Runner resonance risk assessment method based on blade Karman vortex three-dimensional calculation

Abstract

The invention discloses a rotating wheel resonance risk assessment method based on blade Karman vortex three-dimensional calculation, and belongs to the technical field of hydraulic machinery, and the method comprises the steps: obtaining speed field and pressure field data based on hydraulic machinery main runner global flow simulation; establishing a runner inlet speed fitting function and a runner outlet pressure fitting function through layered sampling and polynomial fitting; performing runner blade single-runner flow simulation by taking the fitting function as a boundary condition, and monitoring pressure pulsation data caused by Karman vortex shedding; performing spectral analysis on the pressure pulsation data, and determining Karman vortex shedding frequencies at different spanwise positions of a blade outlet; and in combination with the inherent frequency obtained through leaf water wet modal analysis, the resonance risk is evaluated through frequency comparison, high, medium and low risk points are marked, and a risk report is generated. According to the method, the Karman vortex frequency is directly extracted through full-three-dimensional flow simulation, dependence on empirical parameters is eliminated, three-dimensional flow field characteristics and spanwise frequency distribution are accurately captured, and active pre-judgment of resonance risks in the design stage is achieved.

Description

Technical Field

[0001] This invention belongs to the field of hydraulic machinery technology, and in particular relates to a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. Background Technology

[0002] In the field of hydraulic machinery engineering, the hydraulic elastic vibration induced by Karman vortices at the turbine runner blade outlet is a key technical challenge threatening the safe operation of equipment. When a mixed-flow turbine is operating under certain load conditions, the Karman vortex street formed by flow separation at the turbine runner blade outlet edge can easily induce blade resonance, manifesting as a significant high-frequency whistling sound with the frequency increasing with the load. The essence of this resonance is that the Karman vortex frequency coincides with or is close to the underwater natural frequency of the blade. This can lead to abnormal hydraulic machinery noise in mild cases, and blade cracks or even breakage in severe cases, seriously affecting unit reliability and increasing operation and maintenance costs. Therefore, accurately calculating the Karman vortex frequency during the blade design phase is a core technical requirement for preventing resonance hazards. Currently, the industry commonly uses a semi-numerical-semi-empirical method to estimate the Karman vortex frequency: firstly, the relative velocity outside the blade boundary layer is obtained through computational fluid dynamics (CFD) simulation. Then use empirical formulas Perform frequency calculation. In the formula, It is the Struh number (usually 0.18-0.24 for reaction turbines). The wake width includes the thickness of the blade exit edge and the thickness of the boundary layer on both sides. However, this method has two major technical bottlenecks: First, the width of the trail Precise calculations are difficult. The flow field at the blade exit edge is affected by boundary layer development, flow separation characteristics, and three-dimensional flow. Existing CFD methods struggle to accurately capture the boundary layer thickness distribution and wake width under complex flow fields. The calculation error is significant.

[0003] Secondly, the Struhar number Values ​​are derived based on experience. Existing Struhal numbers... The parameters are mostly based on experiments with flow around flat plates or cylinders, which are significantly different from the complex curved surface flow around actual runner blades. The Reynolds number correlation has not been specifically corrected, resulting in insufficient accuracy in frequency estimation. The aforementioned deficiencies make it impossible to reliably predict the Karman vortex frequency during the design phase. The industry can only take reactive remedial measures, such as modifying the blade structure or adjusting operating conditions, after resonance faults occur during unit operation. This not only increases engineering costs but may also lead to irreversible damage due to hysteresis. Therefore, there is an urgent need for a method that can accurately calculate the Karman vortex frequency at the turbine runner blade outlet, enabling the prediction of resonance risk during the design phase and fundamentally solving the problems of passivity and inaccuracy in existing technologies. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the prior art by proposing a runner resonance risk assessment method based on three-dimensional calculation of blade Karman vortices. This method calculates the Karman vortex frequency at the blade outlet during the runner design stage, thereby providing a theoretical basis for avoiding the occurrence of blade hydroelastic resonance due to its proximity to the natural underwater frequency of the runner blade.

[0005] The above objectives are achieved through the following technical solutions: A method for assessing the resonance risk of a hydraulic runner based on three-dimensional calculation of Karman vortices in blades includes the following steps: S1, obtaining velocity and pressure field data within the hydraulic machinery based on full-domain flow simulation of the main flow channel. S2, obtaining the inlet velocity fitting function and outlet pressure fitting function of the runner based on the velocity and pressure field data within the hydraulic machinery. S3, using the inlet velocity fitting function and outlet pressure fitting function as boundary conditions, performing single-channel flow simulation of the hydraulic machinery runner blades after setting pressure pulsation monitoring points, obtaining pressure pulsation data caused by Karman vortex shedding at the pressure pulsation monitoring points. S4, determining the Karman vortex shedding frequency at different spanwise positions at the outlet of the hydraulic machinery runner blades by analyzing the pressure pulsation data at the pressure pulsation monitoring points. S5, obtaining the blade's natural frequency based on wet modal analysis of the blades in water, comparing the Karman vortex shedding frequency with the blade's natural frequency, and assessing the resonance risk of the runner blades caused by Karman vortex shedding based on the comparison results.

[0006] Preferably, in step S1, the full-domain flow simulation of the hydraulic machinery main channel includes the following steps: S11. Based on geometric modeling software, a three-dimensional geometric model of the entire main channel of the hydraulic machinery is established. The entire main channel of the three-dimensional geometric model of the main channel of the hydraulic machinery is divided into several independent sub-domains, and each sub-domain is discretized into hexahedral mesh elements. S12. Import the three-dimensional geometric model of the entire hydraulic machinery main channel after discretization of each subdomain into the fluid dynamics simulation software. In the fluid dynamics simulation software, select the turbulence model and set the fluid property parameters according to the required simulation conditions, and define the boundary conditions. S13, Based on fluid dynamics simulation software, the flow control equations of the entire main channel of hydraulic machinery are discretized, an algorithm and discretization scheme adapted to the flow field characteristics are selected, and the solution control parameters are configured. S14, initiate iterative calculations using the fluid dynamics simulation software. After convergence, output the velocity and pressure distribution information within the hydraulic machinery and save it as a standard format file. The output velocity and pressure distribution information within the hydraulic machinery includes: In fluid dynamics simulation software, locate the inlet face of the hydraulic machinery runner and output the cylindrical coordinates of all mesh nodes on that face. and the corresponding velocity components ; In fluid dynamics simulation software, locate the outlet surface of the hydraulic machinery runner and output the cylindrical coordinates of all mesh nodes on that surface. and the corresponding pressure value .

[0007] Preferably, in step S11, the method for establishing a three-dimensional geometric model of the entire main channel of the hydraulic machinery is as follows: S11-11, Check whether the required three-dimensional geometric model of the main hydraulic machinery channel already exists in the geometric modeling software; if yes, call it directly; if not, proceed to step S11-12. S11-12, obtain point cloud data of hydraulic machinery prototype through 3D scanning technology, or obtain the original model of hydraulic machinery based on design drawings; S11-13: After simplifying the original model or point cloud data in the geometric modeling software, a full-domain three-dimensional geometric model of the main channel of the hydraulic machinery is obtained.

[0008] Preferably, in step S11, the subdomain is divided primarily based on geometric features and secondarily on functional attributes, including the following steps: S11-21 defines the correspondence between the core geometric components and functional areas of the main channel of the hydraulic machinery in the three-dimensional geometric model of the entire main channel. S11-22, based on geometric features, divides the entire flow field of the main channel into geometric subdomains according to geometric boundaries, so that each individual geometric subdomain corresponds to a complete geometric component; S11-23, based on the correspondence between the core geometric components and functional areas of the main channel, all geometric subdomains are divided into different functional subdomains according to their functional attributes, so as to clarify the physical functional attributes of each geometric subdomain; among them, the functional subdomains include the stationary domain and the rotating domain.

[0009] Preferably, in step S11, discretizing the subdomain into hexahedral mesh elements includes the following steps: S11-31, generate hexahedral structured meshes independently for each subdomain, while maintaining the overall consistency of mesh lines with the main flow direction; S11-32 uses the expansion layer technique to generate multi-layered gradient boundary layer meshes on the walls of all subdomains; S11-33, local densification is performed on areas with relatively larger flow gradients, and the densified areas are connected to the undensified areas through a transition layer; S11-34, through mesh quality verification, ensures that the mesh distortion rate and mesh aspect ratio meet the preset requirements, and that all hexahedral mesh elements have no negative volume; S11-35 uses key hydraulic performance parameters that reflect the performance of hydraulic machinery and equipment as the basis for judging the qualification of the number of grids. It obtains the change range of key hydraulic performance parameters with the number of grids and selects the number of grids whose change range of key hydraulic performance parameters is less than the maximum allowable value when the number of grids continues to increase as the final number of grids.

[0010] Preferably, in step S12, depending on whether the required simulation conditions include design conditions and non-design conditions, the selection of the turbulence model and the setting of fluid property parameters include: S12-1, the initial preset defaults to a turbulence model based on the Reynolds time-averaged method; S12-2, set fluid physical property parameters including fluid density, dynamic viscosity and compressibility; S12-3, the calculated values ​​of key hydraulic performance parameters are obtained through initial flow field simulation calculations; S12-4: Compare the calculated values ​​of key hydraulic performance parameters with the experimental values ​​obtained through physical experiments to determine whether the error is within the allowable range; if yes, retain the current turbulence model and fluid property parameter settings; if no, proceed to step S12-5. S12-5: Select the method of adjusting the error according to the required simulation conditions. That is, if it is the design condition, adjust the current turbulence model parameters and return to step S12-3; if it is not the design condition, proceed to step S12-6. S12-6, Determine whether the current turbulence model is a turbulence model based on the scale analysis method; if yes, adjust the parameters of the current turbulence model and return to step S12-3; if no, switch to the turbulence model based on the scale analysis method and return to step S12-3.

