A wear-resistant optimization design method for turbine runner blades

Abstract

The present application relates to a wear-resistant optimization design method for turbine runner blades. The method comprises: obtaining environmental characteristic information during turbine runner blade operation, preprocessing and extracting characteristics to obtain blade surface force and initial distribution characteristics; then, based on these characteristics, calculating the position and peak force of the high-pressure areas at the leading edge and back of the blade, and comparing multiple wear-resistant materials to determine the final material combination; then, inputting the final material combination data into a wear prediction model, using numerical simulation to analyze the blade surface stress distribution and wear rate, and then deriving an estimated wear distribution value for the blade during long-term operation; finally, using an iterative optimization algorithm based on this estimated value to adjust the strength and wear resistance balance parameters in the wear prediction model to obtain the final wear-resistant design scheme. This method achieves an optimal balance between strength and wear resistance, extends the service life of turbine runner blades, reduces equipment maintenance costs and replacement frequency, and improves turbine operation stability and power generation efficiency.

Description

Technical Field

[0001] The invention belongs to the field of water conservancy and hydropower engineering, and in particular relates to an anti-wear optimization design method for turbine runner blades. Background Art

[0002] With the development of water conservancy and hydropower engineering technology, a wear-resistant optimization design technology for turbine runner blades has emerged. In the field of hydropower generation, turbines are core energy conversion equipment, and their operating stability and efficiency are crucial to the benefits of power generation. Turbine runner blades are exposed to complex and harsh working environments for a long time, suffering from the continuous impact of water flow, particle erosion, and complex pressure and velocity changes in the flow field. Especially in waters with high sand content, the friction and impact of sediment particles on the blade surface exacerbate the wear process. In the existing technology, although anti-wear materials and improved structural design are used to alleviate the wear problem, there are still many deficiencies in accurately grasping the force and initial distribution characteristics of the blade surface and comprehensively and efficiently improving the wear resistance of the blade. This not only causes the operating efficiency of the turbine to decrease over time, but frequent equipment maintenance and replacement also significantly increases the cost of power generation, limiting the sustainable and efficient development of the hydropower industry. Summary of the Invention

[0003] Based on this, it is necessary to provide an anti-wear optimization design method for turbine runner blades that can achieve an optimal balance between strength and wear resistance, extend the service life of turbine runner blades, and improve the operating stability and power generation efficiency of the turbine in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for optimizing the wear resistance of turbine runner blades, comprising:

[0005] Acquire environmental characteristic information of turbine runner blades during operation; preprocess and extract characteristics of the environmental characteristic information to obtain blade surface force and initial distribution characteristics; the environmental characteristic information includes at least one of water flow impact data, particle erosion parameters, flow field velocity, pressure distribution and sediment content information.

[0006] Based on the force and initial distribution characteristics of the blade surface, the stress distribution of the blade under different working conditions is calculated to obtain the dynamic load spectrum of the blade surface; the clustering algorithm is used to classify the positions of the high-pressure areas in the dynamic load spectrum, and the stress gradients in different areas are calculated using the formula to obtain the stress gradient distribution map associated with the material wear characteristics.

[0007] Based on the preset fatigue threshold parameters of different material types, the stress gradient distribution diagrams are matched and screened to obtain candidate material combinations; multi-axial stress simulation tests are performed on the candidate material combinations to generate stress-strain response curves of the mechanical behavior of each combination material under different stress states; the peak pressure decay rate index data is extracted based on the stress-strain response curves to obtain the final material combination that meets the peak pressure decay rate index.

[0008] The final material combination data is input into the wear prediction model and numerical simulation is used to analyze the stress distribution and wear rate on the blade surface to obtain an estimated wear distribution value during long-term operation of the blade.

[0009] According to the estimated wear distribution, the iterative optimization algorithm is used to adjust the strength and wear resistance balance parameters in the wear prediction model to obtain the final wear resistance design scheme.

[0010] In one embodiment, environmental feature information is preprocessed and feature extracted to obtain blade surface force and initial distribution characteristics, including:

[0011] Based on the environmental feature information, wavelet transform and sliding window filtering technology are used to filter noise and eliminate outliers to obtain a stable feature data set.

[0012] A convolutional neural network is used to perform feature mining on a feature data set to obtain a potential feature subset related to the blade; the feature subset includes at least one of a water flow impact frequency feature, a particle size distribution feature, and a pressure fluctuation feature.

[0013] The feature subsets were analyzed using a combination of Pearson correlation coefficient and grey relational analysis to obtain key features reflecting the surface forces and initial distribution of the blades.

