Hydropower station radial gate dynamic characteristic measurement method, system, device and medium

Abstract

The application relates to the technical field of water conservancy and hydropower engineering, and discloses a method, a system, equipment and a medium for measuring the dynamic characteristics of an arc gate of a hydropower station, wherein the method comprises the following steps: constructing a finite element numerical model of the arc gate of the hydropower station; identifying modal vibration mode key points through modal analysis and taking the key points as measuring point positions; installing wireless sensing nodes at the measuring point positions; synchronously collecting vibration response signals of the gate under a set working condition of the gate; identifying the measured dynamic characteristic parameters of the gate by using a modal analysis method; taking the minimization of the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters as a target, selecting to-be-corrected parameters sensitive to the dynamic characteristics, solving the to-be-corrected parameters by using a gradient-guided adaptive collaborative optimization method, and obtaining a corrected finite element numerical model; and performing dynamic characteristic analysis on the gate to obtain final dynamic characteristic measurement results. The application can realize high-precision and real-time measurement of the dynamic characteristics of the arc gate of the hydropower station.

Description

Technical Field

[0001] This invention relates to the field of water conservancy and hydropower engineering technology, and in particular to a method, system, equipment and medium for measuring the dynamic characteristics of an arc gate in a hydropower station. Background Technology

[0002] The surface and intermediate arched gates of high dams and large reservoirs in hydropower stations are key structures for water retention and flood discharge. Operating under complex dynamic loads such as high-speed water flow and pulsating pressure for extended periods, they are prone to vibration, which can lead to structural fatigue damage, loosening of connections, or even failure. In the event of an earthquake, they pose a serious threat to the safety of the dam and downstream areas. Therefore, conducting seismic safety research on arched gates of hydropower stations in high seismic intensity zones is crucial.

[0003] The seismic performance study of arched gates involves multiple factors, including reservoir water conditions, boundary conditions, the gate's natural vibration characteristics, and the structural natural vibration characteristics of the dam and gate piers. Furthermore, the fluid-structure interaction between the reservoir water and the gate cannot be ignored, making the gate's seismic resistance extremely complex. Currently, existing standards lack specific provisions for the seismic design and measures of large arched gates. Therefore, it is urgent to conduct in-depth research on the seismic performance of large arched working gates to ensure their normal function during strong earthquakes and to provide technical support for the revision of relevant seismic design standards in the hydropower industry.

[0004] The dynamic characteristics of curved gate structures, including their natural frequencies and mode shapes, are fundamental parameters for studying the seismic performance of gates and important indicators for assessing the structural health of gates. Therefore, accurately understanding the dynamic characteristics of curved gates (such as natural frequencies, mode shapes, and damping ratios) is crucial.

[0005] Currently, the dynamic characteristics of arc-shaped gates are mainly obtained using the following two methods: Pure numerical simulation method: A parametric model of the gate is established using finite element software, and modal analysis or transient dynamic analysis is performed. The disadvantage of this method is that the boundary conditions of the model (such as hinge constraints, rubber waterstop friction, opening and closing forces, etc.) and structural parameters (such as material properties, damage state) differ from the actual structure, resulting in a large deviation between the calculation results and the actual dynamic characteristics, and low reliability.

[0006] Pure prototype testing method: This method involves deploying sensors (such as accelerometers and dynamic strain gauges) on-site to collect vibration signals using environmental or artificial excitation, and then identifying the dynamic characteristics using signal processing techniques (such as spectrum analysis and random subspace identification). The disadvantages of this method are: 1) Limited measurement points: It is difficult to deploy sensors at all key locations on the gate, potentially missing local vibration modes or critical response points. 2) Discrete results: The test results only represent the operating conditions at the time of the test and cannot predict changes in dynamic characteristics under different water levels and opening degrees. 3) Difficulty in separating interference: On-site background noise and multiple excitation sources couple, making accurate identification difficult.

[0007] In summary, numerical simulation is too idealistic, and prototype testing has spatiotemporal limitations. Furthermore, the two are independent of each other and cannot form a self-correcting and continuously optimizing closed-loop system. This makes it difficult to meet the engineering requirements for accurate, comprehensive, and predictable evaluation of the dynamic characteristics of arc gates (such as natural frequency, mode shape, damping ratio, etc.). Summary of the Invention

[0008] To address the aforementioned issues, this invention proposes a method, system, equipment, and medium for measuring the dynamic characteristics of arc-shaped gates in hydropower stations. This method can significantly improve the accuracy and reliability of measuring the dynamic characteristics of large arc-shaped gates in high-dam, large-reservoir hydropower stations, and can also dynamically assess the health status of the arc-shaped gates.

[0009] The technical solution adopted in this invention is as follows: A method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station includes: A finite element numerical model of the arc gate of the hydropower station was constructed. Modal analysis was performed based on the finite element numerical model to identify the key points of the mode shape and use them as the measurement points. Wireless sensor nodes are installed at the measurement points, and the vibration response signals of the gate are collected synchronously under the set operating conditions of the gate. After preprocessing the collected vibration response signals, modal analysis was used to identify the measured dynamic characteristic parameters of the gate. With the goal of minimizing the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters, parameters that are sensitive to dynamic characteristics are selected to be corrected, and the gradient-guided adaptive collaborative optimization method is used to solve them, so as to obtain the corrected finite element numerical model. The dynamic characteristics of the gate are analyzed using the modified finite element numerical model to obtain the final dynamic characteristic measurement results.