[0011] Preferably, in step S12, defining boundary conditions includes defining functional adaptation boundaries for the connection surfaces between different functional subdomains and different geometric subdomains, so as to ensure the continuity of the flow field parameters across the entire main channel. Further, defining functional adaptation boundaries includes defining inlet / outlet boundaries, wall boundaries, rotating domain-stationary domain interfaces, and stationary domain-stationary domain interfaces.

[0012] Preferably, in step S2, obtaining the impeller inlet velocity fitting function includes: the axial coordinate along the impeller inlet surface of the hydraulic machinery. Perform stratified sampling, in each Value below, along the circumferential direction At least 36 sampling points are selected uniformly, and the velocity components of each sampling point are extracted. For each Calculate the circumferential average value of the velocity components at all sampling points under the given value. , obtain axial coordinates Data set A corresponds to the average speed; data processing software is used to perform polynomial fitting on data set A to obtain and save the fitting function for the runner inlet speed. , and .

[0013] Preferably, in step S2, obtaining the impeller outlet pressure fitting function includes: radial coordinates along the outlet surface. Perform stratified sampling, in each Value below, along the circumferential direction At least 36 sampling points were selected evenly, and the pressure values ​​at each point were extracted. For each Calculate the circumferential average pressure value of all sampling points under the given value. , obtain radial coordinates Data set B corresponds to the average pressure; multinomial fitting is performed on data set B using data processing software to obtain and save the impeller outlet pressure fitting function. .

[0014] Preferably, in step S3, the single-channel flow simulation of the hydraulic machinery runner blades includes the following steps: S31. A single-channel fluid domain model of a hydraulic mechanical runner blade is established based on geometric modeling software, and the fluid domain is divided using a hexahedral structured mesh. S32, in the fluid dynamics simulation software, enable the hybrid turbulence model based on the scale analysis method, define the single flow channel as a rotating domain, with the rotational speed consistent with the main flow channel simulation in step S1, and adopt the absolute velocity coordinate system; S33, the inlet boundary calls the impeller inlet velocity fitting function from step S2; the outlet boundary calls the impeller outlet pressure fitting function; the two sides of the flow channel are set as periodic boundaries; the blades, upper crown and lower ring are all set as non-slip walls; S34, several monitoring points covering the upper crown to the lower ring are evenly arranged along the spanwise direction on the center line of the trailing edge wall of the blade exit, and pressure pulsation time history monitoring parameters are set for the monitoring points. S35, based on fluid dynamics simulation software, discretizes the flow control equations of a single-channel fluid domain model of a hydraulic machinery runner blade, selects a pressure-velocity coupling algorithm and discretization scheme that are suitable for the flow field characteristics, and configures the solution control parameters; S36, start the undetermined iterative calculation of the fluid dynamics simulation software, and after the calculation converges, export the pressure pulsation time history data of all monitoring points.

[0015] Preferably, in step S4, determining the Karman vortex shedding frequency includes the following steps: S41, Perform stability preprocessing on the pressure pulsation time history data; S42, use data processing software to perform spectrum analysis on the preprocessed pressure pulsation time history data to obtain the frequency-amplitude spectrum; S43, identify and record the significant peak frequencies and their relative amplitudes in the frequency-amplitude spectrum, thereby obtaining the regional Karman vortex shedding characteristic frequencies; S44. Using the spanwise relative position as the abscissa and the Kármán vortex shedding characteristic frequency as the ordinate, a distribution cloud map of spanwise position-frequency-relative amplitude is plotted to present the Kármán vortex shedding frequency distribution characteristics at different spanwise positions.

[0016] Preferably, in step S5, assessing the resonance risk caused by Karman vortex shedding includes the following steps: S51, the wet modal analysis of the blade in water was carried out using finite element analysis software, the first few natural frequencies were extracted, and the frequency values ​​and corresponding mode shape characteristics of each order were recorded. S52, set the threshold range for dangerous frequency range and attention frequency range according to each frequency order; S53, compare the Karman vortex shedding frequency at different spanwise positions with the natural frequency one by one: those falling into the danger zone are marked as high-risk points, those falling into the concern zone are marked as medium-risk points, and those not falling into either zone are marked as low-risk points. S54 assesses the magnitude of resonance risk caused by Karman vortex shedding based on the proportion of high-risk points, and generates a risk assessment report in conjunction with mode shape characteristics.

[0017] Compared with existing technologies, the advantages of this technical solution are: 1) Eliminating reliance on empirical parameters and improving the objectivity of frequency calculation. This invention directly calculates the Karman vortex shedding frequency through full three-dimensional flow simulation: it captures flow field characteristics through full-domain flow simulation of the main channel, combines it with fine simulation of pressure pulsation in a single channel to monitor pressure fluctuations, and then extracts the Karman vortex shedding frequency through spectrum analysis. The entire process does not rely on empirical parameters such as the Struhal number, fundamentally eliminating the errors caused by empirical values ​​and improving the objectivity and reliability of frequency calculation.

[0018] 2) Accurately capture complex flow field characteristics and improve frequency calculation accuracy. This invention employs full-domain flow simulation of the main channel to accurately obtain the global velocity and pressure fields, laying the foundation for subsequent analysis. Based on the fitting function of inlet velocity and runner outlet pressure, single-channel flow simulation focuses on the blade outlet region. Combined with a mixing turbulence model and pressure pulsation monitoring point layout, it can accurately capture complex flow states such as boundary layer separation and three-dimensional vortices. Through spectral analysis, the Karman vortex shedding frequency is directly extracted from the pressure pulsation data, avoiding indirect estimation of the wake width and significantly improving the accuracy of frequency calculation.

[0019] 3) Considering three-dimensional flow characteristics to reflect the risk distribution across the entire blade domain. This invention, by uniformly arranging monitoring points along the spanwise direction at the blade exit trailing edge and combining them with a spanwise position-frequency-amplitude distribution cloud map, can accurately present the Karman vortex shedding frequency characteristics at different spanwise positions. This more closely matches the actual three-dimensional flow pattern of the runner blade and provides a detailed basis for comprehensively assessing the resonance risk across the entire blade domain.

[0020] 4) Achieve risk prediction during the design phase, transforming passive remediation into proactive prevention. This invention, through full-process three-dimensional calculations during the design phase, can directly obtain the Karman vortex shedding frequency and compare it with the blade's natural frequency, assessing resonance risk in advance. This fundamentally solves the "passivity" problem of existing technologies, providing early theoretical basis for blade design optimization and operational condition avoidance, and significantly reducing unit operation risks and maintenance costs. Attached Figure Description

[0021] Figure 1 This is a basic implementation flowchart for the wheel resonance risk assessment method. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should not be construed that the present invention is limited to the following examples. Without departing from the concept of the present invention, all modifications and improvements of the present invention in this field should be included within the protection scope of the claims of the present invention.

[0023] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure pertains. Words such as “or” and “comprising” as used in this disclosure mean that an element or object preceding that word encompasses the elements or objects listed following that word and their equivalents, but do not exclude other elements or objects.

[0024] Example 1 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, [the method is described in the original text]. Figure 1 As shown, it includes the following steps: S1, based on the full-domain flow simulation of the main flow channel of the hydraulic machinery, obtains the velocity and pressure field data within the hydraulic machinery. The full-domain flow simulation of the main flow channel of the hydraulic machinery is the foundation for subsequent refined analysis, and its core purpose is to extract the inlet and outlet boundary conditions of the runner through overall flow field calculation.

[0025] For the design conditions, the simulation process uses a time-averaged method based on steady Reynolds. , ) turbulence models (such as The model can meet the simulation accuracy requirements (by comparing energy characteristics such as head, output, or efficiency, with a relative error within a certain range). (Approximately), because it can stably capture the characteristics of the average flow field.

[0026] For non-design conditions, the flow field may exhibit complex flows such as strong separation and vortices, potentially exceeding the error limits of steady-state methods. Therefore, unsteady-state methods are required. Or based on scale analysis methods ( , ) turbulence models (such as Model or (Model). Subsequent steps involve... The flow field is averaged over each rotor rotation cycle to ensure the statistical validity of the flow field characteristics.

[0027] Simulation accuracy is judged by the deviation of key performance parameters (head, output, efficiency, etc.) from the experimental values. The deviation must be within the allowable range (e.g., under design conditions). Within, under non-design conditions When the flow field data obtained from the simulation is within the range of 1000-10000, it can be used for subsequent analysis.

[0028] S2, based on velocity and pressure field data within the hydraulic machinery, obtains the inlet velocity fitting function and outlet pressure fitting function of the runner. The flow field at the runner inlet and outlet is affected by the upstream guide vanes and downstream tailrace pipe, exhibiting a three-dimensional non-uniform distribution. Circumferential averaging can eliminate local pulsation interference and preserve the mainstream trend. Polynomial fitting ( (Order) can transform complex discrete data into concise mathematical expressions, reducing the amount of data while ensuring the continuity and representativeness of boundary conditions (residuals). This provides accurate input for single-channel simulations, avoiding a surge in computation caused by directly importing the global mesh.

[0029] S3 uses the inlet velocity fitting function and outlet pressure fitting function of the runner as boundary conditions. After setting pressure pulsation monitoring points, a single-channel flow simulation of the hydraulic mechanical runner blades is performed to obtain pressure pulsation data caused by Karman vortex shedding at the monitoring points. The single-channel simulation replaces the global runner model with periodic boundaries, significantly reducing computational costs while ensuring consistent flow field characteristics. It can be started with... Obtain the initial flow field, and then use Hybrid models (such as) Class and Unsteady calculations can be performed using a class of models, balancing near-wall boundary layer accuracy with trailing vortex resolution. The calculation time step is generally no larger than [number missing]. The total number of steps is not less than Step 1: Ensure the high-frequency characteristics of Karman vortex shedding are captured (frequency resolution meets requirements). Pressure pulsation monitoring points can be set at the centerline of the blade exit trailing edge wall (spanwise). (A point covering the area from the upper crown to the lower ring) is used because this location is the direct area affected by the shedding of the Karman vortex, allowing for precise recording of pressure pulsation signals.