[0014] Principal component analysis, dimensionality reduction and information fusion are performed on key features to obtain fusion feature parameters.

[0015] Based on the fusion characteristic parameter input and the trained fluid-structure coupling analysis model, the blade surface force and initial distribution characteristics are obtained.

[0016] In one embodiment, a clustering algorithm is used to classify the locations of high-pressure areas in a dynamic load spectrum and a formula is used to calculate stress gradients in different areas to obtain a stress gradient distribution map associated with material wear characteristics, including:

[0017] The data of dynamic load spectrum are processed by clustering algorithm to obtain the location classification results of high-pressure area.

[0018] High-voltage area features are extracted from the position classification results to obtain the area division boundaries.

[0019] Based on the regional division boundary and using the stress gradient formula, the stress gradient value of each region is calculated.

[0020] The stress gradient value is calculated using the following stress gradient formula:

[0021] ;

[0022] in, represents the stress gradient value, represents the spatial dimension, represents stress, and Represents the spatial coordinate components of calculated stress changes in different directions, represents the first-order stress derivative, represents the second-order stress derivative, and Indicates the change in the spatial coordinate components of the calculated stress changes in different directions.

[0023] The correlation strength with the wear characteristics is determined based on the stress gradient value to obtain characteristic correlation data.

[0024] Based on the characteristic correlation data, the finite element method combined with high-order interpolation functions is used to fit and interpolate the stress data of different regions to generate a stress gradient distribution map associated with the material wear characteristics.

[0025] In one embodiment, the final material combination data is input into a wear prediction model and numerical simulation is used to analyze the stress distribution and wear rate on the blade surface to obtain an estimated wear distribution value during long-term operation of the blade, including:

[0026] The final material combination data is input into the wear prediction model to obtain material property parameters; the material property parameters include material composition and mechanical property data.

[0027] The blade surface stress field data is obtained by finite element simulation according to the material property parameters.

[0028] The surface stress field data is coupled with the dynamic wear coefficient and the parameters of the wear prediction model are updated to obtain a wear spatial distribution map; the wear spatial distribution map includes predicted values ​​of wear depth in different areas.

[0029] The wear spatial distribution map is matched and verified with the real-time monitoring data, and iterative calculations are performed based on the verification results to obtain the optimized stress field distribution characteristics.

[0030] Based on the stress field distribution characteristics, the wear accumulation curve of multiple time nodes is constructed, and the estimated wear distribution of the blade in long-term operation is calculated.

[0031] In one embodiment, an iterative optimization algorithm is used to adjust the strength and wear resistance balance parameters in the wear prediction model based on the wear distribution estimate to obtain a final wear resistance design solution, including:

[0032] Material property indicators to obtain an estimate of wear distribution.

[0033] The wear prediction model is adjusted according to the material performance indicators to obtain a parameter adjustment strategy; the parameter adjustment strategy is used to balance the weight ratio of strength and wear resistance.

[0034] The parameter adjustment strategy is processed using an iterative optimization algorithm to obtain a parameter update sequence with dynamic feedback.

[0035] The parameter update sequence is input into the wear prediction model, and multiple sets of anti-wear performance simulation results are output.

[0036] According to the anti-wear performance simulation results, the environmental factor variables are extracted to correct the update sequence and generate the optimized anti-wear parameter combination.

[0037] The anti-wear parameter combination is compared with the preset model convergence conditions. If the model convergence conditions are met, the final anti-wear design scheme is output; the final anti-wear design scheme includes the matching relationship between the strength threshold and the anti-wear coefficient.

[0038] In a second aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0039] In a third aspect, the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented.

[0040] The aforementioned wear-resistant optimization design method for turbine runner blades obtains environmental characteristic information during turbine runner operation, including water impact data, particle erosion parameters, flow field velocity, pressure distribution, and sediment content information, and then preprocesses and extracts characteristics to obtain blade surface force and initial distribution characteristics. Based on these characteristics, the location and peak force of the high-pressure areas at the leading edge and back of the blade are calculated, and a variety of wear-resistant materials are compared to determine the final material combination. The final material combination data is then input into a wear prediction model, and numerical simulation is used to analyze the blade surface stress distribution and wear rate, thereby deriving an estimated wear distribution value for the blade during long-term operation. Finally, based on this estimated value, an iterative optimization algorithm is used to adjust the strength and wear resistance balance parameters in the wear prediction model to obtain the final wear-resistant design solution. This method achieves an optimal balance between strength and wear resistance, extends the service life of turbine runner blades, reduces equipment maintenance costs and replacement frequency, improves turbine operation stability and power generation efficiency, and effectively promotes the sustainable development of the hydropower industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A flow chart of a method for optimizing the wear resistance of a turbine runner blade provided by an embodiment of the present invention;

