GCVF critical penetration impact vibration prediction system and method based on vortex flow pattern

Abstract

This invention proposes a critical through-impact vibration prediction system for GCVFs based on vortex flow patterns. The system includes a flow field modeling module, which provides data to a fluid-structure interaction (FSI) analysis module. Simultaneously, the flow field modeling module optimizes the mesh through a dynamic mesh optimization module before outputting the data to the FSI analysis module. A signal processing and feature extraction module analyzes and processes the vibration characteristic signal data output from the FSI analysis module and transmits it to a data fusion and prediction module. The data fusion and prediction module predicts the impact vibration intensity, frequency, and evolution trend of the GCVF using its internal prediction model and transmits the prediction results to a display and alarm module. A level set method is used to establish the GCVF flow field model, and the dynamic evolution of the gas-liquid interface is tracked through a zero-value surface. A re-initialization algorithm is introduced to address the numerical diffusion problem, accurately describing the gas-liquid interaction and fluid transport laws, thus effectively solving the complexity problem of gas-liquid coupling flow field modeling in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of fluid signal processing technology, specifically to a GCVF critical penetration impact vibration prediction system and method based on vortex flow patterns. Background Technology

[0002] With the gradual depletion of fossil fuels and the intensification of environmental problems, the global demand for clean energy continues to grow. Hydropower, as a clean and efficient renewable energy source, plays a crucial role in energy conversion and grid stability. Low-head tidal power plants, with their high responsiveness and energy storage-power generation characteristics, have become an important means of balancing power load and controlling grid frequency. However, in the operation of low-head hydropower plants, the gas-liquid coupling vortex flow (GCVF) phenomenon is widespread. This phenomenon easily induces random shock wave vibrations, which not only damage the performance of the turbine but also lead to instability in the input flow, ultimately reducing hydropower conversion efficiency and threatening the service life of the equipment.

[0003] Currently, research on the GCVF phenomenon has made some progress, including the understanding of vortex flow patterns, gas-liquid coupling dynamics, and vibration generation characteristics. However, due to the influence of gas-liquid interaction and nonlinear random excitation on GCVFs, the evolution mechanism of their impact vibration and the prediction of critical penetration states remain technical challenges. Therefore, designing a system capable of effectively predicting GCVF impact vibrations is of great significance for improving the utilization rate of hydropower energy and the operational stability of equipment. However, existing methods for predicting the critical penetration state of GCVFs have the following main shortcomings: 1. Complexity of gas-liquid coupling flow field modeling: Traditional methods cannot accurately capture the dynamic evolution characteristics of the GCVF gas-liquid interface, and have limited understanding of the transport laws and interface characteristics of multiphase fluids. 2. Inaccurate vibration feature identification: Existing experimental and numerical simulation methods mainly focus on some frequency bands or time-domain components of vibration signals, making it difficult to effectively reveal the random pulsations and nonlinear vibration characteristics under the critical penetration state. 3. Insufficient prediction accuracy: Existing models fail to comprehensively consider the global characteristics of gas-liquid coupling transport, vortex flow patterns, and vortex-induced vibrations, making it impossible to achieve high-precision prediction of GCVF critical penetration impact vibrations. 4. Poor real-time performance: Some prediction methods have high computational complexity, making them difficult to apply to real-time monitoring and control scenarios, especially in industrial environments such as tidal power plants that require high response speeds. Summary of the Invention

[0004] This invention provides a GCVF critical penetration impact vibration prediction system and method based on vortex flow pattern, ensuring high-precision prediction of GCVF critical penetration impact vibration.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] The critical penetration impact vibration prediction system based on vortex flow pattern GCVF includes a flow field modeling module, a fluid-structure interaction analysis module, a dynamic mesh optimization module, a signal processing and feature extraction module, a data fusion and prediction module, and a display and alarm module.

[0007] The flow field modeling module provides data to the fluid-structure interaction analysis module, and after mesh optimization by the dynamic mesh optimization module, it outputs the data to the fluid-structure interaction analysis module. The signal processing and feature extraction module analyzes and processes the vibration characteristic signal data output by the fluid-structure interaction analysis module and transmits it to the data fusion and prediction module. The data fusion and prediction module predicts the impact vibration intensity, frequency and evolution trend of the GCVF through its internal prediction model and transmits the prediction results to the display and alarm module.