[0030] S4. By analyzing the pressure pulsation data at the pressure pulsation monitoring points, the Karman vortex shedding frequency at different spanwise positions at the outlet of the hydraulic machinery runner blades is determined. The periodic shedding of Karman vortices induces pressure pulsations, the frequency of which is consistent with the vortex shedding frequency. This can be achieved using the Fast Fourier Transform (FFT). Analysis can convert time-domain signals into frequency-domain spectra, separating the characteristic frequencies of the Karman vortex (excluding interference such as rotor rotation harmonics); through "continuous" The criterion of "significant peaks appearing at one or more spanwise positions" can avoid misjudging single-point random pulsations and ensure that the identified frequencies are regional Karman vortex characteristics; the spanwise-frequency-amplitude cloud map can intuitively present the frequency distribution pattern and provide a clear frequency range reference for resonance assessment.

[0031] S5. Based on wet modal analysis of the blade in water, the natural frequencies of the blade are obtained. The Karman vortex shedding frequency is compared with the blade's natural frequencies, and the risk of resonance in the runner blade caused by Karman vortex shedding is assessed based on the comparison results. Specifically, the relative deviation between the Karman vortex frequency and a certain natural frequency of the blade can be set... And the participation coefficient of this mode At this point, the risk of resonance is considered high. The resonance risk stems from the coupling between the Karman vortex frequency and the blade's natural frequency. Wet modal analysis of the blade in water must consider the influence of the water body, and the extracted natural frequencies are closer to the actual operating conditions; when the Karman vortex frequency deviates relatively from a certain natural frequency... And the participation coefficient of this mode is relatively large (e.g. When the modality is significantly affected by the frequency matching, it indicates that the modality contributes significantly to the vibration response, and the risk of resonance is high. This evaluation logic takes into account both frequency matching and modal weighting, and can scientifically determine the possibility of resonance, providing a basis for blade design optimization or operational adjustments.

[0032] Example 2 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, based on Embodiment 1, step S1 of the full-domain flow simulation of the main hydraulic channel includes the following steps: S11. A three-dimensional geometric model of the entire main channel of the hydraulic machinery is established based on geometric modeling software. The three-dimensional geometric model of the entire main channel is the foundation of flow simulation, and its accuracy directly affects the accuracy of flow field calculation. Solid scanning is suitable for reverse analysis of existing equipment and can restore the actual channel morphology (including manufacturing errors and wear). Export the model suitable for new model development, based on design parameters. The full-domain 3D geometric model of the main channel of the hydraulic machinery needs to remove non-flow channel structures such as bolt holes and flanges (to avoid increasing mesh complexity and interfering with the flow field), and retain core features such as blade 3D distortion and flow channel curvature (these features directly determine velocity distribution and vortex generation, and simplification may lead to excessive deviation of Karman vortex frequency).

[0033] For the three-dimensional geometric model of the entire main channel of the hydraulic machinery, the entire main channel is divided into several independent subdomains, and each subdomain is discretized into hexahedral mesh elements. The mesh is configured to satisfy the maximum wall surface area. Adapting to the wall function ensures accurate solutions for near-wall flow; consistent mesh density at interfaces across different regions reduces data transfer errors. Mesh independence can be verified using key performance parameters (such as efficiency), i.e., by observing the relative changes of key performance parameters with increasing mesh count. At this time, the number of meshes is reasonable. In this step, dividing the domain into blocks allows for optimization of the mesh shape for different geometric features (such as straight pipe inlets and twisted blade impellers), avoiding mesh distortion caused by overall partitioning. It also facilitates setting appropriate physical models for rotating / stationary domains (such as considering Coriolis forces in rotating coordinate systems). Hexahedral structured meshes are more accurate than tetrahedral meshes, especially suitable for boundary layer simulations. Mesh independence verification is crucial to eliminating the interference of mesh number on the results, ensuring that the flow field calculation is determined by physical laws rather than mesh density.

[0034] S12. Import the discretized 3D geometric model of the entire hydraulic machinery main channel into the fluid dynamics simulation software. Enable the 3D double-precision calculation mode in the fluid dynamics simulation software (generally, in the solver settings interface of the fluid dynamics simulation software, the precision control is selected to double-precision calculation mode to enable 64-bit floating-point arithmetic, achieving a precision of...). Double precision calculations provide a higher number of significant digits, reducing rounding errors during iteration, and are particularly suitable for calculating fine parameters such as pressure pulsations (single precision may not be able to distinguish minute pulsations).

[0035] In the fluid dynamics simulation software, select the turbulence model according to the required simulation conditions (e.g., the default design condition). When the model's non-design operating condition error exceeds the limit, switch to Model) and setting fluid property parameters (e.g.: water density Dynamic viscosity (Default is incompressible), and define boundary conditions. The turbulence model selection must match the operating condition: the design operating condition is stable flow. The model balances accuracy in both near-wall and mainstream regions; however, the flow field under non-design conditions is complex (including strong separation and vortices). wait The model can employ large eddy simulation in the separation zone to accurately capture the three-dimensional flow structure. Fluid properties (density, viscosity) directly affect the Reynolds number calculation, which in turn determines the flow regime and turbulence intensity.

[0036] S13. Based on fluid dynamics simulation software, the flow control equations for the entire main channel of the hydraulic machinery are discretized. A pressure-velocity coupling algorithm and discretization scheme suitable for the flow field characteristics are selected, and the solution control parameters are configured. Specifically, the finite volume method can be used to discretize the flow control equations. A second-order upwind scheme is used for the convection term, and a central difference scheme is used for the diffusion term. The pressure-velocity coupling algorithm is... Algorithms. The finite volume method directly ensures the conservation of mass and momentum by controlling volume integrals, matching the hexahedral mesh structure and enabling accurate calculation of interfacial fluxes (such as mass flow rate). The second-order upwind scheme reduces numerical dispersion in high-velocity regions, preventing the smoothing of local 3D flow characteristics; the central difference scheme ensures second-order accuracy of the diffusion term, meeting the requirements of turbulence models for capturing boundary layer gradients. Pressure-velocity coupled algorithms (such as...) This can avoid "chessboard-like pressure oscillations", ensure stable solution of discrete equations, and provide a reliable basis for flow field parameter calculation.

[0037] S14, initiate iterative calculations in the fluid dynamics simulation software. After the calculations converge, output the velocity and pressure distribution information within the hydraulic machinery and save it as a standard format file (e.g., ...). (Format). Outputting velocity and pressure data at the runner inlet and outlet provides boundary conditions for subsequent single-channel simulations. Cylindrical coordinates adapt to the rotational symmetry of the runner flow field, facilitating axial / radial layered sampling. Standard format files ensure data can be processed by subsequent data processing software (such as...). This process reads raw data to provide for inlet velocity fitting and outlet pressure fitting, ultimately achieving boundary consistency between global simulation and single-channel simulation. The output includes velocity and pressure distribution information within the hydraulic machinery: In fluid dynamics simulation software, locate the inlet face of the hydraulic machinery runner and output the cylindrical coordinates of all mesh nodes on that face. and the corresponding velocity components ; In fluid dynamics simulation software, locate the outlet surface of the hydraulic machinery runner and output the cylindrical coordinates of all mesh nodes on that surface. and the corresponding pressure value .

[0038] Example 3 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, based on embodiment 2, the method for establishing a three-dimensional geometric model of the entire main channel of the hydraulic machinery in step S11 is as follows: S11-11, check if the required full-domain 3D geometric model of the hydraulic machinery's main channel already exists in the geometric modeling software; if so, call it directly; if not, proceed to step S11-2. Directly calling the existing model in the software avoids the loss of geometric information caused by cross-software import (such as decreased surface accuracy and disruption of topological relationships), while also reducing model conversion errors. However, it is necessary to verify whether the model retains core flow characteristics (3D blade distortion, channel curvature, etc.) to ensure it is suitable for flow field simulation requirements.

[0039] S11-12, point cloud data of the hydraulic machinery prototype is obtained through 3D scanning technology, or based on design drawings from... The software obtains the original model of the hydraulic machinery and exports the full-domain 3D geometric model of the main channel (format: , (etc.). Data source selection needs to match the application scenario—physical scanning is suitable for reverse analysis of existing equipment and can truly restore the actual shape of the flow channel (including manufacturing errors and operational wear); Exporting models suitable for new device development, directly building models based on design parameters avoids error accumulation during the scanning process, ensuring the model matches the design intent. Figure 1 To.

[0040] S11-13 describes the process of simplifying the original model or point cloud data in geometric modeling software to obtain a full-domain 3D geometric model of the hydraulic machinery's main channel. The simplification process specifically involves: removing redundant structures such as bolt holes, flange connection surfaces, supports in non-channel areas, and sensor interfaces; filling holes and gaps on the model surface and repairing geometric defects; and retaining core flow characteristics such as the 3D twisted shape of the blades, the curvature changes of the channel, and the inlet / outlet transition surfaces. Redundant structures (such as bolt holes and flanges) do not participate in fluid flow, and retaining them increases the complexity of mesh generation (e.g., generating fine meshes leading to computational divergence) and may even interfere with flow field simulation (e.g., misjudging local eddies). Core characteristics such as blade twist and channel curvature directly determine the fluid velocity distribution, pressure gradient, and vortex generation (e.g., the formation and shedding of Karman vortices). Simplification may lead to excessive deviations in the calculation of Karman vortex frequencies. Therefore, model simplification needs to balance "reducing redundancy" with "retaining core characteristics" to ensure the accuracy and computational efficiency of the flow field simulation.