[0043] Figure 2 A flowchart of preprocessing and feature extraction of environmental feature information to obtain blade surface force and initial distribution characteristics provided by an embodiment of the present invention;

[0044] Figure 3 A flowchart of an embodiment of the present invention for inputting final material combination data into a wear prediction model and analyzing blade surface stress distribution and wear rate using numerical simulation to obtain an estimated value of blade wear distribution during long-term operation;

[0045] Figure 4 The embodiment of the present invention provides a flowchart for adjusting the strength and wear resistance balance parameters in the wear prediction model using an iterative optimization algorithm based on the wear distribution estimate to obtain the final wear resistance design solution. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0047] First, as Figure 1 As shown, the present application provides a wear-resistant optimization design method for turbine runner blades, which may include:

[0048] Step S101, obtaining environmental characteristic information of the turbine runner blades during operation; preprocessing and feature extraction of the environmental characteristic information to obtain blade surface force and initial distribution characteristics; the environmental characteristic information includes at least one of water flow impact data, particle erosion parameters, flow field velocity, pressure distribution and sediment content information.

[0049] First, obtain information about the environmental characteristics of the turbine runner blades during operation. The turbine operating environment is complex and highly variable. Water impact data can directly reflect key factors such as the force and frequency of water impact on the blades, which directly affect blade fatigue and wear. Particle erosion parameters involve the size, hardness, concentration, and erosion angle of particles carried in the water, which significantly erode the blade surface. Flow velocity and pressure distribution determine the flow pattern and pressure variations around the blades, playing a key role in the forces acting on the blades. Sediment content information reflects the amount of sediment in the water. As the primary abrasive medium, sediment content directly affects the rate of blade wear. After obtaining at least one of these environmental characteristics, preprocessing is performed to eliminate noise and outliers, and to perform data normalization to improve data quality. Feature extraction is then performed. Using specific algorithms and techniques, key features are extracted from the preprocessed data to obtain information that accurately describes the forces acting on the blade surface and their initial distribution characteristics.

[0050] Step S102, based on the blade surface force and initial distribution characteristic data, calculate the stress distribution of the blade under different working conditions to obtain the dynamic load spectrum of the blade surface; use the clustering algorithm to classify the high-pressure area position in the dynamic load spectrum and use the formula to calculate the stress gradient of different areas to obtain the stress gradient distribution map associated with the material wear characteristics.

[0051] Step S103, matching and screening the stress gradient distribution map based on the preset fatigue threshold parameters of different material types to obtain candidate material combinations; performing multi-axis stress simulation tests on the candidate material combinations to generate stress-strain response curves of the mechanical behavior of each combination material under different stress states; extracting the peak pressure decay rate index data based on the stress-strain response curves to obtain the final material combination that meets the peak pressure decay rate index.

[0052] Based on the acquired and processed data on the forces and initial distribution characteristics of the blade surface, specialized mechanical models and calculation methods are used to precisely determine the locations of the leading edge and high-pressure areas on the back of the runner blades. These two areas are subject to significant water flow impact and pressure during turbine operation, making them prone to wear. Furthermore, precise calculation of the peak forces experienced in these areas is crucial for assessing blade wear risk. This peak force directly reflects the extreme mechanical conditions faced by the blade at that location and is crucial for assessing blade wear risk. Furthermore, to effectively improve the wear resistance of blades, a comprehensive and detailed comparison of various wear-resistant materials is required. This evaluation considers multiple factors, including hardness, toughness, corrosion resistance, fatigue resistance, and compatibility with the turbine operating environment, to comprehensively weigh the advantages and disadvantages of different materials in addressing wear issues in different blade locations. After rigorous comparative analysis, the optimal material combination for easily worn areas such as the leading edge of the blade and the high-pressure area on the back of the blade was finally determined. This ensures that the turbine runner blades have excellent wear resistance in complex and harsh operating environments, extends their service life, and improves the overall operating efficiency and reliability of the turbine.

[0053] In step S104 , the final material combination data is input into the wear prediction model and numerical simulation is used to analyze the stress distribution and wear rate on the blade surface to obtain an estimated wear distribution value of the blade during long-term operation.

