Vibration Prediction Method for Hydropower Station Buildings

Abstract

The present invention belongs to the technical field of vibration prediction, and specifically discloses a vibration prediction method for a hydropower station powerhouse, which includes constructing a three-dimensional cellular network of the hydropower station powerhouse and determining a plurality of vibration source cells from the cells of the three-dimensional cellular network; determining the predicted acceleration of each vibration source cell at a moment according to the vibration force, damping force of each vibration source cell at the moment and the elastic force generated by the displacement difference between each vibration source cell and the cells in its neighborhood; determining the predicted acceleration of each cell in the three-dimensional cellular network at the moment according to the predicted acceleration of each vibration source cell at the moment; determining the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and correcting the predicted acceleration of each cell according to the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located. The present invention can take into account both physical mechanisms and data-driven, and can quickly and accurately predict the vibration of the hydropower station powerhouse, supporting the construction of intelligent hydropower plants.

Description

Technical Field

[0001] The present invention belongs to the technical field of vibration prediction, and discloses a vibration prediction method for a hydropower station powerhouse. Background Art

[0002] Vibration is an important indicator reflecting the operation technical condition of a structure. Especially for the structure of a hydropower station powerhouse, the vibration problem is particularly serious. With the increase in the scale of the hydropower station powerhouse structure and the increase in unit capacity, the instances of vibration problems during the operation of the units are also increasing. Excessive load vibration during operation will cause abnormal vibration of the hydropower station powerhouse structure. Being in a vibration state for a long time is likely to cause fatigue damage to the structure, and seriously will lead to cracks in the structure, affecting the structural safety.

[0003] Currently, the vibration analysis of a hydropower station powerhouse often relies on finite element numerical simulation. However, the vibration of a hydropower station powerhouse is affected by the coupling of multiple factors such as machinery, hydraulics, and electricity, and has significant non-linear characteristics. When using finite element numerical simulation, complex parameters need to be set, and it depends on a static model. It is difficult to update the water pressure load in the flow channel and the unit operation load in real time dynamically. The calculation is time-consuming and cannot meet the requirements of online early warning. The existing method of simply relying on sensors to monitor the vibration of a hydropower station powerhouse can only reflect the local vibration state, lacks the quantitative analysis of the global risk propagation path, and cannot predict the crack propagation. Moreover, it only knows after something has happened, which belongs to "passive monitoring" and lacks foresight, and is not conducive to preventing the occurrence of dangers. In addition, if simply using the currently popular machine learning method, there is a black box problem. Although the neural network model can achieve certain prediction, it lacks physical interpretability and is not conducive to tracing the source and proposing treatment measures.

[0004] Therefore, there is an urgent need for an efficient prediction tool that combines physical mechanism and data-driven to achieve the upgrade from "passive monitoring" to "active prevention and control", and at the same time can quickly, real-time, efficiently, and accurately predict the vibration of a hydropower station powerhouse in various situations to support the construction of an intelligent hydropower plant. Summary of the Invention

[0005] The purpose of the present invention is to provide a vibration prediction method for a hydropower station powerhouse to solve the technical problems that the existing methods are time-consuming in calculation, cannot meet the requirements of online early warning, and lack physical interpretability.

[0006] The present invention provides a vibration prediction method for a hydropower station powerhouse, including:

[0007] Step 1: Construct a three-dimensional cellular network of a hydropower station powerhouse, and determine a plurality of vibration source cells from the cells of the three-dimensional cellular network.

[0008] Step 2: According to each vibration source cell in The vibration force, damping force at a moment, and the elastic force generated by the displacement difference between each vibration source cell and the cells in its neighborhood determine the predicted acceleration of each vibration source cell at moment.

[0009] Step 3: Determine the predicted acceleration of each cell in the three-dimensional cell network at moment according to the predicted acceleration of each vibration source cell at moment.

[0010] Step 4: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and correct the predicted acceleration of each cell according to the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

[0011] Preferably, step 2 is specifically:

[0012] Determine the equivalent stiffness of each vibration source cell along the direction of its neighborhood.

[0013] Determine the elastic force according to the equivalent stiffness and the displacement difference between each vibration source cell and the cells in its neighborhood.