[0041] Example 4 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, i.e., based on embodiment 2 or 3, step S11 involves subdomain division based primarily on geometric features and secondarily on functional attributes, including the following steps: S11-21 defines the correspondence between core geometric components and functional regions of the main flow channel in hydraulic machinery, based on the full-domain three-dimensional geometric model. For example, a "geometric component-functional attribute" correspondence table can be established: inlet, guide vanes, tailrace pipe, etc., are stationary domains, while impeller, etc., are rotating domains. The main flow channel of hydraulic machinery consists of multiple components with significantly different structures and functions; clarifying the "geometric component-functional attribute" correspondence is the foundation for accurate subdomain division. Stationary domains (such as guide vanes and tailrace pipes) do not participate in rotation, and the flow field follows the flow laws under a fixed coordinate system; rotating domains (impellers) rotate around an axis, and rotational effects such as Coriolis force and centrifugal force must be considered. This correspondence ensures that the subdomain division both conforms to the solid structure and the physical characteristics of the flow field.

[0042] S11-22, based on geometric characteristics, divides the entire flow field of the main channel into geometric subdomains according to geometric boundaries, so that each individual geometric subdomain corresponds to a complete geometric component. For example, a geometric subdomain may include the inlet subdomain (from the flange face to the guide vane leading edge), the guide vane subdomain (from the guide vane leading edge to the trailing edge), and the runner subdomain (downstream of the guide vane trailing edge). The flow field is divided into several subdomains, including the area from the runner diameter to the runner outlet and the tailrace subdomain (from the runner outlet to the diffuser outlet). Geometric boundaries are the physical basis for the natural partitioning of the flow field. Dividing the flow field into first-level subdomains (geometric subdomains) according to the physical contours of the components ensures that each subdomain corresponds to a complete flow unit. For example, the inlet subdomain covers the straightening section from the fluid inlet to the guide vane, the guide vane subdomain focuses on the regulating effect of the guide vane on the water flow, the runner subdomain contains the core energy conversion region, and the tailrace subdomain covers the fluid diffusion and discharge path. Clear geometric boundaries can avoid subdomain intersections or omissions, provide a clear spatial range for subsequent mesh generation and boundary condition settings, and ensure the continuity of the flow field simulation.

[0043] S11-23, based on the correspondence between the core geometric components and functional areas of the main flow channel, divides all geometric subdomains into different functional subdomains according to their functional attributes, i.e., subdividing them into two-level subdomains by function to clarify the physical functional attributes of each geometric subdomain: the stationary domain is set to a fixed coordinate system, and the rotating domain is set to a rotating coordinate system (with the rotational speed consistent with the design). Functional subdivision is key to adapting to the flow field motion characteristics: using a fixed coordinate system in the stationary domain simplifies the flow field calculation of non-rotating components; using a rotating coordinate system in the rotating domain can accurately describe the flow field changes (such as relative velocity distribution) caused by the rotation of the impeller. This subdivision balances computational efficiency and simulation accuracy in key areas.

[0044] Example 5 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortex. As a preferred implementation of this embodiment, based on any one of embodiments 2 to 4, step S11, discretizing the subdomain into hexahedral mesh elements, includes the following steps: S11-31 generates a hexahedral structured mesh independently for each subdomain, maintaining the overall consistency of the mesh lines with the mainstream flow direction. In practice, the mainstream direction of each subdomain must first be determined through flow field pre-analysis (e.g., the mainstream at the inlet is along the axial direction, and the mainstream at the runner is along the combined radial and circumferential directions). Then, the mesh topology is adjusted to ensure the angle between the mesh lines and the mainstream direction. This is to reduce numerical dissipation in flow solutions. For example: 1) The inlet subdomain adopts a straight pipe grid topology with grid lines extending along the axial direction to ensure that the flow direction of water from the inlet to the guide vane is parallel to the grid lines.

[0045] 2) The impeller subdomain adopts a radially gradient grid, and the grid lines are adjusted according to the blade twisting trend to fit the composite flow characteristics of "radial inflow and circumferential outflow" in the impeller.

[0046] When the node distribution of a structured grid aligns with the flow direction, it reduces the truncation error of the convection term discretization, thereby improving the calculation accuracy of the velocity and pressure fields. It is particularly reliable for capturing small-scale flow structures such as the Karman vortex.

[0047] S11-32 uses an expansion layer technique to generate multi-layered gradient boundary layer meshes on the walls of all subdomains. Parameters can be set as follows: 1) Total thickness: This is taken from the characteristic dimensions of the flow channel (e.g., the maximum diameter of the impeller). For example, the diameter of the rotor is When the total thickness of the boundary layer is set to .

[0048] 2) First layer height: Calculated based on the wall shear rate to ensure maximum wall surface area. .

[0049] 3) Number of layers and expansion ratio: Number of boundary layer mesh layers The height expansion ratio of adjacent mesh layers This ensures a smooth transition in mesh size from the wall to the mainstream area, avoiding computational oscillations caused by abrupt gradient changes.

[0050] The boundary layer is the region with the largest fluid velocity gradient and the source of Karman vortex generation (such as the boundary layer separation at the trailing edge of a blade). Multi-layered gradient meshes can accurately capture the velocity distribution near the wall and avoid deviations in the prediction of the boundary layer separation location due to overly coarse meshes.

[0051] S11-33, for flow gradients that are relatively larger (e.g., downstream of the guide vane trailing edge). The areas encompassing the guide vane chord length range, the inlet and outlet edges of the runner blades, and the bends in the tailrace pipe are locally densified, with the densified and undensified areas connected by a transition layer. For example: 1) Encryption size: The grid side length of the encrypted area is equal to that of the surrounding unencrypted areas. For example, the side length of the unencrypted area grid is At that time, the encrypted area is set to .

[0052] 2) Transition processing: A transition is set between the encrypted and unencrypted areas. Layer transition mesh, mesh size change rate (as from) Transition to When, the intermediate layer is set as ), to avoid mesh topology mutations.

[0053] Strong shear flow and vortices exist in regions such as the guide vane wake and the runner blade trailing edge. Local densification can improve the spatial resolution of small-scale vortices (such as the initial morphology of the Karman vortex), thereby enhancing the signal-to-noise ratio of pressure pulsation signals. above.

[0054] S11-34, perform mesh quality verification using the quality verification tool of geometric modeling software to ensure that the mesh distortion rate and aspect ratio meet preset requirements, and that all hexahedral mesh elements have no negative volume. For example, if the following preset requirements are met: 1) Twist rate The distortion rate reflects the degree to which mesh cells deviate from a regular hexahedron; exceeding this range... This can lead to a deterioration of the condition number of the coefficient matrix of the discrete equation, increasing the difficulty of iterative convergence.

[0055] 2) Aspect Ratio The mesh in the main direction can be appropriately stretched (e.g., the aspect ratio of the boundary layer mesh along the normal direction can be increased to a certain value). However, lateral stretching needs to be strictly controlled to avoid excessive diffusion of the vertical flow direction.

[0056] 3) No negative volume: Negative volume will cause the mass conservation equation to not hold, and it needs to be eliminated by repairing geometric gaps and adjusting the mesh topology (such as splitting twisted elements).

[0057] Low-quality meshes can introduce spurious flows (such as non-physical eddies), causing the calculation of the Karman vortex shedding frequency to deviate significantly. High-quality meshes ensure that flow field calculations are governed by physical laws.

[0058] S11-35 uses key hydraulic performance parameters reflecting the performance of hydraulic machinery as the basis for judging the qualification of the number of grids. It obtains the variation range of key hydraulic performance parameters with the number of grids, and selects the number of grids whose variation range of key hydraulic performance parameters is less than the maximum allowable value when the number of grids continues to increase as the final number of grids. The specific steps are as follows: 1) Generation Groups of different numbers of grids (e.g.) Ten thousand, Ten thousand, Ten thousand, (10,000 elements), maintaining consistent mesh quality parameters; 2) Perform flow field simulations based on different numbers of grids and extract the calculated values ​​of key performance parameters.

[0059] 3) Calculate the relative rate of change of parameters for the number of adjacent grid cells. When the rate of change... When selecting this number of grid cells, use it as the final value (usually the percentage of grid cells in the rotary subdomain). (Because the rotating wheel is the core area for energy conversion).

[0060] Mesh independence verification can eliminate the influence of mesh number on the results, ensuring that the simulation results converge to the physical reality solution, for example, for a certain unit in Efficiency calculation value at 10,000 grids , When there are 10,000 grids, rate of change ,but A grid of 10,000 is a reasonable choice.

[0061] Example 6 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, based on any one of embodiments 2 to 5, step S12, according to the required simulation conditions including design conditions and non-design conditions, involves selecting a turbulence model and setting fluid property parameters as follows: S12-1, the initial preset default selection is based on the steady Reynolds time-averaged method (S12-1). The turbulence model is preferred. The model, which is fused The model has high accuracy near the wall and The model's stability in the mainstream region allows it to simultaneously capture both blade boundary layer flow and mainstream turbulence characteristics, making it particularly suitable for simulating attached flow under hydraulic machinery design conditions. For components with complex curvature, such as guide vanes and impellers, The model's shear stress transport equation can more accurately predict the flow separation trend under adverse pressure gradients, and the initial calculation deviation can be controlled within [a certain range]. Within.

[0062] S12-2 sets fluid properties including fluid density, dynamic viscosity, and compressibility. For example: 1) Density ( ):for Clear water, default value If the simulated medium is another liquid (such as oil) or water containing sand, adjustments need to be made according to the actual medium density (the error needs to be adjusted accordingly). Density directly affects the calculation of inertial force, causing parameter deviations. This can lead to Reynolds number deviation. This, in turn, affects the prediction of turbulence intensity.

[0063] 2) Dynamic viscosity ( ): Take clean water Every change in temperature It needs to be corrected according to empirical formulas (such as...) hour Viscosity determines molecular viscous forces and is a key factor influencing boundary layer thickness and the scale of the Karman vortex. Parameter deviations... This may lead to errors in the calculation of vortex shedding frequency. .

