Method for obtaining centrifugal pump blade load distribution curve

Abstract

The invention relates to the technical field of pump body structure design and optimization, in particular to a method for acquiring a centrifugal pump blade load distribution curve, which comprises the following steps of: recording the stress distribution condition of a blade under the action of alternating load in real time through a high-precision multi-channel strain gauge and a laser interference measurement technology; a scanning electron microscope, a transmission electron microscope and an X-ray diffraction technology are combined to analyze microstructure characteristics of a composite material and metal bonding interface; through comparison of experimental data and finite element analysis results, deviation is quantified, error analysis is carried out, and key microstructure parameters influencing load distribution are identified. According to the analysis result, experimental conditions and a finite element model are optimized, a design scheme for adjusting the interlayer thickness of the composite material and optimizing the metal treatment process is provided, and the load bearing capacity and durability of the blade are improved. The method can be widely applied to design and optimization of high-performance centrifugal pump blades.

Description

Technical Field

[0001] The invention relates to the technical field of pump body structure design and optimization, and in particular to a method for obtaining a load distribution curve of a centrifugal pump blade. Background Art

[0002] Centrifugal pumps are widely used in industrial fields such as petroleum, chemical, and electric power. Their efficient operation and reliability are crucial to the performance of the entire system. Among the core components of centrifugal pumps, the structural design and material selection of the blades directly affect the stress distribution and strength performance when subjected to alternating loads, which in turn determines the overall working efficiency and service life of the pump. With the improvement of modern industry's requirements for equipment performance, centrifugal pump blades are increasingly using heterogeneous structures combining composite materials and metals to optimize the blade's load-bearing capacity and fatigue resistance. However, since the blades are subjected to complex alternating loads during operation, especially the complex stress transfer and load distribution changes at the interface between composite materials and metals, how to effectively obtain the load distribution of the blades and optimize the blade structure design has become a difficult problem in the current technical field.

[0003] Existing technologies usually rely on finite element analysis to simulate and predict the stress distribution of blades, but these methods face some significant problems. First, there is a deviation between the simulated data and the stress distribution of blades under actual working conditions. Especially under high load and high frequency working conditions, the assumptions of the finite element analysis model often cannot fully reflect the true stress distribution of the blades. Secondly, although the existing experimental methods can measure the stress distribution through strain gauges, due to the lack of accurate detection methods for the microstructure of the interface between composite materials and metals, it is difficult to reveal the impact of microstructural changes on the overall strength and load distribution of the blades. In addition, the deviation analysis of experimental data and simulation results lacks a systematic optimization method, resulting in a large error between the experimental results and the simulation predictions, which cannot effectively guide the structural optimization design of the blades. Summary of the invention

[0004] The invention provides a method for obtaining a load distribution curve of a centrifugal pump blade.

[0005] A method for obtaining a centrifugal pump blade load distribution curve comprises the following steps:

[0006] S1, setting of experimental conditions and preparation of blade materials: selecting the blade to be tested, the blade material is a heterogeneous material blade composed of thermoplastic carbon fiber reinforced plastic and metal material, the experimental condition setting includes the working environment parameters of the blade, the environmental parameters include the temperature range, pressure environment, speed range, transmission medium type and experimental duration of the blade, and the application method of the alternating load is determined based on the experimental conditions;

[0007] S2, interface microstructure detection: The microstructure of the blade composite material and metal bonding interface is analyzed by scanning electron microscopy and transmission electron microscopy, and the microstructure data of the carbon fiber and metal bonding at the interface are obtained and recorded. The microstructure data includes fiber arrangement, interface bonding state, crack propagation path and interface phase distribution characteristics. Combined with X-ray diffraction technology, the chemical composition of the interface area is analyzed, the thickness of the metal surface oxide layer is identified, and the influencing factors of the interface mechanical properties are revealed by detecting the changes in the crystal structure (such as the lattice matching between the metal and the composite material);

[0008] S3, load application and stress detection: Through high-precision multi-channel strain gauges and laser interferometry measurement technology, the stress distribution of the blade under the action of alternating loads is recorded in real time. The load is applied step by step according to the preset alternating force, and the loading frequency and amplitude are controlled by computer;

[0009] S4, load distribution data collection and analysis: Through the data acquisition system, the stress data of the blade under different working conditions are collected in real time. Based on the collected stress data, the finite element analysis method is used to analyze the blade load distribution and generate the blade load distribution curve under different loading frequencies and amplitudes;

[0010] S5, correlation analysis between interface change and blade load: comparing the microstructure data obtained in step S2 with the load distribution curve generated in step S4, establishing a relationship model between microstructure change and load distribution, and determining the influence of microstructure on load distribution and blade strength through statistical and mechanical analysis methods;

[0011] S6, result verification and optimization: Compare the load distribution curve obtained in the experiment with the existing finite element analysis simulation results to verify the accuracy of the experimental measurement data and the effectiveness of the method. Evaluate the deviation between the experimental data and the simulation data through error analysis, and propose an optimization plan based on the analysis results.

[0012] Optionally, the S1 specifically includes:

[0013] S11, blade material selection: according to the particularity of the centrifugal pump working environment, select the ratio of composite materials to metals. The composite material is carbon fiber reinforced plastic, and the metal material is high-strength titanium alloy. The thickness ratio of the materials is determined in combination with different working environments and requirements.