[0014] Determine the predicted acceleration of each vibration source cell at moment according to the elastic force, the vibration force, and the damping force of each vibration source cell at moment.

[0015] Preferably, step 3 is specifically:

[0016] Determine the distribution weight of the cells in the neighborhood according to the length between each vibration source cell and the cells in its neighborhood.

[0017] Determine the transitional acceleration of the cells in the neighborhood of the vibration source cell according to the distribution weight and the predicted acceleration of the vibration source cell at moment.

[0018] Determine the predicted acceleration of each cell at moment according to the multiple transitional accelerations corresponding to each cell.

[0019] Preferably, step 4 is specifically:

[0020] Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and determine the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

[0021] Determine the correction value of each cell according to the deviation and the distribution weight, and use the correction value to correct the predicted acceleration to obtain the corrected acceleration.

[0022] Determine a correction ratio based on the predicted acceleration and the corrected acceleration, and use the correction ratio to correct the assigned weight until the deviation is within a preset range, so as to obtain the final predicted acceleration of each cell.

[0023] Preferably, determining a correction ratio based on the predicted acceleration and the corrected acceleration, and using the correction ratio to correct the assigned weight is specifically as follows:

[0024] Determine the correction ratio according to the quotient of the corrected acceleration and the predicted acceleration.

[0025] Multiply the correction ratio by the assigned weight to correct the assigned weight.

[0026] Preferably, after step 4, it further includes:

[0027] Step 5: Determine the predicted velocity of each cell at time according to the predicted velocity and predicted acceleration of each cell at time.

[0028] Preferably, after step 5, it further includes:

[0029] Step 6: Determine the predicted displacement of each cell at time according to the predicted displacement and predicted velocity of each cell at time.

[0030] Preferably, after step 6, it further includes:

[0031] Step 7: Determine the predicted stress of each cell at time according to the elastic modulus of each cell, the predicted displacement of each cell at time, and the predicted displacement at

[0032] Preferably, step 1 is specifically as follows:

[0033] Construct a three-dimensional cell network of the hydropower station powerhouse.

[0034] Apply the excitation load of the hydropower station powerhouse to the corresponding cells in the three-dimensional cell network to obtain a plurality of vibration source cells.

[0035] Preferably, the excitation load includes the flow channel water pressure load and the unit operation load.

[0036] The vibration prediction method of the hydropower station powerhouse of the present invention has the following beneficial effects compared with the prior art:

[0037] The vibration prediction method for a hydropower station powerhouse of the present invention can take into account both physical mechanisms and data-driven approaches, achieving an upgrade from "passive monitoring" to "active prevention and control". At the same time, it can predict the vibrations of the hydropower station powerhouse in various situations in real-time, efficiently, and accurately, supporting the construction of an intelligent hydropower plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flowchart of the vibration prediction method for a hydropower station powerhouse in an embodiment of the present invention.

[0039] Figure 2 It is a schematic diagram of the partition of the spiral case and draft tube flow passages in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0041] An embodiment of the present invention provides a vibration prediction method for a hydropower station powerhouse, as Figure 1 shown, including:

[0042] Step 1: Construct a three-dimensional cellular network of the hydropower station powerhouse, and determine multiple vibration source cells from the cells of the three-dimensional cellular network. Specifically:

[0043] Step 1.1: Construct a three-dimensional cellular network of the hydropower station powerhouse.

[0044] In an embodiment of the present invention, the water turbine layer is used as the demarcation line to construct a three-dimensional cellular network with different cell sizes. Specifically, large-volume concrete below the water turbine layer uses coarse-sized cells, and the plate beam structure above the water turbine layer uses fine-sized cells. The combination of coarse and fine sizes balances the calculation efficiency and accuracy. Exemplarily, the cell size of the large-volume concrete below the water turbine layer is 2.5m × 2.5m × 2.5m; the cell size of the plate beam structure above the water turbine layer is 0.5m × 0.5m × 0.5m.

