A method for predicting cavitation position of hydraulic machinery blade

Abstract

The present application provides a kind of hydraulic machinery blade cavitation position prediction method, comprising: three-dimensional model is established to blade drainage area, and numerical simulation calculation is carried out to three-dimensional model by introducing cavitation model, and the characteristic frequency of cavitation vortex shedding and cavitation change period are obtained;Based on the characteristic frequency of cavitation vortex shedding and three-dimensional model, monitoring point array distributed along the normal direction of blade surface is constructed;Based on cavitation model, numerical simulation calculation is carried out to monitoring point array, and the cavitation volume fraction of monitoring point array is obtained;Based on the cavitation volume fraction of monitoring point array and cavitation change period, the cavitation erosion intensity of blade surface is obtained, and then the intensity nephogram of blade surface is drawn;Based on the intensity nephogram of blade surface, the cavitation erosion position of blade surface is predicted.The present application is quickly and accurately predicted the cavitation erosion position of hydraulic machinery blade by setting monitoring point array along the normal direction of blade surface, and it has important significance to reduce cavitation erosion damage and improve the service life of hydraulic machinery.

Description

Technical Field

[0001] This invention relates to the field of hydraulic machinery, and more particularly to a method for predicting the location of cavitation erosion on hydraulic machinery blades. Background Technology

[0002] Hydraulic machinery, as a common type of general-purpose machinery, faces the significant challenge of cavitation during operation, particularly in bladed hydraulic machinery. Cavitation occurs when the pressure at a localized location in the liquid drops below its vaporization pressure, triggering a liquid-gas phase transition and generating numerous bubbles. These bubbles then undergo initial formation, development, and collapse in response to pressure changes. Near the blade surface, the shock waves generated by cavitation collapse create honeycomb-like pores, leading to cavitation erosion. This damages the blade material and severely impacts the service life of the hydraulic machinery.

[0003] Currently, computational fluid dynamics methods can be used to perform good numerical simulations of cavitation within a flow, but there is still a lack of fast and accurate methods for predicting the location of cavitation erosion. Summary of the Invention

[0004] The purpose of this invention is to provide a method for predicting the cavitation location of hydraulic machinery blades, which solves the problem that existing technologies cannot quickly and accurately predict the cavitation location of hydraulic machinery blades. Through numerical simulation, the peak-to-peak value of cavitation volume fraction is used to calculate the cavitation intensity of cavitation on the blade surface, thereby quickly and accurately predicting the cavitation location of hydraulic machinery blades. This is of great significance for improving the service life of hydraulic machinery.

[0005] This invention provides a method for predicting the location of cavitation erosion on hydraulic machinery blades, comprising:

[0006] A three-dimensional model was established for the blade flow domain, and a cavitation model was introduced to perform numerical simulation calculations on the three-dimensional model to obtain the characteristic frequency of cavitation vortex shedding and the cavitation change period.

[0007] Based on the characteristic frequency of cavitation vortex shedding, a monitoring array is constructed along the normal direction of the blade surface, wherein the monitoring array includes at least one layer of array;

[0008] Based on the cavitation model, numerical simulation calculations are performed on the monitoring array to obtain the cavitation volume fraction of the monitoring array.

[0009] Based on the cavitation volume fraction of the monitoring array and the cavitation change period, the cavitation erosion intensity of the blade surface is obtained, and then an intensity cloud map of the blade surface is drawn.

[0010] Based on the intensity cloud map of the blade surface, the location of cavitation on the blade surface is predicted.

[0011] According to the method for predicting the location of cavitation erosion on hydraulic machinery blades provided by the present invention, the step of constructing a monitoring array distributed along the normal direction of the blade surface based on the characteristic frequency of cavitation vortex shedding includes:

[0012] Based on the characteristic frequency of cavitation vortex shedding, a lattice layer of monitoring points at different distances from the blade surface is formed along the normal direction of the blade surface; wherein, the density of the monitoring point lattice is given according to the following empirical formula:

[0013]

[0014] Among them, L P c is the average distance between two adjacent points in the lattice; s V is an empirical coefficient; ∞ f is the circumferential velocity of the impeller's outer diameter. cav This represents the characteristic frequency of cavitation vortex shedding.