[0064] 3) Compressibility: The default is incompressible flow (assuming the liquid has an infinite bulk modulus), only when the flow velocity... Mach (approximately) When using a compressible model (rarely seen in conventional hydraulic machinery), the incompressible assumption simplifies calculations and results in smaller errors, meeting engineering accuracy requirements.

[0065] S12-3, Based on the above settings, initiate the initial flow field simulation calculation and iterate until convergence (e.g., residual). And the import and export flow deviation ), to obtain calculated values ​​of key hydraulic performance parameters, which may include: 1) For water turbines: head under rated operating conditions ( ),efficiency( ), output ( ); 2) For water pumps: Head at rated flow rate ( ), shaft power ( ),efficiency( ).

[0066] S12-4 compares the calculated values ​​of key hydraulic performance parameters with the experimental values ​​obtained through physical experiments to determine if the error is within the allowable range. If yes, retain the current turbulence model and fluid property parameter settings; otherwise, proceed to step S12-5. The allowable error range can be set according to the operating condition type, such as: 1) Design operating conditions: Errors in parameters such as efficiency and head need to be addressed. (Because the flow is stable under the design conditions, the test data has high repeatability). 2) Non-design conditions (such as partial load, overload): The tolerance can be relaxed to... (The flow field exhibits strong separation, resulting in significant dispersion in the experimental data.)

[0067] S12-5: Select the error adjustment method according to the required simulation conditions. Specifically: if it is the design condition, adjust the current turbulence model parameters and return to step S12-3; if it is a non-design condition, proceed to step S12-6; otherwise... In the design condition, the flow is mainly adhering flow, and fine-tuning the turbulence model parameters can significantly improve the boundary layer simulation accuracy, meeting the error requirements without replacing the model. When adjusting the current turbulence model parameters, the following parameters can be adjusted: Turbulence intensity ( The default value for the inlet boundary turbulence intensity is: If the simulated value is too low, you can use the formula... ( (For imported Reynolds number) correction, gradually adjusted to scope; Model constants: for Model, fine-tuning the turbulent diffusion coefficient ( , ), adjustment range each time Recalculate until the error meets the standard.

[0068] S12-6, Determine whether the current turbulence model is based on the scale analysis method ( ) turbulence models (such as , If the model is correct, adjust the current turbulence model parameters and return to step S12-3; otherwise, switch to the turbulence model based on the scale analysis method and return to step S12-3. When adjusting the current turbulence model parameters, the following parameters can be adjusted: Model: Adjusting the analytical scale threshold ( ), default value Adjust to 0 based on the size of the separation zone. This enhances the ability to capture trailing edge vortices; Model: Modify the source term coefficients in the turbulent dissipation rate equation ( The default value is 0. This enhances the sensitivity to unsteady separated flow.

[0069] After iterating until convergence, compare the experimental values ​​again until the error is reached. The flow field under non-design conditions contains a large number of unsteady vortices (such as the Karman vortex street and the wake vortex band). The model can directly simulate vortex motion on a large scale, overcoming... The model has the limitation of insufficient prediction of unsteady flow.

[0070] Example 7 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, based on any of embodiments 2-6, step S12 defines boundary conditions including defining functional adaptation boundaries for the connection surfaces between different functional subdomains and different geometric subdomains. The interface between the stationary and rotating domains is set as a sliding mesh interface (conservative interpolation), and the interface between subdomains of the same function is set as an internal interface, ensuring the continuity of the flow field parameters across the entire main channel. The reasonable definition of subdomain boundaries is crucial for ensuring the conservation of the entire flow field. The sliding mesh interface, by dynamically updating the relative positions of the stationary and rotating domains, can accurately transmit parameters such as pressure and velocity, simulating the dynamic-static interference effect between the guide vane and the runner. The internal interface (such as the interface between the inlet and guide vane domains belonging to the stationary domain) is considered a continuous flow field surface, ensuring natural parameter transition without additional processing. The rigor of the boundary definition avoids non-physical discontinuities in the flow field (such as sudden pressure changes), laying the foundation for the accuracy of subsequent flow field calculations.

[0071] Example 8 This embodiment discloses a method for assessing the risk of runner resonance based on three-dimensional calculation of blade Karman vortex. As a preferred implementation of this embodiment, based on embodiment 7, the functional adaptation boundary is defined by defining the inlet and outlet boundary, wall boundary, rotating domain-stationary domain interface, and stationary domain-stationary domain interface.

[0072] 1. Definition of import and export boundaries Import boundary: Set to "speed import" ( Input the average velocity corresponding to the design flow rate (calculated from flow rate / inlet cross-sectional area). The velocity profile is set to a uniform or parabolic distribution according to the pipe flow characteristics. Under design conditions, a uniform velocity profile is suitable for situations with a short inlet section and good rectification effect. It simplifies calculations and conforms to the ideal flow assumption, with deviations controlled within a certain range. The internal flow rate has minimal impact on the overall simulation results; however, under certain complex inlet conditions, such as the presence of bends or obstacles, the parabolic distribution more closely resembles reality, accurately reflecting the velocity changes at the inlet cross-section and ensuring the consistency error between the inlet flow rate and actual operating conditions is within a certain range. Within this range, reliable boundary conditions are provided for subsequent flow field simulations. Export boundary: Set to "Pressure outlet" ( The system allows input of the outlet pressure value and enables backflow to handle tailrace separation under partial load conditions. Under partial load, the flow velocity in the tailrace decreases, making water separation and backflow more likely. Setting backflow conditions allows the simulation software to dynamically adjust the backflow flow rate and direction based on the outlet pressure, accurately capturing the pressure distribution in the separation zone and avoiding pressure field distortion caused by overly constrained outlet conditions, which would affect the accuracy of the turbine outlet pressure data (error must be controlled within a certain range). This ensures that the simulation results can accurately reflect the flow field characteristics under non-design conditions. 2. Wall boundary definition All solid walls (such as blades, inlet inner walls, etc.) are set as "no-slip walls," meaning the wall velocity equals the wall speed (zero for stationary walls, and the same speed as the impeller for rotating walls). According to viscous fluid theory, the no-slip condition is fundamental to ensuring accurate boundary layer simulation. Ignoring this condition will lead to errors in the calculation of the velocity gradient within the boundary layer, which in turn will distort the pressure distribution near the wall, ultimately causing excessive deviations in the calculation of the Karman vortex frequency, severely affecting the accuracy of the assessment of blade resonance risk. Rough walls are treated as smooth walls by default, with shear stress corrected using "equivalent sand grain roughness". In actual hydraulic machinery operation, wall roughness alters the friction between the fluid and the wall, affecting boundary layer thickness and flow characteristics. For new equipment or components with well-treated surfaces, the assumption for smooth walls is that deviations are within acceptable limits (e.g., ...). However, for walls that have been in operation for a long time and are worn or corroded, using equivalent sand roughness (the value is determined according to the material and degree of wear) can accurately correct the wall shear stress, improve the accuracy of boundary layer simulation, and make the velocity field near the wall more consistent with reality, thus providing a guarantee for accurately simulating the generation and development of Karman vortices near the wall. 3. Definition of static-dynamic interface The interface between the stationary domain (such as a guide vane) and the rotating domain (such as a rotor) is designated as a dynamic-static interface. Pressure and velocity are transmitted using "conservative interpolation," and the interpolation error of the mismatched mesh needs to be addressed. During the operation of hydraulic machinery, there is relative motion between the guide vanes and the runner. The sliding mesh interface can accurately simulate the interference effect between moving and stationary components by dynamically updating the relative positions of the mesh nodes on both sides of the interface, avoiding the loss of high-frequency signals caused by approximation methods such as "frozen rotor". Conservative interpolation ensures that parameters such as pressure and velocity satisfy mass and momentum conservation when transmitted at the interface, guaranteeing the accuracy of the velocity field at the runner inlet and providing reliable boundary conditions for subsequent single-channel simulations.

[0073] The interface mesh needs to be node aligned or mapped one-to-one, or the overall number and distribution should be similar to ensure the continuity of flow field parameters. This can avoid numerical oscillations caused by mesh mismatch, ensure a smooth transition of parameters such as pressure and velocity at the interface, prevent non-physical pressure abrupt changes or velocity discontinuities, maintain the stability and accuracy of flow field calculations, and lay the foundation for capturing complex flow phenomena of Karman vortices near the dynamic-static interface. 4. Definition of subdomain internal boundaries Interfaces between subdomains of the same type (such as inlet and guide vane) are set as static interfaces, directly transmitting flow field parameters. The internal boundary is defined based on the assumption of flow field continuity within the same functional region, which simplifies the calculation process, avoids repeated calculations of complex physical processes at the interface, ensures that flow field parameters (such as pressure and velocity) transition naturally between subdomains, guarantees the continuity and accuracy of the overall flow field simulation, and reduces calculation errors caused by improper interface treatment. 5. Boundary condition verification The relative deviation between import and export flows needs This ensures quality conservation. Flow imbalance can induce spurious pressure gradients, leading to distortions in the velocity and pressure fields, which in turn affects the accuracy of subsequent boundary condition extraction. For example, an inlet flow deviation exceeding [a certain value]... This could lead to abnormal velocity distribution at the runner inlet, causing deviations in the calculation of the Karman vortex generation location and frequency, ultimately affecting the accuracy of the assessment of runner resonance risk. Verifying that the static wall velocity is 0 and the rotating wall velocity matches the rotational speed is crucial to avoid incorrect boundary conditions. Incorrect wall velocity settings will cause the boundary layer flow simulation to deviate completely from reality, rendering the entire flow field calculation invalid and making it impossible to accurately assess the effect of Karman vortices on the blades and potential resonance risks. Rigorous verification of the wall velocity conditions ensures that the boundary conditions conform to actual physical phenomena, providing a foundation for reliable flow field simulation and resonance risk assessment.