[0010] Furthermore, the construction of the finite element numerical model of the hydropower station's arc-shaped gate, and the modal analysis based on the finite element numerical model to identify key points of the modal vibration modes and use them as measurement points, includes: A finite element numerical model was constructed based on the design drawings of the arc gate of the hydropower station. The finite element numerical model includes the dam gate piers, reservoir water flow, gate panel, supporting components, hinged components, and water-stopping components. Initial modal analysis was performed on the constructed finite element numerical model to obtain the mode shapes corresponding to each mode. Key response points of the structure were selected according to the degree of displacement and curvature change of the mode shape, and these key response points were determined as the measurement points for field testing.

[0011] Furthermore, a wireless sensing node is installed at the measuring point location, and the vibration response signal of the gate is synchronously collected under the gate's set operating conditions, including: Wireless sensing nodes are installed at the determined measurement points, and the wireless sensing nodes integrate an acceleration sensing unit and a strain sensing unit. Under normal operating conditions or different preset opening and upstream water level conditions, each wireless sensor node is activated to collect data synchronously; the multi-channel vibration acceleration signal and dynamic strain signal of the gate structure are collected and recorded as the original vibration response signal.

[0012] Furthermore, after preprocessing the acquired vibration response signal, the measured dynamic characteristic parameters of the gate are identified using modal analysis, including: Noise removal and frequency band filtering are performed on the acquired raw vibration response signal to remove interference components and invalid data from the signal; The preprocessed vibration response signal was subjected to modal parameter extraction and calculation using a random subspace identification method. The natural frequencies, mode shape vectors and damping ratios of each order under the corresponding test conditions of the gate were separated from the calculation results and used as measured dynamic characteristic parameters.

[0013] Furthermore, with the goal of minimizing the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters, parameters sensitive to dynamic characteristics are selected for correction, including: An objective function is constructed with the aim of minimizing the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters. The objective function integrates the frequency error term and the mode shape consistency term and assigns weight coefficients to each term. Structural parameters that significantly affect the dynamic characteristics of the gate and whose numerical values ​​are difficult to determine precisely are selected as parameters to be corrected. The consistency between the mode shape calculated by the finite element numerical model and the measured mode shape is measured by the modal confidence criterion, and the mode shape error is included in the objective function calculation.

[0014] Furthermore, the gradient-guided adaptive co-optimization method is used to solve the problem, resulting in a modified finite element numerical model, including: Gradient information guides the search direction of individuals, and position update logic is combined to complete the position iteration of individuals in the population. An adaptive adjustment mechanism of inertial weight, cognitive learning mechanism and social learning mechanism are integrated to adaptively adjust the speed update rules of individuals in the population. The gradient guidance coefficient is adaptively adjusted according to the iteration progress and individual fitness, and a population collaborative update strategy is introduced to improve population diversity. Boundary constraints are applied to individual positions outside the search space, and the optimal parameter combination is obtained through iterative optimization. The optimal parameters are then substituted into the initial finite element numerical model to obtain a corrected finite element numerical model that matches the measured data.

[0015] Furthermore, the modified finite element numerical model is used to perform dynamic characteristic analysis of the gate, and the final dynamic characteristic measurement results are obtained, including: The dynamic response and stress distribution of the gate under preset extreme working conditions are simulated by the modified finite element numerical model to complete the structural safety assessment under the corresponding working conditions. The modified finite element numerical model is used to display the mode shapes at any position and of any order, thus realizing the visualization of the gate's dynamic characteristics. The subsequent field test data is compared with the prediction results of the corrected finite element numerical model to identify changes in structural stiffness and local damage and issue early warnings.

[0016] A system for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station includes: The initial numerical model construction and measurement point optimization module is configured to construct a finite element numerical model of the arc gate of the hydropower station, perform modal analysis based on the finite element numerical model, identify the key points of the mode vibration mode and use them as measurement point locations; The on-site prototype testing and data acquisition module is configured to install wireless sensing nodes at the measurement point location and synchronously acquire the vibration response signal of the gate under the set operating conditions of the gate. The preliminary identification module for dynamic characteristics based on test data is configured to preprocess the collected vibration response signals and then use modal analysis to identify the measured dynamic characteristic parameters of the gate. The model dynamic correction module based on the mutual feedback mechanism is configured to minimize the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters. It selects the parameters to be corrected that are sensitive to the dynamic characteristics and solves them using the gradient-guided adaptive collaborative optimization method to obtain the corrected finite element numerical model. The dynamic characteristic analysis module based on the modified numerical model is configured to perform dynamic characteristic analysis of the gate using the modified finite element numerical model to obtain the final dynamic characteristic measurement results.

[0017] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method for measuring the dynamic characteristics of the arc gate of a hydropower station.

[0018] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for measuring the dynamic characteristics of the arc gate of a hydropower station.

