Valve vibration impact test method and system based on digital twinning

Abstract

The invention discloses a valve vibration impact test method and system based on digital twinning, and belongs to the technical field of industrial equipment test and evaluation. The method comprises the following steps: firstly, deploying a multi-source sensor network, constructing a physical entity layer of a valve vibration impact test, collecting and preprocessing various physical monitoring data, and constructing a digital twinborn body layer; secondly, real-time data assimilation with a physical test bed is realized through parameter calibration and updating of a digital twinborn model based on various physical monitoring data and the digital twinborn layer; and finally, executing a virtual vibration impact test, realizing prediction and evaluation of global response and key part information of the physical entity according to a test result, and finally realizing interaction and performance prediction. And the valve vibration impact test is realized through the valve vibration impact test system. According to the method, the fault diagnosis capability, the early damage identification capability and the valve state prediction and evaluation capability can be remarkably improved, and the defect of insufficient field monitoring information is overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial equipment testing and evaluation, and relates to a method and system for valve vibration impact testing based on digital twins. Background Art

[0002] As core fluid control equipment, the health of valves is directly related to the safe, stable, and efficient operation of the entire system. Vibration and shock are among the main causes of valve failure (such as stem breakage, seat damage, seal leakage, and actuator failure). Therefore, conducting vibration and shock testing on valves helps ensure valve reliability and safety, providing important data support for valve design and optimization.

[0003] Valve vibration and impact testing has been widely studied. Yu Muquan proposed a valve vibration testing device and method (Chinese Invention Patent CN202111147023.1). However, this method is divorced from actual operating conditions, ignores the fluid-structure coupling effect in the pipeline, lacks internal state sensing capabilities, and is unable to monitor key parameters such as sealing surface contact stress and valve stem root stress. Furthermore, destructive testing is costly and requires the destruction of the physical prototype. Based on existing research, traditional valve vibration and impact testing has the following significant drawbacks:

[0004] There is a serious disconnect between the model and the data. Traditional valve design models (such as CAD and FEA static analysis) cannot reflect their real-time behavior under dynamic vibration and impact loads. There is a lack of closed-loop verification of virtual-reality fusion, and it is impossible to use high-fidelity virtual model prediction results to guide physical testing or optimize operations; status warnings and performance evaluations are impossible; physical testing is costly and easily damages the test prototype.

[0005] The rise of digital twin technology provides an opportunity to address the aforementioned issues. Digital twins aim to construct a dynamic virtual mapping of the entire life cycle of physical entities, enabling deep interaction and integration between the physical and information worlds, and completing performance prediction, status warnings, lifespan assessments, and virtual experiment optimization, while reducing the number of physical experiments and lowering testing costs and risks. However, existing research has yet to effectively address this technical issue. Fu Yumin et al. proposed a vibration test method, system, equipment, and medium based on digital twins (Chinese invention patent CN202210124770.1). However, their invention lacks a model update mechanism, relies heavily on preset parameters, and ignores the multi-physical field coupling effect, focusing only on mechanical vibration. Therefore, in order to overcome the shortcomings of existing technologies and achieve more efficient, comprehensive, and intelligent valve performance testing, evaluation, and safety assurance, it is necessary to invent a method and system for valve vibration and impact testing based on digital twins. Summary of the Invention

[0006] In response to the problems and shortcomings in the existing technology, the present invention provides a method and system for valve vibration and impact testing based on digital twins. By constructing the physical entity layer and digital twin layer of the valve vibration and impact test and dynamically integrating them in the data-model fusion driving engine, virtual vibration and impact testing and evaluation are performed to achieve virtual-reality interaction and performance prediction of the valve.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A method for valve vibration and shock testing based on digital twins includes the following steps:

[0009] The first step is to build the physical entity layer of the valve vibration and shock test, as follows:

[0010] Step 1.1: Install the target valve on the vibration and shock test bench and connect it to the external pipeline to build a physical test bench.

[0011] Step 1.2: Based on the physical test bench built in step 1.1, deploy a multi-source sensor network at key locations of the target valve, external pipelines, and supporting mechanisms. Key locations include the valve body, valve cover, valve stem, valve core, actuator, and upstream and downstream pipeline flanges. Specifically:

[0012] High-frequency accelerometers are installed around the valve body, at the top of the valve stem and at the root of the valve seat to capture micro-amplitude high-frequency vibration impact signals.

[0013] The speed sensors are installed around the valve body, the actuator housing and the upstream and downstream pipeline flanges to monitor low-frequency vibration signals and forced vibration signals.

[0014] Dynamic strain gauges are installed at the valve stem stress concentration point, the valve seat root and the valve cover-valve body connection to measure vibration stress and strain.

[0015] Acoustic emission sensors are installed at the valve stem stress concentration point, the valve seat root, the valve body-external pipe connection and the valve cover-bolt connection to realize abnormal sound signal monitoring.

[0016] Temperature sensors are installed in the contact area of ​​the valve seat sealing surface and the valve stem to monitor the temperature rise caused by friction.

[0017] The pressure sensor is installed on the valve core to monitor the fluid acceleration impact force.

