Vortex flow pattern-based GCVF critical penetration impact vibration prediction system and method

Abstract

The invention provides a GCVF critical penetration impact vibration prediction system based on a vortex flow pattern, and the system comprises a flow field modeling module which provides data for a fluid-solid coupling analysis module, carries out the grid optimization through a dynamic grid optimization module, and outputs the data to the fluid-solid coupling analysis module. The signal processing and feature extraction module analyzes and processes vibration feature signal data output by the fluid-solid coupling analysis module and transmits the vibration feature signal data to the data fusion and prediction module; and the data fusion and prediction module predicts the impact vibration intensity, frequency and evolution trend of the GCVF through an internal prediction model and transmits a prediction result to the display and alarm module. A GCVF flow field model is established by adopting a level set method, dynamic evolution of a gas-liquid interface is tracked through a zero-value surface, a numerical diffusion problem is solved by introducing a re-initialization algorithm, and gas-liquid interaction and a fluid transportation rule are accurately described, so that the problem of gas-liquid coupling flow field modeling complexity in the prior art is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid signal processing, and in particular to a GCVF critical penetration impact vibration prediction system and method based on a vortex flow pattern. Background Art

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

[0003] At present, research on the GCVF phenomenon has made some progress, including vortex flow patterns, gas-liquid coupling dynamic behavior and vibration generation characteristics. However, due to the influence of gas-liquid interaction and nonlinear random excitation on GCVF, the evolution mechanism of its impact vibration and the prediction of critical penetration state are still technical difficulties. Therefore, designing a system that can effectively predict the impact vibration of GCVF is of great significance to improving the energy utilization rate of hydropower and the stability of equipment operation. However, there are the following main deficiencies in the existing prediction of the critical penetration state of GCVF: 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, which makes it difficult to effectively reveal the random pulsation and nonlinear vibration characteristics under the critical penetration state. 3. Insufficient prediction accuracy: The existing model fails to comprehensively consider the global characteristics of gas-liquid coupling transport, vortex flow patterns and vortex-induced vibration, and cannot achieve high-precision prediction of GCVF critical penetration impact vibration. 4. Poor real-time performance: Some prediction methods have high computational complexity and are difficult to apply to real-time monitoring and control scenarios, especially in industrial environments such as tidal power stations that require high response speeds. Summary of the invention

[0004] The present invention provides a GCVF critical penetration impact vibration prediction system and method based on a vortex flow pattern, which ensures high-precision prediction of the GCVF critical penetration impact vibration.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: The GCVF critical penetration impact vibration prediction system based on vortex flow pattern includes flow field modeling module, fluid-solid coupling analysis module, dynamic grid optimization module, signal processing and feature extraction module, data fusion and prediction module, and display and alarm module; While the flow field modeling module provides data to the fluid-solid coupling analysis module, it also optimizes the mesh through the dynamic mesh optimization module and outputs it to the fluid-solid coupling analysis module. The signal processing and feature extraction module analyzes and processes the vibration characteristic signal data output by the fluid-solid coupling 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 its evolution trend of the GCVF through the internal prediction model and transmits the prediction results to the display and alarm module.

[0006] The above-mentioned flow field modeling module uses the level set method to establish the GCVF flow field model, tracks the dynamic changes of the gas-liquid interface, and solves the numerical diffusion problem through the reinitialization algorithm, thereby ensuring the accuracy of the interface calculation; it provides the system with dynamic simulation data of gas-liquid interaction and provides the necessary basic flow field data for subsequent modules. The flow field modeling module transmits the real-time evolution data of the gas-liquid interface to the fluid-solid coupling analysis module.

[0007] The above-mentioned fluid-solid coupling analysis module establishes a fluid-solid coupling model of a thin-walled cylindrical shell based on the Flügge shell theory, calculates the impact force of the vortex on the structure and the resulting vibration response; performs frequency domain and time domain analysis on the vortex-induced vibration, extracts key vibration frequencies and random pulse components, and provides basic data for vibration prediction under critical penetration conditions; the fluid-solid coupling analysis module relies on the output data of the flow field modeling module, and at the same time passes the vibration characteristics to the signal processing and feature extraction module.