[0014] S12, temperature and pressure setting: the control accuracy of the experimental temperature is ±1°C, the temperature is set between -40°C and 150°C, the experimental pressure is controlled by a pressure sensor with an accuracy of 0.01MPa. Combined with the operating conditions of the centrifugal pump, the pressure setting range is 0.1MPa to 5MPa;

[0015] S13, speed setting and control: Use high-speed rotating equipment to simulate the speed in actual operation, and accurately control it through the frequency converter to simulate the operating status under various working conditions;

[0016] S14, selection of transmission medium: the experimental medium is liquid water or air, and the temperature of the transmission medium matches the experimental set temperature;

[0017] S15, Experiment time control: The control accuracy of the experiment time is ±1 minute, and the experiment duration is 1 hour to 10 hours. Select the appropriate experiment time according to different working conditions.

[0018] Optionally, the S2 specifically includes:

[0019] S21, Scanning electron microscope fiber arrangement observation: Use scanning electron microscope to perform high-resolution imaging of carbon fiber arrangement in the interface between composite materials and metals. The parameters of fiber arrangement include the depth of fiber embedding in the metal matrix, the distance and angle between fibers, and quantitative analysis of fiber arrangement is performed using image analysis software;

[0020] S22, Transmission electron microscopy interface bonding state analysis: The bonding quality of the composite material and metal interface is analyzed in depth by transmission electron microscopy to observe the bonding state of the microstructure, including whether there are gaps, cracks, unbonded areas or other microscopic defects on the interface;

[0021] S23, crack propagation path observation: Combined with the observation results of scanning electron microscopy and transmission electron microscopy, the crack propagation path at the interface is analyzed, the starting point, propagation direction and crack length of the crack are recorded, the crack propagation behavior in different areas of the interface is analyzed, and the relationship between the crack propagation path and the interface stress distribution is used to determine the influence of the interface bonding state on the crack propagation;

[0022] S24, X-ray diffraction crystal structure detection: Use X-ray diffraction technology to analyze the crystal structure of the interface area, determine the lattice constant of the composite material and the metal at the interface through the displacement and intensity change of the diffraction peak, and calculate the lattice matching degree;

[0023] S25, metal surface oxide layer thickness detection: analyze the thickness of the oxide layer on the metal surface by X-ray diffraction technology.

[0024] Optionally, the S3 includes:

[0025] S31, setting and applying alternating load: the load is applied by a computer-controlled loading system, and the amplitude and frequency of the alternating load are set according to the actual working conditions of the blade;

[0026] S32, strain gauge arrangement and stress sensing: high-precision multi-channel strain gauges are arranged in key areas of the blade (such as the root, middle and top of the blade) to detect the stress distribution on the blade surface and inside in real time;

[0027] S33, Application of laser interferometry technology: Laser interferometer is used to monitor the stress distribution on the blade surface in a non-contact manner. The laser interferometer accurately detects the tiny deformation of the blade surface through the principle of beam reflection and interference, and converts it into stress data;

[0028] S34, data acquisition and processing: The stress data measured by the strain gauge and laser interferometer are recorded in real time through a computer-controlled multi-channel data acquisition system;

[0029] S35, stress conduction path analysis: Based on the blade structure and material properties, a blade internal stress conduction path model is established through the analysis of stress distribution data to analyze the stress distribution of the blade under different loads and frequencies.

[0030] Optionally, the S4 includes:

[0031] S41, data acquisition system settings: a high-precision multi-channel data acquisition system is used, the system includes a multi-channel strain gauge data acquisition module and signal processing software, and the multi-channel data acquisition system is synchronously controlled with the experimental platform;

[0032] S42, real-time collection of stress data: during the load application process, real-time collection of stress data at various key positions of the blade (such as the root, middle and top of the blade);

[0033] S43, data processing and filtering: using signal processing software to process the collected stress data in real time;

[0034] S44, establishing a finite element analysis model: constructing a finite element analysis model of the blade based on the collected stress data;

[0035] S45, generating a load distribution curve: simulating and calculating the stress data of the blade by a finite element analysis method, and generating a blade load distribution curve under different loading frequencies and amplitudes.

[0036] Optionally, the S5 includes:

[0037] S51, preliminary comparative analysis of microstructure data and load distribution curve: preliminary comparison of the microstructure data obtained in step S2 with the load distribution curve generated in step S4, analyzing the changes of the interface between the composite material and the metal under different load conditions, and combining the lattice matching degree obtained in the X-ray diffraction data to determine the correspondence between the stress concentration area at the interface and the microstructure characteristics. The main goal of this step is to identify the key microstructure parameters that affect the load distribution;

[0038] S52, data normalization and quantitative analysis: In order to ensure the validity of data comparison, the microstructure data and the load distribution curve data are normalized. The fiber arrangement, crack propagation path and interface bonding state parameters are quantified through the normalized data. The correlation analysis method in statistics (such as Pearson correlation coefficient) is used to determine the correlation between each microstructure parameter and the blade load distribution.

[0039] S53, key parameter identification and model optimization: Based on comparison and correlation analysis, the microstructural parameters that have the most significant impact on the blade load distribution (such as fiber arrangement density, crack length, etc.) are identified, and based on the identified parameters, a preliminary relationship model between microstructural changes and load distribution is constructed.

[0040] Optionally, S5 further includes:

[0041] S54, preliminary correlation analysis between blade strength and microstructure data: combining the microstructure data obtained in step S2, using a mechanical analysis method to analyze the strength of the blade, simulating the stress and strain distribution of the blade by a finite element analysis method, and performing a preliminary correlation analysis between the microstructure data (including fiber arrangement, interface bonding state, and crack propagation path) and the blade strength;

[0042] S55, establishment of nonlinear regression model of strength data: based on the correlation between blade strength and microstructure data, a nonlinear regression model is constructed to describe the relationship between blade strength and microstructure;

[0043] S56, Sensitivity analysis of microstructural changes on blade strength: In order to further quantify the influence of microstructural parameters on blade strength, a sensitivity analysis was performed and the partial derivative method was used to calculate the partial derivatives of blade strength with respect to fiber arrangement density and crack length.

