Method for measuring shaping pressure pulsation amplitude of runner crown

Abstract

The invention relates to the technical field of mechanical vibration and fluid excitation coupling effect analysis, in particular to a runner crown modification pressure pulsation amplitude measurement method, which comprises the following steps of: extracting pressure gradient distribution and cavitation phase change characteristic parameters of a runner surface through fluid dynamics simulation; targeted arrangement of a high-frequency pressure sensor array in a vortex core area, a boundary layer separation critical position and a modification structure mutation area is guided, and the signal acquisition precision is improved by adopting an annular encryption and axial equal-interval arrangement strategy. And establishing a dynamic error feedback mechanism of actual measurement and simulation. And verifying an abnormal frequency band proportion through frequency domain kurtosis and analyzing envelope spectrum harmonic waves, and screening a modification parameter combination with the pressure pulsation amplitude decreasing to reach the standard and stable frequency band distribution. According to the method, the problem of measurement errors caused by multi-source high-frequency noise and effective signal frequency domain aliasing is solved, a high-precision quantification basis is provided for runner modification optimization, and the method is suitable for the field of mechanical system dynamic characteristic testing.

Description

Technical Field

[0001] The present invention relates to the technical field of analysis of the coupling effect between mechanical vibration and fluid excitation, and particularly to a method for measuring the amplitude of pressure pulsation during the crown modification of a runner. Background Art

[0002] During the crown modification process of a steam turbine runner in a power plant, the measurement of the pressure pulsation amplitude can be achieved by the cooperation of a high-frequency pressure sensor array and a dynamic signal analyzer. The specific method is as follows: First, based on the geometric parameters and hydrodynamic model of the runner crown, a multi-point pressure sensor is arranged along the surface of the flow passage under specific working conditions to synchronously collect the transient pressure signals corresponding to different modification schemes; Subsequently, a joint time-frequency domain analysis method is used to extract the main frequency components and amplitude characteristics of the pressure pulsation, and combined with the fluid-structure interaction simulation results, the suppression effect of the modified structure on unsteady flows such as vortex band cavitation is quantified; Finally, through the comparison of the amplitude spectrum and the calculation of the energy ratio, an optimized scheme with a significant reduction in the pressure pulsation amplitude and a stable frequency distribution is selected.

[0003] The technical pain point in measuring the pressure pulsation amplitude during the crown modification of a steam turbine runner in a power plant is that there is an aliasing phenomenon in the frequency domain between the multi-source high-frequency noise caused by the unsteady flow in the flow passage and the real pressure pulsation signal. Due to the influence of the rotation effect and boundary layer separation on the flow field in the crown modification area of the runner, the pressure pulsation signal includes both low-frequency components generated by the cavitation vortex band and high-frequency interference caused by the coupling of turbulent pulsation and mechanical vibration. Conventional time-frequency analysis methods are difficult to accurately separate the effective signal components with overlapping frequency bands, resulting in an increase in the amplitude measurement error. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method for measuring the amplitude of pressure pulsation during the crown modification of a runner. The present invention solves the problem that the multi-source high-frequency noise and the effective signal are aliased in the frequency domain during the measurement of the pressure pulsation during the crown modification of the runner, resulting in the inability of conventional time-frequency analysis methods to accurately separate the effective components, thereby causing an amplitude measurement error.

[0005] To solve the above technical problems, the specific technical solution of the present invention is as follows: The method for measuring the amplitude of pressure pulsation during the crown modification of a runner provided by the present invention includes: Obtain the three-dimensional geometric data of the crown modification flow passage of the runner, and combine the hydrodynamic simulation to generate the pressure gradient distribution and cavitation phase change characteristic parameters on the surface of the flow passage; Based on the pressure gradient distribution and cavitation phase change characteristic parameters, arrange a high-frequency pressure sensor array on the surface of the crown modification flow passage of the runner, and dynamically adjust the arrangement position of the high-frequency pressure sensor array in the vortex core area, the critical position of boundary layer separation, and the area with sudden changes in the modified structure; Establish a trigger signal synchronized with the rotation period of the runner, collect the transient pressure signals under all working conditions with different modification parameter configurations, synchronously record the rotational speed, flow rate, and vibration parameters, and generate an original signal dataset with timestamp marks; Perform periodic segmentation processing on the original signal dataset, eliminate low-frequency offsets through baseline drift correction, filter out mechanical vibration noise using an adaptive band-stop filter, and separate turbulent pulsation noise through wavelet packet decomposition and narrowband pass filtering to generate a denoised cavitation characteristic signal; Perform joint time-frequency domain analysis on the cavitation characteristic signal, extract the amplitude characteristics of the main frequency components of the cavitation vortex band, and combine the amplitude spectrum comparison results calibrated by fluid-structure interaction simulation to screen out the modification parameter combinations whose pressure pulsation amplitude reduction exceeds the first preset threshold and whose frequency band distribution conforms to the stability standard.

[0006] Furthermore, for the method for measuring the pressure pulsation amplitude of the runner crown modification in the present invention, based on the pressure gradient distribution and cavitation phase change characteristic parameters, arranging a high-frequency pressure sensor array on the surface of the runner crown modification flow passage, and dynamically adjusting the arrangement position of the high-frequency pressure sensor array in the vortex core area, the critical position of boundary layer separation, and the modification structure mutation area includes: Based on the pressure gradient distribution and cavitation phase change characteristic parameters, adopt a circular layout in the vortex core area, where the sensor spacing is dynamically adjusted according to the simulation data of the local pressure gradient change rate, and is arranged at equal intervals along the axial direction of the flow passage at the critical position of boundary layer separation.

[0007] Furthermore, for the method for measuring the pressure pulsation amplitude of the runner crown modification in the present invention, the periodic segmentation processing of the original signal dataset, eliminating low-frequency offsets through baseline drift correction, filtering out mechanical vibration noise using an adaptive band-stop filter, and separating turbulent pulsation noise through wavelet packet decomposition and narrowband pass filtering to generate a denoised cavitation characteristic signal includes: Perform wavelet packet decomposition on the original signal dataset, divide it into multiple frequency sub-bands, and identify the high-frequency random noise sub-bands dominated by turbulent pulsation; Perform soft threshold function shrinkage processing on the high-frequency random noise sub-bands, and retain the low-frequency sub-bands related to the characteristics of the cavitation vortex band; Perform narrowband pass filtering on the low-frequency sub-bands, and enhance the periodic cavitation components through time-domain synchronous superposition averaging.

[0008] Furthermore, for the method for measuring the pressure pulsation amplitude of the runner crown modification in the present invention, based on the pressure gradient distribution and cavitation phase change characteristic parameters, adopting a circular layout in the vortex core area, where the sensor spacing is dynamically adjusted according to the simulation data of the local pressure gradient change rate, and is arranged at equal intervals along the axial direction of the flow passage at the critical position of boundary layer separation includes: Perform short-time Fourier transform on the denoised cavitation characteristic signal to generate a time-varying amplitude curve; Based on the time-varying amplitude curve, use the wavelet ridge tracking algorithm to identify the cavitation characteristic frequency in the overlapping frequency band region; Combine the cavitation characteristic frequency and the fluid-structure interaction simulation calibration result to generate a cavitation dominant frequency amplitude distribution map.

[0009] Further, the method for measuring the pressure pulsation amplitude of the modified runner crown in the present invention further includes: comparing the error between the extracted cavitation vortex band dominant frequency amplitude feature and the predicted amplitude in the comparison result of the amplitude spectrum after the fluid-structure interaction simulation calibration; If the error exceeds the second preset threshold, adjust the cavitation phase change characteristic parameters and the boundary layer separation criterion of the hydrodynamic simulation model; Through iterative optimization of the cavitation phase change characteristic parameters and the boundary layer separation criterion, converge the error between the simulation predicted amplitude and the measured amplitude to within the second preset threshold.

[0010] Further, the method for measuring the pressure pulsation amplitude of the modified runner crown in the present invention further includes: according to the original signal dataset with timestamp marks and the denoised cavitation characteristic signal of different modification parameters, statistically analyze the historical distribution of the cavitation dominant frequency amplitude; Based on the historical distribution, set a dynamic evaluation threshold, and calculate the frequency domain energy proportion of the cavitation suppression effect of the modification parameters; Screen out the set of modification parameters whose amplitude reduction exceeds the dynamic evaluation threshold and the dominant frequency energy proportion is higher than the third preset ratio.

[0011] Further, the method for measuring the pressure pulsation amplitude of the modified runner crown in the present invention further includes: Based on the cavitation dominant frequency amplitude distribution map, verify the frequency domain kurtosis index of the denoised cavitation characteristic signal, and calculate the abnormal frequency band whose frequency domain kurtosis value exceeds the fourth preset threshold; At the same time, perform envelope spectrum analysis on the cavitation characteristic signal, and extract the harmonic component amplitude corresponding to the runner rotation period in the envelope spectrum; According to the verification result that the proportion of the abnormal frequency band is less than 5% and the amplitude fluctuation range of the harmonic component is lower than the fifth preset threshold, confirm that the amplitude retention degree of the cavitation dominant frequency component reaches more than 90% and the high-frequency noise suppression effect meets the signal-to-noise ratio ≥ 15 dB; Generate a test report including the cavitation dominant frequency amplitude distribution map, the timestamp marked data and the selected set of modification parameters, and output the target set of modification parameters.