[0018] The displacement sensor is installed on the valve stem, valve seat and actuator push rod to measure the actual displacement and small deformation.

[0019] Install the flow meter at the inlet and outlet of the external pipeline to monitor the actual flow.

[0020] The thrust sensor is installed on the actuator push rod to monitor the actuator status.

[0021] In the second step, based on the multi-source sensor network deployed in the first step, various physical monitoring data are collected and preprocessed as follows:

[0022] Step 2.1: Collect various physical monitoring data in real time, synchronously, and with high precision based on a multi-source sensor network.

[0023] In step 2.2, a deep cleaning is performed based on the various physical monitoring data obtained in step 2.1, by removing outliers, reducing noise, and interpolating missing data to obtain the cleaned data.

[0024] In step 2.3, based on the cleaned data obtained in step 2.2, time synchronization is achieved by compensating for time shift.

[0025] Step 2.4, based on the aligned data obtained in step 2.3, feature extraction is performed, including a time domain feature set, a frequency domain feature set, and a time-frequency domain feature set. The time domain feature set includes RMS value, peak value, and kurtosis. The frequency domain feature set includes spectrum, envelope spectrum, and cepstrum. The time-frequency domain feature set is a wavelet transform.

[0026] The third step is to build the digital twin layer of the valve vibration and shock test. Based on the physical entity layer of the valve vibration and shock test built in the first step and the various physical monitoring data pre-processed in the second step, a digital twin model is constructed and model order reduction is performed to achieve dynamic virtual mapping of the physical entity layer of the valve vibration and shock test, as follows:

[0027] Step 3.1: Build the geometric model. Based on the physical layer of the valve vibration and shock test constructed in the first step, a precise CAD model of the valve including key details is constructed, where the key details mainly include the valve disc, valve stem, and flow channel.

[0028] Step 3.2: Build a multi-physics field attribute model. Based on the physical entity layer of the valve vibration and shock test built in the first step, this includes establishing a library of material attributes, flow attributes, and boundary conditions. Specifically:

[0029] The material properties are established by accurately inputting the mechanical properties of each component material at the service temperature, including elastic modulus, Poisson's ratio, density, yield strength, tensile strength, and thermophysical properties, including thermal conductivity, specific heat capacity, and thermal expansion coefficient.

[0030] The fluid properties are established by defining the physical properties of the medium, including density, viscosity, and compressibility.

[0031] The boundary condition library is established: typical working conditions are established, including boundary condition templates for steady state, opening and closing transient, water hammer, earthquake test, and safety valve discharge.

[0032] Step 3.3: Construct a structural dynamics simulation model. Based on the geometric model constructed in step 3.1, a dynamics simulation model of the valve structure is established using the finite element method to ensure support for transient dynamics analysis while taking into account nonlinear simulation calculations, as shown in formula (1):

[0033] (1)

[0034] in, is the displacement; for speed; is the acceleration; For time; is the fluid pressure; is the mass matrix; is the damping matrix; is the stiffness matrix; For external motivation; is the contact force; is the fluid force.

[0035] Step 3.4: Build a computational fluid dynamics (CFD) simulation model. Based on the geometric model built in step 3.1, calculate the complex flow field, pressure pulsation, and transient hydraulic or aerodynamic forces inside the valve and the leakage path through transient turbulence simulation to ensure accurate simulation of the flow field inside the valve, as shown in formulas (2) and (3):

[0036] (2)

[0037] (3)

[0038] in, is the flow rate; For time; is the fluid pressure; is the fluid density; is the kinematic viscosity; For external forces; is the Del operator.

[0039] Step 3.5: Construct a fluid-structure interaction (FSI) simulation model. Based on the structural dynamics simulation model constructed in Step 3.3 and the computational fluid dynamics (CFD) simulation model constructed in Step 3.4, a fluid-structure interaction simulation is used to establish bidirectional coupled data transfer between the two models. This involves transferring displacement to fluid mesh deformation and fluid pressure to structural loads. This allows for the effects of fluid loads on structural deformation or vibration, as well as the reaction of structural deformation to the flow field, to achieve force balance and motion coordination at the coupling interface.

[0040] Step 3.6: Construct a friction and wear model. Based on the physical entity layer of the valve vibration impact test constructed in the first step, the Archard wear model is used to predict the fretting wear of the key friction pairs including the seal pair, valve stem, and packing under vibration impact, as shown in formula (4):

[0041] (4)

[0042] in, is the wear volume; is the dimensionless wear coefficient; is the normal load; is the sliding distance; is the Brinell hardness.

[0043] Step 3.7: Construct a fatigue damage model. Based on the various physical monitoring data pre-processed in the second step, the SWT model in the critical plane method is used to predict the fatigue crack initiation life of key parts using dynamic stress or strain results, as shown in formula (5):

[0044] (5)

[0045] in, For SWT parameters, is the maximum stress, is the strain amplitude.

[0046] Then the above steps 3.1 to 3.7 complete the construction of the digital twin model.

[0047] Step 3.8: Reduce the order of the digital twin model. The digital twin model constructed based on steps 3.1 to 3.7 is a high-dimensional full-order model. By using proper orthogonal decomposition or dynamic modal analysis, while ensuring the accuracy of key dynamic characteristics (including main modes and stress concentration areas), the high-dimensional full-order model is converted into a low-order proxy model suitable for real-time or near-real-time calculations, and a reduced-order model library is established.