[0045] Before predicting the vibration of the hydropower station powerhouse in an embodiment of the present invention, it is necessary to define the state of each cell in the three-dimensional cellular network. Specifically, the state of each cell includes state variables and dynamic risk parameters. Among them, the state variables include mechanical parameters and material properties. The mechanical parameters are velocity, acceleration, displacement, and stress; the material properties are density, elastic modulus, and damping coefficient. The dynamic risk parameters are the width of concrete cracks and the excitation loads composed of unit operation loads and flow passage water pressure loads.

[0046] The initial states of all cells in the embodiments of the present invention are as follows: the velocity, displacement, acceleration, and concrete crack width are all 0, and the stress is the self-weight stress of the structure, that is . Among them the stress at the position of the th cell; is the unit weight of all structures above the th cell, and the unit weight of concrete is taken as 25 kN / m³; is the height of all structures above the

[0047] Step 1.2: Apply the excitation load of the hydropower station powerhouse to the corresponding cells in the three-dimensional cell network to obtain multiple vibration source cells.

[0048] The excitation loads in the embodiments of the present invention include the unit operation load and the flow channel water pressure load.

[0049] The main sources of vibration of the hydropower station powerhouse are mainly unit operation and water flow pulsation. Therefore, in the embodiments of the present invention, when constructing the three-dimensional cell network, the unit operation load and the flow channel water pressure load are applied to the corresponding cells, so as to effectively simulate non-linear dynamic behaviors such as unit operation and water flow pulsation.

[0050] When determining the excitation load in the embodiments of the present invention, it is assumed that each excitation load is a simple harmonic load, and the phases of each excitation load are the same, that is, each excitation load reaches the vibration amplitude at the same time, which actually considers the most unfavorable load combination.

[0051] The flow channel water pressure load specifically includes the internal water pressure in the spiral case and the draft tube flow channel, and is simplified into a periodic load in zones. The zones in the embodiments of the present invention can be divided according to the flow channel pulsation pressure test or experience. Multiple zones can be divided in both the spiral case and the draft tube flow channel, or the spiral case can be taken as one zone and the draft tube flow channel can be taken as one zone. Generally, according to experience, the spiral case is divided into one zone every 90 degrees along the water flow direction, that is, the spiral case is divided into 4 zones, and the draft tube is divided into 2-3 zones.

[0052] The flow channel water pressure load in the embodiments of the present invention is characterized by the vibration force. The loads in the X, Y, and Z directions are the same, so the vibration forces are also the same, ; is the maximum vibration force in zone ; respectively represent different zones in the spiral case and the draft tube flow channel, as shown in Figure 2 ; is the corresponding frequency in zone is the time; is The maximum vibration force in the X direction of area is The maximum vibration force in the Y direction of area is The maximum vibration force in the Z direction of area

[0053] The operating loads of the unit in the embodiments of the present invention include the upper frame vibration load, the lower frame vibration load, and the stator foundation vibration load, all of which include three directions in the X, Y, and Z spaces.

[0054] The vibration loads in the X, Y, and Z directions of the upper frame are characterized by vibration forces, which are respectively: , , ; where , , are respectively the vibration forces of the upper frame in the X, Y, and Z directions; , , are respectively the unit loads acting on the upper frame in the X, Y, and Z directions; is the frequency corresponding to the generator set; is the time.

[0055] The vibration loads in the X, Y, and Z directions of the lower frame are characterized by vibration forces, which are respectively: , , ; where , , are respectively the vibration forces of the lower frame in the X, Y, and Z directions; , , are respectively the unit loads acting on the lower frame in the X, Y, and Z directions; is the frequency corresponding to the generator set; is the time.

[0056] The vibration loads in the X, Y, and Z directions of the stator foundation are characterized by vibration forces, which are respectively: , , ; where , , are respectively the vibration forces of the stator foundation in the X, Y, and Z directions; , , are respectively the unit loads acting on the stator foundation in the X, Y, and Z directions; is the frequency corresponding to the generator set; is the time.

[0057] Step 2. Determine the predicted acceleration of each vibration source cell at moment based on the vibration force, damping force of each vibration source cell and the elastic force generated by the displacement difference between each vibration source cell and the cells in its neighborhood, specifically: moment, specifically:

[0058] Step 2.1. Determine the equivalent stiffness of each vibration source cell along the direction of its neighborhood.