[0019] The beneficial effects of this invention are as follows: 1. This invention establishes a measurement system that integrates prototype testing and numerical simulation through finite element numerical model construction, optimized measurement point layout, on-site vibration acquisition, measured modal identification, dynamic model correction, and dynamic characteristic analysis. This system solves the problems of traditional dynamic characteristic measurements, such as the reliance on experience for measurement point placement and the difficulty in correcting deviations between the numerical model and the actual structure. By using model correction as a link between measured data and simulation analysis, the measurement results simultaneously possess the authenticity of on-site measurements and the globality of numerical analysis, enabling a more accurate reflection of the actual dynamic characteristics of the arc-shaped gate.

[0020] 2. This invention selects measurement points based on the modal characteristics of a refined finite element model, which can scientifically locate the key response positions of the structure, reduce the randomness of measurement point layout, and ensure the effectiveness of vibration signal acquisition. It adopts integrated wireless sensing nodes to synchronously collect vibration responses under multiple operating conditions, which can cover different operating states of the gate and obtain multi-dimensional structural vibration data. With the help of signal preprocessing and modal recognition methods, it can stably extract dynamic parameters such as the gate's natural frequency, mode shape, and damping ratio, providing reliable data support for subsequent model correction, and improving the overall efficiency and data quality of on-site testing.

[0021] 3. This invention employs an objective function that integrates frequency error and mode shape consistency to perform model correction. This function constrains the correction direction from two dimensions: overall frequency and spatial mode shape distribution, avoiding the result deviation caused by correction of a single index. Combined with a gradient-guided adaptive collaborative optimization method, this invention performs inverse solving of uncertainties such as boundary stiffness and material parameters, which can improve the convergence efficiency and solution accuracy of parameter optimization. This results in a high degree of consistency between the corrected finite element numerical model and the measured dynamic characteristics, effectively reducing the assumption errors in the initial modeling and forming a numerical model that can be dynamically updated according to the structural state.

[0022] 4. Based on the modified numerical model, this invention can overcome the limitation of the number of on-site measurement points, realize the visualization of vibration modes at any position of the entire structure, and simulate the dynamic response and stress distribution of the gate under extreme working conditions. It can also supplement the operating scenarios that cannot be covered by on-site testing and expand the application scope of dynamic characteristic measurement. At the same time, by comparing the measured data with the model prediction results regularly, it can identify the risk of structural stiffness degradation and local damage in a timely manner, providing early warning support for the long-term safe operation and maintenance of the gate, and has the dual function of dynamic characteristic measurement and structural safety assessment. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for measuring the dynamic characteristics of an arc gate in a hydropower station, according to Embodiment 1 of the present invention. Detailed Implementation

[0024] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments are now described. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0025] Example 1 like Figure 1 As shown in the figure, this embodiment provides a method for measuring the dynamic characteristics of an arc gate in a hydropower station, including: A finite element numerical model of the arc gate of the hydropower station was constructed. Modal analysis was performed based on the finite element numerical model to identify the key points of the mode shape and use them as the measurement points. Wireless sensor nodes are installed at the measurement points, and the vibration response signals of the gate are collected synchronously under the set operating conditions of the gate. After preprocessing the collected vibration response signals, modal analysis was used to identify the measured dynamic characteristic parameters of the gate. With the goal of minimizing the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters, parameters that are sensitive to dynamic characteristics are selected to be corrected, and the gradient-guided adaptive collaborative optimization method is used to solve them, so as to obtain the corrected finite element numerical model. The dynamic characteristics of the gate are analyzed using the modified finite element numerical model to obtain the final dynamic characteristic measurement results.

[0026] Preferably, a finite element numerical model is constructed based on the design drawings of the arc gate of the hydropower station. The finite element numerical model includes the dam gate piers, reservoir water flow, gate panel, supporting components, hinged components, and water-stopping components. Initial modal analysis is performed on the constructed finite element numerical model to obtain the mode shapes corresponding to each mode. Key response points of the structure are selected according to the degree of displacement change and curvature change of the mode shape, and the key response points are determined as the measurement points for field testing.

[0027] Specifically, when constructing the finite element numerical model, the complete set of design drawings of the gate are retrieved to reconstruct the solid structure of the dam piers, the fluid computation domain of the reservoir water, and the detailed structure of the gate panel, main beam support arms and other supporting components, hinged components and water-stop sealing components. The model is assembled according to the actual material properties and connection relationships of each component. After applying corresponding boundary constraints to the assembled finite element numerical model, modal calculations are run to output the mode shape cloud map and displacement and curvature distribution data under each mode. The displacement amplitude and curvature amplitude distribution in the mode shape are traversed, and the positions with higher displacement or curvature values ​​are selected as key points of structural response. After optimizing the arrangement of the points, the measurement points for field testing are determined.

[0028] It should be noted that a refined finite element numerical model can more closely approximate the actual structural form of the gate. The method of selecting measurement points based on mode characteristics can avoid the blindness of empirical point placement, ensure that the measurement points can capture the core vibration response information, and improve the accuracy of subsequent testing and model correction.

[0029] Preferably, wireless sensor nodes are installed at the determined measurement points, and the wireless sensor nodes integrate acceleration sensing units and strain sensing units; when the gate is in the normal operating environment or under preset different opening degrees and different upstream water levels, each wireless sensor node is activated to collect data synchronously; the multi-channel vibration acceleration signal and dynamic strain signal of the gate structure are collected and recorded as the original vibration response signal.