[0015] According to the method for predicting the location of cavitation erosion on hydraulic machinery blades provided by the present invention, the step of obtaining the cavitation erosion intensity on the blade surface based on the cavitation volume fraction of the monitoring array and the cavitation change period, and then drawing an intensity cloud map of the blade surface, includes:

[0016] Based on the cavitation volume fraction of the monitoring array, for any monitoring point of the monitoring array, any cavitation change cycle is selected, and the cavitation change cycle is divided into at least one time period.

[0017] Calculate the peak-to-peak value of the cavitation volume fraction for each time period; wherein, the peak-to-peak value of the cavitation volume fraction is the difference between the maximum and minimum values ​​of the cavitation volume fraction for each time period;

[0018] Based on the peak-to-peak value of the cavitation volume fraction within each time period, the average peak-to-peak value of the cavitation volume fraction peak-to-peak value is obtained;

[0019] The average peak-to-peak value of each monitoring point is projected onto the blade surface along the normal direction and superimposed to obtain the cavitation erosion intensity of the blade surface.

[0020] Based on the cavitation intensity of the blade surface, an intensity cloud map of the blade surface is drawn.

[0021] According to the method for predicting the cavitation location of hydraulic machinery blades provided by the present invention, the step of projecting the average peak-to-peak value of each monitoring point along the normal direction of the blade surface onto the blade surface and superimposing them to obtain the cavitation intensity of the blade surface includes:

[0022] Calculate the equivalent intensity of the average peak-to-peak value of each monitoring point projected onto the blade surface along the normal direction of the blade surface;

[0023] The cavitation intensity of the blade surface is obtained by superimposing the equivalent intensity of the average peak-to-peak value of each monitoring point projected along the normal of the blade surface onto the blade surface.

[0024] This invention provides a method for predicting the location of cavitation erosion on hydraulic machinery blades. A three-dimensional model of the blade's flow domain is established, and a cavitation model is introduced to perform numerical simulation calculations on the three-dimensional model to obtain the characteristic frequency of cavitation vortex shedding and the cavitation variation period. Based on the cavitation vortex shedding characteristic frequency, a monitoring array distributed along the normal direction of the blade surface is constructed, wherein the monitoring array includes at least one layer. Based on the cavitation model, numerical simulation calculations are performed on the monitoring array to obtain the cavitation volume fraction of the monitoring array. Based on the cavitation volume fraction of the monitoring array and the cavitation variation period, the cavitation erosion intensity of the blade surface is obtained, and an intensity cloud map of the blade surface is then plotted. Based on the intensity cloud map of the blade surface, the location of cavitation erosion on the blade surface is predicted. This invention, through numerical simulation, sets up a monitoring array along the normal direction of the blade surface, and uses the peak-to-peak superposition of the cavitation volume fraction to calculate the intensity of the cavitation effect on the blade surface, quickly and accurately predicting the location of cavitation erosion on hydraulic machinery blades. This is of great significance for reducing cavitation erosion damage and improving the service life of hydraulic machinery. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating the method for predicting the cavitation location of hydraulic machinery blades provided by the present invention.

[0027] Figure 2 This is a schematic diagram of the cross-section of a hydraulic impeller blade provided by the present invention;

[0028] Figure 3 These are the different normal distance positions on the blade surface provided by this invention;

[0029] Figure 4 (a) is a schematic diagram of the cavitation vortex shedding characteristic frequency analysis provided by the present invention;

[0030] Figure 4 (b) is a schematic diagram of the cavitation change cycle analysis provided by the present invention;

[0031] Figure 5 This is a schematic diagram of the monitoring point array arrangement provided by the present invention;

[0032] Figure 6It is an intensity cloud map of the blade surface. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] The following is combined with Figures 1-6 The present invention describes a method for predicting the location of cavitation erosion on hydraulic machinery blades.