[0059] The neighborhood rule adopted in the embodiment of the present invention is: Use the Moore - type three - dimensional neighborhood (that is, each cell directly interacts with 26 surrounding cells) to define the inter - connection between cells, ensuring the reliable propagation of vibrations in three - dimensional space.

[0060] The embodiment of the present invention uses to represent the equivalent stiffness, that is, is the equivalent stiffness formed by the th vibration source cell along the th neighborhood direction.

[0061] Step 2.2. Determine the elastic force according to the equivalent stiffness and the displacement difference between each vibration source cell and the cells in its neighborhood.

[0062] Step 2.3. Determine the predicted acceleration of each vibration source cell at moment based on the elastic force, vibration force and damping force of each vibration source cell at moment.

[0063] Exemplarily, the predicted acceleration of the th vibration source cell at moment is , which includes three directions in the X, Y, Z space, respectively denoted as: , , .

[0064] (1)

[0065] (2)

[0066] (3)

[0067] In the formula, is the mass of the th vibration source cell, , is the density, the th vibration source cell forms a volume in the three - dimensional grid; , , are respectively the sum of elastic forces generated by the displacement differences of the th vibration source cell and the cells within its neighborhood ( is the total number of neighborhood cells of the th vibration source cell) along the X, Y, and Z directions. Among them, , , are respectively the displacements of the th vibration source cell at time in the X, Y, and Z directions; , , are respectively the displacements of the th vibration source cell corresponding to the th neighborhood cell in the X, Y, and Z directions at time ; is the equivalent stiffness of the th vibration source cell along the th neighborhood direction, ; is the equivalent moment of inertia of the cross-section perpendicular to the th neighborhood direction of the th vibration source cell along the th neighborhood direction. is the elastic modulus of the th vibration source cell, , is the elastic modulus of concrete; is the maximum allowable operating crack width (empirically taken as 1 mm); is the crack width of the th vibration source cell at time ; is the stiffness degradation coefficient of the th vibration source cell at time , , is the stress of the th vibration source cell at time , , is the allowable value of the material's tensile strength. For the part with steel bars, it takes 0.4 - 0.6 times the allowable value of the steel bar strength, and for the part without steel bars, it takes the allowable value of the tensile strength of the concrete material itself; , , are respectively the stresses of the th vibration source cell in the X, Y, and Z directions at time . When , , When it is compressive stress, the stress in this direction is not considered; when , ; when , , is the stress of the th vibration source cell at the moment; when , . is the damping force of the th vibration source cell, where is the damping coefficient of the th vibration source cell (the damping is set according to the material properties, and 0.07 is taken according to the standard workshop), , , are the velocities in the X, Y, and Z directions of the th vibration source cell at the moment.

[0068] , , are the vibration forces in the X, Y, and Z directions of the excitation load of the th vibration source cell at the moment. The vibration forces of the vibration source cells in the flow channel range are respectively ; the vibration forces of the vibration source cells in the upper frame part are respectively , , ; the vibration forces of the vibration source cells in the lower frame part are respectively , , ; the vibration forces of the vibration source cells in the stator base part are respectively , , .

[0069] Step 3: Determine the predicted acceleration of each cell in the three-dimensional cell network at moment according to the predicted acceleration of each vibration source cell at moment.

[0070] In the embodiment of the present invention, the transfer rule of the cell acceleration is: the weights are distributed according to the neighboring cells and attenuate successively to the surrounding according to the ratio. The acceleration of each cell is the superposition effect of different excitation loads transmitted here.

[0071] Then step 3 is specifically:

[0072] Step 3.1: Determine the allocation weight of the cells in the neighborhood according to the length between each vibration source cell and the cells in its neighborhood, as shown in formula (4).

[0073] (4)

[0074] In the formula, is the allocation weight corresponding to the th vibration source cell for the th neighborhood, is the length of the th vibration source cell in the direction of the th neighborhood cell, is the attenuation coefficient, which can be obtained through experiments or empirical formulas. Exemplarily, , A is a material characteristic constant, such as 0.12 for concrete, is the elastic modulus of concrete, is the material density, is the vibration source frequency, is the material damping coefficient.