[0012] Advantages of the present invention; The present invention extracts the pressure gradient distribution on the flow channel surface and the characteristic parameters of cavitation phase change through hydrodynamic simulation, guides the targeted dynamic layout of the high-frequency pressure sensor array in the vortex core region, the critical position of boundary layer separation, and the mutation region of the modified structure, combines the strategies of annular encryption and axial equidistant arrangement to improve the spatial resolution and dynamic response ability of signal acquisition; adopts multi-stage signal processing techniques, including baseline drift correction, adaptive band-stop filtering, wavelet packet decomposition, and narrow-band pass filtering, effectively separates the turbulent pulsation noise and mechanical vibration interference, and enhances the extraction accuracy of the main cavitation frequency characteristics; calibrates the model parameters through fluid-structure interaction simulation, establishes a dynamic error feedback mechanism between the measured data and the simulation prediction, combines the frequency-domain kurtosis verification and envelope spectrum analysis to quantify the cavitation suppression effect and screen the combination of modified parameters that meet the stability standard, systematically solves the measurement error problem caused by the frequency-domain aliasing of multi-source high-frequency noise and effective signals, provides a high-precision quantitative basis for the optimization of runner modification, and meets the accuracy and reliability requirements for measuring the amplitude of cavitation vortex band pressure pulsation in the field of dynamic characteristic testing of mechanical systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the drawings.

[0014] Figure 1 It is a flowchart of the method for measuring the amplitude of pressure pulsation of the crown modification of the runner provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments and corresponding drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention will be described in detail below in conjunction with the drawings. To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0016] Please refer to Figure 1 , the method for measuring the amplitude of pressure pulsation of the crown modification of the runner provided by the present invention includes: Step 1: Obtain the three-dimensional geometric data of the flow channel of the crown modification of the runner, and generate the pressure gradient distribution on the flow channel surface and the characteristic parameters of cavitation phase change in combination with hydrodynamic simulation; When obtaining the three-dimensional geometric data of the modified runner crown flow passage, three-dimensional laser scanning technology or computer-aided design models are used to perform topological reconstruction on the surface of the modified flow passage, and the curvature distribution of the flow passage, the width change trend, and the local geometric mutation characteristics of the modified area are extracted. The three-dimensional geometric data is aligned with the original design model through a point cloud registration algorithm to eliminate measurement errors and generate a high-precision flow passage surface mesh model, providing geometric input conditions for subsequent hydrodynamic simulations.

[0017] When generating the pressure gradient distribution on the flow passage surface by combining hydrodynamic simulations, a numerical model is constructed based on the Reynolds-averaged Navier-Stokes equations, and a cavitation phase change model is used to describe the characteristics of vapor-liquid two-phase flow. During the simulation process, the inlet boundary conditions are set as the flow rate and rotational speed parameters of the actual working conditions, the outlet boundary conditions are set as pressure-free outflow, and the medium physical property parameters are set according to the properties of the working fluid. The pressure gradient distribution cloud map of the flow passage surface is obtained through transient calculations, the extreme points of the pressure fluctuation amplitude in the vortex core region, the critical position of boundary layer separation, and the modified structure mutation region are identified, and the cavitation phase change characteristic parameters are extracted. The cavitation phase change characteristic parameters include the cavitation inception threshold, the void fraction distribution, and the pressure oscillation amplitude in the vortex core region, and are output as a spatially coordinated dataset through a post-processing module to guide the targeted layout strategy of the sensor array.

[0018] The above steps establish a quantitative analysis basis for the flow characteristics of the modified flow passage through a progressive process of geometric data acquisition, numerical modeling, and simulation calculations. The three-dimensional geometric data provides accurate geometric input for the simulation, and the hydrodynamic simulation reveals the dynamic characteristics of the flow field through parametric modeling. The two are logically related to form a complete mapping from the physical structure to the flow characteristics, meeting the technical requirements for the collaborative optimization of simulation and measured data in the field of mechanical system dynamic characteristic testing.

[0019] Step 2: Based on the pressure gradient distribution and cavitation phase change characteristic parameters, arrange a high-frequency pressure sensor array on the surface of the modified runner crown flow passage, and dynamically adjust the arrangement position of the high-frequency pressure sensor array in the vortex core region, the critical position of boundary layer separation, and the modified structure mutation region; Based on the pressure gradient distribution and cavitation phase change characteristic parameters, in the vortex core region, an annular layout strategy is adopted, and high-frequency pressure sensors are evenly arranged circumferentially with the center of the vortex core as the center. The sensor spacing is dynamically adjusted according to the simulation data of the local pressure gradient change rate. Specifically, in the region where the pressure gradient change rate exceeds the preset threshold, the sensor spacing is reduced to 5% - 10% of the vortex core diameter to capture the high-frequency pressure pulsation characteristics; in the region with a gentle gradient, the spacing is increased to reduce redundant data. The diameter of the annular layout is set according to the simulated predicted lateral expansion range of the vortex core, and the number of sensors is positively correlated with the pressure fluctuation amplitude, forming a high-density monitoring network for the dynamic characteristics of the vortex core.

[0020] At the critical position of boundary layer separation, a sensor array is arranged at equal intervals along the axial direction of the flow channel, and the interval is set to 1% - 3% of the axial length of the flow channel. The determination of the interval needs to combine the wavelength characteristics of the main frequency of the pressure pulsation in the separation zone to ensure that the distance between adjacent sensors is not greater than one-fourth of the main frequency wavelength, so as to avoid signal distortion caused by spatial aliasing. The coordinates of the flow separation point obtained through simulation are used to arrange sensors at equal intervals along the axial extension direction of the flow channel surface to monitor the unsteady flow signals caused by boundary layer separation in real time.

[0021] For the modified structure mutation region, according to the curvature radius of the geometric mutation point and the mutation amplitude of the pressure gradient predicted by simulation, sensors are arranged more densely upstream and downstream of the mutation point respectively. The upstream arrangement density is higher than that of the downstream to form a gradient monitoring network for tracking the dynamic process of the start and development of flow separation. The encrypted interval is dynamically adjusted according to the range of the pressure fluctuation amplitude in the mutation region. The greater the range of the amplitude, the smaller the sensor interval, so as to improve the spatial resolution of signal acquisition in the mutation region.

[0022] When dynamically adjusting the sensor arrangement position, by comparing the local differences between the measured pressure pulsation amplitude and the simulation predicted value in real time, it is judged whether the flow field state deviates from the initial model. If the measured amplitude exceeds 15% of the simulation predicted value, a sensor position optimization program is triggered: recalculate the diameter of the annular layout in the vortex core region and the sensor interval, and update the axial equal interval parameters in the boundary layer separation zone. The adjustment instruction is sent to the sensor array through the wireless transmission module, and the sensor is driven by the micro servo mechanism to move to the target coordinates to complete the online position calibration. This closed-loop feedback mechanism realizes the synchronous optimization of sensor arrangement and flow field evolution through the dynamic interaction between the measured data and the simulation model, avoids the signal acquisition blind area caused by flow instability, and meets the requirements of measurement accuracy and real-time performance for the dynamic characteristics test of the mechanical system.

[0023] Step 3: Establish a trigger signal synchronized with the rotation period of the runner, collect the transient pressure signals under all working conditions with different modified parameter configurations, synchronously record the rotational speed, flow rate and vibration parameters, and generate an original signal dataset with timestamp marks; When establishing a trigger signal synchronized with the rotation period of the runner, an encoder or Hall sensor is used to monitor the rotation phase of the runner in real time to generate a pulse trigger signal that is strictly aligned with the rotation period. The rising edge of the trigger signal corresponds to a specific geometric mark point of the runner, and the phase deviation caused by the rotational speed fluctuation is eliminated through the phase-locked loop circuit, so that the data acquisition system is synchronized with the runner rotation in terms of time reference. The frequency resolution of the trigger signal is set to one-thousandth of the rotation period to meet the synchronous accuracy requirements under different rotational speed working conditions.

[0024] When collecting transient pressure signals under all working conditions with different profile modification parameter configurations, configure the high-frequency pressure sensor array in a multi-channel synchronous sampling mode, and set the sampling frequency to more than 5 times the estimated value of the cavitation dominant frequency, meeting the requirements of the Nyquist sampling theorem for signal fidelity. For the dynamic switching requirements of profile modification parameters, use an automated fixture or an electronically controlled adjustment device to adjust the flow channel structure parameters in real time, realizing continuous acquisition of pressure signals for different profile modification schemes within the full working condition coverage range. When synchronously recording the rotational speed, flow rate, and vibration acceleration parameters, associate the data streams of each sensor through the same time stamp marking system. The rotational speed parameter is directly obtained by an encoder, the flow rate parameter is measured in real time by an electromagnetic flowmeter, and the vibration parameter is collected by a piezoelectric acceleration sensor, with the data synchronization error controlled within the microsecond level.