[0035] In one specific embodiment, Figure 1 The flowchart of the method for predicting the cavitation location of hydraulic machinery blades provided by the present invention is as follows: Figure 1 As shown, the method for predicting the cavitation location of hydraulic machinery blades provided by the present invention includes:

[0036] Step S110: Establish a three-dimensional model for the blade flow area, and introduce a cavitation model to perform numerical simulation calculations on the three-dimensional model to obtain the characteristic frequency of cavitation vortex shedding and the cavitation change period.

[0037] Step S120: Based on the cavitation vortex shedding characteristic frequency, construct a monitoring array distributed along the normal direction of the blade surface, wherein the monitoring array includes at least one layer of array;

[0038] Step S130: Based on the cavitation model, perform numerical simulation calculations on the monitoring array to obtain the cavitation volume fraction of the monitoring array;

[0039] Step S140: Based on the cavitation volume fraction of the monitoring array and the cavitation change period, the cavitation erosion intensity of the blade surface is obtained, and then an intensity cloud map of the blade surface is drawn.

[0040] Step S150: Based on the intensity cloud map of the blade surface, predict the cavitation location on the blade surface.

[0041] In this embodiment, the blade flow domain refers to the entire area through which the flowing medium passes from the inlet to the outlet of the turbomachinery. Mesh discretization is a widely used method in this technical field and a necessary prerequisite for numerical simulation calculations in subsequent steps; therefore, it will not be elaborated upon further in this application. The cavitation model refers to the modeling of the phase change of the fluid medium in numerical simulation calculations. The monitoring point array is a set of monitoring points composed of a large number of monitoring points, which can monitor the changes in specific physical quantities at each monitoring point location in the blade flow domain over time in real time, intuitively reflecting the flow conditions in the blade flow domain. The cavitation volume fraction refers to the proportion of gas phase per unit volume, with a value range of (0, 1); the larger the value, the more severe the cavitation. The intensity cloud map refers to the cavitation intensity cloud map obtained by superimposing equivalent cavitation intensities on the blade surface.

[0042] In this embodiment, a three-dimensional model of the blade flow domain is constructed, and the three-dimensional model is discretized to obtain a discrete mesh. A cavitation model is introduced, and hydrodynamic numerical simulation calculations are performed on the discrete mesh to obtain the cavitation volume fraction of the discrete mesh. The frequency characteristics of the cavitation volume fraction are obtained through Fourier transform. Based on the dominant frequency of the frequency characteristics of the cavitation volume fraction, the characteristic frequency of cavitation vortex shedding is obtained. The reciprocal of the characteristic frequency of cavitation vortex shedding is calculated to obtain the cavitation variation period. Specifically, this includes obtaining the cavitation volume fraction α based on the previous hydrodynamic numerical simulation calculation results. cav The variation over time was analyzed, and the cavitation volume fraction α was obtained using a fast Fourier transform. cav Frequency variation characteristics, to find cavitation volume fraction α cav The dominant frequency of the frequency variation characteristics, and the cavitation volume fraction α cav The characteristic frequency f of cavitation vortex shedding is obtained by analyzing the dominant frequency. cav Calculate the characteristic frequency f of cavitation vortex shedding. cav The reciprocal of the cavitation change period T is obtained. cav Then, based on the characteristic frequency of cavitation vortex shedding and the three-dimensional model, a monitoring lattice consisting of at least one layer of points distributed along the normal direction of the blade surface is constructed. Based on the cavitation model, numerical simulation calculations are performed on the monitoring lattice to obtain the cavitation volume fraction of all monitoring points in the lattice. Based on the cavitation volume fraction of all monitoring points in the lattice and the known cavitation change period, the cavitation erosion intensity of the blade surface is obtained, and then an intensity cloud map of the blade surface is drawn. Based on the intensity cloud map of the blade surface, the cavitation erosion location on the blade surface is determined.