[0044] Optionally, the S6 includes:

[0045] S61, comparative analysis of experimental data and simulation results: Compare the blade load distribution curve obtained during the experiment with the stress distribution simulation results generated by the finite element analysis software in the early stage of design. The comparison items include stress distribution under different loading frequencies and amplitudes, stress concentration areas and load transfer paths. Through curve comparison, identify the differences between the experimental data and the simulation results in terms of stress peak value, stress distribution shape and stress transfer path;

[0046] S62, quantification of the deviation between experimental data and simulation results: quantify the deviation between experimental data and simulation results using the deviation formula;

[0047] S63, Deviation Source Analysis: Analyze the source of the deviation based on the deviation quantification results.

[0048] Optionally, S6 further includes:

[0049] S64, Optimization and adjustment of experimental conditions: If the deviation mainly comes from the experimental conditions, optimize the experimental settings;

[0050] S65, optimization of finite element analysis model: if the deviation mainly comes from the finite element analysis model, the model is optimized according to the deviation analysis results;

[0051] S66, Proposing an optimization plan: Based on the comparison results of experimental data and simulation data, a specific optimization plan for blade design is proposed;

[0052] S67, result verification and iterative optimization: Based on the optimized experimental conditions and finite element model, the experiment and simulation analysis are repeated and a new load distribution curve is generated.

[0053] Beneficial effects of the present invention:

[0054] The present invention systematically detects the microstructural characteristics of the interface between composite materials and metals, including fiber arrangement, interface bonding state, crack propagation path, and crystal structure changes, by combining scanning electron microscopy, transmission electron microscopy, and X-ray diffraction technology. The quantitative comparative analysis of the microstructural data obtained by these precision instruments and the load distribution curve can accurately reveal the impact of microstructural changes on the stress distribution and strength of the blades, providing accurate data support for the subsequent optimization design of blade structures, and ensuring the long-term stability and high load bearing capacity of the blades under complex working conditions.

[0055] The present invention collects blade stress data in real time through multi-channel strain gauges and laser interferometry technology, combines the stress distribution curve generated by the finite element analysis model, and systematically compares the experimental and simulation results. Based on the quantitative deviation analysis method, through the error formula and sensitivity analysis, the key differences between the experimental and simulation data can be quickly identified, and the experimental settings and finite element analysis models can be optimized. This method effectively controls the error between the experimental results and the simulation results (the deviation is less than 5%), ensures high consistency of the data, greatly improves the accuracy of the experimental data and the simulation results, and provides a reliable basis for the mechanical analysis of complex blades.

[0056] The present invention provides a complete result verification and optimization path. Combining microstructure detection data and load distribution curves, the invention proposes a blade design optimization scheme based on actual working conditions through multiple rounds of iterative optimization of experimental conditions and finite element models. The optimization scheme includes adjusting the thickness ratio of composite materials and metal layers, optimizing metal surface treatment processes, etc., which fundamentally improves the stress transfer path in the stress concentration area, significantly improves the load bearing capacity and fatigue resistance of the blade, and ensures the long-term reliable operation of the blade in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0059] Figure 2 Schematic diagram of S4 process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0061] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0062] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0063] like Figure 1-Figure 2 As shown, a method for obtaining a centrifugal pump blade load distribution curve comprises the following steps:

[0064] S1, setting of experimental conditions and preparation of blade materials: selecting the blade to be tested, the blade material is a heterogeneous material blade composed of thermoplastic carbon fiber reinforced plastic and metal material, the experimental condition setting includes the working environment parameters of the blade, the environmental parameters include the temperature range, pressure environment, speed range, transmission medium type and experimental duration of the blade, and the application method of the alternating load is determined based on the experimental conditions. The applied alternating load range is 10N to 500N, the frequency is 1Hz to 20Hz, and the loading direction is clearly defined as a comprehensive direction of radial and tangential directions;

[0065] S2, interface microstructure detection: The microstructure of the blade composite material and metal bonding interface is analyzed by scanning electron microscopy and transmission electron microscopy, and the microstructure data of the carbon fiber and metal bonding at the interface are obtained and recorded. The microstructure data includes fiber arrangement, interface bonding state, crack propagation path and interface phase distribution characteristics. Combined with X-ray diffraction technology, the chemical composition of the interface area is analyzed, the thickness of the metal surface oxide layer (1 micron to 5 microns) is identified, and the influencing factors of the interface mechanical properties are revealed by detecting the changes in the crystal structure (such as the lattice matching between the metal and the composite material);

[0066] S3, load application and stress detection: Through high-precision multi-channel strain gauges and laser interferometry measurement technology, the stress distribution of the blade under the action of alternating loads is recorded in real time. The load is applied step by step according to the preset alternating force, and the loading frequency and amplitude are controlled by computer;

[0067] S4, load distribution data collection and analysis: Through the data acquisition system (including multi-channel data acquisition device and signal processing software), the stress data of the blade under different working conditions are collected in real time. Based on the collected stress data, the finite element analysis method is used to analyze the blade load distribution and generate the blade load distribution curve under different loading frequencies and amplitudes;

[0068] S5, correlation analysis between interface change and blade load: comparing the microstructure data obtained in step S2 with the load distribution curve generated in step S4, establishing a relationship model between microstructure change and load distribution, and determining the influence of microstructure on load distribution and blade strength through statistical and mechanical analysis methods;

[0069] S6, result verification and optimization: Compare the load distribution curve obtained in the experiment with the existing finite element analysis simulation results (such as the stress distribution simulation results generated by finite element software in the early stage of blade design) to verify the accuracy of the experimental measurement data and the effectiveness of the method. Evaluate the deviation between the experimental data and the simulation data through error analysis. If the deviation is large, further adjust the experimental conditions or optimize the blade design parameters. According to the analysis results, propose optimization plans, such as adjusting the thickness between composite material layers or the metal treatment process, to improve the load bearing capacity and durability of the blade.