[0048] The fourth step is to build a data-model fusion driving engine. Based on the various physical monitoring data preprocessed in the second step and the valve vibration and impact test digital twin layer constructed in the third step, the parameters of the digital twin model are calibrated and updated to achieve real-time data assimilation with the physical test bench. The details are as follows:

[0049] Step 4.1: Digital twin model parameter calibration. Based on the various physical monitoring data preprocessed in step 2, a genetic algorithm is used to calibrate the key uncertain parameters of the digital twin model constructed in step 3, including contact stiffness, damping coefficient, and boundary conditions.

[0050] Step 4.2: Update the digital twin model. Based on the various physical monitoring data preprocessed in step 2 and the digital twin model calibrated in step 4.1, the Kalman filter algorithm is used to dynamically integrate the real-time physical monitoring data (especially the vibration impact response) with the predicted output of the digital twin model to achieve digital twin model updates. This includes updates to the digital twin model state, digital twin model parameters, and digital twin model structure. Specifically:

[0051] The digital twin model state update estimates the current true "hidden state" of the target valve, including internal stress, microcrack extension and contact state.

[0052] The digital twin model parameter update: online adjustment of model parameters, including local stiffness degradation, damping change, and friction coefficient, so that the prediction results of the digital twin model are as close to the actual response of the physical entity as possible.

[0053] The digital twin model result update: when significant performance drift or damage is detected, the model reconstruction is triggered and replaced with a suitable reduced-order model.

[0054] The fifth step is to conduct virtual vibration and shock testing and evaluation. Based on the valve vibration and shock test digital twin layer processed in the fourth step, various virtual vibration and shock test scenarios are defined on the digital twin model platform. Virtual vibration and shock testing is then executed, and the global response of the physical entity and key component information are predicted and evaluated based on the test results.

[0055] Step 6: Based on the virtual vibration and shock test results obtained in step 5, interaction and performance prediction are achieved as follows:

[0056] Step 6.1: Visualization and Interaction. The digital twin model's status, such as real-time response, stress cloud diagrams, and prediction results, is overlaid on the target valve through a 3D visualization platform, providing an intuitive and interactive experience.

[0057] Step 6.2: Abnormal diagnosis and early warning. By integrating and analyzing the characteristics of physical monitoring data with the predicted features after virtual vibration and shock testing, and utilizing machine learning-driven intelligent diagnostic algorithms, early fault detection, type identification, severity assessment, and location are achieved. When an abnormality is detected or predicted damage approaches a set threshold, a multi-level early warning is automatically triggered.

[0058] Step 6.3: State Prediction and Assessment. The processed digital twin model is used to predict internal valve conditions that are difficult to measure directly, including contact stress distribution on the sealing surface, stress concentration at the valve stem root, and microcrack growth trends. Simultaneously, fatigue damage accumulation analysis and prediction are performed based on the fatigue damage model constructed in step 3 to assess the remaining service life of key valve components.

[0059] A valve vibration impact test system based on digital twinning, each step in the valve vibration impact test method based on digital twinning is processed in a module, the valve vibration impact test system based on digital twinning includes a physical perception module, a digital twin model management module, a data-driven engine module, a virtual test module, and an intelligent application and interaction module.

[0060] The physical perception module is mainly composed of a target valve physical experiment table, a multi-source sensor network, and a data acquisition and processing unit, a multi-source sensor network is deployed on the built physical test table, high-precision, multi-channel, and synchronous data acquisition is used to acquire physical monitoring data, and data preprocessing is performed in the data processing unit to ensure data integrity and accuracy, and to provide high-quality input data for subsequent modules.

[0061] The digital twin model management module accesses the physical perception module, is used to integrate and call the solver of the digital twin model to solve the model, and uses a model reduction toolkit to realize a reduction algorithm, and simultaneously completes storage, loading, and scheduling of full-order models and reduced-order proxy models, realizes dynamic virtual mapping of the physical entity layer of the valve vibration impact test.

[0062] The data-driven engine module accesses the digital twin model management module and the physical perception module, realizes real-time data assimilation with the physical test table through model calibration and updating.

[0063] The virtual test module accesses the data-driven engine module, the digital twin model management module, and the physical perception module, is used to manage definition of a virtual test scene, load application, virtual vibration impact test task submission and monitoring, test result reading, realizes prediction and evaluation of global response of the physical entity and key part information.

[0064] The intelligent application and interaction module accesses the virtual test module, the data-driven engine module, the digital twin model management module, and the physical perception module, is mainly composed of a visualization platform, an intelligent diagnosis and prediction module, and an analysis and evaluation module, displays physical data, digital twin state, analysis results, and early warning information, and simultaneously realizes valve fault diagnosis and life prediction by integrating a machine learning algorithm library.