[0043] This invention provides a method for predicting the location of cavitation erosion on hydraulic machinery blades. The method involves establishing a three-dimensional model of the blade's flow domain and discretizing the model into a discrete mesh. A cavitation model is introduced, and numerical simulations are performed on the discrete mesh to obtain the characteristic frequency of cavitation vortex shedding and the cavitation variation period. Based on the cavitation vortex shedding characteristic frequency and the three-dimensional model, at least one layer of monitoring points distributed along the blade surface normal is constructed. Based on the cavitation model, numerical simulations are performed on the monitoring points to obtain the cavitation volume fraction of the monitoring points. Based on the cavitation volume fraction and cavitation variation period of the monitoring points, the cavitation erosion intensity of the blade surface is obtained, and an intensity cloud map of the blade surface is plotted. Based on the intensity cloud map of the blade surface, the location of cavitation erosion on the blade surface is determined. This invention, through numerical simulation, sets up a monitoring point array along the blade surface normal, and uses the cavitation volume fraction of the monitoring points to plot the intensity cloud map of the blade surface, thereby quickly and accurately predicting the location of cavitation erosion on hydraulic machinery blades. This is of great significance for reducing cavitation erosion damage and improving the service life of hydraulic machinery.

[0044] In one specific embodiment, according to the hydraulic machinery blade cavitation location prediction method provided by the present invention, the step of constructing a monitoring array distributed along the normal direction of the blade surface based on the characteristic frequency of cavitation vortex shedding includes:

[0045] Based on the characteristic frequency of cavitation vortex shedding, a lattice layer of monitoring points at different distances from the blade surface is formed along the normal direction of the blade surface; wherein, the density of the monitoring point lattice is given according to the following empirical formula:

[0046]

[0047] Among them, L P c is the average distance between two adjacent points in the lattice; s V is an empirical coefficient; ∞ f is the circumferential velocity of the impeller's outer diameter. cav This represents the characteristic frequency of cavitation vortex shedding.

[0048] In this embodiment, the density of the monitoring dot matrix is ​​L, which is the average distance L between two adjacent dots in the dot matrix. P To represent; c s This is an empirical coefficient, typically ranging from 0.1 to 10.

[0049] In this embodiment, a structured monitoring array is set along the normal direction of the hydraulic machinery impeller blade, covering different distances h from the blade surface. Generally, several different h positions are selected to establish the monitoring array, and h is related to the maximum blade thickness t. b Regarding this, h = 0.2t is generally chosen. b h = 0.4t b h = 0.6t b h = 0.8t band h = 1.0t b The height of the monitoring points is used to set up a 5-layer monitoring array. The specific density of the monitoring points is given by the empirical formula mentioned above, and will not be repeated here.

[0050] It should be emphasized that, in this application, the average distance L between two adjacent points in the monitoring array P Specifically, this refers to the spacing between two adjacent points in the lattice layer on the blade surface when the distance from the blade surface is h = 0. In other words, when the distance from the blade surface is 0, the monitoring layer on the blade surface only sets points and does not take values; this lattice layer on the blade surface is only used to calibrate the density of the entire monitoring lattice.

[0051] This invention provides a method for predicting the location of cavitation erosion on hydraulic machinery blades. By further clarifying the specific path for obtaining the monitoring point array, it strongly supports the rapid and accurate prediction of the location of cavitation erosion on hydraulic machinery blades, which is of great significance for reducing cavitation erosion damage and improving the service life of hydraulic machinery.

[0052] In one specific embodiment, according to the hydraulic machinery blade cavitation location prediction method provided by the present invention, the step of obtaining the cavitation intensity of the blade surface based on the cavitation volume fraction of the monitoring array and the cavitation change period, and then drawing an intensity cloud map of the blade surface, includes:

[0053] Based on the cavitation volume fraction of the monitoring array, for any monitoring point of the monitoring array, any cavitation change cycle is selected, and the cavitation change cycle is divided into at least one time period.