[0008] The aforementioned flow field modeling module uses the level set method to establish a GCVF flow field model, tracks the dynamic changes of the gas-liquid interface, and solves the numerical diffusion problem through a re-initialization algorithm, thereby ensuring the accuracy of the interface calculation. It provides the system with dynamic simulation data of gas-liquid interaction, provides the necessary basic flow field data for subsequent modules, and transmits real-time evolution data of the gas-liquid interface to the fluid-structure interaction analysis module.

[0009] The aforementioned fluid-structure interaction (FSI) analysis module establishes a fluid-structure interaction model of a thin-walled cylindrical shell based on Flügge shell theory, calculates the impact force of eddies on the structure and the resulting vibration response, performs frequency and time domain analysis on eddy-induced vibration, extracts key vibration frequencies and random pulse components, and provides basic data for vibration prediction under critical penetration conditions. The FSI analysis module relies on the output data of the flow field modeling module and simultaneously transmits the vibration characteristics to the signal processing and feature extraction module.

[0010] The aforementioned dynamic mesh optimization module optimizes the mesh in real time by employing spring-smoothing mesh technology and local mesh reconstruction strategy, thereby solving the high distortion problem that may occur in the calculation and ensuring the stability and accuracy of numerical calculation. It also optimizes the mesh for the flow field data provided by the fluid-structure interaction analysis module, ensuring the accuracy of data transmission and providing high-quality mesh support for the fluid-structure interaction analysis module.

[0011] The aforementioned signal processing and feature extraction module performs time-domain, frequency-domain, and time-frequency-domain analysis on the vibration signal output by the fluid-structure interaction analysis module, and uses short-time Fourier transform and power spectral density (PSD) analysis to extract key vibration features. These features help identify the critical penetration state of the GCVF and provide the necessary input parameters for system prediction. The vibration feature information extracted by the signal processing module is finally transmitted to the data fusion and prediction module for further analysis and prediction.

[0012] The aforementioned data fusion and prediction module integrates multi-source data from various modules, combining simulated data from the fluid-structure interaction analysis model with real-world information from vibration sensors. Through comprehensive feature analysis of the simulated data in the time, frequency, and time-frequency domains, it predicts and confirms the GCVF's critical penetration state before it enters the critical penetration stage. A prediction model is established using machine learning algorithms. This module comprehensively analyzes historical data and real-time input to predict the impact vibration intensity, frequency, and evolution trend of the GCVF, providing operators with actionable prediction results and suggestions. This module is the core of the entire system, responsible for generating prediction results and transmitting them to the display and alarm modules.

[0013] Using the prediction method of the GCVF critical penetration impact vibration prediction system based on the above vortex flow pattern, the specific workflow within the flow field modeling module is as follows:

[0014] Level set method (LSM) utilizes high-level functions The zero-value tracking of the real-time motion of the gas-liquid interface; where, the function =0 indicates the interface. 0 indicates the fluid above the interface. 0 represents the fluid below the interface; the transport equation of LSM is as follows:

[0015] ;

[0016] Due to numerical diffusion, It is no longer a distance function; to address the above issues, through... The described reinitialization process is used to re-initialize the distance function; mean curvature k The normal vector n can be represented by the function n. And gradient normal interface calculation:

[0017] ;

[0018] ;

[0019] Density in GCVF flow field r i and viscosity m i Dependent on level set functions; can be utilized Physical parameters determine the transition region caused by variables. In the interface transition, the Heaviside function can smooth the physical properties of the fluid, such as density and viscosity.

[0020] ;

[0021] In the formula, e To simulate interface thickness, e =1.5 a , a The value is in the grid space; the governing equations for the fluid properties are:

[0022] ;

[0023] In the formula, subscripts a and b represent the upper and lower fluid layers, respectively; assuming that the liquid and gas phases are incompressible fluids, the surface tension is calculated using the CSF model in conjunction with Continuum surface forces, and the Navier-Stokes equations are modified accordingly.