[0065] The application has the following advantages:

[0066] This invention significantly improves fault diagnosis, early damage identification, and valve status prediction and assessment capabilities by constructing a physical entity layer and a digital twin layer for valve vibration and shock testing, and dynamically integrating these layers within a data-model fusion drive engine. This overcomes the limitations of insufficient on-site monitoring information. Furthermore, virtual vibration and shock testing replaces most high-risk, high-cost physical destructive testing, reducing the number of prototype tests and the test cycle, significantly lowering costs and risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of a valve vibration impact test method based on digital twins of the present invention.

[0068] Figure 2 This is a system flow diagram of a valve vibration impact test based on digital twins of the present invention. DETAILED DESCRIPTION

[0069] The present invention is further described below with reference to an embodiment and a comparative example of a vibration impact test applied to a high-pressure, large-diameter ball valve at an LNG receiving station.

[0070] Example 1

[0071] A method for valve vibration and shock testing based on digital twins includes the following steps:

[0072] The first step is to build the physical entity layer of the valve vibration and shock test, as follows:

[0073] Step 1.1: Install the target valve on the vibration and shock test bench and connect it to the external pipeline to build a physical test bench.

[0074] Step 1.2: Based on the physical test bench built in step 1.1, deploy a multi-source sensor network at key locations of the target valve, external pipelines, and supporting structures. Key locations include the valve body, valve cover, valve stem, valve core, actuator, and upstream and downstream pipeline flanges. The details are as follows:

[0075] High-frequency accelerometers are installed around the valve body, at the top of the valve stem and at the root of the valve seat to capture micro-amplitude high-frequency vibration impact signals.

[0076] The speed sensors are installed around the valve body, the actuator housing and the upstream and downstream pipeline flanges to monitor low-frequency vibration signals and forced vibration signals.

[0077] Dynamic strain gauges are installed at the valve stem stress concentration point, the valve seat root and the valve cover-valve body connection to measure vibration stress and strain.

[0078] Acoustic emission sensors are installed at the stress concentration of the valve stem, the root of the valve seat, the connection between the valve body and the external connecting pipe, and the connection between the valve cover and the bolt, to monitor abnormal acoustic signals.

[0079] Temperature sensors are installed at the contact area of the valve seat sealing surface and the valve stem to monitor the temperature rise caused by friction.

[0080] Pressure sensors are installed at the valve core to monitor the fluid acceleration impact force.

[0081] Displacement sensors are installed at the valve stem, valve seat, and actuator push rod to measure actual displacement and slight deformation.

[0082] Flow meters are installed at the inlet and outlet of the external connecting pipe to monitor the actual flow.

[0083] Thrust sensors are installed at the actuator push rod to monitor the state of the actuator.

[0084] Step 2, based on the multi-source sensor network deployed in step 1, collect and preprocess multiple physical monitoring data, as follows:

[0085] Step 2.1, based on the multi-source sensor network deployed in step 1, collect multiple physical monitoring data in real time, synchronously, and with high precision.

[0086] Step 2.2, based on the physical monitoring data obtained in step 2.1, perform deep cleaning by removing outliers, noise reduction, and interpolating missing data.

[0087] Step 2.3, based on the cleaned data obtained in step 2.2, achieve time synchronization alignment through time shift compensation.

[0088] Step 2.4, based on the aligned data obtained in step 2.3, perform feature extraction, including time domain feature set, frequency domain feature set, and time-frequency domain feature set. The time domain feature set includes RMS value, peak value, and kurtosis. The frequency domain feature set includes spectrum, envelope spectrum, and cepstrum. The time-frequency domain feature set is wavelet transform.

[0089] Step 3, construct the valve vibration impact test digital twin layer. Based on the physical entity layer of the valve vibration impact test constructed in step 1 and the multiple physical monitoring data preprocessed in step 2, construct a digital twin model and perform model reduction to achieve dynamic virtual mapping of the physical entity layer of the valve vibration impact test, as follows:

[0090] Step 3.1, construct a geometric model. Based on the physical entity layer of the valve vibration impact test constructed in step 1, construct an accurate CAD model of the valve including key details, mainly including the valve disc, valve stem, and flow passage.

[0091] Step 3.2: Build a multi-physics field attribute model. Based on the physical entity layer of the valve vibration and shock test built in the first step, this includes establishing a library of material attributes, flow attributes, and boundary conditions. Specifically:

[0092] The material properties are established by accurately inputting the mechanical properties of each component material at the service temperature, including elastic modulus, Poisson's ratio, density, yield strength, tensile strength, and thermophysical properties, including thermal conductivity, specific heat capacity, and thermal expansion coefficient.

[0093] The fluid properties are established by defining the physical properties of the medium, including density, viscosity, and compressibility.

[0094] The boundary condition library is established: typical working conditions are established, including boundary condition templates for steady state, opening and closing transient, water hammer, earthquake test, and safety valve discharge.

[0095] In this embodiment, the valve body and valve core materials are Stainless steel, at a working temperature of -162°C, has a modulus of 214 GPa, a Poisson's ratio of 0.29, and a density of , yield strength is 550MPa, tensile strength is 850MPa, thermal conductivity is , the specific heat capacity is , the thermal expansion coefficient is The fluid is liquefied natural gas (LNG), with a density of , the dynamic viscosity is , the kinematic viscosity is , the compression ratio is For various typical working conditions, a corresponding boundary condition library should be established with reference to national or international standards.