[0025] When generating the original signal data set with time stamp markings, encode the transient pressure signals, rotational speed, flow rate, and vibration parameters collected from multiple channels according to a unified time reference to form a multi-dimensional data matrix. The time stamp markings are generated by a high-precision clock chip with a time resolution reaching the nanosecond level, and each data point is associated with the profile modification parameter configuration identifier, working condition label, and sensor spatial coordinate information. The data set is stored in a structured binary format, supporting fast retrieval and seamless docking with subsequent signal processing modules, providing a complete data basis for noise separation and feature extraction.

[0026] The above steps achieve high-fidelity capture and precise association of transient signals under all working conditions through an integrated design of trigger signal synchronization, multi-parameter collaborative acquisition, and data encoding. The unity of the time reference of the trigger signal ensures data phase alignment, the multi-channel synchronous sampling mode avoids time drift between signals, and the time stamp marking and parameter association mechanism provides traceable context information for subsequent analysis, meeting the systematic requirements for complex working condition data acquisition in the field of mechanical system dynamic characteristic testing.

[0027] Step 4: Perform periodic segmentation processing on the original signal data set, eliminate low-frequency offset through baseline drift correction, filter out mechanical vibration noise using an adaptive band-stop filter, and separate turbulent pulsation noise through wavelet packet decomposition and narrowband filtering to generate a denoised cavitation characteristic signal; When performing periodic segmentation processing on the original signal data set, divide the continuous signal into multiple equal-length segments according to the phase signal of the runner rotation period to eliminate the period length deviation caused by rotational speed fluctuations. The phase signal is obtained in real time by an encoder or a Hall sensor, and the segmented signal segments are strictly aligned with the rotation phase through time stamp markings to form a periodic segmentation data set, providing input with consistent time reference for subsequent signal processing.

[0028] When eliminating low-frequency drift through baseline drift correction, a polynomial fitting algorithm is used to model the low-frequency trend term of each signal segment, and the least squares method is adopted to remove the low-frequency interference introduced by sensor zero drift or environmental temperature changes. The corrected signal retains the high-frequency pressure pulsation component, avoiding the interference of low-frequency drift on cavitation feature analysis. When using an adaptive band-stop filter to filter out mechanical vibration noise, the center frequency of the filter is dynamically set according to the natural frequency of the runner support structure and the vibration acceleration test data, and the bandwidth is adaptively adjusted according to the frequency-domain distribution of vibration energy, suppressing the mechanical vibration harmonic components coupled with the runner rotation frequency in real time.

[0029] When using the wavelet packet decomposition technique to divide the signal into multiple frequency sub-bands, the Daubechies wavelet basis function is selected for multi-level decomposition, and the decomposition level is determined according to the estimated range of the cavitation dominant frequency. By calculating the energy ratio and variance characteristics of each sub-band, the high-frequency random noise sub-band dominated by turbulent pulsation is identified, and a soft threshold function is used to perform coefficient shrinkage processing on the high-frequency sub-band, suppressing random noise while retaining the low-frequency effective signal related to the cavitation vortex band. When performing narrow-band pass filtering on the retained low-frequency sub-band, the passband range is set in combination with the cavitation frequency prediction value of the fluid-structure interaction simulation, and the stopband attenuation is not less than 40 dB, extracting the signal components near the cavitation dominant frequency. The filtered signal is enhanced through time-domain synchronous superposition averaging. Based on the signal segments aligned by the rotation period phase, multiple cycle signals at the same phase are superimposed and averaged to suppress non-periodic turbulent noise, generating the denoised cavitation feature signal.

[0030] The above steps form a complete denoising link from the original signal to the cavitation feature signal through sequential processing of periodic segmentation, baseline correction, band-stop filtering, wavelet decomposition, and narrow-band pass filtering. Periodic segmentation eliminates the influence of rotational speed fluctuations, baseline correction and band-stop filtering respectively suppress low-frequency drift and mechanical vibration interference, wavelet packet decomposition realizes frequency-domain noise separation, narrow-band pass filtering and time-domain averaging enhance the cavitation dominant frequency components, and the logic of each step is closely connected, meeting the technical requirements of signal separation accuracy and fidelity in the field of mechanical system dynamic characteristic testing.

[0031] Step 5: Perform a joint time-frequency domain analysis on the cavitation feature signal, extract the amplitude characteristics of the cavitation vortex band dominant frequency component, and combine the calibrated amplitude spectrum comparison results of the fluid-structure interaction simulation to screen out the modified parameter combinations whose pressure pulsation amplitude reduction exceeds the first preset threshold and whose frequency band distribution meets the stability standard.

[0032] When performing joint time-frequency domain analysis on the cavitation characteristic signal, short-time Fourier transform is used to generate a time-varying amplitude curve. The signal segment is intercepted by a Hamming window and the spectrum is calculated to reflect the characteristic of the main cavitation frequency amplitude evolving with time. Combining with the wavelet ridge tracking algorithm, the Morlet wavelet basis function is selected for continuous wavelet transform. The local maximum points of energy are detected on the time-frequency plane, and the candidate points are connected by a path optimization algorithm to form the main ridge line, extracting the cavitation characteristic frequency trajectory and the corresponding amplitude to solve the signal separation problem in the frequency band overlapping region. The frequency trajectory and amplitude data of the main ridge line generate the amplitude characteristic set of the main frequency component of the cavitation vortex band through integral processing.

[0033] When combining the comparison results of the amplitude spectrum calibrated by fluid-structure interaction simulation, the measured main cavitation frequency amplitude is frequency-point matched with the simulation predicted spectrum, the corresponding points with a frequency deviation less than 1% are identified, and the relative amplitude error is calculated. The error data is associated with the coordinate of the sensor arrangement on the flow channel surface to generate a heat map of the error distribution, positioning the regional deviation between the simulation model and the measured data. If the error exceeds the first preset threshold, the model parameter optimization program is started, adjusting the cavitation phase change characteristic parameters (such as the cavitation bubble rupture rate) and the boundary layer separation criterion (such as the vorticity-pressure gradient coupling criterion), and iteratively optimizing through the gradient descent method until the error converges within the threshold.

[0034] When screening the modified parameter combinations with the pressure pulsation amplitude reduction meeting the standard, the threshold of the main frequency amplitude reduction is set not less than 20%, and the stability standard of the frequency band distribution is that the main frequency energy accounts for more than 70% of the total frequency band energy and the amplitude fluctuation range of the sub-frequency component is less than 10%. The multi-objective optimization algorithm is used to perform Pareto front analysis on the modified parameters, and the conflicting solutions are eliminated by combining with the coupling effect matrix between the parameters, retaining the parameter set with the coordinated optimization of the amplitude reduction and stability. The screening results are further verified by the frequency domain kurtosis to verify the proportion of the abnormal frequency band and the envelope spectrum harmonic analysis to confirm the signal fidelity, and finally the modified parameter combination meeting the requirements of cavitation suppression effect and pressure pulsation stability is output, providing a quantitative basis for the optimization of the runner structure. The above steps systematically solve the amplitude measurement error problem under multi-source noise interference through a closed-loop process of time-frequency analysis, model calibration and multi-objective screening, meeting the accuracy and reliability requirements for the screening of modified parameters in the field of mechanical system dynamic characteristic testing.

[0035] The specific implementation manner of the method for measuring the pressure pulsation amplitude of the crown modification of the runner provided by the present invention is as follows: In Step 1, when obtaining the three-dimensional geometric data of the crown trimming runner, it is necessary to extract the topological structure and geometric parameters of the runner surface based on three-dimensional laser scanning or computer-aided design models, including curvature distribution, runner width variation, and local geometric mutation characteristics of the trimming area. Through hydrodynamic simulation, the Reynolds-averaged Navier-Stokes equations are combined with a cavitation model to simulate the unsteady flow characteristics in the runner under different operating conditions. During the simulation, the boundary conditions are set as the rotational speed, flow rate, and medium physical property parameters of the actual operating conditions, and the pressure gradient distribution and cavitation phase change characteristic parameters on the runner surface are calculated. The cavitation phase change characteristic parameters include the cavitation inception threshold, void fraction distribution, and pressure fluctuation amplitude in the vortex core region. The simulation results are exported as pressure gradient contour maps and spatial coordinate data of the cavitation phase change region through a post-processing module, which are used to guide subsequent sensor placement.

[0036] In Step 2, based on the pressure gradient distribution and cavitation phase change characteristic parameters obtained in Step 1, an annular layout strategy is adopted in the vortex core region, and a high-frequency pressure sensor array is circumferentially distributed along the vortex core center. The sensor spacing is dynamically adjusted according to the simulation data of the local pressure gradient change rate. Specifically, a smaller sensor spacing is used in the region with a higher pressure gradient change rate to capture high-frequency pressure pulsations, while the spacing is increased in the gently varying gradient region to reduce redundant data. At the critical position of boundary layer separation, sensors are arranged at equal intervals along the axial direction of the runner, and the spacing is set to 1% - 3% of the axial length of the runner to adapt to the flow instability caused by boundary layer separation. For the trimming structure mutation region, combined with the curvature radius of the geometric mutation point and the predicted pressure fluctuation amplitude from the simulation, the sensor layout is densified upstream and downstream of the mutation region to form a gradient monitoring network.