[0024] ;

[0025] In the formula, n is the normal vector of the gas-liquid interface. d ( ) is the surface δ function, s The surface tension coefficient, k The average curvature of the interface.

[0026] The specific workflow within the fluid-structure interaction analysis module described above is as follows:

[0027] The axial wavenumber displacement solution of the Flügge equation is as follows:

[0028] ;

[0029] In the formula, U ms , V ms , W ms These are the shell components in cylindrical coordinates ( x , i , r Displacement amplitude in three directions, k ms The axial wave number, m For circumferential mode number, It is the angular frequency;

[0030] Assuming the fluid is a viscosity-free, incompressible medium, and its motion exhibits anisotropic and non-rotational characteristics, the wave equation for the flow field is obtained:

[0031] ;

[0032] In the formula, C f Let be the wave velocity of the sound field. Consider using the method of separation of variables to solve the above equation; the sound pressure field satisfying the wave equation is as follows:

[0033] ;

[0034] In the formula, k rs is the radial wavenumber. P ms Indicates the amplitude of the sound pressure field. Y m ( t )express n Bessel function of order 1;

[0035] The excitation of fluid impact on the shell has nonlinear characteristics. The random excitation under fluid impact is simulated by axial cosine distributed harmonic load:

[0036] ;

[0037] In the formula, ( x () is the unit impulse function. F x Let represent the force per unit perimeter; by combining the local Fourier transform method to solve the above fluid-structure interaction process, the displacement response is derived as follows:

[0038] ;

[0039] The fluid-structure interaction analysis module can solve for the radial displacement at any point and obtain the acceleration characteristics; based on the displacement and acceleration response, the law between the critical penetration state of GCVF and the transition of shock vibration wave can be obtained.

[0040] This invention provides a critical penetration impact vibration prediction system and method based on vortex flow modulus GCVF, which has the following technical advantages:

[0041] 1) To address the complexity of gas-liquid coupled flow field modeling; based on the flow field modeling module in the technical solution, a GCVF flow field model is established using the level set method, and the dynamic evolution of the gas-liquid interface is tracked through the zero-value surface. At the same time, a re-initialization algorithm is introduced to solve the numerical diffusion problem, accurately describing the gas-liquid interaction and fluid transport laws, thereby effectively solving the complexity of gas-liquid coupled flow field modeling in the existing technology.

[0042] 2) To address the problem of inaccurate vibration feature identification; based on the signal processing and feature extraction module in the technical solution, combined with local Fourier transform, power spectral density (PSD) analysis and time-frequency analysis methods, the key features of GCVF impact vibration, including random pulse components and nonlinear vibration frequencies, were extracted, which significantly improved the identification accuracy of vibration features under critical penetration conditions, thereby effectively solving the problem of inaccurate vibration feature identification in the existing technology.

[0043] 3) To address the problem of insufficient prediction accuracy; based on the data fusion and prediction module in the technical solution, the data outputs of the flow field modeling module and the fluid-structure interaction analysis module are integrated, and a high-precision prediction model is constructed using machine learning algorithms to dynamically identify the critical penetration state of GCVF and accurately predict the impact vibration intensity and frequency, thus solving the problem of insufficient prediction accuracy in the existing technology.

[0044] 4) To address the issue of poor real-time performance; based on the dynamic mesh optimization module in the technical solution, the computational efficiency under high distortion conditions is significantly improved through spring smoothing mesh technology and local mesh reconstruction strategy. Furthermore, by combining the fifth-order weighted non-oscillating WENO spatial discretization and the third-order Runge-Kutta TVD-RK time discretization method, the solution speed of the system is optimized, and the ability to monitor the critical penetration state of GCVF in real time is realized, thereby solving the problem of poor real-time performance in the existing technology. Attached Figure Description

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0046] Figure 1 This is a system block diagram of the present invention;

[0047] Figure 2 This is a schematic diagram of the flow field modeling module of the present invention;

[0048] Figure 3 This is a schematic diagram of the fluid-structure interaction analysis module of the present invention;

[0049] Figure 4 This is a schematic diagram of the dynamic mesh optimization module of the present invention;

[0050] Figure 5 This is a schematic diagram of the signal processing and feature extraction module of the present invention;

[0051] Figure 6 This is a schematic diagram of the data fusion and prediction module of the present invention;

[0052] Figure 7 This is a schematic diagram illustrating the study of mesh independence in the numerical model in this embodiment of the invention.