[0070] S1 specifically includes:

[0071] S11, blade material selection: according to the particularity of the centrifugal pump working environment, select the ratio of composite materials to metals. The composite material is carbon fiber reinforced plastics, and the fiber content is controlled within the range of 30% to 70%. The metal material is high-strength titanium alloy. Combined with different working environments and requirements, determine the material thickness ratio. The thickness ratio of carbon fiber reinforced plastics to metal materials is between 1:3 and 1:5.

[0072] S12, temperature and pressure setting: the control accuracy of the experimental temperature is ±1°C, the temperature is set between -40°C and 150°C, the experimental pressure is controlled by a pressure sensor with an accuracy of 0.01MPa. Combined with the operating conditions of the centrifugal pump, the pressure setting range is 0.1MPa to 5MPa;

[0073] S13, speed setting and control: Use high-speed rotating equipment to simulate the actual speed in operation, with a speed range of 1000RPM to 3000RPM, and use the frequency converter for precise control, with a speed change step of 50RPM to simulate the operating status under various working conditions;

[0074] S14, selection of transmission medium: the experimental medium is liquid water or air, the temperature of the transmission medium matches the experimental set temperature, the flow rate range of liquid water is 0.1m / s to 1m / s, and the flow rate range of air is 1m / s to 5m / s;

[0075] S15, Experiment time control: The control accuracy of the experiment time is ±1 minute, and the experiment duration is 1 hour to 10 hours. Select the appropriate experiment time according to different working conditions;

[0076] By setting experimental conditions and preparing blade materials, the working environment of the blade under different working conditions can be accurately simulated, especially the comprehensive control of temperature, pressure, speed and medium, making the subsequent stress and load analysis more reliable and consistent.

[0077] S2 specifically includes:

[0078] S21, Scanning electron microscope fiber arrangement observation: Use scanning electron microscope to perform high-resolution imaging of the carbon fiber arrangement in the interface between composite materials and metals. The parameters of fiber arrangement include the depth of fiber embedding in the metal matrix, the distance and angle between fibers. The fiber arrangement is quantitatively analyzed by image analysis software to determine that the fiber spacing is in the range of 0.5 microns to 2 microns, ensuring effective contact between the interface fibers and the metal matrix, thereby optimizing the stress transfer path;

[0079] S22, Transmission electron microscopy interface bonding state analysis: The bonding quality of the composite material and metal interface is analyzed in depth by transmission electron microscopy, and the bonding state of the microstructure is observed, including whether there are gaps, cracks, unbonded areas or other microscopic defects on the interface. The adhesion and stress transfer at the interface between the composite material and the metal are analyzed in detail, and the interface bonding quality is recorded by high-resolution images to ensure that the mechanical properties of the interface meet the design requirements;

[0080] S23, crack propagation path observation: Combined with the observation results of scanning electron microscopy and transmission electron microscopy, the crack propagation path at the interface is analyzed, the starting point, propagation direction and crack length of the crack are recorded, and the propagation behavior of the crack in different areas of the interface is analyzed. Through the relationship between the crack propagation path and the interface stress distribution, the influence of the interface bonding state on the crack propagation is judged. The width of the crack propagation should be controlled within 50 microns to prevent the crack from causing interface failure;

[0081] S24, X-ray diffraction crystal structure detection: Use X-ray diffraction technology to analyze the crystal structure of the interface area. Through the displacement and intensity change of the diffraction peak, determine the lattice constant of the composite material and the metal at the interface, and calculate the lattice matching degree. The lattice matching degree formula is:

[0082]

[0083] Where M is the lattice matching degree, a1 and a2 are the lattice constants of the composite material and the metal respectively, and the matching degree should be controlled within ±5% to ensure effective stress transfer at the interface and avoid interface stress concentration;

[0084] S25, metal surface oxide layer thickness detection: The oxide layer thickness on the metal surface is analyzed by X-ray diffraction technology. The thickness of the oxide layer is calculated by the half-width of the diffraction peak using the Debye-Scherrer formula, which is expressed as:

[0085]

[0086] Where D is the thickness of the oxide layer, K is the shape factor (usually 0.9), λ is the wavelength of the X-ray, B is the half-height width of the diffraction peak, and θ is the Bragg angle. The thickness range of the oxide layer on the metal surface is calculated by the change in the width of the diffraction peak to ensure that the oxide layer thickness is between 1 micron and 5 microns to avoid excessive oxide layer thickness affecting the interface bonding quality and stress transfer performance;

[0087] By combining scanning electron microscopy and transmission electron microscopy with X-ray diffraction technology, the microstructural characteristics of the interface between composite materials and metals can be accurately analyzed, including fiber arrangement, interface bonding state, crack propagation path, and thickness of the oxide layer. This detection method provides detailed basic data for subsequent stress analysis and load distribution, ensuring the mechanical properties of the interface under alternating loads.