[0075] Step 3.2: Determine the transition acceleration of the cells in the neighborhood of the vibration source cell according to the allocation weight and the predicted acceleration of the vibration source cell at time.

[0076] Exemplarily, determine the transition acceleration of each cell in the neighborhood according to the product of the allocation weight and the predicted acceleration of the vibration source cell at time.

[0077] Step 3.3: Determine the predicted acceleration of each cell at time according to the multiple transition accelerations corresponding to each cell.

[0078] Since each cell is affected by multiple vibration source cells, therefore, the predicted acceleration of each cell at time is the superposition of the transition accelerations applied to the cell by multiple vibration source cells.

[0079] In the embodiment of the present invention, to reduce the prediction error, the monitored acceleration is introduced to modify the predicted acceleration, as described in the following step 4.

[0080] Step 4: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and correct the predicted acceleration of each cell according to the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

[0081] The monitoring equipment in the embodiment of the present invention includes, in addition to the accelerometer, a stress gauge, a displacement sensor, etc.

[0082] The above step 4 is specifically as follows:

[0083] Step 4.1: Determine the cell where the accelerometer monitoring the hydropower station building is located, and determine the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

[0084] In determining the predicted acceleration, the embodiment of the present invention uses, in addition to the above-mentioned physical driving rules, a data-driven rule to correct the predicted acceleration based on the monitored acceleration, thereby reducing the prediction error.

[0085] The embodiment of the present invention first uses 80% of the existing monitored acceleration to correct the predicted acceleration, and the remaining 20% is used for verification after correction to ensure the reliability of the prediction model. Then the time interval of the monitored acceleration is compared with the time step of the three-dimensional cellular network. Alignment can be performed by interpolation. Finally, the existing monitored acceleration is extended to all cells of the three-dimensional cellular network to construct a spatial correlation matrix.

[0086] The process of determining the deviation in the embodiment of the present invention is as follows: the position of the accelerometer monitoring the hydropower station powerhouse is approximately mapped to the corresponding cell in the three-dimensional cellular network, its acceleration value is extracted and recorded as the monitoring acceleration, and a monitoring set is established. , extract the predicted acceleration of the cell at the corresponding position and establish the prediction set .in is the number of monitoring locations, The embodiment of the present invention uses a simplified The monitored acceleration is represented and the predicted acceleration is represented by the simplified Y. The deviation between the monitored acceleration and the predicted acceleration of the corresponding cell is shown in formula (5).

[0087] (5)

[0088] In the formula is the deviation set, For the The deviation of the monitoring position.

[0089] Step 4.2: Determine the correction value of each cell based on the deviation and the assigned weight, and use the correction value to correct the predicted acceleration to obtain the corrected acceleration.

[0090] The embodiment of the present invention adopts the deviation diffusion principle to correct the correction value of each cell according to the following steps.

[0091] 1) Define the initial correction matrix: Assume that the initial correction value of the corresponding cell at the monitoring position is , the initial correction amount of the remaining cells .

[0092] 2) Three-dimensional neighborhood diffusion: For each time step, the deviation at each monitoring position is sequentially weighted according to the neighborhood and diffused to all surrounding cells, enabling the deviation to spread in the cells. The deviation at each monitoring position is the superposition effect of the deviations transmitted from different monitoring positions (for each cell, the deviations from as many monitoring positions as there are are superimposed), that is: ( is the deviation when the deviation at the th monitoring position is diffused to the th cell). The predicted acceleration after correction (corrected acceleration) is the original predicted acceleration + deviation, that is:

[0093] .

[0094] Step 4.3: Determine the correction ratio based on the predicted acceleration and the corrected acceleration, and use the correction ratio to correct the allocation weight until the deviation is within the preset range to obtain the final predicted acceleration for each cell.

[0095] Specifically, determining the correction ratio based on the predicted acceleration and the corrected acceleration and using the correction ratio to correct the allocation weight means: determining the correction ratio according to the quotient of the corrected acceleration and the predicted acceleration; multiplying the correction ratio by the allocation weight to correct the allocation weight.

[0096] Among them, the correction ratio is specifically as shown in formula (6):

[0097] (6)

[0098] Specifically, the above-mentioned correction of the allocation weight is as shown in formula (7):

[0099] (7)

[0100] In the formula, is the corrected allocation weight.