[0037] In Step 3, a trigger signal synchronized with the runner rotation period is generated through an encoder or Hall sensor, so that the data acquisition system is phase-synchronized with the runner rotation. A high-frequency pressure sensor array is used to collect the transient pressure signals under all operating conditions with different trimming parameter configurations, and the rotational speed, flow rate, and vibration acceleration signals are recorded synchronously. The data acquisition system is configured in a multi-channel synchronous sampling mode, and the sampling frequency is set to more than 5 times the pre-estimated cavitation dominant frequency to meet the Nyquist sampling theorem. The original signal dataset is marked with time stamps and associated with the corresponding trimming parameter configurations, rotational speed, and flow rate conditions to form a multi-dimensional data matrix for subsequent signal processing and parameter comparison analysis.

[0038] In step 4, when performing periodic segmentation on the original signal dataset, the continuous signal is divided into multiple equal-length segments according to the rotation period of the runner to eliminate the periodic deviation caused by rotational speed fluctuations. The polynomial fitting algorithm is used to correct the baseline drift of each signal segment to remove the low-frequency interference introduced by temperature drift or sensor zero offset. The adaptive band-stop filter is used to filter out the mechanical vibration noise, where the band-stop frequency range is dynamically adjusted according to the natural frequency of the runner support structure and the vibration test data. The signal is divided into multiple frequency sub-bands through wavelet packet decomposition, the high-frequency random noise sub-band dominated by turbulent pulsation is identified, and the soft threshold function is used to shrink the high-frequency sub-band to suppress the random noise. The low-frequency sub-band related to the characteristics of the cavitation vortex band is retained, the signal components near the main cavitation frequency are extracted through a narrow-band pass filter, and the repetitive characteristics of the periodic cavitation components are enhanced by combining time-domain synchronous superposition averaging to generate the denoised cavitation characteristic signal.

[0039] In step 5, when performing joint time-frequency domain analysis on the denoised cavitation characteristic signal, the short-time Fourier transform is used to obtain the time-varying amplitude curve of the signal, and the cavitation characteristic frequency in the frequency band overlapping region is identified by combining the wavelet ridge tracking algorithm. The amplitude characteristics of the main frequency component of the cavitation vortex band are extracted from the amplitude spectrum calibrated by the fluid-structure interaction simulation, and a dynamic error comparison is made with the simulation prediction results. When screening the modified parameter combinations with the pressure pulsation amplitude reduction exceeding the first preset threshold, the main frequency amplitude reduction threshold is set not less than 20%, and at the same time, it is required that the frequency band distribution meets the stability standard, that is, the main frequency energy accounts for more than 70% of the total frequency band energy and the amplitude fluctuation range of the sub-frequency component is less than 10%. By iteratively optimizing the cavitation phase change characteristic parameters and boundary layer separation criteria of the hydrodynamic model, the amplitude error between the simulation prediction and the measured data converges to within 5%, and finally, a set of modified parameters that meet the requirements of cavitation suppression effect and pressure pulsation stability is output.

[0040] The above steps solve the measurement error problem caused by the frequency domain aliasing of multi-source high-frequency noise and effective signals through a closed-loop process of simulation-guided sensor layout, dynamic signal acquisition, multi-source noise separation, and cavitation feature extraction, providing a high-precision quantitative basis for runner modification optimization. A technical closed-loop is formed between the steps through data transfer and feedback mechanisms. For example, the simulation results guide the sensor layout, and the measured data feeds back to the model calibration, and the signal processing results drive the parameter screening.

[0041] Specifically, for the method for measuring the pressure pulsation amplitude of the crown modification of the runner described in the present invention, based on the pressure gradient distribution and cavitation phase change characteristic parameters, arranging a high-frequency pressure sensor array on the surface of the crown modification flow channel of the runner, and dynamically adjusting the arrangement position of the high-frequency pressure sensor array in the vortex core area, the critical position of boundary layer separation, and the area of sudden change of the modification structure includes: Based on the above-mentioned pressure gradient distribution and cavitation phase change characteristic parameters, an annular layout is adopted in the vortex core region, where the sensor spacing is dynamically adjusted according to the simulation data of the local pressure gradient change rate, and is arranged at equal intervals along the axial direction of the flow channel at the critical position of boundary layer separation.

[0042] When arranging the high-frequency pressure sensor array, first, based on the pressure gradient distribution cloud map of the flow channel surface generated by hydrodynamic simulation, identify the spatial position of the vortex core region and the extreme value region of the pressure fluctuation amplitude. The specific annular layout of the vortex core region is as follows: with the center of the vortex core as the center of the circle, a number of sensors are evenly distributed along the circumferential direction. The annular diameter is determined according to the simulation data of the transverse expansion range of the vortex core. The number of sensors is positively correlated with the pressure gradient change amplitude in the vortex core region. For the region where the pressure gradient change rate exceeds the preset threshold, an adaptive encryption algorithm is used to reduce the sensor spacing in the annular layout, and the spacing adjustment step is 5% - 10% of the vortex core diameter to capture the local high-frequency pressure pulsation characteristics.

[0043] At the critical position of boundary layer separation, combined with the simulation coordinates of the flow separation point on the flow channel surface, arrange the sensor array at equal intervals along the axial direction of the flow channel. The setting of the equal interval is based on 1% - 3% of the axial length of the boundary layer separation region of the flow channel, and the specific value is adjusted according to the wavelength characteristics of the main frequency of the pressure pulsation in the separation region, so that the spacing between adjacent sensors is not greater than one-fourth of the main frequency wavelength to avoid spatial aliasing. For the modified structure mutation region, based on the curvature radius of the geometric mutation point and the pressure gradient mutation amplitude, sensors are arranged upstream and downstream of the mutation point respectively to form a gradient monitoring network, and the upstream layout density is higher than that of the downstream to track the dynamic process of the initiation and development of flow separation.

[0044] When dynamically adjusting the sensor arrangement position, by comparing the local amplitude difference between the pressure gradient change rate predicted by simulation and the measured signal in real time, judge whether the flow state in the vortex core region or the boundary layer separation region deviates from the initial simulation model. If the measured pressure pulsation amplitude exceeds 15% of the simulation prediction value, trigger the sensor position optimization program: recalculate the diameter and sensor spacing of the annular layout in the vortex core region according to the current pressure gradient distribution, and at the same time update the axial equal interval parameters at the critical position of boundary layer separation. The adjusted sensor array receives the update instruction through the wireless transmission module, and the sensor is driven by the micro servo mechanism to move to the target coordinate to complete the online position calibration. This dynamic adjustment mechanism realizes the synchronous optimization of sensor arrangement and flow field evolution through closed-loop feedback, avoiding signal acquisition blind areas caused by flow instability.

[0045] Specifically, for the method of measuring the amplitude of pressure pulsation in the crown modification of the runner in the present invention, the periodic segmentation process of the original signal dataset, eliminating the low-frequency offset through baseline drift correction, filtering out mechanical vibration noise using an adaptive band-stop filter, and separating turbulent pulsation noise through wavelet packet decomposition and narrowband pass filtering to generate the denoised cavitation characteristic signal includes: Perform wavelet packet decomposition on the original signal dataset, divide it into multiple frequency sub-bands, and identify the high-frequency random noise sub-band dominated by turbulent pulsation; Adopt a soft threshold function shrinkage process for the high-frequency random noise sub-band, and retain the low-frequency sub-bands related to the characteristics of the cavitation vortex band; Perform narrowband pass filtering on the low-frequency sub-bands, and enhance the periodic cavitation component through time-domain synchronous superposition averaging.

[0046] During the signal processing, first perform periodic segmentation on the original signal dataset according to the runner rotation period. Using the runner rotation phase signal obtained by an encoder or Hall sensor, the continuously collected transient pressure signals are divided into multiple signal segments synchronized with the rotation period, eliminating the period length deviation caused by rotational speed fluctuations. Each signal segment is aligned with the corresponding rotation period phase through timestamp marking to form a periodic segmentation dataset.

[0047] When performing baseline drift correction on the periodic segmentation dataset, use a polynomial fitting algorithm to model the low-frequency trend term of each signal segment, and eliminate the low-frequency offset introduced by sensor zero drift or environmental temperature changes through the least squares method. The corrected signal segments are filtered out mechanical vibration noise through an adaptive band-stop filter, where the center frequency of the band-stop filter is dynamically set according to the natural frequency of the runner support structure and vibration test data, and the bandwidth is adaptively adjusted according to the frequency domain distribution of vibration energy. The filter parameters are updated in real time by online monitoring the vibration acceleration signal to suppress the mechanical vibration interference coupled with the runner rotation frequency.

[0048] Use the wavelet packet decomposition algorithm to divide the corrected signal into multiple frequency sub-bands. The wavelet basis function selects the Daubechies series with compact support characteristics, and the decomposition level is determined according to the estimated range of the cavitation dominant frequency. By calculating the energy ratio and variance characteristics of each sub-band, identify the high-frequency random noise sub-band dominated by turbulent pulsation. Adopt a soft threshold function for shrinkage processing of the high-frequency sub-band, and the threshold is set based on the statistical characteristics of the noise sub-band. Retain the low-frequency sub-bands related to the characteristics of the cavitation vortex band by shrinking the random noise coefficients. The low-frequency sub-bands extract the signal components near the cavitation dominant frequency through a narrowband pass filter, and the passband range of the filter is set in combination with the cavitation frequency prediction value of the fluid-structure interaction simulation, and the stopband attenuation is not less than 40 dB.