[0088] S3 includes:

[0089] S31, setting and applying alternating load: The load is applied by a computer-controlled loading system. The amplitude and frequency of the alternating load are set according to the actual working conditions of the blade. The specific settings are:

[0090] The load frequency starts from 1Hz and increases gradually with a frequency increment of 1Hz. The maximum frequency reaches 20Hz. The load amplitude starts from 10N and increases by 10N each time. The maximum applied load is 500N. The load application direction is adjusted in real time by the controller to ensure that the load is applied in a radial and tangential composite loading mode to simulate the actual working stress environment of the centrifugal pump blades.

[0091] S32, strain gauge arrangement and stress sensing: high-precision multi-channel strain gauges are arranged in key areas of the blade (such as the root, middle and top of the blade). The strain gauge arrangement in each area should ensure complete coverage of the blade surface and internal stress conduction path. The sensitivity of the strain gauge is set to ±0.01N to detect the stress distribution on the blade surface and inside in real time to ensure high-precision collection of stress data during load application;

[0092] S33, Application of laser interferometry technology: Laser interferometer is used to conduct non-contact monitoring of the stress distribution on the blade surface. The laser interferometer accurately detects the tiny deformation of the blade surface through the principle of light beam reflection and interference, and converts it into stress data. The measurement accuracy reaches the nanometer level, ensuring that the changes in the stress distribution on the blade surface are accurately captured during the application of high-frequency and high-amplitude loads;

[0093] S34, data acquisition and processing: The stress data measured by the strain gauge and laser interferometer are recorded in real time through a computer-controlled multi-channel data acquisition system. The sampling frequency is set to 1000 Hz to ensure that key stress data will not be lost under high-frequency loading conditions;

[0094] S35, stress conduction path analysis: Combined with the blade structure and material properties, through the analysis of stress distribution data, the internal stress conduction path model of the blade is established to analyze the stress distribution of the blade under different loads and frequencies, especially the stress concentration area at the interface between composite materials and metals. The establishment of the stress conduction path model can help identify stress concentration points and potential failure areas;

[0095] Through high-precision multi-channel strain gauges and laser interferometry measurement technology, the stress distribution and conduction of the blades under alternating loads can be recorded and analyzed in real time, ensuring that stress changes on the blade surface and inside are accurately captured, providing reliable stress data for subsequent load distribution analysis, and optimizing blade design to improve its fatigue resistance and durability.

[0096] S4 includes:

[0097] S41, data acquisition system settings: a high-precision multi-channel data acquisition system is used. The system includes a multi-channel strain gauge data acquisition module and signal processing software. The multi-channel data acquisition system is synchronously controlled with the experimental platform to ensure that the data acquisition frequency is consistent with the load application frequency. The acquisition frequency is set to 1000Hz to ensure that the stress data acquisition accuracy is not affected when high-frequency loads are applied. The acquisition system filters noise through real-time processing algorithms to ensure data accuracy;

[0098] S42, real-time collection of stress data: During the load application process, stress data of key positions of the blade (such as the root, middle and top of the blade) are collected in real time. Data collection covers the entire loading process, including stress changes in the initial, middle and high-frequency loading states. The collected data includes stress distribution on the blade surface and internal stress conduction conditions to ensure the integrity and accuracy of the stress conduction path;

[0099] S43, data processing and filtering: using signal processing software to process the collected stress data in real time. The processing steps include filtering and noise removal of the original stress data. The filtering method uses a low-pass filter to eliminate external interference and high-frequency noise to ensure the accuracy of the collected data.

[0100] S44, establishment of finite element analysis model: based on the collected stress data, a finite element analysis model of the blade is constructed. The model needs to accurately define the geometric shape and material properties (including elastic modulus, Poisson's ratio, etc.) of the blade, and set corresponding boundary conditions and loading conditions. The loading conditions are synchronized with the experimental data to ensure that the simulation environment of the finite element model is consistent with the experimental environment;

[0101] In finite element analysis, the stress-strain relationship is described as:

[0102] σ=E·ε;

[0103] Among them, σ is stress, E is elastic modulus, and X is strain. According to the load application and stress distribution, the stress distribution of the blade under different working conditions is calculated;

[0104] S45, generation of load distribution curve: simulate and calculate the stress data of the blade by finite element analysis method, and generate the load distribution curve of the blade under different loading frequency and amplitude conditions. The load distribution curve describes the stress distribution of the blade under radial and tangential alternating loads and its change over time. The curve is further optimized by data fitting method to ensure that the curve can accurately reflect the stress transfer and load bearing capacity of the blade;

[0105] By combining high-precision data acquisition and finite element analysis, the stress distribution of the blade under different working conditions can be acquired and analyzed in real time, and the load distribution curve can be generated, providing a reliable theoretical basis for blade design and performance optimization. This method ensures the accurate description of the stress transfer path of the blade under alternating loads and optimizes the strength design of the blade.

[0106] S5 includes:

[0107] S51, preliminary comparative analysis of microstructure data and load distribution curve: preliminary comparison of the microstructure data (including fiber arrangement, interface bonding state, crack propagation path, interface phase distribution characteristics) obtained in step S2 with the load distribution curve generated in step S4, analyzing the changes of the composite material and metal interface under different load conditions, and combining the lattice matching degree obtained in the X-ray diffraction data to determine the corresponding relationship between the stress concentration area at the interface bonding and the microstructure characteristics. The main goal of this step is to identify the key microstructure parameters that affect the load distribution;

[0108] S52, data normalization and quantitative analysis: In order to ensure the validity of data comparison, the microstructure data and the load distribution curve data are normalized. The fiber arrangement, crack propagation path and interface bonding state parameters are quantified through the normalized data. The correlation analysis method in statistics (such as Pearson correlation coefficient) is used to determine the correlation between each microstructure parameter and the blade load distribution. Assuming the correlation coefficient between stress distribution and microstructure is r, r is calculated as:

[0109]

[0110] Among them, X is the microstructure data, Y is the load distribution curve data, σ X and σ Y are the standard deviations of X and Y, respectively, and Cov is the covariance. This formula can be used to quantify the influence of microstructural characteristics on load distribution;

[0111] S53, key parameter identification and model optimization: Based on comparison and correlation analysis, the microstructural parameters (such as fiber arrangement density, crack length, etc.) that have the most significant impact on the blade load distribution are identified. Based on the identified parameters, a preliminary relationship model between microstructural changes and load distribution is constructed. The expression of the model is:

[0112] σ load =f(microstructure);

[0113] Among them, σ load is the stress value of load distribution, microstructure represents the microstructure parameter set, and the model is optimized through regression analysis and the accuracy of the model is verified to ensure that the model can accurately reflect the impact of microstructure changes on load distribution;

[0114] Through preliminary data comparison, normalized quantitative processing and model construction, the specific impact of microstructural changes at the interface between composite materials and metals on the blade load distribution was clarified, providing basic theoretical support for blade design optimization.

[0115] The S5 also includes:

[0116] S54, preliminary correlation analysis between blade strength and microstructure data: Combined with the microstructure data obtained in step S2, the strength of the blade is analyzed using a mechanical analysis method, the stress and strain distribution of the blade is simulated by a finite element analysis method, and a preliminary correlation analysis is performed between the microstructure data (including fiber arrangement, interface bonding state, and crack propagation path) and the blade strength, especially the areas with poor interface bonding state and crack propagation, and their influence on the overall strength of the blade should be the focus of analysis;

[0117] S55, establishment of nonlinear regression model of strength data: Based on the correlation between blade strength and microstructure data, a nonlinear regression model is constructed to describe the relationship between blade strength and microstructure. Assuming that the blade strength S and microstructure parameters X1 (fiber arrangement density) and X2 (crack length) show a nonlinear relationship, the model expression is:

[0118]

[0119] Where S is the blade strength, X1 is the fiber arrangement density (unit: fiber per square millimeter, representing the distribution of carbon fiber in the interface area), X2 is the crack length (unit: micrometer, representing the length of the interface crack propagation path), a, b, c, d and e are regression coefficients, which are determined by fitting experimental data;

[0120] This formula is used to analyze the effect of fiber arrangement density and crack length on blade strength, ensuring that the model can accurately predict the strength performance of blades under different microstructural conditions;

[0121] S56, sensitivity analysis of microstructure changes on blade strength: In order to further quantify the influence of microstructure parameters on blade strength, a sensitivity analysis was conducted. The partial derivative method was used to calculate the partial derivatives of blade strength with respect to fiber arrangement density and crack length. The sensitivity analysis was expressed as:

[0122]

[0123] in, It indicates the effect of fiber arrangement density on blade strength, with the unit being MPa / (fiber per square millimeter). It indicates the effect of crack length on blade strength, in MPa / micrometer;

[0124] Through this formula, the sensitivity of each microstructural parameter (fiber arrangement density and crack length) to blade strength is quantified, the microstructural features that most significantly affect blade strength are identified, and how to optimize the fatigue resistance of the blade by regulating fiber arrangement density and crack extension is determined;

[0125] Through nonlinear regression model and sensitivity analysis, the effects of fiber arrangement density and crack extension on blade strength were clarified, which helped optimize the interface design and improve the strength and service life of the blade under alternating loads.

[0126] S6 includes:

[0127] S61, comparative analysis of experimental data and simulation results: Compare the blade load distribution curve obtained during the experiment with the stress distribution simulation results generated by the finite element analysis software in the early stage of design. The comparison items include stress distribution under different loading frequencies and amplitudes, stress concentration areas and load transfer paths. Through curve comparison, identify the differences between the experimental data and the simulation results in terms of stress peak value, stress distribution shape and stress transfer path;

[0128] S62, quantification of the deviation between experimental data and simulation results: The deviation between experimental data and simulation results is quantified using the deviation formula, which is calculated as:

[0129]

[0130] Among them, δ represents the deviation percentage, σ exp is the experimentally measured stress value, σ sim is the simulated stress value of finite element analysis;

[0131] If the deviation δ exceeds 5%, it means that there is a significant difference between the experimental results and the simulation results, and further adjustments are needed. If the deviation is controlled within 5%, it verifies that the experimental results are in good agreement with the simulation results.

[0132] S63, Deviation Source Analysis: Based on the deviation quantification results, analyze the sources of deviation, which include measurement errors of the experimental device, insufficient stability of load application, inaccurate material properties in the finite element model, and unreasonable boundary conditions. By comparing the specific working conditions of the experiment and simulation, identify the key error sources and optimize the experimental and simulation settings.

[0133] The S6 also includes:

[0134] S64, Optimization and adjustment of experimental conditions: If the deviation is mainly caused by experimental conditions, optimize the experimental settings, including recalibrating the load application system to ensure the accuracy and stability of load application, optimize the accuracy of measurement equipment, such as increasing the resolution of stress sensors or adjusting their layout to improve the measurement accuracy of blade surface and internal stress transfer paths, and optimize the experimental time, load frequency and amplitude settings according to different working conditions to ensure that the experimental data more accurately reflects the actual working conditions;

[0135] S65, Optimization of finite element analysis model: If the deviation mainly comes from the finite element analysis model, optimize the model according to the deviation analysis results, adjust the key parameters of the material, such as elastic modulus, Poisson's ratio, density, etc., to ensure that these parameters are consistent with the material properties in the actual experiment, optimize the setting of boundary conditions, including redefining the position and state of fixed boundaries and free boundaries, to ensure that the boundary conditions are more in line with the actual working environment, and at the same time, optimize the meshing of the finite element model according to the stress concentration in complex areas to improve the analysis accuracy;