[0053] Figure 8 This is a time-frequency domain waveform diagram of GCVF in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the following will describe the specific technical solutions of this invention systematically and completely in conjunction with the accompanying drawings provided by this invention. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0055] Example 1:

[0056] like Figure 1-6 As shown, this invention proposes a GCVF gas-liquid coupled vortex critical penetration impact vibration prediction system based on vortex flow patterns. The system consists of multiple modules, which work closely together through data streams and control signals to ensure high-precision prediction of GCVF critical penetration impact vibration.

[0057] It includes the following modules: flow field modeling module, fluid-structure interaction analysis module, dynamic mesh optimization module, signal processing and feature extraction module, data fusion and prediction module, and display and alarm module.

[0058] Specifically, the flow field modeling module uses the level set method to establish a GCVF flow field model, tracks the dynamic changes of the gas-liquid interface, and solves the numerical diffusion problem through a re-initialization algorithm, thereby ensuring the accuracy of the interface calculation. This module provides the system with dynamic simulation data of gas-liquid interactions, providing necessary flow field foundation data for subsequent modules. The flow field modeling module is closely related to other modules, transmitting real-time evolution data of the gas-liquid interface to the fluid-structure interaction analysis module.

[0059] Among them, the level set method (LSM) utilizes high-level functions. The zero-value tracking of the real-time motion of the gas-liquid interface. Among them, the function... =0 indicates the interface. 0 indicates the fluid above the interface. 0 represents the fluid below the interface; the transport equation of LSM is as follows:

[0060]

[0061] Due to numerical diffusion, It is no longer a distance function. To address the above issues, through... The described reinitialization process is used to re-initialize the distance function; mean curvature k The normal vector n can be represented by the function n. And gradient normal interface calculation:

[0062] ;

[0063] ;

[0064] Density in GCVF flow field r i and viscosity m i It depends on the level set function. It can be utilized... Physical parameters determine the transition region caused by variables; in the interface transition, the Heaviside function can smooth the physical properties of the fluid, such as density and viscosity.

[0065] ;

[0066] In the formula, e To simulate interface thickness, e =1.5 a , a The value is in the grid space; the governing equations for the fluid properties are:

[0067] ;

[0068] In the formula, subscripts a and b represent the upper and lower fluid layers, respectively. Assuming the liquid and gas phases are incompressible fluids, and combining the continuous surface forces, the CSF model calculates surface tension, thus modifying the Navier-Stokes equations:

[0069] ;

[0070] In the formula, n is the normal vector of the gas-liquid interface. d ( ) is the surface δ function, s The surface tension coefficient, k The average curvature of the interface.

[0071] Specifically, the fluid-structure interaction (FSI) analysis module establishes a thin-walled cylindrical shell FSI model based on Flügge shell theory to calculate the impact force of eddies on the structure and the resulting vibration response. This module performs frequency and time domain analysis on eddy-induced vibration, extracting key vibration frequencies and random pulse components to provide fundamental data for vibration prediction under critical penetration conditions. The FSI analysis module relies on the output data from the flow field modeling module and simultaneously transmits vibration characteristics to the signal processing and feature extraction module.

[0072] The axial wavenumber displacement solution of the Flügge equation is as follows:

[0073] ;

[0074] In the formula, U ms , Vms , W ms These are the shell components in cylindrical coordinates ( x , i , r Displacement amplitude in three directions, k ms The axial wave number, m For circumferential mode number, It is the angular frequency.