[0054] The final material combination data is fully input into a pre-built wear prediction model. This wear prediction model, built on extensive experimental data, theoretical analysis, and complex mathematical algorithms, accurately simulates the wear process of turbine runner blades under actual operating conditions. Leveraging advanced numerical simulation techniques, the model conducts an in-depth analysis of the surface stress distribution of the blades under various operating conditions. By comprehensively incorporating factors such as blade geometry, material properties, and operating environment parameters, the numerical simulation meticulously simulates the dynamic changes in blade surface stress under complex forces such as water impact and particle erosion. The model also accurately calculates the blade wear rate, taking into account factors such as material wear characteristics, stress conditions, and the interaction between water flow and particles. By continuously simulating different stages of the blade's long-term operation, an estimate of the blade's wear distribution over its entire operating cycle is derived. This estimate is presented in intuitive and detailed data and charts.

[0055] Step S105 , adjusting the strength and anti-wear balance parameters in the wear prediction model using an iterative optimization algorithm according to the wear distribution estimate, to obtain a final anti-wear design solution.

[0056] The wear distribution estimate intuitively and in detail presents the expected wear conditions at various blade locations during long-term operation. An iterative optimization algorithm, through continuous iterations, fine-tunes the strength and wear balance parameters within the wear prediction model. The strength parameter correlates to the blade material's ability to resist external forces, while the wear balance parameter determines how well the material resists abrasive forces such as water erosion and particle erosion. During this adjustment process, the algorithm intelligently determines the deviation between the current model parameter settings and the expected wear performance based on the wear distribution estimate. If the wear estimate is found to be excessive in certain areas, indicating that the current balance between strength and wear performance is tilted toward insufficient strength or poor wear performance, the algorithm will accordingly increase the weight of the wear balance parameter or fine-tune the strength parameter, or vice versa. Through multiple rounds of iterative calculations, the model parameters gradually approach the optimal solution, ultimately resulting in a final wear design that accurately reflects the actual wear requirements of the blade. This design comprehensively considers the wear characteristics of various blade components under complex operating conditions, ensuring that strength requirements are met while maximizing the overall wear performance of the blade, providing a solid foundation for the efficient, stable, and long-term operation of turbine runner blades.

[0057] The aforementioned wear-resistant optimization design method for turbine runner blades obtains environmental characteristic information during turbine runner operation, including water impact data, particle erosion parameters, flow field velocity, pressure distribution, and sediment content information, and then preprocesses and extracts characteristics to obtain blade surface force and initial distribution characteristics. Based on these characteristics, the location and peak force of the high-pressure areas at the leading edge and back of the blade are calculated, and a variety of wear-resistant materials are compared to determine the final material combination. The final material combination data is then input into a wear prediction model, and numerical simulation is used to analyze the blade surface stress distribution and wear rate, thereby deriving an estimated wear distribution value for the blade during long-term operation. Finally, based on this estimated value, an iterative optimization algorithm is used to adjust the strength and wear resistance balance parameters in the wear prediction model to obtain the final wear-resistant design solution. This method achieves an optimal balance between strength and wear resistance, extends the service life of turbine runner blades, reduces equipment maintenance costs and replacement frequency, improves turbine operation stability and power generation efficiency, and effectively promotes the sustainable development of the hydropower industry.

[0058] In one embodiment, Figure 2 As shown, preprocessing and feature extraction of environmental feature information to obtain blade surface force and initial distribution characteristics may include the following steps:

[0059] Step S201 : noise filtering is performed based on environmental feature information using wavelet transform and sliding window filtering technology, and outliers are removed to obtain a stable feature data set.

[0060] Step S202: Using a convolutional neural network to perform feature mining on the feature data set, a potential feature subset related to the blade is obtained; the feature subset includes at least one of a water flow impact frequency feature, a particle size distribution feature, and a pressure fluctuation feature.

[0061] Step S203 , performing correlation analysis on the feature subset using a method combining Pearson correlation coefficient and grey correlation analysis to obtain key features reflecting the surface forces and initial distribution of the blade.

[0062] Step S204: perform principal component analysis, dimensionality reduction, and information fusion on the key features to obtain fused feature parameters.

[0063] Step S205 , inputting the trained fluid-structure coupling analysis model based on the fusion characteristic parameters, and obtaining the blade surface force and initial distribution characteristics.