[0074] Example 9 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, based on any one of embodiments 2 to 8, step S2, obtaining the runner inlet velocity fitting function includes: First, the axial coordinate along the inlet face of the hydraulic machinery runner. Perform stratified sampling (e.g., per Take one (value), in each On the corresponding circumferential section, along the circumferential direction At least 36 sampling points were selected evenly (each One, covering (Full circumference), extract the cylindrical coordinates of each sampling point. With the corresponding velocity component The flow field at the runner inlet exhibits circumferential non-uniformity due to the influence of the upstream guide vanes (such as velocity pulsations caused by the guide vane wake). The 36 sampling points can fully cover the circumferential variations and capture the circumferential distribution characteristics of the velocity. Axial stratified sampling can reflect the variation law of velocity along the depth of the flow channel, providing comprehensive data support for subsequent fitting.

[0075] Secondly, for each Calculate the circumferential average value of the velocity components at all sampling points under the given value. , obtain axial coordinates Data set A corresponding to the average velocity, i.e., "axial coordinates" - Average speed" dataset A Specifically: average radial velocity ; average circumferential velocity ; average axial velocity ;in, Indicates the sampling point number. , and They represent the first Radial velocity components at each sampling point axial velocity component and axial velocity components Circumferential averaging can eliminate local pulsations such as guide vane wakes, retain the mainstream velocity trend, and make the boundary conditions more representative. Without averaging, directly using discrete point data will result in redundant pulsations in the inlet conditions of a single-channel simulation, inducing non-physical vortices and affecting the accuracy of the Karman vortex simulation.

[0076] Then, use data processing software to perform polynomial fitting on dataset A (recommended). (order), obtain and save the impeller inlet velocity fitting function. , and .like: Radial velocity fitting function (Taking a 3rd order example); Circumferential velocity fitting function (Taking a 3rd order example); Axial velocity fitting function (For example, the 3rd order).

[0077] Retain function coefficients ( , , , ) and expressions.

[0078] The first-order polynomial can balance fitting accuracy and function smoothness, capturing nonlinear changes in the velocity field (such as velocity gradients at the inlet) while avoiding overfitting (such as oscillations) caused by higher-order fitting. The fitting function simplifies millions of grid node data into a small number of coefficients, significantly reducing the computational cost of single-channel simulation while ensuring the continuity of boundary conditions.

[0079] Furthermore, the fitting accuracy can be verified by calculating the residuals between the fitted values ​​and the original average values: Requires maximum residual In the single-channel simulation, the impeller inlet velocity fitting function was called to verify the deviation between the inlet flow rate and the simulation results of the main flow channel across the entire channel. Residual control ensures that the fitted function does not lose key flow information; flow continuity verification ensures the consistency of the boundary between single-channel simulation and global simulation, avoiding errors in the calculation of Karman vortex frequency due to deviations in inlet conditions.

[0080] Example 10 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortex. As a preferred implementation of this embodiment, based on any one of embodiments 2 to 9, step S2, obtaining the runner outlet pressure fitting function includes: First, along the radial coordinates of the exit surface Perform stratified sampling (e.g., per Take one (value), in each At the radial position corresponding to the value, along the circumferential direction At least 36 sampling points were selected evenly (each One, covering (Full circumference), extract the cylindrical coordinates of each sampling point. and the corresponding pressure value The pressure distribution at the runner outlet mainly varies radially (affected by centrifugal force and tailrace diffusion effect). Radial stratified sampling can accurately capture the radial pressure gradient; 36 circumferential sampling points can cover the circumferential pressure fluctuations caused by the dynamic and static interference between the guide vanes and the runner, providing sufficient data for subsequent averaging.

[0081] Secondly, for each Calculate the circumferential average pressure value of all sampling points under the given value. , to obtain the radial coordinates Data set B, corresponding to the average pressure, yields the "radial coordinates". - Pressure Average Dataset B .like: ;in, Indicates the sampling point number. Indicates the first Pressure values ​​at each sampling point Circumferential averaging can eliminate periodic pressure pulsations caused by impeller rotation (such as harmonics of blade number × rotational speed), preserving the mainstream pressure distribution characteristics along the radial direction. Directly using unaveraged pressure data will result in redundant fluctuations at the outlet boundary of a single-channel simulation, interfering with the stable simulation of trailing edge Karman vortices.

[0082] Finally, data processing software was used to process dataset B. Polynomial fitting of order 1 is used to obtain and save the fitting function of the impeller outlet pressure. .like: (Taking a 3rd order function as an example). Save the function coefficients ( , , and ) and expressions. A polynomial of order one can accurately describe the nonlinear change of pressure along the radial direction (such as the pressure decay law from the impeller outlet to the tailrace pipe), and compared with linear fitting, it can reduce residuals (controlled within a certain range). Meanwhile, the concise function form can reduce the computational load of single-channel simulation and avoid convergence difficulties caused by complex boundary conditions.

[0083] Furthermore, the fitting accuracy can be verified by calculating the residuals between the fitted values ​​and the original average values: Requires maximum residual In the single-channel simulation, the impeller outlet pressure fitting function was called to verify the deviation between the average pressure at the outlet section and the global simulation results. This ensures the continuity of the pressure field. Residual control ensures that the fitted function accurately reflects the pressure distribution pattern; pressure continuity verification avoids the disconnect between the outlet boundary of the single-channel simulation and the global simulation, and prevents the displacement of the Karman vortex shedding position due to pressure gradient anomalies.

[0084] Example 11 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, based on any one of embodiments 2 to 10, step S3 of the single-channel flow simulation of the hydraulic machinery runner blade includes the following steps: S31. A single-channel fluid domain model (including the upper crown, lower ring, and blade) of a hydraulic machinery runner blade is established based on geometric modeling software, ensuring that the model contains only a single flow channel core region (using periodic boundaries to replace the global model). A hexahedral structured mesh is used to layer the single-channel fluid domain model of the hydraulic machinery runner blade (e.g., "blade wall → trailing edge → flow channel core region"), as follows: Blade wall boundary layer: setting Mesh above layer, expansion ratio To ensure the maximum surface area of ​​the blade wall , trailing edge wall .

[0085] Trailing edge reinforcement zone: set along the thickness direction (height) of the trailing edge. Layered grid, with flow direction (direction of water flow) and span (from upper crown to lower ring) densified to extend to the surrounding area. ,pass Layer transition meshes enable smooth size transitions.

[0086] Spanning node arrangement: in the blade span ( to )Every The location is set with grid nodes to provide a coordinate reference for the pressure pulsation monitoring points.

[0087] Mesh quality verification: Twist rate Aspect Ratio , with no negative volume units.

[0088] Single-channel meshes need to balance accuracy and efficiency: blade wall surface It can accurately simulate viscous boundary layer flow, avoiding prediction errors in the initial formation location of Karman vortices due to excessively coarse near-wall meshes; trailing edge refinement ( The layer can distinguish the three-dimensional morphology of vortices (such as spanwise vorticity distribution), preventing small-scale vortices from being masked by numerical dissipation; the uniform distribution of spanwise nodes ensures the accuracy of monitoring point coordinates, providing a reliable spatial reference for subsequent frequency analysis.

[0089] S32, enable the mixed turbulence model in the fluid dynamics simulation software, such as: enable Mixed turbulence model ( Transitional model), automatically activated on the near-wall surface of the blade. The model's trailing vortex region has been switched to... Model. The single flow channel is defined as a "rotational domain," with the rotational speed consistent with the main flow channel simulation in step S1 (e.g., the design rotational speed). ), and the use of an absolute velocity coordinate system. Model combination and Advantages: near the wall Ensure the accuracy of boundary layer calculations (solve) (Excessive mesh requirements) Trailing edge Directly simulates the vortex generation-shedding process, overcoming the limitations of traditional methods. The deficiency in predicting unsteady flows (the error in calculating the Karman vortex frequency can be reduced to...) Within); the absolute velocity coordinate system facilitates the direct output of velocity parameters consistent with the global simulation, avoiding relative velocity conversion errors.

[0090] S33, the inlet boundary can be accessed via the simulation software interface by calling the impeller inlet velocity fitting function from step S2. , and This achieves a continuous velocity distribution along the axial coordinate. The outlet boundary utilizes the impeller outlet pressure fitting function. It is configured as a pressure outlet. The two sides of the flow channel are set as periodic boundaries to ensure continuous circumferential flow and pressure. The blades, upper crown, and lower ring are all configured as non-slip walls, integrated with the mesh. Control matching. The fitting functions for the impeller inlet velocity and the impeller outlet pressure ensure consistency between the single-channel boundary and the global simulation, avoiding non-physical vortices caused by boundary mismatch; the periodic boundary utilizes the circumferential symmetry of the flow channel to reduce the computational domain to ( (The number of blades is used to significantly reduce the computational load.) The no-slip wall conforms to the motion laws of viscous fluids, ensuring that the flow velocity at the wall is 0 (stationary wall) or the same as that of the impeller (rotating wall), providing realistic constraints for the generation of Karman vortices.

[0091] S34, several (e.g., 21) monitoring points are evenly distributed along the spanwise direction on the midline of the trailing edge wall at the blade exit, covering the upper crown ( ) to the lower ring ( ) monitoring points, each One spanwise location is used (coordinates obtained through mesh generation software). Pressure pulsation time history monitoring parameters are set for each monitoring point. For example, in fluid dynamics simulation software, monitoring is set to monitor only static pressure, with a recording time of one time step, continuously recording the pressure pulsation time history. The blade trailing edge is the direct area affected by Karman vortex shedding. 21 spanwise monitoring points can cover the entire blade area, avoiding the omission of local vortex characteristics (such as the difference in vortex frequency between the blade tip and root) due to single-point monitoring. Static pressure monitoring directly reflects the pressure fluctuations caused by vortex shedding, and high-frequency data recording (stored every time step) ensures the capture of high-frequency Karman vortex signals (typically hundreds of times). ).