[0075] Assuming the fluid is a viscosity-free, incompressible medium, and its motion exhibits anisotropic and non-rotational characteristics, the wave equation for the flow field is obtained:

[0076] ;

[0077] In the formula, C f Let be the wave velocity of the sound field. Consider using the method of separation of variables to solve the above equation. The sound pressure field satisfying the wave equation is as follows:

[0078] ;

[0079] In the formula, k rs is the radial wavenumber. P ms Indicates the amplitude of the sound pressure field. Y m ( t )express n The order Bessel function.

[0080] The excitation of fluid impact on the shell has nonlinear characteristics. The random excitation under fluid impact is simulated by axial cosine distributed harmonic load:

[0081] ;

[0082] In the formula, ( x () is the unit impulse function. F x This represents the force per unit perimeter. This paper uses the local Fourier transform method to solve the above fluid-structure interaction process, and the resulting displacement response is derived as follows:

[0083] ;

[0084] Fluid-structure interaction systems can solve for radial displacement at any point and obtain acceleration characteristics. Based on the displacement and acceleration responses, the relationship between the critical penetration state of GCVF and the transition of shock vibration waves can be obtained.

[0085] Specifically, the dynamic mesh optimization module employs spring-smoothing mesh technology and a local mesh reconstruction strategy to optimize the mesh in real time, addressing potential high distortion issues in computation and ensuring the stability and accuracy of numerical calculations. It optimizes the mesh for the flow field data provided by the fluid-structure interaction module, guaranteeing the accuracy of data transmission and providing high-quality mesh support for the fluid-structure interaction analysis module.

[0086] Since the number of mesh elements significantly impacts simulation accuracy, a study on mesh independence is necessary to ensure the accuracy and repeatability of the simulation results. This paper obtains GCVF radial velocity curves for three different mesh densities N1~N3: 376541, 521786, and 698470. Figure 7 It can be seen that at a lower grid density N1, the velocity values ​​exhibit a significant deviation at the radial coordinate of 0.0025m, with a numerical error of 9.01%. Due to the suction force of the GCVF, the velocity gradient of the flow field changes rapidly, making it difficult to obtain accurate results at lower grid densities. However, when the grid density reaches a certain value, the velocity values ​​of curves N2 and N3 show a uniform distribution, with a relative error of 3.15%. Therefore, grid densities N2 and N3 can meet the grid independence requirement, ensuring the accuracy and repeatability of the numerical calculations.

[0087] Specifically, the signal processing and feature extraction module performs time-domain, frequency-domain, and time-frequency-domain analyses on the vibration signals output by the fluid-structure interaction analysis module, using short-time Fourier transform and power spectral density (PSD) analysis to extract key vibration features. These features help identify the critical penetration state of the GCVF and provide the necessary input parameters for system prediction. The vibration feature information extracted by the signal processing module is finally transmitted to the data fusion and prediction module for further analysis and prediction.

[0088] The sampling frequency in the time domain is 100 Hz. Based on the time-domain waveform, it can be inferred that as the GCVF dynamically evolves, the amplitude of the vibration signal increases with the size of the bubble and contains many random components, resulting in a nonlinear waveform. Specifically, at low flow velocities, the signal amplitude is weak, and the vibration wave exhibits obvious step-increase and transient decrease characteristics. As the flow velocity increases, the release of flow field energy causes severe vibration of the shell, generating many nonlinear vibration components, resulting in abrupt peaks in the vibration signal. These phenomena indicate that the signal strength and transient distortion characteristics of the GCVF critical penetration stage are related to the flow rate. Based on the aforementioned transient characteristics of vibration amplitude, the critical penetration point of the GCVF can be detected using vibration sensors. This is essential for vortex suppression control during the operation of tidal power plant turbines, and the data measured by the vibration sensors also provides a basis for subsequent predictions.

[0089] In the frequency domain analysis, the GCVF impact vibration signal exhibits high energy values ​​under critical penetration conditions, with the frequency of maximum energy concentrated in the 30–50 Hz range. Under critical penetration conditions, the flow field experiences strong excitation forces, leading to increased signal intensity and highly nonlinear characteristics. The peak frequency and energy amplitude of the power spectral density (PSD) show different evolutionary characteristics with varying flow rates, but a large number of random pulse components are present under critical penetration conditions. This phenomenon can serve as a key characteristic for detecting the critical penetration state of GCVFs.