[0049] When performing time-domain synchronous superposition averaging on the filtered low-frequency sub-band, based on the signal segments aligned by the rotational period phase, multiple periodic signals with the same phase are superposed and averaged to suppress non-periodic turbulent pulsation noise. The number of superpositions is dynamically adjusted according to the requirement of SNR improvement. The averaged signal retains the repetitive characteristics of the periodic cavitation component, generating a denoised cavitation characteristic signal. The above processing steps effectively separate the cavitation vortex band characteristics and multi-source interference signals through the synergistic effect of frequency-domain decomposition, threshold denoising, and time-domain enhancement, providing high-fidelity input data for subsequent time-frequency analysis.

[0050] The logical relationship between each step is as follows: Periodic segmentation eliminates the influence of rotational speed fluctuations, baseline correction and band-stop filtering suppress low-frequency and mechanical vibration interference, wavelet packet decomposition realizes frequency-domain noise separation, and narrow-band pass filtering and time-domain averaging enhance cavitation characteristics. The technical solution strips the noise components layer by layer through multi-level signal processing, forming a complete denoising link from the original signal to the cavitation characteristic signal, meeting the requirements of signal separation accuracy in the field of mechanical system dynamic characteristic testing.

[0051] Specifically, in the method for measuring the pressure pulsation amplitude of the crown modification of the runner of the present invention, based on the pressure gradient distribution and cavitation phase change characteristic parameters, an annular layout is adopted in the vortex core area, where the sensor spacing is dynamically adjusted according to the simulation data of the local pressure gradient change rate, and is arranged at equal intervals along the axial direction of the flow channel at the critical position of boundary layer separation, including: Perform short-time Fourier transform on the denoised cavitation characteristic signal to generate a time-varying amplitude curve; Based on the time-varying amplitude curve, use the wavelet ridge tracking algorithm to identify the cavitation characteristic frequency in the frequency band overlapping area; Combine the cavitation characteristic frequency and the fluid-structure interaction simulation calibration result to generate a cavitation dominant frequency amplitude distribution map.

[0052] When performing short-time Fourier transform on the denoised cavitation characteristic signal, use the Hamming window as the time-domain window function, and set the window length to 2-3 times the estimated period of the cavitation dominant frequency to balance the time-frequency resolution. The sliding step of the window function is determined according to the phase resolution of the runner rotation period, usually 1 / 4 of the window length. Signal segments are intercepted by segment-by-segment windowing and the spectrum is calculated to generate a time-varying amplitude curve. The horizontal axis of the time-varying amplitude curve is the time series, the vertical axis is the frequency component, and the amplitude is represented by color mapping, reflecting the characteristics of the amplitude of the cavitation characteristic frequency evolving with time.

[0053] When applying the wavelet ridge tracking algorithm, the Morlet wavelet is selected as the basis function, and its central frequency matches the estimated range of the main cavitation frequency. The time-frequency energy distribution of the signal is calculated through continuous wavelet transform, and local energy maximum points are detected on the time-frequency plane to form a preliminary set of ridge candidate points. A path optimization algorithm is used to connect the candidate points to eliminate the false ridges caused by noise or cross terms and retain the main ridge that reflects the dynamic characteristics of the cavitation vortex band. The frequency trajectory of the main ridge is the cavitation characteristic frequency, and its corresponding amplitude is extracted through ridge energy integration to form a cavitation characteristic frequency-amplitude correlation data set.

[0054] When calibrating the results by combining fluid-structure interaction simulation, the measured cavitation characteristic frequency is frequency-domain aligned with the frequency-amplitude distribution predicted by the simulation. The corresponding points with a frequency deviation less than 1% between the simulation and the measured data are identified through a frequency point matching algorithm, and the amplitude of the cavitation phase change parameters of the simulation model is corrected. The corrected model outputs the three-dimensional spatial distribution of the main cavitation frequency amplitude. Combining with the spatial coordinates of the sensor arrangement positions, the Kriging interpolation algorithm is used to generate the distribution map of the main cavitation frequency amplitude on the flow channel surface. Different color regions in the map represent the amplitude intensity, and the flow channel geometric contour lines are superimposed to visually display the spatial evolution law of the cavitation vortex band.

[0055] The above steps convert the denoised cavitation characteristic signal into an amplitude map with spatial distribution characteristics through progressive processing of time-frequency analysis, ridge extraction, and data fusion. The short-time Fourier transform provides the basic data of time-varying amplitude, the wavelet ridge tracking realizes the precise separation of the frequency band overlapping region, and the fluid-structure interaction simulation calibration establishes the dynamic correlation between the measurement and the simulation. The technical solution quantifies the spatio-temporal distribution characteristics of the main cavitation frequency amplitude through multi-source data collaborative processing, providing a visual basis for the optimization of the runner modification parameters. A data closed-loop is formed between the steps. For example, the simulation calibration depends on the measured ridge data, and the map generation in turn feeds back the iterative optimization of the sensor arrangement strategy, reflecting the systematicness and operability of the technical solution.

[0056] Specifically, the method for measuring the pressure pulsation amplitude of the runner crown modification in the present invention further includes: comparing the error between the extracted main frequency amplitude characteristics of the cavitation vortex band and the predicted amplitude in the comparison result of the amplitude spectrum after the fluid-structure interaction simulation calibration; If the error exceeds the second preset threshold, adjust the cavitation phase change characteristic parameters and the boundary layer separation criterion of the hydrodynamic simulation model; Through iterative optimization of the cavitation phase change characteristic parameters and the boundary layer separation criterion, the error between the simulation predicted amplitude and the measured amplitude is converged within the second preset threshold.

[0057] In the error comparison stage, the main frequency amplitude characteristics of the extracted cavitation vortex band are aligned with the amplitude spectrum calibrated by the fluid-structure interaction simulation through the frequency point matching algorithm. The frequency point matching algorithm sets the matching tolerance range based on the frequency deviation threshold between the measured and simulated data, establishes the amplitude mapping relationship for the frequency points with a frequency deviation less than 1%, and calculates the relative amplitude error of the corresponding frequency points. The error data is associated with the sensor layout positions on the flow channel surface according to the spatial coordinates to generate an error distribution heat map for locating the regional deviation between the simulation model and the measured data.

[0058] When the error exceeds the second preset threshold, start the parameter optimization program of the hydrodynamic simulation model. The adjustment of the cavitation phase change characteristic parameters includes the cavitation initiation threshold, the cavitation bubble rupture rate, and the phase change energy transfer coefficient. The parameters that have a significant impact on the amplitude error are determined through sensitivity analysis. The optimization of the boundary layer separation criterion is based on the dynamic correlation characteristics of the pressure gradient and velocity vector in the separation zone, and the vorticity-pressure gradient coupling criterion is used to update the triggering condition of the separation criterion. During the parameter adjustment process, the objective function is optimized based on the gradient descent method. The objective function is defined as the root mean square error between the measured and simulated amplitudes, and the iteration step size is dynamically adjusted according to the error convergence speed.

[0059] During the iterative optimization process, the fluid-structure interaction simulation is re-executed after each parameter adjustment to generate an updated amplitude spectrum prediction result. The new predicted data is compared with the measured data for a second error comparison. If the error still exceeds the threshold, the parameter weights are adjusted according to the spatial gradient direction of the error distribution heat map, and the corresponding parameters in the high-error region are preferentially corrected. This process is executed cyclically through a closed-loop feedback mechanism until the amplitude error between the measured and simulated values converges within the second preset threshold range. The convergence determination condition is that the error change rate for three consecutive iterations is less than 0.5%, and at the same time, the standard deviation of the amplitude error at each frequency point drops below 20% of the initial value.

[0060] The above steps realize the dynamic calibration of the simulation model and the measured data through the collaborative mechanism of data mapping, parameter optimization, and closed-loop verification. The frequency point matching algorithm establishes the data association benchmark, the parameter adjustment program drives the model to approximate the real flow characteristics, and the iterative convergence mechanism ensures the stability of the optimization process. The technical solution improves the prediction accuracy of the simulation model for the dynamic characteristics of the cavitation vortex band through the collaborative correction of multi-physical field parameters, providing a reliable quantitative basis for the screening of modification parameters. A positive feedback link is formed between the steps. For example, error analysis guides the parameter adjustment direction, and the optimized model feeds back to a higher-precision simulation prediction, reflecting the self-adaptability and systematicness of the technical solution.

[0061] Specifically, the method for measuring the amplitude of the pressure pulsation of the crown modification of the runner described in the present invention further includes: statistically analyzing the historical distribution of the main frequency amplitude of cavitation according to the original signal data set with time stamp marks and the denoised cavitation characteristic signals of different modification parameters; A dynamic evaluation threshold is set based on the historical distribution, and the frequency domain energy ratio of the cavitation suppression effect of the modification parameters is calculated; A set of modification parameters whose amplitude reduction exceeds the dynamic evaluation threshold and whose main frequency energy proportion is higher than a third preset proportion is screened out.