[0096] Step 3.3: Construct a structural dynamics simulation model. Based on the geometric model constructed in step 3.1, a dynamics simulation model of the valve structure is established using the finite element method to ensure support for transient dynamics analysis while taking into account nonlinear simulation calculations, as shown in formula (1).

[0097] In this embodiment, transient dynamics simulation is performed in Ansys-Workbench to analyze the dynamic response of the valve structure.

[0098] Step 3.4: Build a computational fluid dynamics (CFD) simulation model. Based on the geometric model built in step 3.1, calculate the complex flow field, pressure pulsation, and transient hydraulic or aerodynamic forces inside the valve and in the leakage path through transient turbulence simulation to ensure accurate simulation of the flow field inside the valve, as shown in formulas (2) and (3).

[0099] In this embodiment, a fluid simulation model of the internal flow channel of the ball valve is constructed in Ansys-Fluent to simulate the flow field at different openings, including pressure distribution, flow velocity, and turbulence, with special attention paid to the flow near the ball.

[0100] Step 3.5: Construct a fluid-structure interaction (FSI) simulation model. Based on the structural dynamics simulation model constructed in Step 3.3 and the computational fluid dynamics (CFD) simulation model constructed in Step 3.4, a fluid-structure interaction simulation is used to establish bidirectional coupled data transfer between the two models. This involves transferring displacement to fluid mesh deformation and fluid pressure to structural loads. This allows for the effects of fluid loads on structural deformation or vibration, as well as the reaction of structural deformation to the flow field, to achieve force balance and motion coordination at the coupling interface.

[0101] In this embodiment, Ansys-System-Coupling is used to implement bidirectional fluid-structure coupling simulation, transmit the fluid pressure to the structure, and feed back the displacement to the fluid to update the mesh.

[0102] Step 3.6: Construct a friction and wear model. Based on the physical entity layer of the valve vibration impact test constructed in the first step, the Archard wear model is used to predict the fretting wear of the key friction pairs including the seal pair, valve stem, and packing under vibration impact, as shown in formula (4).

[0103] Step 3.7: Construct a fatigue damage model. Based on the various physical monitoring data pre-processed in the second step, the SWT model in the critical plane method is used to predict the fatigue crack initiation life of key parts using dynamic stress or strain results, as shown in formula (5).

[0104] Then the above steps 3.1 to 3.7 complete the construction of the digital twin model.

[0105] Step 3.8: Reduce the order of the digital twin model. The digital twin model constructed based on steps 3.1 to 3.7 is a high-dimensional full-order model. By using proper orthogonal decomposition or dynamic modal analysis, while ensuring the accuracy of key dynamic characteristics (including main modes and stress concentration areas), the high-dimensional full-order model is converted into a low-order proxy model suitable for real-time or near-real-time calculations, and a reduced-order model library is established.

[0106] In this embodiment, modal analysis is performed on the full-order finite element model to extract the first 50 modes, covering 0-1500 Hz. The intrinsic orthogonal decomposition method is applied to establish a reduced-order model library, covering combined operating conditions of different openings and different pressure levels, and verifying the accuracy of the reduced-order model.

[0107] The fourth step is to build a data-model fusion driving engine. Based on the various physical monitoring data preprocessed in the second step and the valve vibration and impact test digital twin layer constructed in the third step, the real-time data assimilation with the physical test bench is achieved through digital twin model parameter calibration and model update, as follows:

[0108] Step 4.1: Calibrate the parameters of the digital twin model. Based on the various physical monitoring data preprocessed in the second step, a genetic algorithm is used to calibrate the key uncertain parameters of the digital twin model constructed in the third step, including contact stiffness, damping coefficient, and boundary conditions.

[0109] Step 4.2: Update the digital twin model. Based on the various physical monitoring data preprocessed in step 2 and the digital twin model calibrated in step 4.1, the Kalman filter algorithm is used to dynamically integrate the real-time physical monitoring data (especially the vibration impact response) with the predicted output of the digital twin model to achieve digital twin model updates, including updates to the digital twin model state, digital twin model parameters, and digital twin model structure:

[0110] Digital twin model state update: Estimate the current true “hidden state” of the target valve, including internal stress, microcrack growth, and contact status.

[0111] Digital twin model parameter update: Online adjustment of model parameters, including local stiffness degradation, damping changes, and friction coefficient, to ensure that the prediction results of the digital twin model are as close to the actual response of the physical entity as possible.

[0112] Digital twin model result update: When significant performance drift or damage is detected, the model is rebuilt and replaced with an appropriate reduced-order model.

[0113] The fifth step is to conduct virtual vibration and shock testing and evaluation. Based on the valve vibration and shock test digital twin layer processed in the fourth step, various virtual vibration and shock test scenarios are defined on the digital twin model platform. Virtual vibration and shock testing is then executed, and the global response of the physical entity and key component information are predicted and evaluated based on the test results.

[0114] Step 6: Based on the virtual vibration and shock test results obtained in step 5, interaction and performance prediction are achieved as follows:

[0115] Step 6.1: Visualization and Interaction. The digital twin model's status, such as real-time response, stress cloud diagrams, and prediction results, is overlaid on the target valve through a 3D visualization platform, providing an intuitive and interactive experience.