[0008] The above-mentioned dynamic mesh optimization module optimizes the mesh in real time by adopting spring smoothing mesh technology and local mesh reconstruction strategy, solves the high distortion problem that may occur in the calculation, and ensures the stability and accuracy of the numerical calculation; it optimizes the mesh of the flow field data provided by the fluid-solid coupling analysis module to ensure the accuracy of data transmission and provide high-quality mesh support for the fluid-solid coupling analysis module.

[0009] The above-mentioned 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 coupling analysis module, and uses short-time Fourier transform and power spectral density PSD analysis to extract key vibration features; these features help to identify the critical penetration state of GCVF and provide the required 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.

[0010] The above-mentioned data fusion and prediction module fuses multi-source data from various modules, combines the simulated data information of the fluid-solid coupling analysis model with real information such as on-site vibration sensors, and performs comprehensive feature analysis of the simulated data information in the time domain, frequency domain, and time-frequency domain. It predicts and confirms before entering the critical penetration state of GCVF, and establishes a prediction model through machine learning algorithms. This module comprehensively analyzes historical data and real-time inputs, predicts the impact vibration intensity, frequency and evolution trend of GCVF, and provides operators with actionable prediction results and suggestions. This module is the core of the entire system and is responsible for generating prediction results and transmitting them to the display and alarm module.

[0011] Using the prediction method of the GCVF critical penetration impact vibration prediction system based on the vortex flow pattern mentioned above, the specific workflow in the flow field modeling module is as follows: Level set method, LSM uses advanced functions The zero value of tracks the real-time motion of the gas-liquid interface; among them, the function =0 means interface, 0 represents the fluid above the interface, 0 represents the fluid below the interface; the transport equation of LSM is as follows: ; Due to numerical diffusion, is no longer a distance function; for the above problem, The reinitialization process described above is used to re-initialize the distance function; the mean curvature k and the normal vector n can be respectively expressed by the function And the gradient normal interface calculation: ; ; GCVF flow field density r i and viscosity m i Depends on the level set function; can be used The physical parameters determine the transition region caused by the variables. In the interface transition, the Heaviside function can smooth the physical properties of the fluid such as density and viscosity: ; In the formula, e To simulate the interface thickness, e =1.5 a , a The value of is in grid space; the governing equations for the fluid properties are: ; In the formula, subscripts a and b represent the upper and lower fluids, respectively. Assuming that the liquid and gas phases are incompressible fluids, the surface tension is calculated by combining the continuum surface forces (CSF) model to correct the Navier-Stokes equations: ; Where n is the normal vector of the gas-liquid interface, d ( ) is the surface delta function, s is the surface tension coefficient, k is the average curvature of the interface.

[0012] The specific workflow in the above-mentioned fluid-structure coupling analysis module is as follows: The axial wavenumber shift solution of the Flügge equation is as follows: ; In the formula, U ms , V ms , W ms They are the shell components in cylindrical coordinates ( x , i , r ) displacement amplitudes in three directions, k ms is the axial wave number, m is the circumferential mode number, is the circular frequency; Assuming that the fluid is an inviscid, incompressible medium whose motion is anisotropic and non-rotating, the wave equation of the flow field is obtained: ; In the formula, C f is the wave velocity of the sound field. Consider using the variable separation method to solve the above equation. The sound pressure field that satisfies the wave equation is as follows: ; In the formula, k rs is the radial wave number, P ms represents the amplitude of the sound pressure field, Y m ( t )express n order Bessel function; The excitation of fluid impacting 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 pulse function, F x represents the force per unit circumference; combined with the local Fourier transform method to solve the above fluid-solid coupling process, the displacement response is derived as follows: ; The fluid-solid coupling analysis module can solve the radial displacement of any point and obtain the acceleration characteristics; based on the displacement and acceleration responses, the law between the critical penetration state of GCVF and the transition of impact vibration waves can be obtained.