[0030] Specifically, wireless sensor nodes are fixedly installed on the corresponding structural surface of the gate according to the determined measurement point locations, ensuring that the sensor unit is in close contact with the gate structure. Each sensor node synchronously collects acceleration signals in three directions and dynamic strain signals on the structural surface. During the test, the natural excitation condition of the gate's normal operation and test conditions with different opening degrees and different upstream water storage heights are set respectively. Under each condition, all sensor nodes are activated through a synchronous triggering mechanism to collect data. The collected multi-channel signals are stored and transmitted to the data processing terminal as raw vibration response data.

[0031] It should be noted that wireless nodes integrating multiple types of sensing units can simultaneously acquire various vibration response signals. The multi-condition acquisition settings can cover the actual operating scenarios of the gate, and the synchronous acquisition method can ensure the time consistency of multi-channel data, providing a reliable raw data foundation for subsequent modal identification.

[0032] Preferably, noise removal and frequency band filtering are performed on the acquired raw vibration response signal to remove interference components and invalid data in the signal; the modal parameter extraction calculation is performed on the preprocessed vibration response signal using a random subspace identification method, and the natural frequencies, mode shape vectors and damping ratios of each order under the corresponding test conditions of the gate are separated from the calculation results as measured dynamic characteristic parameters.

[0033] Specifically, the acquired raw vibration response signals are first preprocessed to eliminate noise signals caused by environmental interference through a denoising algorithm. Then, filtering parameters are set according to the vibration frequency range of the gate structure to filter out clutter and invalid data segments outside the frequency band. The preprocessed multi-channel response signals are input into a random subspace recognition algorithm. By constructing matrices, performing matrix decomposition and determining the system order, the modal parameters of the gate structure under the corresponding test conditions are extracted, including the natural frequency values ​​of each order, the mode shape vectors of the corresponding order, and the damping ratios of each order. These parameters are then organized into a set of measured dynamic characteristic parameters.

[0034] It should be noted that signal preprocessing can effectively improve the signal-to-noise ratio of the original data and ensure the accuracy of subsequent identification results; the random subspace identification method can stably extract the modal parameters of the structure under environmental excitation, adapt to the working conditions of the gate field test, and the obtained measured dynamic characteristic parameters can be directly used for subsequent model correction.

[0035] Preferably, an objective function is constructed with the aim of minimizing the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters. The objective function integrates the frequency error term and the mode shape consistency term and assigns weight coefficients to each term. Structural parameters that have a significant impact on the dynamic characteristics of the gate and whose numerical values ​​are difficult to determine precisely are selected as parameters to be corrected. The consistency between the mode shape calculated by the finite element numerical model and the measured mode shape is measured by the modal confidence criterion and incorporated into the mode shape error calculation of the objective function.

[0036] Specifically, when constructing the objective function, the relative errors between the natural frequencies output by the finite element numerical model and the measured natural frequencies are calculated to form a frequency error term. At the same time, the correlation between the mode shape calculated by the model and the measured mode shape is calculated using the modal confidence criterion and converted into a mode shape consistency error term. Corresponding weight coefficients are set for the two errors, and the weighted two errors are summed to form the objective function used for optimization. When selecting parameters to be corrected, structural parameters that have a high impact on the dynamic characteristics of the gate and whose actual values ​​are difficult to determine accurately in the initial modeling are selected, including the boundary stiffness of the hinge, the friction parameters of the water-stopping components, and the equivalent elastic parameters of the structural materials, and these are set as variables for optimization.

[0037] It should be noted that the objective function that integrates the errors of both frequency and mode shape can constrain the direction of model correction from both the overall and distributed levels, avoiding the deviation caused by correction of a single index. Selecting boundary and material uncertain parameters as correction objects can fit the characteristics of parameters that are difficult to accurately determine in engineering practice, thereby improving the engineering applicability of model correction.

[0038] Preferably, the search direction of individuals is guided by gradient information, and the position iteration of individuals in the population is completed by combining position update logic; the speed update rules of individuals in the population are adaptively adjusted by integrating inertial weight adaptive adjustment mechanism, cognitive learning mechanism and social learning mechanism; the gradient guidance coefficient is adaptively adjusted according to the iteration progress and individual fitness, and a population collaborative update strategy is introduced to improve population diversity; boundary constraint processing is performed on the position of individuals outside the search space, and the optimal parameter combination is obtained after iterative optimization; the optimal parameters are substituted into the initial finite element numerical model to obtain a corrected finite element numerical model that matches the measured data.

[0039] Specifically, when using the gradient-guided adaptive collaborative optimization method, the positions and velocities of individuals in the population are first initialized, and the fitness value of each individual is calculated. During the iteration process, the gradient information at the individual's position is calculated, and a gradient unit vector is generated to guide the individual's position update direction. In the velocity update stage, the adaptively changing inertial weights are combined with the individual's historically optimal cognitive learning term and the population's globally optimal social learning term, while also incorporating a gradient-guided term to complete the velocity update. As the iteration progresses, the magnitude of the gradient-guided coefficient is dynamically adjusted according to the current iteration number and the individual's fitness. At the same time, information interaction between individuals is achieved through the population collaboration factor to update the state of the individual population. For individuals that exceed the parameter value range, a boundary absorption strategy is used to constrain them within the search space. The iteration continues until the convergence condition is met, and the optimal parameter combination is output. This optimal parameter combination is substituted into the corresponding parameter terms in the initial finite element numerical model to complete the dynamic correction of the model, resulting in a corrected finite element numerical model that highly matches the measured dynamic characteristics.

