulation results of temperature field, pressure field and stress field, and adjust the model output error; Integrate the calibrated model into a digital twin that is updated in real time and outputs operating status data synchronously.

6. The method according to claim 1, characterized in that The system architecture of the digital twin includes: Real-time database: stores the operating parameters collected by sensors; Model driving layer: drives thermal and stress simulation models to generate multi-physics field data; Application development layer: provides function modules of status monitoring, life prediction and operation optimization; Communication interface: realize two-way transmission of real-time data and simulation results.

7. The method according to claim 1, characterized in that The operation optimization includes: Adjust the hot side flow rate or cold side outlet temperature setting value according to the real-time heat transfer efficiency and pressure drop loss; Combined with high stress area distribution, dynamically limit flow or temperature thresholds to reduce fatigue risk.

8. An electronic device, characterized in that: include: one or more processors; A storage unit, used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the heat exchanger system management method based on digital twin according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the heat exchanger system management method based on digital twin according to any one of claims 1 to 7.

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