[0101] Repeat steps 4.1 to 4.3 several times (usually 2 - 3 times is sufficient) so that the deviation at the monitoring position is controlled within 5% of the monitored acceleration.

[0102] In the embodiment of the present invention, a new predicted acceleration is recalculated based on the corrected , and then the remaining 20% of the monitored acceleration is compared with the final predicted acceleration. The root mean square error of the obtained result is less than 5%, indicating the reliability of the final predicted acceleration.

[0103] In order to improve the comprehensiveness of vibration prediction, after step 4, the embodiment of the present invention further includes:

[0104] Step 5. Determine the predicted velocity of each cell at moment according to the predicted velocity and predicted acceleration of each cell at moment, specifically: , , . Among them, , , are the predicted velocities in the X, Y, and Z directions of the -th cell at moment respectively; , , are the predicted velocities in the X, Y, and Z directions of the -th cell at moment respectively; , , are the predicted accelerations in the X, Y, and Z directions of the -th cell at moment respectively; is the time step.

[0105] To further improve the comprehensiveness of vibration prediction in the embodiments of the present invention, after Step 5, it further includes:

[0106] Step 6. Determine the predicted displacement of each cell at moment according to the predicted displacement and predicted velocity of each cell at moment, specifically: , , . Among them, , , are the predicted displacements in the X, Y, and Z directions of the -th cell at moment respectively; , , are the predicted displacements in the X, Y, and Z directions of the -th cell at moment respectively; , , are the predicted velocities in the X, Y, and Z directions of the -th cell at moment respectively; is the time step.

[0107] To further improve the comprehensiveness of vibration prediction in the embodiments of the present invention, after Step 6, it further includes:

[0108] Step 7. Determine the predicted stress of each cell at moment according to the elastic modulus of each cell, the predicted displacement of each cell at moment, and the predicted displacement of each cell at moment. Specifically, , , . Among them, , , are the predicted stresses in the X, Y, and Z directions of the th cell at moment respectively; , , are the predicted displacements in the X, Y, and Z directions of the th cell at moment respectively; , , are the predicted displacements in the X, Y, and Z directions of the th cell at moment respectively; is the elastic modulus of the th cell, ; , , are the lengths of the th cell in the X, Y, and Z directions respectively.

[0109] With the aid of monitoring acceleration, the present invention dynamically updates the cell state, predicts the data conditions of various indicators (stress, displacement, velocity, acceleration) caused by vibration in future time steps, generates corresponding prediction results, and combines with the structural safety standard to set the standard value, so as to quickly capture the dangerous positions and assist in locating the high-risk areas. The present invention can also be further combined with digital graphic processing methods to generate a map for display.

[0110] The present invention proposes a prediction method combining dynamic risk propagation and cellular automata to predict the vibration situation of a hydropower station powerhouse. Through the method of the present invention, the vibration characteristics of the hydropower station powerhouse can be quickly evaluated, and the anti-vibration risk of the structure can be reduced. Specifically, by quantifying the potential hazards of vibration propagation (such as crack propagation, resonance probability, stress concentration, etc.) to form spatial distribution characteristics, the predicted value of each cell is dynamically updated to reflect the real-time risk propagation path, and the relationship between local interaction and global dynamic evolution in the cell can be quickly displayed. Through hierarchical modeling, the present invention can not only macroscopically reflect the overall vibration mode but also refine to the microscopic response of local components. Combining with visualization tools on platforms such as Matlab, the diffusion process of vibration energy in the powerhouse can be intuitively displayed to assist engineers in quickly locating the weak links.

[0111] Through the discretization of space and local evolution rules, the present invention can effectively simulate nonlinear dynamic behaviors such as unit vibration and water flow pulsation, avoiding the computational burden of the traditional finite element method when solving high-dimensional differential equations; the present invention can dynamically update the cell state according to real-time monitoring data to capture the instantaneous changes of vibration. Compared with the limitation of the finite element method that the load needs to be pre-selected, the model autonomously evolves the vibration propagation path through the neighborhood interaction rule, can autonomously change the boundary and load conditions, adapt to the emergencies under different working conditions, and update the vibration situation caused by the power generation process in real time, and realize the vibration response efficiently and quickly.