[0040] It should be noted that the optimization method combining gradient guidance and adaptive collaborative mechanism can balance global search capability and local convergence accuracy, improving the efficiency and accuracy of parameter optimization; boundary constraint treatment can ensure the stability of the solution process, and the final corrected model can accurately reflect the actual dynamic characteristics of the gate, providing a high-fidelity numerical basis for subsequent analysis.

[0041] Preferably, the gate's dynamic response and stress distribution under preset extreme working conditions are simulated using a modified finite element numerical model to complete the structural safety assessment under the corresponding working conditions; the modified finite element numerical model displays the mode shapes at any position and of any order, realizing the visualization of the gate's dynamic characteristics; the subsequent field test data are compared with the prediction results of the modified finite element numerical model to identify changes in structural stiffness and local damage and issue early warnings.

[0042] Specifically, based on the modified finite element numerical model, firstly, modal shapes of any order can be retrieved to view the vibration displacement and deformation at any position of the gate, and the dynamic characteristics can be visualized through cloud maps and animations. Secondly, extreme conditions such as ultra-high water levels, full-open flood discharge, and strong earthquakes can be set to simulate and calculate the dynamic response time history and structural stress distribution of the gate under the corresponding conditions, and the structural safety of the gate under extreme conditions can be evaluated based on the calculation results. During the long-term operation and maintenance of the gate, on-site prototype tests are conducted regularly to obtain new test data. The dynamic characteristic parameters obtained from the new tests are compared with the prediction results of the modified model. When there is a significant decrease in natural frequency, deviation of mode shape, or decrease in modal confidence, it is determined that the structure has stiffness degradation or local damage risk, and a structural safety warning is issued simultaneously. The approximate area of ​​damage can also be located through model inversion.

[0043] It should be noted that the revised high-fidelity model can overcome the limitation of the number of on-site measurement points and realize the dynamic characteristics of the entire structure; the simulation and prediction of extreme working conditions can make up for the inability of on-site testing to cover extreme scenarios and identify safety risks in advance; the damage early warning mechanism of regular comparison can realize the dynamic monitoring of the gate's dynamic characteristics and provide technical support for the operation and maintenance management of the structure.

[0044] Accordingly, this embodiment also provides a dynamic characteristic measurement system for an arc gate in a hydropower station, comprising: The initial numerical model construction and measurement point optimization module is configured to construct a finite element numerical model of the arc gate of the hydropower station, perform modal analysis based on the finite element numerical model, identify the key points of the mode vibration mode and use them as measurement point locations; The on-site prototype testing and data acquisition module is configured to install wireless sensing nodes at the measurement point location and synchronously acquire the vibration response signal of the gate under the set operating conditions of the gate. The preliminary identification module for dynamic characteristics based on test data is configured to preprocess the collected vibration response signals and then use modal analysis to identify the measured dynamic characteristic parameters of the gate. The model dynamic correction module based on the mutual feedback mechanism is configured to minimize the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters. It selects the parameters to be corrected that are sensitive to the dynamic characteristics and solves them using the gradient-guided adaptive collaborative optimization method to obtain the corrected finite element numerical model. The dynamic characteristic analysis module based on the modified numerical model is configured to perform dynamic characteristic analysis of the gate using the modified finite element numerical model to obtain the final dynamic characteristic measurement results.

[0045] Specifically, the initial numerical model construction and measurement point optimization module is used to retrieve gate design data, build a refined finite element numerical model, complete initial modal calculations, and output an optimized measurement point layout scheme; the on-site prototype testing and data acquisition module is used to manage the deployment and operation of on-site wireless sensor nodes, trigger synchronous acquisition according to set working conditions, and store and transmit raw vibration response data; the preliminary identification module of dynamic characteristics based on test data is used to preprocess the acquired data and extract the measured dynamic characteristic parameters through modal recognition algorithms; the model dynamic correction module based on the feedback mechanism is used to construct the correction objective function, select the parameters to be corrected, solve the optimal parameters through intelligent optimization algorithms, and complete the dynamic correction of the finite element model; the dynamic characteristic analysis module based on the corrected numerical model is used to carry out mode visualization, extreme working condition simulation, and damage early warning analysis, and output the final dynamic characteristic measurement and evaluation results.

[0046] It should be noted that the modular system architecture enables step-by-step control and data flow of the measurement process. Each module has a clear division of labor and smooth connection, which can support the entire process of measuring the dynamic characteristics of the arc gate, and improve the efficiency and standardization of the measurement work.

[0047] Example 2 This embodiment provides a method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station, including the following steps: S1: Initial numerical model construction and optimized layout of measurement points Based on the design drawings of the arc-shaped gate, a detailed finite element model was established, including details such as the dam piers, reservoir water, and the gate's panel, main beam, support arms, hinges, and waterstop. Modal analysis was performed based on this initial model. According to the modal vibration results, key points with large mode displacements or curvatures were identified as the initial optimal placement locations for the prototype test sensors. This overcomes the drawbacks of traditional testing methods where measuring point placement relies on experience and may miss crucial information.