[0116] In this embodiment, physical sensor data and images are displayed in real time through Unity-3D, status information obtained from real-time virtual testing is mapped and displayed, and potential damage locations and severity estimated by Kalman filtering are marked.

[0117] Step 6.2: Abnormal diagnosis and early warning. By integrating and analyzing the characteristics of physical monitoring data with the predicted features after virtual vibration and shock testing, and utilizing machine learning-driven intelligent diagnostic algorithms, early fault detection, type identification, severity assessment, and location are achieved. When an abnormality is detected or predicted damage approaches a set threshold, a multi-level early warning is automatically triggered.

[0118] Step 6.3: State Prediction and Assessment. The processed digital twin model is used to predict internal valve conditions that are difficult to measure directly, including contact stress distribution on the sealing surface, stress concentration at the valve stem root, and microcrack growth trends. Simultaneously, fatigue damage accumulation analysis and prediction are performed based on the fatigue damage model constructed in step 3 to assess the remaining service life of key valve components.

[0119] A valve vibration and shock testing system based on digital twins is designed to process each step of the valve vibration and shock testing method based on digital twins in a modular manner. The system includes a physical perception module, a digital twin model management module, a data-driven engine module, a virtual testing module, and an intelligent application and interaction module. Specifically:

[0120] The physical perception module is mainly composed of a target valve physical test bench, a multi-source sensor network, and a data acquisition and processing unit. By deploying a multi-source sensor network on the constructed physical test bench, the data acquisition unit is used to collect physical monitoring data with high precision, multi-channel, and synchronization, and the data is pre-processed in the data processing unit to ensure data integrity and accuracy, while providing high-quality input data for subsequent modules.

[0121] The digital twin model management module is connected to the physical perception module, responsible for integrating and calling the digital twin model solver to solve the model, and using the model reduction toolkit to implement the reduction algorithm. At the same time, it completes the storage, loading and scheduling of the full-order model and the reduced-order proxy model, realizing dynamic virtual mapping of the physical entity layer of the valve vibration and impact test.

[0122] The data-driven engine module is connected to the digital twin model management module and the physical perception module, and realizes real-time data assimilation with the physical test bench by calibrating and updating the model.

[0123] The virtual testing module is connected to the data-driven engine module, the digital twin model management module and the physical perception module, and is responsible for managing the definition of virtual test scenarios, load application, submission and monitoring of virtual vibration and shock test tasks, and reading of test results, to achieve the prediction and evaluation of the global response of the physical entity and key part information.

[0124] The intelligent application and interaction module is connected to the virtual testing module, data-driven engine module, digital twin model management module and physical perception module. It is mainly composed of a visualization platform, an intelligent diagnosis and prediction module, and an analysis and evaluation module. It displays physical data, digital twin status, analysis results, and early warning information. At the same time, by integrating a machine learning algorithm library, it realizes valve fault diagnosis and life prediction.

[0125] Comparative Example 1

[0126] To demonstrate the superiority of the present invention, a seismic test of a high-pressure, large-diameter ball valve at an LNG receiving station was used as an example. As shown in Table 1, compared to traditional seismic testing based on GB / T-2424.25-2024, "Environmental Testing Part 3: Test Guidelines, Seismic Test Method," the present invention reduces seismic testing costs by over 99%, increases test efficiency by over 100 times, and provides extensive test coverage. Fault warnings are provided more than six months in advance, effectively predicting service life and avoiding significant losses due to unplanned downtime. Valve reliability is also improved. Table 1 demonstrates that the digital twin-based valve vibration and impact testing method and system designed by the present invention, driven by digital twins, transforms valve management from "passive response" to "active prediction and optimization," creating significant economic and safety benefits.

[0127] Table 1, specific implementation examples of the present invention to improve the effect

[0128]

[0129] The above-described embodiments merely express the implementation methods of the present invention, but should not be understood as limiting the scope of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. A method for valve vibration and shock testing based on digital twins, characterized in that: The method comprises the following steps: The first step is to build the physical entity layer of the valve vibration and impact test; In the second step, based on the multi-source sensor network deployed in the first step, various physical monitoring data are collected and pre-processed; The third step is to construct the digital twin layer for the valve vibration and shock test. Based on the physical entity layer of the valve vibration and shock test constructed in the first step and the various physical monitoring data pre-processed in the second step, a digital twin model is constructed and then reduced to achieve dynamic virtual mapping of the physical entity layer of the valve vibration and shock test. The fourth step is to build a data-model fusion driving engine. Based on the various physical monitoring data preprocessed in the second step and the valve vibration and impact test digital twin layer constructed in the third step, the parameters of the digital twin model are calibrated and updated to achieve real-time data assimilation with the physical test bench. The fifth step is to conduct virtual vibration and shock testing and evaluation. Based on the valve vibration and shock test digital twin layer processed in the fourth step, various virtual vibration and shock test scenarios are defined on the digital twin model platform. Virtual vibration and shock testing is then performed, and the global response of the physical entity and key part information are predicted and evaluated based on the test results. Step 6: Based on the virtual vibration shock test results obtained in step 5, interaction and performance prediction are achieved.