[0062] When counting the historical distribution of the cavitation main frequency amplitude, the original signal data sets of different modification parameters and the denoised cavitation characteristic signals are first classified and stored according to the parameter configuration based on the timestamp mark. For multiple groups of signal data under each modification parameter, the time domain peak value and frequency domain integrated energy of the cavitation main frequency amplitude are extracted to construct an amplitude history database indexed by the modification parameter. The sliding window statistical method is used to perform time-period aggregation analysis on the historical data, and the mean, variance and extreme value distribution characteristics of the cavitation main frequency amplitude corresponding to each parameter are calculated to generate a trend curve of the cavitation amplitude changing with the modification parameter.

[0063] When setting the dynamic evaluation threshold based on historical distribution, an adaptive threshold generation algorithm is used, with the historical amplitude mean as the benchmark, and the evaluation threshold range is dynamically adjusted in combination with variance data. Specifically, the dynamic evaluation threshold is determined by the weighted average of 70% of the benchmark amplitude and the historical maximum reduction amplitude, and the weight coefficient is dynamically allocated according to the working condition coverage of the modification parameters. For new modification parameter combinations, the interpolation method is used to combine the historical distribution data of neighboring parameters to predict the initial threshold, and the threshold accuracy is gradually corrected through the online learning mechanism.

[0064] When calculating the frequency domain energy ratio, the power spectrum density analysis is performed on the denoised cavitation characteristic signal, the energy integral value within the cavitation main frequency bandwidth is extracted, and the ratio is calculated with the total energy of the full frequency band. The main frequency bandwidth range is set according to the cavitation frequency prediction results of the fluid-solid coupling simulation, and the bandwidth width does not exceed ±5% of the predicted main frequency. The frequency domain energy ratio calculation results are compared with the same parameter data in the historical database to generate quantitative indicators of the cavitation suppression effect under various modification parameters.

[0065] When screening the modification parameter set, the cavitation main frequency amplitude reduction must exceed 20% of the dynamic evaluation threshold, and the main frequency energy ratio must be higher than 65% of the third preset ratio. A multi-objective optimization algorithm is used to perform Pareto frontier analysis on the modification parameters to select parameter combinations that meet the requirements of amplitude reduction and energy ratio. During the screening process, the coupling effect matrix between parameters is combined to eliminate conflicting modification schemes, retain parameter sets with significant synergistic effects and satisfactory stability, and form the final optimization solution library.

[0066] The above steps achieve a quantitative evaluation of the cavitation suppression effect of the profiling parameters through a progressive process of historical data modeling, dynamic threshold generation, and multi-objective screening. The historical distribution statistics provide a data benchmark, the dynamic threshold avoids the limitations of static evaluation, the energy ratio calculation quantifies the frequency domain characteristics, and the multi-objective screening combines the performance and stability requirements. The technical solution provides a traceable decision-making basis for the optimization of the runner profiling parameters through a data-driven evaluation system, meeting the requirements for the scientificity and reliability of the evaluation method in the field of dynamic characteristic testing of mechanical systems. A data closed-loop is formed among the steps. For example, the screening results are fed back to the historical database to update the statistical model, and the dynamic threshold is continuously optimized with the accumulation of data, reflecting the adaptive iterative ability of the technical solution.

[0067] Specifically, the method for measuring the amplitude of the pressure pulsation of the upper crown of the runner described in the present invention further includes: Based on the cavitation dominant frequency amplitude distribution map, verify the frequency domain kurtosis index of the denoised cavitation characteristic signal, and calculate the abnormal frequency band where the frequency domain kurtosis value exceeds the fourth preset threshold; At the same time, perform envelope spectrum analysis on the cavitation characteristic signal, and extract the amplitude of the harmonic components corresponding to the rotation period of the runner in the envelope spectrum; According to the verification result that the proportion of the abnormal frequency band is less than 5% and the fluctuation amplitude of the harmonic component amplitude is lower than the fifth preset threshold, confirm that the amplitude retention of the cavitation dominant frequency component reaches more than 90% and the high-frequency noise suppression effect meets the signal-to-noise ratio ≥ 15 dB; Generate a test report including the cavitation dominant frequency amplitude distribution map, the timestamp marked data, and the selected set of profiling parameters, and output the target set of profiling parameters.

[0068] In the frequency domain kurtosis index verification stage, based on the cavitation dominant frequency amplitude distribution map, perform frequency domain kurtosis calculation on the denoised cavitation characteristic signal. The frequency domain kurtosis quantifies the steepness of the energy distribution of the signal spectrum through the fourth-order moment statistical method, calculates the kurtosis value of each frequency point, and generates a kurtosis distribution curve. The abnormal frequency band is identified based on the set of continuous frequency points where the kurtosis value exceeds the fourth preset threshold. The threshold is set by referring to the mean kurtosis of the normal working conditions in the historical data plus twice the standard deviation to exclude the instantaneous spike interference caused by random noise. The proportion of the abnormal frequency band is determined by calculating the ratio of the number of abnormal frequency points to the total number of analyzed frequency points, and is used to evaluate the remaining degree of non-stationary interference in the signal.

[0069] In envelope spectrum analysis, the cavitation characteristic signal is subjected to Hilbert transform to generate an analytic signal, its envelope waveform is calculated, and Fourier transform is performed to obtain the envelope spectrum. The frequency components corresponding to the fundamental rotation frequency of the runner and its integer multiple harmonics in the envelope spectrum are extracted, and the fluctuation range of the amplitudes of each harmonic is quantified. The amplitude fluctuation range of the harmonic components is obtained by calculating the ratio of the standard deviation of the amplitudes in different rotation periods to the mean value of the same harmonic, and is used to characterize the intensity of periodic interference caused by mechanical vibration or assembly clearance.

[0070] When verifying the amplitude retention degree of the main cavitation frequency component, the frequency-domain kurtosis and the envelope spectrum analysis results are combined: if the proportion of the abnormal frequency band is less than 5%, it indicates that the suppression effect of high-frequency noise in the signal meets the standard; if the amplitude fluctuation range of the harmonic components is lower than the fifth preset threshold (usually set to 15% of the harmonic mean value), it is determined that the periodic interference has been effectively filtered out. At the same time, the energy proportion within the main cavitation frequency bandwidth is calculated through power spectral density integration, and the suppression effect of high-frequency noise is verified in combination with the signal-to-noise ratio evaluation formula to meet the requirement of signal-to-noise ratio ≥ 15 dB. The joint verification of the above indicators confirms that the amplitude retention degree of the main cavitation frequency of the denoised signal exceeds 90%, and the residual noise is within the controllable range.

[0071] When generating the test report, the amplitude distribution map of the main cavitation frequency, the timestamp marked data, and the selected set of modified parameters are integrated. The map uses three-dimensional visualization technology to superimpose the geometric contour of the flow channel, marks the amplitude intensity distribution with different color scales, and marks the critical positions of the vortex core area and the boundary layer separation. The timestamp marked data contains the synchronous recording information of the rotational speed, flow rate, and vibration parameters, and is associated with the corresponding signal segments according to the time series. The set of modified parameters is stored in matrix form, including parameter names, optimization amplitudes, and cavitation suppression effect scores, and finally outputs the target set of modified parameters in the industrial standard format for direct use in the optimization of the runner structure.

[0072] The above steps achieve the dual verification of signal quality and modification effect through a progressive process of kurtosis verification, envelope analysis, and comprehensive evaluation. The frequency-domain kurtosis detects the residual non-stationary interference, the envelope spectrum analysis suppresses the periodic noise, and the comprehensive evaluation index quantifies the fidelity of the cavitation characteristics and the noise suppression level. The technical solution forms a complete link from signal processing to optimization decision-making through multi-dimensional data fusion and standardized output, meeting the requirements of data reliability and application guidance in the field of mechanical system dynamic testing. The logical closed-loop between the steps is reflected in that the verification results drive the parameter screening and optimization, and the optimized parameters are fed back to the historical database through the test report to support subsequent iterative upgrades.

[0073] The technical features of the technical solution of the present invention are explained as follows: Modified upper crown runner flow path: Refers to the fluid passage of the upper crown of a steam turbine runner after structural modification. Its geometric shape is obtained through three-dimensional laser scanning or computer-aided design models, including curvature distribution, flow path width variation, and local geometric mutation characteristics in the modified area, used to optimize flow stability and suppress cavitation.

[0074] Pressure gradient distribution: Data on the rate of change of pressure on the flow path surface generated through hydrodynamic simulation, reflecting the pressure differences in different regions when the fluid flows in the flow path, used to identify high-dynamic pressure fluctuation regions such as the vortex core region and boundary layer separation region, guiding the targeted layout of sensors.

[0075] Cavitation phase change characteristic parameters: Quantitative indicators describing the cavitation phase change process of the fluid in the low-pressure region, including the cavitation inception threshold (critical pressure for initiating cavitation), void volume fraction (proportion of voids in unit volume), and amplitude of pressure fluctuation in the vortex core region, used for calibration of simulation models and optimization of sensor layout.