[0048] S2: On-site prototype testing and data acquisition At the optimized initial measurement points determined in step S1, miniature, low-power wireless intelligent sensing nodes (such as high-precision, low-noise triaxial accelerometers and strain gauges) are installed. Under normal gate operation (environmental excitation) or specific operating conditions (such as different opening degrees and different upstream water levels), the response signals (acceleration, dynamic strain, etc.) of the gate under multi-channel environmental excitation or hammer impact are synchronously collected.

[0049] S3: Preliminary identification of dynamic characteristics based on test data After preprocessing (denoising and filtering) the acquired vibration response signals, modal analysis methods such as random subspace identification are used to identify the actual dynamic characteristic parameters of the gate under the current test conditions, including the natural frequencies of each order. , vibration shape Damping ratio .

[0050] S4: Dynamic Model Correction Based on Feedback Mechanism (1) Definition of objective function: The dynamic characteristic parameters (frequency) obtained by numerical model calculation , vibration shape ) and the measured parameters (frequency) identified in step S3 , vibration shape The objective function is defined as follows: (The goal is to minimize the error between the two sides.)

[0051] in, For frequency weighting coefficients, This is the mode shape weighting coefficient. It is a modal confidence criterion used to measure the consistency between calculated and measured mode shapes.

[0052] (2) Selection of correction parameters: Select model parameters that are sensitive to dynamic characteristics but difficult to obtain accurately as parameters to be corrected, such as the boundary stiffness of the hinge, the friction coefficient of the rubber waterstop, and the equivalent elastic modulus of the material.

[0053] (3) Optimization Algorithm Solution: The Gradient-guided Adaptive Synergistic Optimization Algorithm (GASOA) is adopted. This algorithm simulates the adaptive growth and branching process of roots searching for nutrients in the soil. Through the balance of individual memory, group communication, and random exploration, it efficiently searches the parameter space, thereby obtaining a corrected numerical model that highly matches the measured data. The GASOA optimization algorithm is an intelligent optimization algorithm that integrates gradient information, adaptive strategy, and collaborative optimization mechanism. Its core formula is constructed around three major modules: gradient-guided search, adaptive parameter adjustment, and collaborative update strategy. The following are the key formulas of this algorithm: 1) Gradient-guided position update The GASOA algorithm guides individuals to search in a better direction using gradient information, and combines the position update logic of traditional particle swarm optimization. The core formula is:

[0054] In the formula: : Gradient unit vector, used to determine the search direction; Minimum value (e.g., 10) -6 ), to avoid the denominator being 0; Gradient guidance coefficient: controls the degree of influence of gradient information and is adaptively adjusted.

[0055] : No. , In the nth iteration The position vectors of each individual; :individual fitness value; Fitness function in The gradient vector at that point; : No. , In the nth iteration The velocity vector of each individual.

[0056] 2) Adaptive speed update The speed update integrates adaptive adjustment of inertia weights, cognitive learning, social learning, and gradient guidance, as shown in the formula:

[0057] Among them, adaptive inertia weights The calculation formula is:

[0058] In the formula: : The maximum and minimum values ​​of the inertia weight; Maximum number of iterations; : No. The maximum and minimum fitness values ​​of the population in each iteration; Synergy factor: Balances the influence of individual fitness on inertia weight.

[0059] 3) Gradient guiding coefficient Adaptive adjustment The formula is as follows: Based on the iterative progress and individual fitness, the changes adapt accordingly.

[0060] In the formula: : The maximum and minimum values ​​of the gradient guiding coefficient; Early stage of iteration Larger size, enhances global search; later iterations Reduce the focus and seek local optimization.

[0061] 4) Collaborative optimization population renewal strategy To enhance population diversity, a synergistic factor was introduced. The formula for achieving information exchange between individuals is:

[0062] In the formula: Population size; It reflects the degree of coordination between the historical best and the global best of an individual in the population; the larger the value, the stronger the coordination of the population.

[0063] 5) Boundary constraint handling formula When the individual's location is outside the search space At this time, a boundary absorption strategy is adopted:

[0064] S5: Dynamic Characteristic Analysis Based on Modified Numerical Model Using the corrected numerical model obtained in step S4, the following analysis is performed: (1) Visualization of dynamic characteristics: Display the vibration mode at any position and of any order on the model, overcoming the limitation of limited test points.

[0065] (2) Working condition prediction: Simulate and calculate the dynamic response and stress distribution of the gate under extreme working conditions that may occur in the past or in the future (such as ultra-high water level, full opening flood discharge, strong earthquake) to conduct a safety assessment.

[0066] (3) Damage identification and early warning: Conduct prototype tests regularly and compare the new test data with the model prediction data. If phenomena such as a significant decrease in natural frequency, abnormal vibration mode, or decrease in MAC value occur, it can be used to warn that the structure may have stiffness degradation or local damage, and the approximate area of ​​damage can be located through model inversion.