[0092] S35, based on fluid dynamics simulation software, discretizes the flow control equations of a single-channel fluid domain model for a hydraulic machinery runner blade, such as using the finite volume method. The equations use a second-order upwind scheme for the convection term and a central difference scheme for the diffusion term. A pressure-velocity coupling algorithm adapted to the flow field characteristics is selected (e.g., pressure-velocity coupling algorithm). Algorithms) and discrete schemes (e.g., using a second-order implicit scheme, iterating at each time step). (Secondly to ensure convergence), configure the solution control parameters. The second-order scheme can reduce numerical dispersion and prevent the high-frequency characteristics of the Karman vortex from being smoothed; Algorithm comparison The algorithm converges faster, especially suitable for unsteady flows; iteration count control ( (Time step) ensures flow field convergence at each time step, preventing error accumulation from affecting the quality of pressure pulsation data.

[0093] S36, Initiate iterative calculations in the fluid dynamics simulation software. After the calculations converge, export the pressure pulsation time history data for all monitoring points. Iteration convergence criterion: residuals. And the import and export flow deviation Total number of calculation steps: Step, covering at least One Karman vortex shedding cycle (based on estimated frequency); Output: Export Pressure pulsation time history data (TXT format, including timestamps and pressure values) from each monitoring point are simultaneously output as a trailing edge vortex morphology cloud map. The above calculations ensure that the pressure pulsation data is statistically representative (excluding interference from the initial unsteady state); flow deviation control ensures mass conservation and avoids false pressure pulsations; synchronous output of vortex morphology can intuitively verify the rationality of the Karman vortex simulation (such as the vortex street arrangement pattern), providing a physical basis for subsequent frequency analysis.

[0094] Example 12 This embodiment discloses a method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices. As a preferred implementation of this embodiment, based on any one of embodiments 2 to 10, step S4, determining the Karman vortex shedding frequency, includes the following steps: S41 performs stability preprocessing on the pressure pulsation time history data. For example, in the post-processing module of fluid dynamics simulation software, the data is exported... Pressure pulsation time history data (format can be TXT) from each monitoring point, including complete timestamps and corresponding static pressure values; remove the unstable segments at the beginning of the data (the initial stage before the flow field reaches a periodic state), retaining only the stabilized pressure pulsation sequence, ensuring the data length covers at least [missing information - likely related to pressure pulsation time history]. One Karman vortex shedding cycle (based on the estimated frequency, such as...) correspond The cycle is The flow field exhibits transient fluctuations in the initial stage of the simulation (such as initiation vortices or insufficiently developed boundary layers). Removing unstable data from the early stages eliminates unsteady-state disturbances, ensuring that the analysis focuses on stable Karman vortex shedding signals. Data length coverage... More than one cycle can guarantee the frequency resolution of spectrum analysis and avoid frequency identification deviation due to insufficient data.

[0095] S42, use data processing software / programs to perform spectrum analysis on the preprocessed pressure pulsation time history data, and perform pressure time series analysis on each monitoring point. The transformation yields a "frequency-amplitude" spectrum, where the amplitude is normalized to the maximum peak-to-peak value across all monitoring points. This eliminates amplitude differences between different monitoring points. The spectrum is smoothed (using a 5-point moving average method) to reduce high-frequency noise interference and highlight characteristic frequency peaks.

[0096] S43, identify and record the significant peak frequencies and their relative amplitudes in the frequency-amplitude spectrum, thereby obtaining the regional karman vortex shedding characteristic frequencies. For example, in the frequency-amplitude spectrum, identify the relative amplitudes... Significant peak frequencies were identified, excluding numerical perturbation errors; for each spanwise position, significant peak frequencies and their relative amplitudes were recorded; when a frequency exhibited significant peaks (relative amplitudes) at three or more consecutive spanwise positions, the significant peak frequencies were recorded. When the value is [value], it is determined to be the characteristic frequency of Karman vortex shedding in that region. Karman vortex shedding is a global flow phenomenon at the blade trailing edge, and its characteristic frequency should be consistent across continuous spanwise positions. The criterion of "three consecutive spanwise positions" can eliminate misjudgments of single-point random fluctuations (such as isolated peaks caused by turbulent noise). Relative amplitude Ensure that the pressure pulsation energy corresponding to this frequency is significant enough to represent the true vortex shedding effect; eliminate the interference of numerical perturbation errors on the Karman vortex frequency.

[0097] S44, with span relative position The x-axis represents the characteristic frequency of Karman vortex shedding. The vertical axis represents the relative amplitude. To map the frequencies using color (e.g., red indicates high amplitude), plot a distribution contour map of spanwise location-frequency-relative amplitude to represent the frequency distribution characteristics of Karman vortex shedding at different spanwise locations. Mark the identified Karman vortex characteristic frequency ranges in the contour map to clarify their spanwise distribution range (e.g., ...). The area is concentrated The spanwise coverage ratio corresponding to each characteristic frequency is statistically analyzed to form a frequency-spanwise coverage statistical table. The distribution cloud map can intuitively present the variation pattern of the Karman vortex frequency along the blade height, clearly identify whether there is a continuously distributed "dangerous frequency band", and provide a visual basis for subsequent resonance risk assessment. The spanwise coverage ratio statistics can quantify the influence range of the characteristic frequency; the higher the coverage ratio, the greater the risk of the frequency causing global resonance.

[0098] Example 13 This embodiment discloses a method for assessing the resonance risk of a turbine runner based on three-dimensional calculation of the Karman vortex in the blades. As a preferred implementation of this embodiment, based on any one of embodiments 2 to 12, step S5, assessing the resonance risk caused by the shedding of the Karman vortex, includes the following steps: S51. Underwater wet modal analysis of the blade is performed using finite element analysis software. The first few natural frequencies are extracted, and the frequency values ​​and corresponding mode shapes are recorded. For example: a solid model of the runner blade is constructed using 3D modeling software, retaining the connection structure between the blade root and the hub, and deleting non-structural load-bearing components; the model is imported into finite element analysis software for mesh generation, ensuring mesh refinement in stress concentration areas such as the blade trailing edge and root; material properties are set and constraints are applied (e.g., the blade root is rigidly connected to the hub, and the contact points between the upper crown and lower ring and the water are set to frictionless constraints); modal analysis is performed, extracting at least the first 50 natural frequencies (including bending and torsional modes), recording the frequency values ​​and corresponding mode shape contours (marking the location of the maximum amplitude), and modal participation factors (reflecting the contribution weight of the mode to the vibration response). Underwater wet modal analysis needs to consider the added mass effect of the water on the blade, which is closer to the actual operating state. The first 50 natural frequencies cover the main vibration modes of the blade (low-order bending, high-order torsion, etc.), and the modal participation factors... The modes play a dominant role in the vibration response and are the core focus of resonance risk assessment. Mesh refinement ensures the accuracy of modal calculations and avoids deviations in natural frequencies due to insufficient discretization.

[0099] S52 sets the threshold ranges for dangerous frequency ranges and frequency ranges of interest based on each frequency order. Define the "dangerous frequency range": the inherent frequency of each order. The scope covers frequency shifts caused by frequency calculation errors and operating condition fluctuations; the "frequency range of interest" is defined as: the natural frequencies of each order. The range is used to warn of potential risks; the exclusion zone is clearly defined: beyond which... The frequency range was determined to be low risk. The danger zone should be referenced in hydraulic machinery industry standards to ensure coverage of frequency coupling ranges that may cause resonance. The focus range allows for design optimization or operational adjustments, preventing escalation of risks due to sudden fluctuations in operating conditions (such as sudden load changes). Threshold settings balance safety and engineering feasibility, and risk control with unit operational flexibility.

[0100] S53, compare the Karman vortex shedding frequencies at different spanwise positions with the natural frequencies one by one: if the Karman vortex frequency falls into the "dangerous frequency range" of a certain natural frequency, and the modal participation coefficient of that order... Marked as "high-risk point"; if it falls into the "frequency of concern range" and the modal participation coefficient is... Marked as "medium risk point"; if none fall into the range or modal participation coefficient These are marked as "low-risk points." A comparison table of "spanning position - Karman vortex frequency - natural frequency" can be generated, indicating the corresponding mode shape (e.g., first-order bending, second-order torsional) for each high / medium-risk point. Frequency matching is a necessary condition for resonance, while the modal participation coefficient reflects the distribution of vibrational energy—modes with high participation coefficients will produce larger amplitudes at resonance. Combining these two factors can avoid misjudgments such as "similar frequencies but minimal impact" (e.g., high-order modes have low participation coefficients; even with frequency matching, the resonance hazard is relatively small), thus improving the accuracy of risk assessment.

[0101] S54 assesses the magnitude of resonance risk caused by Karman vortex shedding based on the proportion of high-risk points, and generates a risk assessment report in conjunction with mode shape characteristics. The proportion of statistically significant risk points is as follows: Risk level classification: High risk. Immediate optimization of blade design is required (e.g., modifying the trailing edge shape to change the Karman vortex frequency); medium risk. Restricting operation under specific conditions (such as avoiding corresponding load ranges); low risk. Normal operation is maintained, but vibration data needs to be monitored every 3 months. A risk assessment report is generated, including a spanwise risk distribution cloud map, a comparison curve of natural frequencies and Karman vortex frequencies, mode shape analysis, and targeted prevention and control recommendations. Resonance risk is positively correlated with the proportion of high-risk points—the higher the proportion, the wider the range of Karman vortex excitation across the blade, and the greater the risk of fatigue damage. Graded measures are formulated based on the severity of the risk, avoiding increased costs due to over-control while ensuring effective management of key risks, providing a quantitative basis for the safe operation of the unit.