[0136] S66, Propose an optimization plan: Based on the comparison results of experimental data and simulation data, propose a specific optimization plan for blade design. The optimization plan includes:

[0137] (1) Adjustment of the thickness of the composite material and metal interface: For stress concentration areas, adjust the thickness ratio between the composite material layer and the metal layer to improve the stress transfer path, reduce the load in the stress concentration area, and improve the overall structural strength;

[0138] (2) Optimization of metal surface treatment process: Microscopic surface treatment is performed on the metal interface of the blade, such as increasing the precise control of the oxide layer thickness or performing surface nano-treatment to enhance the bonding strength between the composite material and the metal interface, thereby improving the durability of the blade under alternating loads;

[0139] S67, result verification and iterative optimization: Based on the optimized experimental conditions and finite element model, re-perform the experiment and simulation analysis, and generate a new load distribution curve, compare the new experimental data with the simulation results, and end the optimization process if the deviation is significantly reduced and the optimization scheme is verified to be effective. If there is still a large difference in the deviation, it is necessary to continue to iterate the optimization of the experimental conditions or the finite element model. The optimization cycle continues until the deviation between the experimental results and the simulation results is controlled within an acceptable range (e.g., less than 5%).

[0140] By adjusting the experimental conditions and optimizing the finite element analysis model, an optimization scheme is proposed and verified, which can significantly improve the load-bearing capacity and durability of the blades. The optimized design ensures that the blades can operate reliably for a long time under actual working conditions and effectively reduces the risk of failure in stress concentration areas.

[0141] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0142] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for obtaining a centrifugal pump blade load distribution curve, characterized in that: The following steps are involved: S1, setting of experimental conditions and preparation of blade materials: selecting the blade to be tested, the blade material is a heterogeneous material blade composed of thermoplastic carbon fiber reinforced plastic and metal material, the experimental condition setting includes the working environment parameters of the blade, the environmental parameters include the temperature range, pressure environment, speed range, transmission medium type and experimental duration of the blade, and the application method of the alternating load is determined based on the experimental conditions; S2, interface microstructure detection: The microstructure of the blade composite material and metal bonding interface is analyzed by scanning electron microscopy and transmission electron microscopy, and the microstructure data of the carbon fiber and metal bonding at the interface are obtained and recorded. The microstructure data includes fiber arrangement, interface bonding state, crack propagation path and interface phase distribution characteristics. Combined with X-ray diffraction technology, the chemical composition of the interface area is analyzed, the thickness of the metal surface oxide layer is identified, and the influencing factors of the interface mechanical properties are revealed by detecting the changes in the crystal structure; S3, load application and stress detection: Through multi-channel strain gauges and laser interferometry measurement technology, the stress distribution of the blade under the action of alternating load is recorded in real time. The load is applied step by step according to the preset alternating force, and the loading frequency and amplitude are controlled by computer; S4, load distribution data collection and analysis: Through the data acquisition system, the stress data of the blade under different working conditions are collected in real time. Based on the collected stress data, the finite element analysis method is used to analyze the blade load distribution and generate the blade load distribution curve under different loading frequencies and amplitudes; S5, correlation analysis between interface change and blade load: comparing the microstructure data obtained in step S2 with the load distribution curve generated in step S4, establishing a relationship model between microstructure change and load distribution, and determining the influence of microstructure on load distribution and blade strength through statistical and mechanical analysis methods; S6, result verification and optimization: Compare the load distribution curve obtained in the experiment with the existing finite element analysis simulation results to verify the accuracy of the experimental measurement data and the effectiveness of the method. Evaluate the deviation between the experimental data and the simulation data through error analysis, and propose an optimization plan based on the analysis results.

2. A method for obtaining a centrifugal pump blade load distribution curve according to claim 1, characterized in that: The S1 specifically includes: S11, blade material selection: according to the particularity of the centrifugal pump working environment, select the ratio of composite materials to metals. The composite material is carbon fiber reinforced plastic and the metal material is titanium alloy. The thickness ratio of the materials is determined in combination with different working environments and requirements. S12, temperature and pressure setting: the control accuracy of the experimental temperature is ±1°C, the temperature is set between -40°C and 150°C, the experimental pressure is controlled by a pressure sensor with an accuracy of 0.01MPa. Combined with the operating conditions of the centrifugal pump, the pressure setting range is 0.1MPa to 5MPa; S13, speed setting and control: Use rotating equipment to simulate the speed in actual operation, and control it through the frequency converter to simulate the operating status under various working conditions; S14, selection of transmission medium: the experimental medium is liquid water or air, and the temperature of the transmission medium matches the experimental set temperature; S15, Experiment time control: The control accuracy of the experiment time is ±1 minute, and the experiment duration is 1 hour to 10 hours. The experiment time is selected according to different working conditions.