2. A valve vibration impact test method based on digital twin according to claim 1, characterized in that: The first step is specifically as follows: Step 1.1: Install the target valve on the vibration and shock test bench and connect it to the external pipeline to build a physical test bench; Step 1.2: Based on the physical test bench built in Step 1.1, deploy a multi-source sensor network at key locations of the target valve, external pipelines, and supporting mechanisms. Key locations include the valve body, valve cover, valve stem, valve core, actuator, and upstream and downstream pipeline flanges. Deploying the multi-source sensor network specifically includes: High-frequency accelerometers are installed around the valve body, at the top of the valve stem, and at the base of the valve seat to capture slight high-frequency vibration impact signals. Install speed sensors around the valve body, actuator housing, and upstream and downstream pipeline flanges to monitor low-frequency vibration signals and forced vibration signals; Install dynamic strain gauges at the valve stem stress concentration point, the valve seat root and the valve cover-valve body connection to measure vibration stress and strain; Install acoustic emission sensors at the valve stem stress concentration point, valve seat root, valve body-external pipe connection point, and valve cover-bolt connection point to monitor abnormal acoustic signals; Install temperature sensors in the contact area of ​​the valve seat sealing surface and the valve stem to monitor the temperature rise caused by friction; Install the pressure sensor on the valve core to monitor the fluid acceleration impact force; Install the displacement sensor on the valve stem, valve seat and actuator push rod to measure the actual displacement and small deformation; Install the flow meter at the inlet and outlet of the external pipeline to monitor the actual flow; The thrust sensor is installed on the actuator push rod to monitor the actuator status.

3. The method for valve vibration and shock testing based on digital twin according to claim 2, characterized in that: The second step is as follows: Step 2.1: Real-time, synchronous, and high-precision collection of various physical monitoring data based on a multi-source sensor network; Step 2.2: Based on the various physical monitoring data obtained in step 2.1, deep cleaning is performed to remove outliers, reduce noise, and interpolate missing data to obtain cleaned data. Step 2.3: Based on the cleaned data obtained in step 2.2, time synchronization is achieved by compensating for time shift; Step 2.4, based on the aligned data obtained in step 2.3, feature extraction is performed, including a time domain feature set, a frequency domain feature set, and a time-frequency domain feature set; the time domain feature set includes RMS value, peak value, and kurtosis; the frequency domain feature set includes spectrum, envelope spectrum, and cepstrum; the time-frequency domain feature set is a wavelet transform.

4. The method for valve vibration and shock testing based on digital twin according to claim 3, characterized in that: The third step is as follows: Step 3.1: Build the geometric model. Based on the physical layer of the valve vibration and shock test constructed in the first step, build an accurate CAD model of the valve including key details, including the disc, stem, and flow path. Step 3.2, constructing a multi-physics field attribute model; based on the physical entity layer of the valve vibration and shock test constructed in the first step, including establishing a library of material attributes, flow attributes, and boundary conditions; Step 3.3, constructing a structural dynamics simulation model; Based on the geometric model constructed in step 3.1, a dynamic simulation model of the valve structure is established using the finite element method to ensure support for transient dynamic analysis while taking into account nonlinear simulation calculations, as shown in formula (1): （1） in, is the displacement; for speed; is the acceleration; For time; is the fluid pressure; is the mass matrix; is the damping matrix; is the stiffness matrix; For external motivation; is the contact force; is the fluid force; Step 3.4: Construct a computational fluid dynamics (CFD) simulation model. Based on the geometric model constructed in step 3.1, calculate the complex flow field, pressure pulsation, and transient hydraulic or aerodynamic forces inside the valve and the leakage path through transient turbulence simulation to ensure accurate simulation of the flow field inside the valve, as shown in formulas (2) and (3): （2） （3） in, is the flow rate; For time; is the fluid pressure; is the fluid density; is the kinematic viscosity; For external forces; is the Del operator; Step 3.5: Construct a fluid-structure coupling (FSI) simulation model. Based on the structural dynamics simulation model constructed in step 3.3 and the computational fluid dynamics (CFD) simulation model constructed in step 3.4, establish bidirectional coupling data transfer between the two through fluid-structure coupling simulation, i.e., transfer displacement to fluid mesh deformation and fluid pressure to structural load. This allows for the influence of fluid load on structural deformation or vibration, as well as the reaction of structural deformation to the flow field, to achieve force balance and motion coordination at the coupling interface. Step 3.6, construct a friction and wear model; based on the physical entity layer of the valve vibration impact test constructed in the first step, the Archard wear model is used to predict the fretting wear of the key friction pairs including the seal pair, valve stem and packing under vibration impact, as shown in formula (4): （4） in, is the wear volume; is the dimensionless wear coefficient; is the normal load; is the sliding distance; is the Brinell hardness; Step 3.7: Construct a fatigue damage model. Based on the various physical monitoring data pre-processed in the second step, the SWT model in the critical plane method is used to predict the fatigue crack initiation life of key parts using dynamic stress or strain results, as shown in formula (5): （5） in, For SWT parameters, is the maximum stress, is the strain amplitude; Then the above steps 3.1 to 3.7 complete the construction of the digital twin model; Step 3.8, order reduction processing of the digital twin model; the digital twin model constructed based on steps 3.1 to 3.7 is a high-dimensional full-order model. By using intrinsic orthogonal decomposition or dynamic modal analysis, the high-dimensional full-order model is converted into a low-order proxy model suitable for real-time or near real-time operations, and a reduced-order model library is established.