[0090] Furthermore, the time-frequency domain waveform of the GCVF is obtained by employing the short-time Fourier transform method, where the three axes are time, frequency, and energy amplitude, respectively. From Figure 8 As can be seen from this, before the critical penetration state, the vibration energy amplitude is concentrated at 0.2. 10 -11 (m·s -2 ) 2 As shown in Figure (a), the spectral structure increases in a step-like manner, with the peak frequency reaching 0.9 × 10⁻⁶. -11 (m·s -2 ) 2 The critical state is predicted by using the state before the critical breakthrough obtained from this feature.

[0091] Specifically, in the data fusion and prediction module, the system integrates multi-source data from various modules, combining simulated data from the fluid-structure interaction analysis model with real-world information from vibration sensors. Through comprehensive feature analysis of the simulated data in the time, frequency, and time-frequency domains, it predicts and confirms the GCVF's critical penetration state before it reaches it, and establishes a prediction model using machine learning algorithms. This module comprehensively analyzes historical data and real-time input to predict the impact vibration intensity, frequency, and evolution trend of the GCVF, providing operators with actionable prediction results and suggestions. This module is the core of the entire system, responsible for generating prediction results and transmitting them to the display and alarm modules.

[0092] Specifically, the display and alarm module shows the system's prediction results and trend analysis in real time through a graphical interface. When the predicted vibration state exceeds a preset threshold, the module triggers an alarm and provides timely audible and visual alerts. This module also supports data storage and backtracking functions, providing support for subsequent analysis and optimization.

[0093] The various modules are interconnected through tight data flow and control signals, forming a complete closed-loop system. Within this system, the flow field modeling module first provides basic data, the fluid-structure interaction analysis module calculates the vibration response, the dynamic mesh optimization module ensures accuracy, the signal processing module extracts vibration characteristics, the data fusion and prediction module performs comprehensive analysis, and the display and alarm module provides feedback on the prediction results. Through the integration and collaboration of this system, accurate prediction of critical through-impact vibration of GCVFs can be achieved, thereby improving hydropower energy utilization and providing real-time monitoring and optimization support for equipment operation.

Claims

1. A critical penetration impact vibration prediction system based on vortex flow GCVF, characterized in that, It includes a flow field modeling module, a fluid-structure interaction analysis module, a dynamic mesh optimization module, a signal processing and feature extraction module, a data fusion and prediction module, and a display and alarm module; While the flow field modeling module provides data to the fluid-structure interaction analysis module, it also optimizes the mesh through the dynamic mesh optimization module and outputs the data to the fluid-structure interaction analysis module. The signal processing and feature extraction module analyzes and processes the vibration characteristic signal data output by the fluid-structure interaction analysis module and transmits it to the data fusion and prediction module. The data fusion and prediction module predicts the impact vibration intensity, frequency and evolution trend of the GCVF through its internal prediction model and transmits the prediction results to the display and alarm module. The flow field modeling module uses the level set method to establish a GCVF flow field model, tracks the dynamic changes of the gas-liquid interface, and solves the numerical diffusion problem through a re-initialization algorithm; it provides dynamic simulation data of gas-liquid interaction for the system, provides basic flow field data for subsequent modules, and transmits real-time evolution data of the gas-liquid interface to the fluid-structure interaction analysis module. The specific workflow within the flow field modeling module is as follows: Level set method (LSM) utilizes high-level functions The zero-value tracking of the real-time motion of the gas-liquid interface; where, the function =0 indicates the interface. 0 indicates the fluid above the interface. 0 represents the fluid below the interface; the transport equation of LSM is as follows: ； Due to numerical diffusion, No longer a distance function; through The described reinitialization process is used to re-initialize the distance function; mean curvature κ The normal vector n can be represented by the function n. And gradient normal interface calculation: ； ； use Physical parameters determine the transition region caused by variables. In the interface transition, the Heaviside function smooths the density and viscosity physical properties of the fluid. ； In the formula, ε To simulate interface thickness, ε =1.5 a , a The value is in the grid space; the governing equations for the fluid properties are: ； In the formula, subscripts a and b represent the upper and lower fluid layers, respectively; assuming that the liquid and gas phases are incompressible fluids, the surface tension is calculated using the CSF model in conjunction with Continuum surface forces, and the Navier-Stokes equations are modified accordingly. ； In the formula, n is the normal vector of the gas-liquid interface. δ ( ) is the surface δ function, σ The surface tension coefficient, κ The average curvature of the interface.

2. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern as described in claim 1, characterized in that, The fluid-structure interaction (FSI) analysis module establishes a fluid-structure interaction model of a thin-walled cylindrical shell based on Flügge shell theory, calculates the impact force of eddies on the structure and the resulting vibration response; performs frequency and time domain analysis on eddy-induced vibration, extracts key vibration frequencies and random pulse components, and provides data for vibration prediction under critical penetration conditions; the FSI analysis module relies on the output data of the flow field modeling module and simultaneously transmits the vibration characteristics to the signal processing and feature extraction module.

3. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern as described in claim 2, characterized in that, The dynamic mesh optimization module optimizes the mesh in real time by employing spring-smoothing mesh technology and local mesh reconstruction strategy to solve the high distortion problem that may occur in the calculation; it also optimizes the mesh of the flow field data provided by the fluid-structure interaction analysis module to ensure the accuracy of data transmission and provide mesh support for the fluid-structure interaction analysis module.

4. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern as described in claim 3, characterized in that, The signal processing and feature extraction module performs time-domain, frequency-domain, and time-frequency-domain analysis on the vibration signal output by the fluid-structure interaction analysis module, and uses short-time Fourier transform and power spectral density (PSD) analysis to extract key vibration features; providing the necessary input parameters for system prediction; the vibration feature information extracted by the signal processing module is finally transmitted to the data fusion and prediction module for further analysis and prediction.

5. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern as described in claim 4, characterized in that, The data fusion and prediction module integrates multi-source data from various modules, combining simulated data from the fluid-structure interaction analysis model with real information from vibration sensors on site. By performing comprehensive feature analysis of the simulated data in the time domain, frequency domain, and time-frequency domain, it predicts and confirms the GCVF before it enters the critical penetration state. It also establishes a prediction model through machine learning algorithms, comprehensively analyzes historical data and real-time input, and predicts the impact vibration intensity, frequency, and evolution trend of the GCVF, providing operators with actionable prediction results and suggestions. The prediction results are generated and transmitted to the display and alarm module.

6. The prediction method of the GCVF critical penetration impact vibration prediction system based on vortex flow pattern as described in claim 5, characterized in that, The specific workflow within the fluid-structure interaction analysis module is as follows: The axial wavenumber displacement solution of the Flügge equation is as follows: ； In the formula, U ms , V ms , W ms These are the shell components in cylindrical coordinates ( x , θ , r Displacement amplitude in three directions, k ms The axial wave number, m For circumferential mode number, It is the angular frequency; Assuming the fluid is a viscosity-free, incompressible medium, and its motion exhibits anisotropic and non-rotational characteristics, the wave equation for the flow field is obtained: ； In the formula, C f Let be the wave velocity of the sound field. Consider using the method of separation of variables to solve the above equation; the sound pressure field satisfying the wave equation is as follows: ； In the formula, k rs is the radial wavenumber. P ms Indicates the amplitude of the sound pressure field. Y m ( τ )express n Bessel function of order 1; The excitation of fluid impact on the shell has nonlinear characteristics. The random excitation under fluid impact is simulated by axial cosine distributed harmonic load: ； In the formula, ( x () is the unit impulse function. F x Let represent the force per unit perimeter; by combining the local Fourier transform method to solve the above fluid-structure interaction process, the displacement response is derived as follows: ； The fluid-structure interaction analysis module can solve for the radial displacement at any point and obtain the acceleration characteristics; based on the displacement and acceleration response, the law between the critical penetration state of GCVF and the transition of shock vibration wave can be obtained.

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