[0076] High-frequency pressure sensor array: Multiple high-frequency response pressure sensors arranged according to a specific spatial strategy, with a circular layout in the vortex core region, equally spaced along the axial direction of the flow path in the boundary layer separation region, and densified upstream and downstream of the modified mutation region, used to capture transient pressure pulsation signals and improve spatial resolution.

[0077] Vortex core region: The core region of the rotating vortex formed during fluid flow, where the rate of change of pressure gradient is significantly higher than the surrounding region. After being located through the simulation pressure contour map, the spacing is dynamically adjusted using a circular sensor layout to capture high-frequency cavitation pulsation signals.

[0078] Critical position of boundary layer separation: The starting point where the fluid separates from the wall surface due to a sudden change in flow velocity on the flow path surface. After determining the coordinates through simulation, sensors are arranged along the axial direction of the flow path, with the spacing set to 1% - 3% of the flow path length, used to monitor the unsteady flow caused by separation.

[0079] Modified structure mutation region: The local region where the geometric shape of the upper crown of the runner undergoes mutation after modification (such as a curvature mutation point). Based on the predicted amplitude of pressure gradient mutation through simulation, sensors are densified upstream and downstream to form a gradient monitoring network to track the initiation and development process of flow separation.

[0080] Baseline drift correction: Using a polynomial fitting algorithm to eliminate the low-frequency trend term introduced by sensor zero drift or environmental temperature change in the signal, retaining the high-frequency components of the true pressure pulsation, and avoiding the influence of low-frequency interference on subsequent analysis.

[0081] Adaptive band-stop filter: A filter that dynamically adjusts the stopband range according to the natural frequency of the runner support structure and the measured vibration data, used to filter out noise in specific frequency bands coupled with mechanical vibration and suppress rotational frequency harmonic interference.

[0082] Wavelet packet decomposition: A technique that decomposes the signal into multiple frequency sub-bands based on the Daubechies wavelet basis function. The high-frequency noise sub-bands dominated by turbulent pulsation are identified through energy proportion analysis. The random noise coefficient is shrunk using a soft threshold function to retain the low-frequency effective signals related to the cavitation vortex band.

[0083] Narrow bandpass filtering: The passband is set based on the main frequency range of cavitation predicted by fluid-structure coupling simulation, and the stopband attenuation is not less than 40dB. It is used to extract signal components near the cavitation characteristic frequency and suppress out-of-band noise interference.

[0084] Time domain synchronous superposition averaging: Based on the signal segments aligned with the phase of the rotor rotation period, multiple periodic signals with the same phase are superimposed and averaged to enhance the repetitive characteristics of the periodic cavitation components and suppress non-periodic turbulence noise.

[0085] Short-time Fourier transform: A Hamming window is used to intercept signal segments and calculate the spectrum to generate a time-varying amplitude curve, which reflects the characteristics of the evolution of the cavitation main frequency amplitude over time and provides a time-frequency analysis basis for wavelet ridge tracing.

[0086] Wavelet ridge tracing algorithm: Based on the continuous wavelet transform of Morlet wavelet, the local energy maximum points are detected on the time-frequency plane, and the candidate points are connected through path optimization to form the main ridge line, and the cavitation characteristic frequency trajectory and corresponding amplitude are extracted to solve the signal separation problem in the frequency band overlapping area.

[0087] Fluid-solid coupling simulation calibration: Compare the measured cavitation main frequency amplitude with the simulation predicted spectrum, calculate the error through the frequency point matching algorithm, iteratively adjust the cavitation phase change parameters (such as cavitation bubble rupture rate) and boundary layer separation criteria (vorticity-pressure gradient coupling criterion) to make the simulation model close to the real flow characteristics.

[0088] Dynamic evaluation threshold: Based on the weighted calculation of the average hollowing main frequency amplitude and the maximum amplitude reduction value in historical data, it is used to screen the modification parameter combinations with amplitude reduction exceeding 20% ​​and main frequency energy proportion exceeding 65%, so as to avoid evaluation deviation caused by static threshold.

[0089] Frequency domain kurtosis verification: The steepness of the spectrum energy distribution is quantified through fourth-order moment statistics, abnormal frequency bands whose kurtosis values ​​exceed two standard deviations of the historical mean are identified, and the non-stationary noise residue in the signal is evaluated. The abnormal frequency band ratio is required to be less than 5% to confirm the noise suppression effect.

[0090] Envelope spectrum analysis: Perform Hilbert transform on the signal to generate an envelope waveform, extract the amplitude fluctuation of the fundamental frequency of the wheel rotation and its harmonic components, and require the harmonic amplitude fluctuation to be less than 15% of the mean value to verify the filtering effect of periodic mechanical vibration interference.

[0091] Cavitation dominant frequency amplitude distribution map: A three-dimensional spatial distribution map generated by integrating the measured data of sensors and simulation results using the Kriging interpolation algorithm. The amplitude intensity is marked by color scale, and the geometric contour of the flow channel is superimposed to visually display the spatial evolution law of the cavitation vortex band, providing a visual basis for the optimization of modification parameters.

[0092] The specific implementation manner of the present invention is as follows: In the measurement of the amplitude of the pressure pulsation of the crown modification of the runner, first, three-dimensional geometric data of the flow channel are obtained based on three-dimensional laser scanning or a computer-aided design model, including the curvature distribution, the flow channel width, and the local mutation characteristics of the modification area. Through hydrodynamic simulation, the Reynolds-averaged Navier-Stokes equation is combined with the cavitation model to simulate the flow field characteristics under actual working conditions, and the pressure gradient distribution on the surface of the flow channel and the cavitation phase change characteristic parameters are generated. The cavitation phase change characteristic parameters include the cavitation initiation threshold, the void fraction distribution, and the pressure fluctuation amplitude in the vortex core region. The simulation results are exported as a pressure gradient cloud map and cavitation region coordinate data, which are used to guide the subsequent sensor arrangement.

[0093] Based on the simulation results, in the vortex core region, an annular layout strategy is adopted, and high-frequency pressure sensors are evenly arranged circumferentially with the center of the vortex core as the center. The sensor spacing is dynamically adjusted according to the local pressure gradient change rate. When the pressure gradient change rate exceeds the preset threshold, the spacing is reduced to 5% - 10% of the vortex core diameter. At the critical position of the boundary layer separation, sensors are arranged at equal intervals of 1% - 3% of the flow channel length along the axial direction of the flow channel, and the spacing is not greater than one-fourth of the main frequency wavelength of the pressure pulsation. In the region of the modification structure mutation, sensors are densely arranged upstream and downstream according to the curvature radius of the geometric mutation point and the amplitude of the pressure gradient mutation to form a gradient monitoring network. A trigger signal synchronized with the rotation of the runner is generated by an encoder or a Hall sensor, and transient pressure signals are collected in a multi-channel synchronous sampling mode. The sampling frequency is set to more than 5 times the pre-estimated value of the cavitation dominant frequency, and the rotational speed, flow rate, and vibration acceleration parameters are synchronously recorded to generate an original signal dataset with timestamp marks.

[0094] The original signal dataset is processed by periodic segmentation, divided into equal-length signal segments according to the rotation period of the runner, and the influence of rotational speed fluctuation is eliminated. The polynomial fitting algorithm is used to correct the baseline drift, and the low-frequency interference introduced by the zero offset and temperature drift of the sensor is removed. An adaptive band-stop filter is used to filter out the mechanical vibration noise, whose center frequency is dynamically adjusted according to the natural frequency of the runner support structure, and the bandwidth is adaptively optimized according to the vibration energy distribution. The signal is divided into multiple frequency sub-bands through wavelet packet decomposition, the high-frequency random noise sub-band dominated by turbulent pulsation is identified, and the soft threshold function shrinkage processing is used to suppress the noise, and the low-frequency sub-bands related to the characteristics of the cavitation vortex band are retained. The low-frequency sub-bands are filtered by narrow-band pass filters, the passband range is set in combination with the predicted cavitation frequency value of the fluid-structure interaction simulation, the stopband attenuation is not less than 40 dB, and the periodic cavitation component is enhanced by time-domain synchronous superposition averaging to generate the denoised cavitation characteristic signal.

[0095] The short-time Fourier transform is performed on the denoised signal to generate a time-varying amplitude curve, the continuous wavelet transform is performed using the Morlet wavelet basis function, the wavelet ridge line is extracted through the path optimization algorithm, and the cavitation characteristic frequency is identified. Combining the calibration results of the fluid-structure interaction simulation, the measured frequency is aligned with the simulation prediction, and the Kriging interpolation is used to generate the cavitation dominant frequency amplitude distribution map. The amplitude error between the measurement and the simulation is calculated through the frequency point matching algorithm. If the error exceeds the second preset threshold, the cavitation phase change characteristic parameters and the boundary layer separation criterion are adjusted, and the iteration is optimized until the error converges. The historical distribution of the cavitation dominant frequency amplitude of different modification parameters is statistically analyzed, and the parameter combinations with an amplitude reduction of more than 20% and a dominant frequency energy ratio higher than 65% are screened based on the dynamic evaluation threshold. Finally, it is verified through the frequency domain kurtosis that the abnormal frequency band ratio is less than 5%, and the harmonic amplitude fluctuation of the envelope spectrum analysis is lower than 15%, confirming that the signal fidelity and noise suppression effect meet the standards, and a test report including the amplitude distribution map, timestamp data, and optimized parameter set is output, providing a quantitative basis for the runner modification.