Claims

1. A method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices, characterized in that, Includes the following steps: S1, based on the full-domain flow simulation of the main channel of the hydraulic machinery, obtains the velocity field and pressure field data within the hydraulic machinery; S2, Based on the velocity field and pressure field data within the hydraulic machinery, obtain the inlet velocity fitting function and the outlet pressure fitting function of the runner; S3, using the inlet velocity fitting function and outlet pressure fitting function of the runner as boundary conditions, and after setting pressure pulsation monitoring points, a single-channel flow simulation of the hydraulic mechanical runner blades is performed to obtain pressure pulsation data caused by the shedding of Karman vortices at the pressure pulsation monitoring points. S4. By analyzing the pressure pulsation data at the pressure pulsation monitoring point, the Karman vortex shedding frequency at different spanwise positions of the hydraulic machinery runner blade outlet is determined. S5. Based on wet modal analysis of the blade in water, the natural frequency of the blade is obtained. The Karman vortex shedding frequency is compared with the natural frequency of the blade. The risk of resonance of the runner blade caused by Karman vortex shedding is assessed based on the comparison results.

2. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 1, characterized in that, In step S1, the full-domain flow simulation of the main hydraulic machinery channel includes the following steps: S11. Based on geometric modeling software, a three-dimensional geometric model of the entire main channel of the hydraulic machinery is established. The entire main channel of the three-dimensional geometric model of the main channel of the hydraulic machinery is divided into several independent sub-domains, and each sub-domain is discretized into hexahedral mesh elements. S12. Import the three-dimensional geometric model of the entire hydraulic machinery main channel after discretization of each subdomain into the fluid dynamics simulation software. In the fluid dynamics simulation software, select the turbulence model and set the fluid property parameters according to the required simulation conditions, and define the boundary conditions. S13, Based on fluid dynamics simulation software, the flow control equations of the entire main channel of hydraulic machinery are discretized, an algorithm and discretization scheme adapted to the flow field characteristics are selected, and the solution control parameters are configured. S14, initiate iterative calculations using the fluid dynamics simulation software. After convergence, output the velocity and pressure distribution information within the hydraulic machinery and save it as a standard format file. The output velocity and pressure distribution information within the hydraulic machinery includes: In fluid dynamics simulation software, locate the inlet face of the hydraulic machinery runner and output the cylindrical coordinates of all mesh nodes on that face. and the corresponding velocity components ; In fluid dynamics simulation software, locate the outlet surface of the hydraulic machinery runner and output the cylindrical coordinates of all mesh nodes on that surface. and the corresponding pressure value .

3. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S11, the method for establishing the full-domain three-dimensional geometric model of the hydraulic machinery's main channel is as follows: S11-11, Check whether the required three-dimensional geometric model of the main hydraulic machinery channel already exists in the geometric modeling software; if yes, call it directly; if not, proceed to step S11-12. S11-12, obtain point cloud data of hydraulic machinery prototype through 3D scanning technology, or obtain the original model of hydraulic machinery based on design drawings; S11-13: After simplifying the original model or point cloud data in the geometric modeling software, a full-domain three-dimensional geometric model of the main channel of the hydraulic machinery is obtained.

4. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S11, the subdomain is divided primarily based on geometric features and secondarily on functional attributes, including the following steps: S11-21 defines the correspondence between the core geometric components and functional areas of the main channel of the hydraulic machinery in the three-dimensional geometric model of the entire main channel. S11-22, based on geometric features, divides the entire flow field of the main channel into geometric subdomains according to geometric boundaries, so that each individual geometric subdomain corresponds to a complete geometric component; S11-23, based on the correspondence between the core geometric components and functional areas of the main channel, all geometric subdomains are divided into different functional subdomains according to their functional attributes, so as to clarify the physical functional attributes of each geometric subdomain; among them, the functional subdomains include the stationary domain and the rotating domain.

5. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S11, discretizing the subdomain into hexahedral mesh elements includes the following steps: S11-31, generate hexahedral structured meshes independently for each subdomain, while maintaining the overall consistency of mesh lines with the main flow direction; S11-32 uses the expansion layer technique to generate multi-layered gradient boundary layer meshes on the walls of all subdomains; S11-33, local densification is performed on areas with relatively larger flow gradients, and the densified areas are connected to the undensified areas through a transition layer; S11-34, through mesh quality verification, ensures that the mesh distortion rate and mesh aspect ratio meet the preset requirements, and that all hexahedral mesh elements have no negative volume; S11-35 uses key hydraulic performance parameters that reflect the performance of hydraulic machinery and equipment as the basis for judging the qualification of the number of grids. It obtains the change range of key hydraulic performance parameters with the number of grids and selects the number of grids whose change range of key hydraulic performance parameters is less than the maximum allowable value when the number of grids continues to increase as the final number of grids.

6. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S12, depending on whether the required simulation conditions include design conditions or non-design conditions, the selection of the turbulence model and the setting of fluid property parameters include: S12-1, the initial preset defaults to a turbulence model based on the Reynolds time-averaged method; S12-2, set fluid physical property parameters including fluid density, dynamic viscosity and compressibility; S12-3, the calculated values ​​of key hydraulic performance parameters are obtained through initial flow field simulation calculations; S12-4: Compare the calculated values ​​of key hydraulic performance parameters with the experimental values ​​obtained through physical experiments to determine whether the error is within the allowable range; if yes, retain the current turbulence model and fluid property parameter settings; if no, proceed to step S12-5. S12-5: Select the method of adjusting the error according to the required simulation conditions. That is, if it is the design condition, adjust the current turbulence model parameters and return to step S12-3; if it is not the design condition, proceed to step S12-6. S12-6, Determine whether the current turbulence model is a turbulence model based on the scale analysis method; if yes, adjust the parameters of the current turbulence model and return to step S12-3; if no, switch to the turbulence model based on the scale analysis method and return to step S12-3.

7. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S12, defining boundary conditions includes defining functional adaptation boundaries for the connection surfaces between different functional subdomains and between different geometric subdomains, so as to ensure the continuity of the flow field parameters across the entire main channel.

8. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 7, characterized in that, The defined functional adaptation boundaries include defining the inlet / outlet boundaries, wall boundaries, the rotation domain-stationary domain interface, and the stationary domain-stationary domain interface.

9. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S2, obtaining the impeller inlet velocity fitting function includes: Axial coordinates along the inlet face of the hydraulic machinery runner Perform stratified sampling, in each Value below, along the circumferential direction At least 36 sampling points are selected uniformly, and the velocity components of each sampling point are extracted. ; For each Calculate the circumferential average value of the velocity components at all sampling points under the given value. , obtain axial coordinates Dataset A corresponding to the average velocity; Data processing software was used to perform polynomial fitting on dataset A to obtain and save the fitting function for the impeller inlet velocity. , and .

10. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S2, obtaining the impeller outlet pressure fitting function includes: radial coordinates along the exit surface Perform stratified sampling, in each Value below, along the circumferential direction At least 36 sampling points were selected evenly, and the pressure values ​​at each point were extracted. ; For each Calculate the circumferential average pressure value of all sampling points under the given value. , obtain radial coordinates Dataset B corresponds to the average pressure. Data processing software was used to perform multinomial fitting on dataset B to obtain and save the fitting function for the impeller outlet pressure. .

11. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S3, the single-channel flow simulation of the hydraulic mechanical runner blades includes the following steps: S31. A single-channel fluid domain model of a hydraulic mechanical runner blade is established based on geometric modeling software, and the fluid domain is divided using a hexahedral structured mesh. S32, in the fluid dynamics simulation software, enable the hybrid turbulence model based on the scale analysis method, define the single flow channel as a rotating domain, with the rotational speed consistent with the main flow channel simulation in step S1, and adopt the absolute velocity coordinate system; S33, the inlet boundary calls the impeller inlet velocity fitting function from step S2; the outlet boundary calls the impeller outlet pressure fitting function; the two sides of the flow channel are set as periodic boundaries; the blades, upper crown and lower ring are all set as non-slip walls; S34, several monitoring points covering the upper crown to the lower ring are evenly arranged along the spanwise direction on the center line of the trailing edge wall of the blade exit, and pressure pulsation time history monitoring parameters are set for the monitoring points. S35, based on fluid dynamics simulation software, discretizes the flow control equations of a single-channel fluid domain model of a hydraulic machinery runner blade, selects a pressure-velocity coupling algorithm and discretization scheme that are suitable for the flow field characteristics, and configures the solution control parameters; S36, start the undetermined iterative calculation of the fluid dynamics simulation software, and after the calculation converges, export the pressure pulsation time history data of all monitoring points.

12. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S4, determining the Karman vortex shedding frequency includes the following steps: S41, Perform stability preprocessing on the pressure pulsation time history data; S42, use data processing software to perform spectrum analysis on the preprocessed pressure pulsation time history data to obtain the frequency-amplitude spectrum; S43, identify and record the significant peak frequencies and their relative amplitudes in the frequency-amplitude spectrum, thereby obtaining the regional Karman vortex shedding characteristic frequencies; S44. Using the spanwise relative position as the abscissa and the Kármán vortex shedding characteristic frequency as the ordinate, a distribution cloud map of spanwise position-frequency-relative amplitude is plotted to present the Kármán vortex shedding frequency distribution characteristics at different spanwise positions.

13. The method for assessing runner resonance risk based on three-dimensional calculation of blade Karman vortices as described in claim 2, characterized in that, In step S5, assessing the resonance risk caused by Karman vortex shedding includes the following steps: S51, the wet modal analysis of the blade in water was carried out using finite element analysis software, the first few natural frequencies were extracted, and the frequency values ​​and corresponding mode shape characteristics of each order were recorded. S52, set the threshold range for dangerous frequency range and attention frequency range according to each frequency order; S53, compare the Karman vortex shedding frequency at different spanwise positions with the natural frequency one by one: those falling into the danger zone are marked as high-risk points, those falling into the concern zone are marked as medium-risk points, and those not falling into either zone are marked as low-risk points. S54 assesses the magnitude of resonance risk caused by Karman vortex shedding based on the proportion of high-risk points, and generates a risk assessment report in conjunction with mode shape characteristics.

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