[0067] Example 3 This embodiment is based on embodiment 2: This embodiment provides a method for measuring the dynamic characteristics of an arc gate in a hydropower station, and will be described in detail using a large medium-sized arc gate in a hydropower station as an example.

[0068] Step S1: Based on the gate's design drawings, a three-dimensional finite element model is established using large-scale finite element analysis software such as ANSYS or ABAQUS, employing shell and beam elements for simulation. Initial modal analysis is performed to obtain the first few vibration modes, mainly characterized by local vibration of the panel and overall torsional vibration. Based on the vibration mode results, wireless acceleration nodes are ultimately determined to be placed at the mid-sections of the side arms, the mid-section of the lower panel, and the mid-span of the main beam.

[0069] Step S2: When the gate is at a stable opening (e.g., partial flood discharge), activate all wireless acceleration nodes to continuously sample acceleration data from environmental excitation or 15-20 hammer blows at a certain sampling frequency (e.g., 200Hz) for 30 minutes. The data is transmitted wirelessly to the gateway on the gate pier, and then sent to the server in the power plant's central control room via a fiber optic network.

[0070] Step S3: After the received acceleration data is detrended and filtered by the analysis platform in the server, the first few natural frequencies and mode shapes of the gate are identified by fitting the resonance peaks using the improved frequency domain decomposition method (EFDD).

[0071] Step S4: The platform initiates the mutual feedback correction process. This specifically includes: (1) Definition of target quantity: the first n natural frequencies identified by environmental excitation or hammer blows. and mode As a benchmark target.

[0072] (2) Parametric modeling: In the finite element model, the boundary conditions of the hinge are defined as elastic supports, and the stiffness of its six degrees of freedom is set. Set as the parameter variable to be corrected.

[0073] (3) Construction of objective function: Construct the multi-objective optimization function as shown below. :

[0074] in, For frequency weighting coefficients, This is the mode shape weighting coefficient. It is a modal confidence criterion used to measure the consistency between calculated and measured mode shapes.

[0075] (4) Automated correction: The GASOA optimization algorithm is adopted, with the objective function... Minimize as the optimization objective, and automatically iterate and adjust the stiffness parameters defined in step (2).

[0076] (5) Convergence Criterion: When the optimization algorithm satisfies the following two conditions, the model is considered to have converged, and the finite element model under the current parameters is a high-fidelity model: a) For all First mode, satisfying ; b) For all First mode, satisfying .

[0077] Step S5: Using this high-fidelity model, the platform generates a dynamic response cloud map of the gate when it is fully open during flood discharge, predicting the location of the maximum dynamic stress and providing key areas for structural health monitoring. Simultaneously, an automatic test and model analysis comparison are performed at specified intervals to determine the gate's health status. Based on the measured values, the current simulation model is further corrected, and simulation analysis and prediction continue. By continuously repeating the above steps, long-term status tracking can be achieved.

[0078] Example 4 This embodiment is based on embodiment 1: This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method for measuring the dynamic characteristics of an arc gate in a hydropower station as described in Embodiment 1. The computer program can be in the form of source code, object code, executable file, or some intermediate form.

[0079] Specifically, the memory is used to store the computer program, finite element model data, field-collected vibration data, and intermediate calculation results corresponding to the measurement method of the dynamic characteristics of the arc gate of the hydropower station; the processor calls the computer program in the memory and executes all the steps of model construction and measurement point optimization, data acquisition and control, modal parameter identification, model dynamic correction, and dynamic characteristic analysis in sequence, and outputs the corresponding measurement and analysis results.

[0080] It should be noted that this computer equipment can integrate the calculation process of the measurement method, reduce manual intervention, ensure the consistency of the calculation process and the reproducibility of the results, and is suitable for various application scenarios in engineering sites and laboratories.

[0081] Example 5 This embodiment is based on embodiment 1: This embodiment provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the method for measuring the dynamic characteristics of an arc gate in a hydropower station as described in Embodiment 1. The computer program can be in the form of source code, object code, executable file, or some intermediate form. The storage medium includes any entity or device capable of carrying computer program code, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0082] Specifically, the storage medium contains executable computer program code, which includes an instruction set for the entire process of dynamic characteristic measurement. When the processor reads and runs the computer program in the storage medium, it can completely execute all the methods and steps for measuring the dynamic characteristics of the arc gate of the hydropower station and output the corresponding measurement and analysis results.

[0083] It should be noted that the storage medium enables convenient portability and deployment of the measurement method program, facilitating the loading and running of the dynamic characteristic measurement method on different computing devices, thereby improving the application flexibility and scalability of the technical solution.

[0084] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

[0085] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. A method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station, characterized in that, include: A finite element numerical model of the arc gate of the hydropower station was constructed. Modal analysis was performed based on the finite element numerical model to identify the key points of the mode shape and use them as the measurement points. Wireless sensor nodes are installed at the measurement points, and the vibration response signals of the gate are collected synchronously under the set operating conditions of the gate. After preprocessing the collected vibration response signals, modal analysis was used to identify the measured dynamic characteristic parameters of the gate. With the goal of minimizing the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters, parameters that are sensitive to dynamic characteristics are selected to be corrected, and the gradient-guided adaptive collaborative optimization method is used to solve them, so as to obtain the corrected finite element numerical model. The dynamic characteristics of the gate are analyzed using the modified finite element numerical model to obtain the final dynamic characteristic measurement results.