[0096] The above implementation method solves the problem of frequency domain aliasing between multi-source noise and effective signals in the measurement of the pressure pulsation of the runner crown modification through a closed-loop process of simulation-guided sensor layout, multi-level signal processing, and dynamic model calibration. Technical linkages are formed through data feedback between each step. For example, the optimization of the sensor layout depends on the feedback of the measured signal, and the calibration of the model parameters drives the modification screening with higher accuracy, meeting the requirements for measurement accuracy and reliability in the field of mechanical system dynamic characteristic testing.

[0097] The present invention solves the problem of amplitude measurement error caused by frequency domain aliasing between multi-source high-frequency noise and effective signals in the measurement of the pressure pulsation of the runner crown modification through the following technical solutions: First, based on hydrodynamic simulation, extract the pressure gradient distribution and cavitation phase change characteristic parameters on the surface of the flow channel to guide the targeted layout of the high-frequency pressure sensor array in the vortex core region, the critical position of boundary layer separation, and the modified structure mutation region. Adopt an annular layout strategy in the vortex core region, and dynamically adjust the sensor spacing according to the local pressure gradient change rate. Arrange the sensors at equal intervals along the axial direction of the flow channel in the boundary layer separation region, and densely arrange them upstream and downstream of the modified mutation region to form a gradient monitoring network. This layout strategy accurately locates the high-frequency noise source and the sensitive region of the effective signal through the closed-loop optimization of simulation data and measured feedback, improving the spatial resolution of signal acquisition.

[0098] Secondly, perform multi-level signal processing on the collected transient pressure signals to separate the frequency-domain aliasing components. Eliminate the low-frequency offset through baseline drift correction, suppress mechanical vibration noise with an adaptive band-stop filter, and separate the turbulent pulsation interference through wavelet packet decomposition and narrowband pass filtering. The retained cavitation characteristic signals are enhanced in periodic components through time-domain synchronous superposition averaging, and the cavitation dominant frequency amplitude characteristics are extracted by combining the short-time Fourier transform and the wavelet ridge tracking algorithm. This processing link effectively strips the frequency band overlap between the high-frequency noise and the dominant frequency of the cavitation vortex band through frequency-domain decomposition, threshold denoising, and time-frequency joint analysis.

[0099] Finally, calibrate the model parameters through fluid-structure interaction simulation, and establish a dynamic error feedback mechanism for measured data and simulation prediction. Compare the cavitation dominant frequency amplitude characteristics with the simulation spectrum, iteratively optimize the cavitation phase change characteristic parameters and the boundary layer separation criterion to make the error converge to the preset threshold. Combine the frequency-domain kurtosis verification and envelope spectrum analysis to confirm the signal fidelity and noise suppression effect, and generate the cavitation dominant frequency amplitude distribution map and the modified parameter set. This solution quantifies the cavitation suppression effect through multi-source data fusion and closed-loop calibration, providing a high-precision basis for the runner modification optimization and meeting the technical requirements of mechanical system dynamic characteristic testing.

Claims

1. A method for measuring the pressure pulsation amplitude of a crown shaping wheel, characterized in that: include: Obtain the three-dimensional geometric data of the crown-shaped flow channel on the runner, and generate the pressure gradient distribution and cavitation phase change characteristic parameters on the flow channel surface by combining fluid dynamics simulation; Based on the pressure gradient distribution and cavitation phase change characteristic parameters, a high-frequency pressure sensor array is arranged on the surface of the runner crown modified flow channel, and the arrangement position of the high-frequency pressure sensor array is dynamically adjusted in the vortex core area, the critical position of boundary layer separation and the modified structure mutation area; Establish a trigger signal synchronized with the runner rotation cycle, collect transient pressure signals of all working conditions under different trimming parameter configurations, synchronously record speed, flow and vibration parameters, and generate a raw signal data set with a timestamp; The original signal data set is processed periodically in segments, low-frequency offset is eliminated by baseline drift correction, mechanical vibration noise is filtered out by an adaptive band-stop filter, and turbulent pulsation noise is separated by wavelet packet decomposition and narrow-bandpass filtering to generate a denoised cavitation characteristic signal; A joint time-frequency domain analysis is performed on the cavitation characteristic signal to extract the amplitude characteristics of the main frequency component of the cavitation vortex. Combined with the amplitude spectrum comparison results after fluid-solid coupling simulation calibration, a modification parameter combination is screened whose pressure pulsation amplitude reduction exceeds a first preset threshold and whose frequency band distribution meets the stability standard.

2. The method for measuring the pressure pulsation amplitude of the crown shaping of a runner according to claim 1, characterized in that: The arrangement of a high-frequency pressure sensor array on the surface of the runner upper crown modified flow channel based on the pressure gradient distribution and the cavitation phase change characteristic parameters, and the dynamic adjustment of the arrangement position of the high-frequency pressure sensor array in the vortex core area, the boundary layer separation critical position and the modified structure mutation area includes: Based on the pressure gradient distribution and cavitation phase change characteristic parameters, an annular layout is adopted in the vortex core area, wherein the sensor spacing is dynamically adjusted according to the simulation data of the local pressure gradient change rate, and the sensors are arranged at equal intervals along the axial direction of the flow channel at the critical position of boundary layer separation.

3. The method for measuring the pressure pulsation amplitude of the crown shaping of a runner according to claim 1, characterized in that: The process of performing periodic segmentation processing on the original signal data set, eliminating low-frequency offset by baseline drift correction, filtering out mechanical vibration noise by using an adaptive band-stop filter, and separating turbulent pulsation noise by wavelet packet decomposition and narrow-bandpass filtering to generate a denoised cavitation characteristic signal includes: Decomposing the original signal data set by wavelet packets, dividing it into multiple frequency sub-bands, and identifying the high-frequency random noise sub-band dominated by turbulent pulsation; The high-frequency random noise sub-band is shrunk by a soft threshold function to retain the low-frequency sub-band related to the cavitation vortex band characteristics; The low frequency sub-band is subjected to narrow band pass filtering, and the periodic cavitation component is enhanced by synchronous superposition and averaging in the time domain.

4. The method for measuring the pressure pulsation amplitude of the crown shaping of a runner according to claim 1, characterized in that: Based on the pressure gradient distribution and cavitation phase change characteristic parameters, an annular layout is adopted in the vortex core area, wherein the sensor spacing is dynamically adjusted according to the simulation data of the local pressure gradient change rate, and the sensors are arranged at equal intervals along the axial direction of the flow channel at the critical position of boundary layer separation, including: Performing short-time Fourier transform on the denoised cavitation characteristic signal to generate a time-varying amplitude curve; Based on the time-varying amplitude curve, a wavelet ridge tracking algorithm is used to identify the cavitation characteristic frequency in the frequency band overlap region; The cavitation characteristic frequency is combined with the fluid-solid coupling simulation calibration result to generate a cavitation main frequency amplitude distribution spectrum.

5. The method for measuring the pressure pulsation amplitude of the crown shaping of a runner according to claim 4, characterized in that: Also includes: Performing an error comparison between the extracted amplitude characteristics of the main frequency of the cavitation vortex and the predicted amplitude in the amplitude spectrum comparison result after the fluid-solid coupling simulation calibration; If the error exceeds a second preset threshold, adjusting the cavitation phase change characteristic parameters and the boundary layer separation criterion of the fluid dynamics simulation model; By iteratively optimizing the cavitation phase change characteristic parameters and the boundary layer separation criterion, the error between the simulation prediction amplitude and the measured amplitude is converged to within the second preset threshold.

6. The method for measuring the pressure pulsation amplitude of the crown shaping of a runner according to claim 5, characterized in that: Also includes: According to the original signal data set with time stamps and the cavitation characteristic signal after denoising with different shaping parameters, the historical distribution of the cavitation main frequency amplitude is counted; A dynamic evaluation threshold is set based on the historical distribution, and the frequency domain energy ratio of the cavitation suppression effect of the modification parameters is calculated; A set of modification parameters whose amplitude reduction exceeds the dynamic evaluation threshold and whose main frequency energy proportion is higher than a third preset proportion is screened out.

7. The method for measuring the pressure pulsation amplitude of the crown shaping of a runner according to claim 6, characterized in that: Also includes: Based on the cavitation main frequency amplitude distribution spectrum, verifying the cavitation characteristic signal after denoising by a frequency domain kurtosis index, and calculating the abnormal frequency band whose frequency domain kurtosis value exceeds a fourth preset threshold; At the same time, an envelope spectrum analysis is performed on the cavitation characteristic signal to extract the amplitude of the harmonic component corresponding to the rotation period of the rotor in the envelope spectrum; According to the verification result that the proportion of the abnormal frequency band is less than 5% and the amplitude fluctuation of the harmonic component is lower than the fifth preset threshold, it is confirmed that the amplitude retention of the cavitation main frequency component is more than 90% and the high-frequency noise suppression effect satisfies the signal-to-noise ratio ≥ 15dB; A test report including the cavitation main frequency amplitude distribution map, the timestamp mark data and the screened modification parameter set is generated, and a target modification parameter set is output.

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