[0013] The present invention provides a GCVF critical penetration impact vibration prediction system and method based on vortex flow pattern, which has the following technical effects: 1) Aiming at the problem of complexity in modeling the gas-liquid coupled flow field, according to the flow field modeling module in the technical solution, the level set method is used to establish the GCVF flow field model, and the dynamic evolution of the gas-liquid interface is tracked through the zero-value surface. At the same time, a reinitialization algorithm is introduced to solve the numerical diffusion problem, accurately describing the gas-liquid interaction and fluid transport laws, thereby effectively solving the problem of complexity in modeling the gas-liquid coupled flow field in the existing technology.

[0014] 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 recognition accuracy of vibration features under critical penetration states, thereby effectively solving the problem of inaccurate vibration feature identification in the prior art.

[0015] 3) To address the problem of insufficient prediction accuracy; according to the data fusion and prediction module in the technical solution, the data output of the flow field modeling module and the fluid-solid coupling analysis module are integrated, and a high-precision prediction model is constructed using a machine learning algorithm 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.

[0016] 4) To address the problem of poor real-time performance; according to the dynamic grid optimization module in the technical solution, the spring smoothing grid technology and local grid reconstruction strategy are used to significantly improve the computational efficiency under high distortion conditions. The fifth-order weighted non-oscillatory WENO spatial discretization and the third-order Runge-Kutta TVD-RK time discretization method are combined to optimize the system's solution speed and realize the ability to monitor the critical penetration state of GCVF in real time, thus solving the problem of poor real-time performance in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 is a system block diagram of the present invention; Figure 2 This is a schematic diagram of a flow field modeling module of the present invention; Figure 3 It is a schematic diagram of the fluid-solid coupling analysis module of the present invention; Figure 4 This is a schematic diagram of a dynamic grid optimization module of the present invention; Figure 5 It is a schematic diagram of the signal processing and feature extraction module of the present invention; Figure 6 This is a schematic diagram of the data fusion and prediction module of the present invention; Figure 7 Schematic diagram of the study on grid independence of the numerical model in the embodiment of the present invention; Figure 8 It is a time-frequency domain waveform diagram of GCVF in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention clearer, the following content will systematically and completely describe the specific technical solutions of the present invention in combination with the drawings provided according to the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] Embodiment 1: like Figure 1-6 As shown in the present invention, a GCVF gas-liquid coupled vortex critical penetration impact vibration prediction system based on vortex flow type is proposed. The system is composed of multiple modules, and each module closely cooperates through data flow and control signal to ensure high-precision prediction of GCVF critical penetration impact vibration.

[0020] It includes the following modules: flow field modeling module, fluid-solid coupling analysis module, dynamic grid optimization module, signal processing and feature extraction module, data fusion and prediction module, display and alarm module.

[0021] Specifically, the flow field modeling module uses the level set method to establish the GCVF flow field model, track the dynamic changes of the gas-liquid interface, and solve the numerical diffusion problem through the reinitialization algorithm to ensure the accuracy of the interface calculation. This module provides the system with dynamic simulation data of gas-liquid interaction and provides the necessary basic flow field data for subsequent modules. The flow field modeling module is closely related to other modules, and transmits the real-time evolution data of the gas-liquid interface to the fluid-solid coupling analysis module.

[0022] Among them, the level set method, LSM uses advanced functions The zero value of tracks the real-time motion of the gas-liquid interface. =0 means interface, 0 represents the fluid above the interface, 0 represents the fluid below the interface; the transport equation of LSM is as follows:

[0023] Due to numerical diffusion, is no longer a distance function. To solve the above problem, The reinitialization process described above is used to re-initialize the distance function; the mean curvature k and the normal vector n can be respectively expressed by the function And the gradient normal interface calculation: ; ; GCVF flow field density r i and viscosity m i Depends on the level set function. It can be used Physical parameters determine the transition region caused by the variable; in the interface transition, the Heaviside function can smooth the physical properties of the fluid such as density and viscosity: ; In the formula, e To simulate the interface thickness, e =1.5 a , a The value of is in grid space; the governing equations for the fluid properties are: ; In the formula, the subscripts a and b represent the upper and lower fluids, respectively. Assuming that the liquid and gas phases are incompressible fluids, the surface tension is calculated by combining the Continuum surface forces, CSF model, and the Navier-Stokes equations are modified: ; Where n is the normal vector of the gas-liquid interface, d ( ) is the surface delta function, s is the surface tension coefficient, k is the average curvature of the interface.