3. The method for obtaining a centrifugal pump blade load distribution curve according to claim 2, characterized in that: The S2 specifically includes: S21, Scanning electron microscope fiber arrangement observation: Scanning electron microscope is used to image the carbon fiber arrangement in the interface between composite materials and metals. The parameters of fiber arrangement include the depth of fiber embedding in the metal matrix, the distance and angle between fibers. The fiber arrangement is quantitatively analyzed by image analysis software. S22, Transmission electron microscopy interface bonding state analysis: The bonding quality of the composite material and metal interface is analyzed by transmission electron microscopy to observe the bonding state of the microstructure, including whether there are gaps, cracks, unbonded areas or other microscopic defects on the interface; S23, crack propagation path observation: Combined with the observation results of scanning electron microscopy and transmission electron microscopy, the crack propagation path at the interface is analyzed, the starting point, propagation direction and crack length of the crack are recorded, the crack propagation behavior in different areas of the interface is analyzed, and the relationship between the crack propagation path and the interface stress distribution is used to determine the influence of the interface bonding state on the crack propagation; S24, X-ray diffraction crystal structure detection: Use X-ray diffraction technology to analyze the crystal structure of the interface area, determine the lattice constant of the composite material and the metal at the interface through the displacement and intensity change of the diffraction peak, and calculate the lattice matching degree; S25, metal surface oxide layer thickness detection: analyze the thickness of the oxide layer on the metal surface by X-ray diffraction technology.

4. The method for obtaining a centrifugal pump blade load distribution curve according to claim 3, characterized in that: The S3 includes: S31, setting and applying alternating load: the load is applied by a computer-controlled loading system, and the amplitude and frequency of the alternating load are set according to the actual working conditions of the blade; S32, strain gauge arrangement and stress sensing: Multi-channel strain gauges are arranged in key areas of the blade to detect the stress distribution on the blade surface and inside in real time; S33, Application of laser interferometry technology: Laser interferometer is used to monitor the stress distribution on the blade surface in a non-contact manner. The laser interferometer detects the deformation of the blade surface through the principle of light beam reflection and interference and converts it into stress data; S34, data acquisition and processing: The stress data measured by the strain gauge and laser interferometer are recorded in real time through a computer-controlled multi-channel data acquisition system; S35, stress conduction path analysis: Based on the blade structure and material properties, a blade internal stress conduction path model is established through the analysis of stress distribution data to analyze the stress distribution of the blade under different loads and frequencies.

5. The method for obtaining a centrifugal pump blade load distribution curve according to claim 4, characterized in that: The S4 includes: S41, data acquisition system settings: a multi-channel data acquisition system is used, the system includes a multi-channel strain gauge data acquisition module and signal processing software, and the multi-channel data acquisition system is synchronously controlled with the experimental platform; S42, real-time collection of stress data: during the load application process, real-time collection of stress data at various key positions of the blade; S43, data processing and filtering: using signal processing software to process the collected stress data in real time; S44, establishing a finite element analysis model: constructing a finite element analysis model of the blade based on the collected stress data; S45, generating a load distribution curve: simulating and calculating the stress data of the blade by a finite element analysis method, and generating a blade load distribution curve under different loading frequencies and amplitudes.

6. The method for obtaining a centrifugal pump blade load distribution curve according to claim 5, characterized in that: The S5 includes: S51, preliminary comparative analysis of microstructure data and load distribution curve: preliminary comparison of the microstructure data obtained in step S2 with the load distribution curve generated in step S4, analyzing the changes of the interface between the composite material and the metal under different load conditions, and combining the lattice matching degree obtained in the X-ray diffraction data to determine the corresponding relationship between the stress concentration area at the interface and the microstructure characteristics; S52, data normalization and quantitative analysis: normalize the microstructure data and the load distribution curve data, quantify the fiber arrangement, crack propagation path and interface bonding state parameters through the normalized data, and use the correlation analysis method in statistics to determine the correlation between each microstructure parameter and the blade load distribution; S53, key parameter identification and model optimization: Based on comparison and correlation analysis, the microstructural parameters that have the most significant impact on the blade load distribution are identified, and based on the identified parameters, a preliminary relationship model between microstructural changes and load distribution is constructed.

7. The method for obtaining a centrifugal pump blade load distribution curve according to claim 6, characterized in that: The S5 further includes: S54, preliminary correlation analysis between blade strength and microstructure data: combining the microstructure data obtained in step S2, using a mechanical analysis method to analyze the strength of the blade, simulating the stress and strain distribution of the blade by a finite element analysis method, and performing a preliminary correlation analysis between the microstructure data and the blade strength; S55, establishment of nonlinear regression model of strength data: based on the correlation between blade strength and microstructure data, a nonlinear regression model is constructed to describe the relationship between blade strength and microstructure; S56, Sensitivity analysis of microstructural changes on blade strength: A sensitivity analysis was performed and the partial derivatives of blade strength with respect to fiber density and crack length were calculated using the partial derivative method.

8. The method for obtaining a centrifugal pump blade load distribution curve according to claim 7, characterized in that: The S6 includes: S61, comparative analysis of experimental data and simulation results: Compare the blade load distribution curve obtained during the experiment with the stress distribution simulation results generated by the finite element analysis software in the early stage of design. The comparison items include stress distribution under different loading frequencies and amplitudes, stress concentration areas and load transfer paths. Through curve comparison, identify the differences between the experimental data and the simulation results in terms of stress peak value, stress distribution shape and stress transfer path; S62, quantification of the deviation between experimental data and simulation results: quantify the deviation between experimental data and simulation results using the deviation formula; S63, Deviation Source Analysis: Analyze the source of the deviation based on the deviation quantification results.

9. The method for obtaining a centrifugal pump blade load distribution curve according to claim 8, characterized in that: The S6 further includes: S64, Optimization and adjustment of experimental conditions: If the deviation mainly comes from the experimental conditions, optimize the experimental settings; S65, optimization of finite element analysis model: if the deviation mainly comes from the finite element analysis model, the model is optimized according to the deviation analysis results; S66, Proposing an optimization plan: Based on the comparison results of experimental data and simulation data, an optimization plan for blade design is proposed; S67, result verification and iterative optimization: Based on the optimized experimental conditions and finite element model, the experiment and simulation analysis are repeated and a new load distribution curve is generated.

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