[0054] Calculate the peak-to-peak value of the cavitation volume fraction for each time period; wherein, the peak-to-peak value of the cavitation volume fraction is the difference between the maximum and minimum values ​​of the cavitation volume fraction for each time period;

[0055] Based on the peak-to-peak value of the cavitation volume fraction within each time period, the average peak-to-peak value of the cavitation volume fraction peak-to-peak value is obtained;

[0056] The average peak-to-peak value of each monitoring point is projected onto the blade surface along the normal direction and superimposed to obtain the cavitation erosion intensity of the blade surface.

[0057] Based on the cavitation intensity of the blade surface, an intensity cloud map of the blade surface is drawn.

[0058] In this embodiment, the cavitation volume fraction α based on the monitoring lattice is... cav For any monitoring point in the monitoring matrix, select any cavitation change period T. cav , a cavitation change period T cavThe system is divided into several time periods n (n is a positive integer); the peak-to-peak value of the cavitation volume fraction in each time period is calculated, which is the difference between the maximum and minimum cavitation volume fraction values ​​in each time period; based on the peak-to-peak value of the cavitation volume fraction in each time period, the average peak-to-peak value Δα of the cavitation volume fraction peak-to-peak value is obtained. cav The average peak-to-peak value Δα at each monitoring point cav The cavitation erosion intensity of the blade surface is obtained by projecting the cavitation erosion intensity along the normal direction onto the blade surface and then superimposing the projections. Based on the cavitation erosion intensity, an intensity contour map of the blade surface is plotted. The average peak-to-peak value Δα of the cavitation volume fraction peak-to-peak value is also included. cav The calculation formula is as follows:

[0059]

[0060] This invention provides a method for predicting the location of cavitation erosion on hydraulic machinery blades. By further disclosing the specific implementation path of drawing an intensity cloud map of the blade surface using the cavitation volume fraction of the monitoring array and the cavitation change period, this invention strongly supports the rapid and accurate prediction of the location of cavitation erosion on hydraulic machinery blades, which is of great significance for reducing cavitation erosion damage and improving the service life of hydraulic machinery.

[0061] In one specific embodiment, according to the method for predicting the cavitation location of hydraulic machinery blades provided by the present invention, the step of projecting the average peak-to-peak value of each monitoring point along the normal direction of the blade surface onto the blade surface and superimposing them to obtain the cavitation intensity of the blade surface includes:

[0062] Calculate the equivalent intensity of the average peak-to-peak value of each monitoring point projected onto the blade surface along the normal direction of the blade surface;

[0063] The cavitation intensity of the blade surface is obtained by superimposing the equivalent intensity of the average peak-to-peak value of each monitoring point projected along the normal of the blade surface onto the blade surface.

[0064] In this embodiment, the equivalent intensity E of the average peak-to-peak value of each monitoring point projected onto the blade surface along the normal direction of the blade surface is... ce The functional relationship between the distance h from the corresponding monitoring point to the blade surface is as follows:

[0065]

[0066] Among them, E ce For the equivalent strength, k is a constant, Δα cav t represents the average peak-to-peak value of the cavitation volume fraction peak, h is the distance from the monitoring point along the normal direction to the blade surface, and t is the average peak-to-peak value of the ... cavitation volume fraction peak. b This represents the maximum thickness of the blade.

[0067] The equivalent intensity E of projecting the average peak-to-peak value of each monitoring point along the normal direction of the blade surface onto the blade surface is calculated. ce The total cavitation intensity of the blade surface is obtained by superimposing the results.

[0068] This invention provides a method for predicting the location of cavitation erosion on hydraulic machinery blades. By further disclosing the equivalent intensity of the average peak-to-peak value of each monitoring point projected along the normal direction of the blade surface onto the blade surface and superimposing them, the cavitation erosion intensity of the blade surface is obtained. This method strongly supports the rapid and accurate prediction of the location of cavitation erosion on hydraulic machinery blades and is of great significance for reducing cavitation erosion damage and improving the service life of hydraulic machinery.