[0024] Specifically, the fluid-structure coupling analysis module establishes a fluid-structure coupling model of a thin-walled cylindrical shell based on the Flügge shell theory, and calculates the impact force of vortex on the structure and the resulting vibration response. This module performs frequency and time domain analysis on vortex-induced vibration, extracts key vibration frequencies and random pulse components, and provides basic data for vibration prediction under critical penetration conditions. The fluid-structure coupling analysis module relies on the output data of the flow field modeling module and passes the vibration characteristics to the signal processing and feature extraction module.

[0025] Among them, the axial wave number shift solution of the Flügge equation is as follows: ; In the formula, U ms , V ms , W ms They are the shell components in cylindrical coordinates ( x , i , r ) displacement amplitudes in three directions, k ms is the axial wave number, m is the circumferential mode number, is the circular frequency.

[0026] Assuming that the fluid is an inviscid, incompressible medium whose motion is anisotropic and non-rotating, the wave equation of the flow field is obtained: ; In the formula, C f is the wave velocity of the sound field. Consider using the variable separation method to solve the above equation. The sound pressure field that satisfies the wave equation is as follows: ; In the formula, k rs is the radial wave number, P ms represents the amplitude of the sound pressure field, Y m ( t )express n Bessel function of order.

[0027] The excitation of fluid impacting 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 pulse function, F xRepresents the force per unit circumference. This paper combines the local Fourier transform method to solve the above fluid-solid coupling process, and the displacement response is derived as follows: ; The fluid-solid coupling system can solve the radial displacement of any point and obtain the acceleration characteristics. According to the displacement and acceleration response, the law between the critical penetration state of GCVF and the transition of shock vibration wave can be obtained.

[0028] Specifically, the dynamic mesh optimization module optimizes the mesh in real time by using spring smoothing mesh technology and local mesh reconstruction strategy to solve the high distortion problem that may occur in the calculation and ensure the stability and accuracy of the numerical calculation. It optimizes the mesh of the flow field data provided by the fluid-structure coupling module to ensure the accuracy of data transmission and provide high-quality mesh support for the fluid-structure coupling analysis module.

[0029] Among them, since the number of grids has a great influence on the simulation accuracy, it is necessary to conduct grid independence research to ensure the accuracy and repeatability of the simulation results. This paper obtains the GCVF radial velocity curves of three different grid densities N1~N3: 376541, 521786, and 698470. Figure 7 It can be seen that at a lower grid density N1, the velocity value has a significant deviation at the radial coordinate of 0.0025m, and the numerical error is 9.01%. Due to the suction force of the GCVF, the velocity gradient of the flow field changes rapidly, and it is difficult to obtain accurate results at a lower grid density. However, when the grid density reaches a certain value, the velocity values ​​of curves N2 and N3 are uniformly distributed, and the relative error is 3.15%. Therefore, the grid densities N2 and N3 can meet the grid independence requirements and ensure the accuracy and repeatability of numerical calculations.

[0030] Specifically, the signal processing and feature extraction module analyzes the vibration signal output by the fluid-structure interaction analysis module in the time domain, frequency domain, and time-frequency domain, 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 required input parameters for system prediction. The vibration feature information extracted by the signal processing module is ultimately transmitted to the data fusion and prediction module for further analysis and prediction.

[0031] Among them, the sampling frequency of the time domain is 100 Hz. According to the signal time domain waveform, it can be inferred that with the dynamic evolution of GCVF, the amplitude of the vibration signal increases with the increase of the bubble scale and there are many random components, which makes the time domain waveform present nonlinear characteristics. Specifically, at low flow rates, the signal amplitude is weak, and the vibration wave has obvious step increase and transient decrease characteristics. When the flow rate increases, the energy release of the flow field causes the shell to vibrate violently, and generates many nonlinear vibration components, which makes the vibration signal have a sudden change peak. The above phenomenon shows that the signal intensity and transient distortion characteristics of the critical penetration stage of GCVF are related to the flow rate. According to the above-mentioned transition characteristics of the vibration amplitude, the critical penetration point of GCVF can be detected by vibration sensors, which is essential for vortex suppression control during the operation of tidal power station turbines. At the same time, the data measured by the vibration sensor also provides a basis for subsequent predictions.