2. The method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station according to claim 1, characterized in that, The construction of the finite element numerical model for the arc-shaped gate of the hydropower station involves modal analysis based on the finite element numerical model to identify key points of the modal vibration modes and use them as measurement points, including: A finite element numerical model was constructed based on the design drawings of the arc gate of the hydropower station. The finite element numerical model includes the dam gate piers, reservoir water flow, gate panel, supporting components, hinged components, and water-stopping components. Initial modal analysis was performed on the constructed finite element numerical model to obtain the mode shapes corresponding to each mode. Key response points of the structure were selected according to the degree of displacement and curvature change of the mode shape, and these key response points were determined as the measurement points for field testing.

3. The method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station according to claim 1, characterized in that, Wireless sensing nodes are installed at the measured point locations, and the vibration response signals of the gate are synchronously collected under the gate's set operating conditions, including: Wireless sensing nodes are installed at the determined measurement points, and the wireless sensing nodes integrate an acceleration sensing unit and a strain sensing unit. Under normal operating conditions or different preset opening and upstream water level conditions, each wireless sensor node is activated to collect data synchronously; the multi-channel vibration acceleration signal and dynamic strain signal of the gate structure are collected and recorded as the original vibration response signal.

4. The method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station according to claim 1, characterized in that, After preprocessing the collected vibration response signals, modal analysis is used to identify the measured dynamic characteristic parameters of the gate, including: Noise removal and frequency band filtering are performed on the acquired raw vibration response signal to remove interference components and invalid data from the signal; The preprocessed vibration response signal was subjected to modal parameter extraction and calculation using a random subspace identification method. The natural frequencies, mode shape vectors and damping ratios of each order under the corresponding test conditions of the gate were separated from the calculation results and used as measured dynamic characteristic parameters.

5. The method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station according to claim 1, characterized in that, With the objective of minimizing the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters, parameters sensitive to dynamic characteristics are selected for correction, including: An objective function is constructed with the aim of minimizing the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters. The objective function integrates the frequency error term and the mode shape consistency term and assigns weight coefficients to each term. Structural parameters that significantly affect the dynamic characteristics of the gate and whose numerical values ​​are difficult to determine precisely are selected as parameters to be corrected. The consistency between the mode shape calculated by the finite element numerical model and the measured mode shape is measured by the modal confidence criterion, and the mode shape error is included in the objective function calculation.

6. The method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station according to claim 5, characterized in that, The gradient-guided adaptive co-optimization method is used to solve the problem, resulting in a modified finite element numerical model, including: Gradient information guides the search direction of individuals, and position update logic is combined to complete the position iteration of individuals in the population. An adaptive adjustment mechanism of inertial weight, cognitive learning mechanism and social learning mechanism are integrated to adaptively adjust the speed update rules of individuals in the population. The gradient guidance coefficient is adaptively adjusted according to the iteration progress and individual fitness, and a population collaborative update strategy is introduced to improve population diversity. Boundary constraints are applied to individual positions outside the search space, and the optimal parameter combination is obtained through iterative optimization. The optimal parameters are then substituted into the initial finite element numerical model to obtain a corrected finite element numerical model that matches the measured data.

7. The method for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station according to claim 1, characterized in that, The modified finite element numerical model is used to perform dynamic characteristic analysis of the gate, and the final dynamic characteristic measurement results are obtained, including: The dynamic response and stress distribution of the gate under preset extreme working conditions are simulated by the modified finite element numerical model to complete the structural safety assessment under the corresponding working conditions. The modified finite element numerical model is used to display the mode shapes at any position and of any order, thus realizing the visualization of the gate's dynamic characteristics. The subsequent field test data is compared with the prediction results of the corrected finite element numerical model to identify changes in structural stiffness and local damage and issue early warnings.

8. A system for measuring the dynamic characteristics of an arc-shaped gate in a hydropower station, characterized in that, include: The initial numerical model construction and measurement point optimization module is configured to construct a finite element numerical model of the arc gate of the hydropower station, perform modal analysis based on the finite element numerical model, identify the key points of the mode vibration mode and use them as measurement point locations; The on-site prototype testing and data acquisition module is configured to install wireless sensing nodes at the measurement point location and synchronously acquire the vibration response signal of the gate under the set operating conditions of the gate. The preliminary identification module for dynamic characteristics based on test data is configured to preprocess the collected vibration response signals and then use modal analysis to identify the measured dynamic characteristic parameters of the gate. The model dynamic correction module based on the mutual feedback mechanism is configured to minimize the error between the dynamic characteristic parameters calculated by the finite element numerical model and the measured dynamic characteristic parameters. It selects the parameters to be corrected that are sensitive to the dynamic characteristics and solves them using the gradient-guided adaptive collaborative optimization method to obtain the corrected finite element numerical model. The dynamic characteristic analysis module based on the modified numerical model is configured to perform dynamic characteristic analysis of the gate using the modified finite element numerical model to obtain the final dynamic characteristic measurement results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method for measuring the dynamic characteristics of the arc gate of a hydropower station as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for measuring the dynamic characteristics of the arc gate of a hydropower station as described in any one of claims 1-7.

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