[0069] To better illustrate the technical solution of this application, such as Figure 2-6 As shown, Figure 2 In the diagram, S represents the streamline direction, and P represents the blade span direction. Figure 3 The dashed line represents the normal direction, where h = 0.2t. b 0.4t b 0.6t b 0.8t b and 1.Ot b The height position; Figure 4 (a) Displays the cavitation volume fraction α at a specific monitoring point. cav The pattern of change with time T Figure 4 (b) Display the cavitation volume fraction α at a specific monitoring point. cav The frequency variation characteristics of f; Figure 5 (a) is a schematic diagram of the cavitation monitoring array layout, and (b), (c), (d) and (e) are schematic diagrams of the monitoring array monitoring the occurrence, development and shedding of cavitation vortices. The gray area in the figure is the cavitation coverage area at different times. Figure 6 The intensity of cavitation erosion is displayed by the strength of grayscale. The following section, using a water turbine as an example, details the process of predicting the location of cavitation erosion on hydraulic machinery blades, based on actual values:

[0070] 1. For example Figure 2 As shown, a three-dimensional model of the turbine impeller flow domain is established, with the blade length set to 100 mm, the blade span width to 75 mm, and the maximum blade thickness to be t. b The mesh size is 9mm, and the 3D model is discretized to obtain a discrete mesh.

[0071] 2. Computational fluid dynamics numerical simulations were performed on the impeller flow domain, which had been established in three dimensions and discretized by mesh, and the Zwart cavitation model was added for numerical simulation calculations.

[0072] 3. For example Figure 4 As shown, based on the numerical simulation results in step 2, the cavitation volume fraction α at each monitoring point is obtained.cav The cavitation volume fraction α was obtained by using a fast Fourier transform to measure the change over time T, where T = 0.3 s. cav Frequency characteristics, to find the cavitation volume fraction α cav The dominant frequency of the frequency characteristics is obtained from the cavitation vortex shedding characteristic frequency f. cav f cav =10H z Calculate f cav The reciprocal of the cavitation change period T is obtained. cav T cav =0.1s.

[0073] 4. For example Figure 3 As shown, a structured monitoring array with different distances along the normal direction is formed on the surface of the turbine impeller blades, typically with h = 0.2t. b h = 0.4t b h = 0.6t b h = 0.8t b and h = 1.0t b At the height position, i.e., setting up a 5-layer monitoring array, t b Substituting 9mm, we get h = 1.8mm, h = 3.6mm, h = 5.4mm, h = 7.2mm, and h = 9.0mm. The monitoring lattice density is given according to an empirical formula:

[0074]

[0075] Among them, L P c is the average distance between two adjacent points in the lattice; s Let c be the empirical coefficient. s =0.2; V ∞ Let V be the circumferential velocity of the impeller's outer diameter. ∞ =1m / s; f cav f is the characteristic frequency of cavitation vortex shedding. cav =10H z L can be obtained P =3.2mm.

[0076] 5. Continue to conduct two cavitation cycles T cav Unsteady calculations exceeding time limits are required, along with the cavitation volume fraction α at each monitoring point in the monitoring array. cav Monitoring was conducted, and the last cavitation cycle T was selected. cav The analysis was conducted to determine the cavitation period T. cav The data is divided into 10 time periods (n=10). The peak-to-peak value of the cavitation volume fraction is calculated for each time period. Finally, the average peak-to-peak value Δαcav of the cavitation volume fraction over the 10 time periods is calculated, as is the average peak-to-peak value Δα of the cavitation volume fraction peak-to-peak value.cav The calculation formula is as follows:

[0077]

[0078] 6. Calculate the average peak-to-peak value Δα for each monitoring point. cav The cavitation erosion intensity is obtained by superimposing the values ​​from monitoring points at each layer onto the blade surface along the normal direction. During projection, the equivalent intensity E of the average peak-to-peak value of each monitoring point projected onto the blade surface along the normal direction is obtained. ce The functional relationship between the distance h from the corresponding monitoring point to the blade surface is as follows:

[0079]

[0080] Among them, E ce For the equivalent strength, k is a constant, taken as k = 0.15, Δα cav t represents the average peak-to-peak value of the cavitation volume fraction peak, h is the distance from the monitoring point along the normal direction to the blade surface, and t is the average peak-to-peak value of the ... cavitation volume fraction peak. b This represents the maximum thickness of the blade.