[0032] In the frequency domain analysis, the GCVF impact vibration signal has a higher energy value in the critical penetration state and the frequency of the maximum energy value is concentrated in the range of 30~50hz. In the critical penetration state, there is a strong excitation force in the flow field, which causes the signal intensity to increase and presents highly nonlinear characteristics. The peak frequency and energy amplitude of the power spectral density PSD show different evolution characteristics with the change of flow rate, but there are a large number of random pulse components in the critical penetration state. This phenomenon can be used as a key feature for GCVF critical penetration state detection.

[0033] Furthermore, the short-time Fourier transform method is used to obtain the GCVF time-frequency domain waveform, where the three axes are time, frequency, and energy amplitude. Figure 8 It can be seen that 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 spectrum structure increases in a step-like manner, with the frequency peak reaching 0.9×10 -11 (m·s -2 ) 2 The state before critical penetration obtained from this feature is used to predict the critical state.

[0034] Specifically, in the data fusion and prediction module, the system fuses multi-source data from various modules, combines the simulated data information of the fluid-solid coupling analysis model with real information such as vibration sensors on site, and performs comprehensive feature analysis of the simulated data information in the time domain, frequency domain, and time-frequency domain. It predicts and confirms before entering the critical penetration state of GCVF, and establishes a prediction model through machine learning algorithms. This module comprehensively analyzes historical data and real-time inputs to predict the impact vibration intensity, frequency, and evolution trend of GCVF, and provides operators with actionable prediction results and suggestions. This module is the core of the entire system, responsible for generating prediction results and passing them to the display and alarm module.

[0035] Specifically, the display and alarm module displays the system's prediction results and trend analysis in real time through a graphical interface. When the predicted vibration state exceeds the preset threshold, the module triggers an alarm and provides timely sound and light prompts. The module also supports data storage and backtracking functions to provide support for subsequent analysis and optimization.

[0036] Each module is connected through tight data flow and control signals to form a complete closed-loop system. In this system, the flow field modeling module first provides basic data, the fluid-solid coupling analysis module calculates the vibration response, the dynamic grid optimization module ensures accuracy, the signal processing module extracts vibration characteristics, the data fusion and prediction module performs comprehensive analysis, and the prediction results are fed back through the display and alarm module. Through the integration and collaboration of this system, accurate prediction of GCVF critical penetration impact vibration can be achieved, thereby improving the utilization rate of hydropower energy and providing real-time monitoring and optimization support for equipment operation.

Claims

1. GCVF critical penetration impact vibration prediction system based on vortex flow pattern, characterized by: It includes flow field modeling module, fluid-solid coupling analysis module, dynamic grid optimization module, signal processing and feature extraction module, data fusion and prediction module, and display and alarm module; While the flow field modeling module provides data to the fluid-solid coupling analysis module, it also optimizes the mesh through the dynamic mesh optimization module and outputs it to the fluid-solid coupling analysis module. The signal processing and feature extraction module analyzes and processes the vibration characteristic signal data output by the fluid-solid coupling 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 its evolution trend of the GCVF through the internal prediction model and transmits the prediction results to the display and alarm module.

2. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern according to claim 1, characterized in that: 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 reinitialization algorithm; it provides the system with dynamic simulation data of gas-liquid interaction and basic flow field data for subsequent modules. The flow field modeling module transmits real-time evolution data of the gas-liquid interface to the fluid-solid coupling analysis module.

3. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern according to claim 2, characterized in that: The fluid-solid coupling analysis module establishes a fluid-solid coupling model of a thin-walled cylindrical shell based on the Flügge shell theory, calculates the impact force of the vortex on the structure and the resulting vibration response; performs frequency domain and time domain analysis on the vortex-induced vibration, extracts key vibration frequencies and random pulse components, and provides data for vibration prediction under critical penetration conditions; the fluid-solid coupling 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.

4. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern according to claim 3, characterized in that: The dynamic grid optimization module optimizes the grid in real time by adopting spring smoothing grid technology and local grid reconstruction strategy to solve the high distortion problem that may occur in the calculation; it optimizes the grid of the flow field data provided by the fluid-solid coupling analysis module to ensure the accuracy of data transmission and provide grid support for the fluid-solid coupling analysis module.

5. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern according to claim 4, 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-solid coupling analysis module, and uses short-time Fourier transform and power spectral density PSD analysis to extract key vibration features; provides the required 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.

6. The GCVF critical penetration impact vibration prediction system based on vortex flow pattern according to claim 5, characterized in that: The data fusion and prediction module fuses the multi-source data from each module, combines the simulated data information of the fluid-solid coupling analysis model with the real information such as the vibration sensor on site, and performs comprehensive feature analysis of the simulated data information in the time domain, frequency domain, and time-frequency domain to predict and confirm before entering the critical penetration state of the GCVF. A prediction model is established through a machine learning algorithm, and historical data and real-time input are comprehensively analyzed to predict the impact vibration intensity, frequency and evolution trend of the GCVF, so as to provide the operator with actionable prediction results and suggestions. Generate prediction results and pass them to the display and alarm module.

7. A prediction method using the GCVF critical penetration impact vibration prediction system based on the vortex flow type as described in claim 6, characterized in that: The specific workflow in the flow field modeling module is as follows: Level set method, LSM uses advanced functions The zero value of tracks the real-time motion of the gas-liquid interface; among them, the function =0 means interface, 0 represents the fluid above the interface, 0 represents the fluid below the interface; the transport equation of LSM is as follows: ； Due to numerical diffusion, is no longer a distance function; The reinitialization process described above is used to re-initialize the distance function; the mean curvature κ and the normal vector n can be respectively expressed by the function And the gradient normal interface calculation: ； ； use The physical parameters determine the transition region caused by the variables. In the interface transition, the Heaviside function smoothes the physical properties of the fluid such as density and viscosity: ； In the formula, ε To simulate the interface thickness, ε =1.5 a , a The value of is in grid space; the governing equations for the fluid properties are: ； In the formula, subscripts a and b represent the upper and lower fluids, respectively. Assuming that the liquid and gas phases are incompressible fluids, the surface tension is calculated by combining the continuum surface forces (CSF) model to correct the Navier-Stokes equations: ； Where n is the normal vector of the gas-liquid interface, δ ( ) is the surface delta function, σ is the surface tension coefficient, κ is the average curvature of the interface.

8. The prediction method of the GCVF critical penetration impact vibration prediction system based on the vortex flow pattern according to claim 7, characterized in that: The specific workflow in the fluid-solid coupling analysis module is as follows: The axial wavenumber shift solution of the Flügge equation is as follows: ； In the formula, U ms , V ms , W ms They are the shell components in cylindrical coordinates ( x , θ , r ) displacement amplitudes in three directions, k ms is the axial wave number, m is the circumferential mode number, is the circular frequency; Assuming that the fluid is an inviscid, incompressible medium whose motion is anisotropic and non-rotating, the wave equation of the flow field is obtained: ； In the formula, C f is the wave velocity of the sound field. Consider using the variable separation method to solve the above equation. The sound pressure field that satisfies the wave equation is as follows: ； In the formula, k rs is the radial wave number, P ms represents the amplitude of the sound pressure field, Y m ( τ )express n order Bessel function; The excitation of fluid impacting 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 pulse function, F x represents the force per unit circumference; combined with the local Fourier transform method to solve the above fluid-solid coupling process, the displacement response is derived as follows: ； The fluid-solid coupling analysis module can solve the radial displacement of any point and obtain the acceleration characteristics; based on the displacement and acceleration responses, the law between the critical penetration state of GCVF and the transition of impact vibration waves can be obtained.

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