[0112] The present invention is a comprehensive vibration analysis method combining a physical mechanism model and real-time monitoring data, which significantly improves the efficiency and globality of prediction and reduces engineering risks.

[0113] The above are only several embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention is disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art, without departing from the scope of the technical solution of the present invention, makes some changes or modifications using the technical content disclosed above, which are all equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A vibration prediction method for a hydropower station powerhouse, characterized in that, Including: Step 1: Construct a three-dimensional cellular network of the hydropower station powerhouse, and determine multiple vibration source cells from the cells of the three-dimensional cellular network; Step 2. Determine the predicted acceleration of each vibration source cell at t the moment of the vibration force, damping force, and elastic force generated by the displacement difference between each vibration source cell and the cells in its neighborhood at t the moment of +1 Step 3. Determine the predicted acceleration of each cell in the three-dimensional cellular network at t the +1 moment according to the predicted acceleration of each vibration source cell at t the +1 moment. Specifically, it is as follows: Determine the distribution weight of the cells in the neighborhood according to the length between each vibration source cell and the cells in its neighborhood; Determine the transitional acceleration of the cells within the neighborhood of the vibration source cell according to the assigned weight and the predicted acceleration of the vibration source cell at t the +1 moment; Determine the predicted acceleration of each cell at t the +1 moment according to the multiple transition accelerations corresponding to each cell; Step 4: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and correct the predicted acceleration of each cell according to the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located. Specifically: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and determine the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located; Determine the correction value of each cell according to the deviation and the distribution weight, and use the correction value to correct the predicted acceleration to obtain the corrected acceleration; Determine the correction ratio according to the predicted acceleration and the corrected acceleration, and use the correction ratio to correct the distribution weight until the deviation is within the preset range to obtain the final predicted acceleration of each cell.

2. The vibration prediction method for a hydropower station powerhouse according to claim 1, characterized in that, Step 2 is specifically: Determine the equivalent stiffness of each vibration source cell along the direction of its neighborhood; Determine the elastic force according to the equivalent stiffness and the displacement difference between each vibration source cell and the cells in its neighborhood; According to the elastic force and the vibration force and damping force of each vibration source cell at t time, determine the predicted acceleration of each vibration source cell at t time + 1.

3. The method for predicting the vibration of a hydropower station powerhouse according to claim 1, wherein Determine the correction ratio according to the predicted acceleration and the corrected acceleration, and use the correction ratio to correct the distribution weight. Specifically: Determine the correction ratio according to the quotient of the corrected acceleration and the predicted acceleration; Multiply the correction ratio by the distribution weight to correct the distribution weight.

4. The method for predicting the vibration of a hydropower station powerhouse according to any one of claims 1 to 3, characterized in that, After step 4, it further includes: Step 5. Determine the predicted speed of each cell at t time according to the predicted speed and predicted acceleration of each cell at t time + 1.

5. The vibration prediction method for a hydropower station powerhouse according to claim 4, wherein After step 5, it further includes: Step 6. Determine the predicted displacement of each cell at t time according to the predicted displacement of each cell at t time and the predicted velocity at t +1 time.

6. The method for predicting the vibration of a hydropower station powerhouse according to claim 5, wherein After step 6, it further includes: Step 7. Determine the predicted stress of each cell at t +1 moment according to the elastic modulus of each cell, the predicted displacement of each cell at the moment of t and the predicted displacement of each cell at the moment of t +1. t and the predicted displacement at the moment of t and the predicted displacement at the moment of t +1 to determine the predicted stress of each cell at t t +1 moment.

7. The vibration prediction method for a hydropower station powerhouse according to claim 1, characterized in that, Step 1 is specifically: Construct a three-dimensional cellular network of the hydropower station powerhouse; Apply the excitation load of the hydropower station powerhouse to the corresponding cells in the three-dimensional cellular network to obtain multiple vibration source cells.

8. The method for predicting the vibration of a hydropower station powerhouse according to claim 7, wherein The excitation load includes the flow channel water pressure load and the unit operation load.

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