[0081] 7. For example Figure 5 and Figure 6 As shown, based on the equivalent intensity of cavitation obtained in step 6, an intensity cloud map of the blade surface is drawn, and the magnitude of the intensity value is evaluated to determine the location of cavitation.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the location of cavitation erosion on hydraulic machinery blades, characterized in that, include: A three-dimensional model was established for the blade flow domain, and a cavitation model was introduced to perform numerical simulation calculations on the three-dimensional model to obtain the characteristic frequency of cavitation vortex shedding and the cavitation change period. Based on the characteristic frequency of cavitation vortex shedding, a monitoring array is constructed along the normal direction of the blade surface, wherein the monitoring array includes at least one layer of array; Based on the cavitation model, numerical simulation calculations are performed on the monitoring array to obtain the cavitation volume fraction of the monitoring array. Based on the cavitation volume fraction of the monitoring array and the cavitation change period, the cavitation erosion intensity of the blade surface is obtained, and then an intensity cloud map of the blade surface is drawn. Based on the intensity cloud map of the blade surface, the location of cavitation on the blade surface is predicted. The cavitation erosion intensity of the blade surface is obtained based on the cavitation volume fraction of the monitoring array and the cavitation change period, and then an intensity cloud map of the blade surface is drawn, including: Based on the cavitation volume fraction of the monitoring array, for any monitoring point of the monitoring array, any cavitation change cycle is selected, and the cavitation change cycle is divided into at least one time period. Calculate the peak-to-peak value of the cavitation volume fraction for each time period; wherein, the peak-to-peak value of the cavitation volume fraction is the difference between the maximum and minimum values ​​of the cavitation volume fraction for each time period; Based on the peak-to-peak value of the cavitation volume fraction within each time period, the average peak-to-peak value of the cavitation volume fraction peak-to-peak value is obtained; The average peak-to-peak value of each monitoring point is projected onto the blade surface along the normal direction and superimposed to obtain the cavitation erosion intensity of the blade surface. Based on the cavitation erosion intensity of the blade surface, an intensity contour map of the blade surface is plotted; wherein, the average peak-to-peak value of the cavitation volume fraction peak-to-peak value is... The calculation formula is as follows: ； in, It is a positive integer. To find the maximum cavitation volume fraction within time period i, To find the minimum cavitation volume fraction within time period i; The step of projecting the average peak-to-peak value of each monitoring point onto the blade surface along the normal direction and superimposing them to obtain the cavitation erosion intensity of the blade surface includes: Calculate the equivalent intensity of the average peak-to-peak value of each monitoring point projected onto the blade surface along the normal direction of the blade surface; The cavitation erosion intensity of the blade surface is obtained by superimposing the equivalent intensity of the average peak-to-peak value of each monitoring point projected along the normal of the blade surface onto the blade surface. The equivalent intensity of the average peak-to-peak value of each monitoring point projected onto the blade surface along the normal direction. Distance from the corresponding monitoring point to the blade surface The functional relationship is presented as follows: ； in, For equivalent strength, It is a constant. The average peak-to-peak value of the cavitation volume fraction peak-to-peak value. To monitor the distance from the monitoring point along the normal direction to the blade surface, This represents the maximum thickness of the blade.

2. The method for predicting the cavitation location of hydraulic machinery blades according to claim 1, characterized in that, The construction of a monitoring array distributed along the normal direction of the blade surface based on the characteristic frequency of cavitation vortex shedding includes: Based on the characteristic frequency of cavitation vortex shedding, a lattice layer of monitoring points at different distances from the blade surface is formed along the normal direction of the blade surface; wherein, the density of the monitoring point lattice is given according to the following empirical formula: ； in, This represents the average distance between two adjacent points in the dot matrix; This is an empirical coefficient; The circumferential velocity of the impeller's outer diameter; This represents the characteristic frequency of cavitation vortex shedding.

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