5. The method for valve vibration and shock testing based on digital twin according to claim 4, characterized in that: The step 3.2 is specifically as follows: Establishing material properties: accurately inputting the mechanical properties of each component material at the service temperature, including elastic modulus, Poisson's ratio, density, yield strength, tensile strength, and thermophysical properties, including thermal conductivity, specific heat capacity, and thermal expansion coefficient; The fluid properties are established as follows: defining the physical properties of the medium, including density, viscosity, and compressibility; The boundary condition library is established: typical working conditions are established, including boundary condition templates for steady state, opening and closing transient, water hammer, earthquake test, and safety valve discharge.

6. The method for valve vibration and shock testing based on digital twin according to claim 4, characterized in that: The fourth step is as follows: Step 4.1: Digital twin model parameter calibration. Based on the various physical monitoring data preprocessed in step 2, a genetic algorithm is used to calibrate the key uncertain parameters of the digital twin model constructed in step 3, including contact stiffness, damping coefficient, and boundary conditions. Step 4.2, update the digital twin model; based on the various physical monitoring data preprocessed in the second step and the digital twin model calibrated in step 4.1, the real-time collected physical monitoring data is dynamically integrated with the predicted output of the digital twin model through the Kalman filter algorithm to achieve the update of the digital twin model, including the update of the digital twin model state, digital twin model parameters and digital twin model structure.

7. The method for valve vibration and shock testing based on digital twin according to claim 6, characterized in that: In step 4.2: The digital twin model state update: estimates the current true "hidden state" of the target valve, including internal stress, microcrack extension, and contact state; The digital twin model parameter update: online adjustment of model parameters, including local stiffness degradation, damping change, and friction coefficient, so that the prediction results of the digital twin model are as close to the actual response of the physical entity as possible; The digital twin model result update: when significant performance drift or damage is detected, the model reconstruction is triggered and replaced with a suitable reduced-order model.

8. The method for valve vibration and shock testing based on digital twin according to claim 6, characterized in that: The sixth step is as follows: Step 6.1, visualization and interaction: superimpose the digital twin model status, such as real-time response, stress cloud diagram, and prediction results, on the target valve through the 3D visualization platform; Step 6.2: Abnormal diagnosis and early warning. By integrating and analyzing the characteristics of physical monitoring data with the predicted characteristics after virtual vibration and shock testing, and utilizing machine learning-driven intelligent diagnostic algorithms, early fault detection, type identification, severity assessment, and location are achieved. When an abnormality is detected or the predicted damage approaches the set threshold, multi-level warnings are automatically triggered. Step 6.3, state prediction and evaluation: Use the processed digital twin model to predict the internal states of the valve that are difficult to measure directly, including the contact stress distribution of the sealing surface, stress concentration at the root of the valve stem, and the trend of microcrack propagation. At the same time, based on the fatigue damage model constructed in the third step, fatigue damage accumulation analysis and prediction are performed to evaluate the remaining service life of the key components of the valve.

9. A valve vibration and impact testing system based on digital twins, characterized in that: The valve vibration and impact testing system is used to implement a valve vibration and impact testing method based on digital twins as described in any one of claims 1-8, including a physical perception module, a digital twin model management module, a data-driven engine module, a virtual testing module, and an intelligent application and interaction module.

10. A valve vibration and shock testing system based on digital twin according to claim 9, characterized in that: The valve vibration impact test system is specifically: The physical sensing module includes a target valve physical test bench, a multi-source sensor network, and a data acquisition and processing unit. By deploying the multi-source sensor network on the constructed physical test bench, the data acquisition unit collects physical monitoring data with high precision, multi-channel, and synchronization, and pre-processes the data in the data processing unit. The digital twin model management module is connected to the physical perception module to integrate and call the digital twin model solver to solve the model, and use the model reduction toolkit to implement the reduction algorithm. At the same time, it completes the storage, loading and scheduling of the full-order model and the reduced-order proxy model, realizing dynamic virtual mapping of the physical entity layer of the valve vibration and impact test; The data-driven engine module is connected to the digital twin model management module and the physical perception module to achieve real-time data assimilation with the physical test bench by calibrating and updating the digital twin model; The virtual test module is connected to the data-driven engine module, the digital twin model management module and the physical perception module to manage the definition of virtual test scenarios, load application, submission and monitoring of virtual vibration and shock test tasks, and reading of test results, thereby realizing the prediction and evaluation of the global response of the physical entity and key part information; The intelligent application and interaction module is connected to the virtual test module, data-driven engine module, digital twin model management module and physical perception module to display physical data, digital twin status, analysis results and early warning information, thereby realizing valve fault diagnosis and life prediction.

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