[0064] First, based on the acquired environmental feature information, wavelet transform and sliding window filtering techniques are used to filter noise and eliminate outliers, thereby obtaining a stable feature data set. Next, a convolutional neural network is used to carry out feature mining on this data set to obtain a subset of potential blade-related features that includes at least one of the following: water flow impact frequency characteristics, particle size distribution characteristics, and pressure fluctuation characteristics. Subsequently, a correlation analysis is performed on the feature subset using a combination of Pearson correlation coefficient and grey correlation analysis to accurately extract key features reflecting the force and initial distribution on the blade surface. Afterwards, principal component analysis is used to reduce the dimension of the key features and fuse the information to generate fused feature parameters. Finally, the fused feature parameters are input into the trained fluid-solid coupling analysis model to successfully obtain the force and initial distribution characteristics on the blade surface.

[0065] Noise filtering and outlier removal are performed through wavelet transform and sliding window filtering techniques, ensuring the reliability and stability of the data and laying a solid foundation for subsequent analysis. The use of convolutional neural networks efficiently mines potential features and broadens the cognitive dimension of blade-related characteristics. The combination of Pearson correlation coefficient and grey correlation analysis accurately locates key features and improves the accuracy of analysis. Principal component analysis dimensionality reduction and information fusion reduce data redundancy and improve computational efficiency, and the fused feature parameters can more comprehensively reflect blade characteristics. Finally, the fluid-solid coupling analysis model, based on the blade surface force and initial distribution characteristics output by the fused feature parameters, provides an extremely critical and accurate basis for anti-wear optimization design, effectively ensuring the stable operation and anti-wear performance improvement of turbine runner blades in complex environments.

[0066] In one embodiment, calculating the positions and peak force values ​​of the leading edge and back high-pressure areas based on the force and initial distribution characteristics of the blade surface and comparing multiple wear-resistant materials to obtain a final material combination may include the following steps:

[0067] Step S301, based on the blade surface force and initial distribution characteristic data, calculate the stress distribution of the blade under different working conditions to obtain a dynamic load map of the blade surface; the dynamic load map includes the location, size, and erosion risk level of the stress concentration area.

[0068] Step S302 , using a clustering algorithm to classify the high-pressure area positions in the dynamic load spectrum and using a formula to calculate the stress gradients of different areas, to obtain a stress gradient distribution diagram associated with the material wear characteristics.

[0069] Step S303 , matching and screening the stress gradient distribution map based on the fatigue threshold parameters preset for different material types to obtain candidate material combinations; the candidate material combinations must simultaneously meet the interface bonding strength constraint and the overall toughness constraint.

[0070] Step S304 : performing a multi-axial stress simulation test on the candidate material combinations to generate stress-strain response curves of the mechanical behavior of each combination of materials under different stress states.

[0071] Step S305 , extracting peak pressure decay rate index data according to the stress-strain response curve to obtain a final material combination that meets the peak pressure decay rate index.

[0072] Specifically, relying on data on the force and initial distribution characteristics of the blade surface, in-depth calculations were performed on the stress distribution of the blade under various operating conditions. This led to the generation of a detailed dynamic load spectrum of the blade surface, which clearly displays key information such as the location, range, peak stress sequence, and erosion risk level of the stress concentration areas. Subsequently, a clustering algorithm was used to classify the high-pressure areas in the dynamic load spectrum, and the stress gradients in different regions were calculated using a specific formula to create a stress gradient distribution map that is closely related to the material wear characteristics. Based on pre-set fatigue threshold parameters for different material types, the stress gradient distribution maps were precisely matched and screened, strictly adhering to the interface bonding strength and overall toughness constraints to obtain candidate material combinations. Next, multiaxial stress simulation tests were conducted on the candidate material combinations to generate stress-strain response curves corresponding to the mechanical behavior of each material combination under different stress states. Finally, peak pressure decay rate index data was extracted from the stress-strain response curves. After detailed comparative analysis, the final material combination that met the peak pressure decay rate index requirements was determined.

[0073] This embodiment calculates the stress distribution under different working conditions and generates dynamic load maps, which can provide a comprehensive and intuitive insight into the stress conditions of the blades in a complex operating environment, providing basic data support for subsequent analysis. Clustering algorithms and stress gradient calculations help to clarify the relationship between stress characteristics and material wear in different regions, and point out the direction for material screening. Matching screening based on fatigue threshold parameters, combined with dual constraints, greatly narrows the range of candidate materials and improves screening efficiency and accuracy. The stress-strain response curve generated by the multi-axis stress simulation test truly shows the mechanical behavior of the material under different stresses, providing an intuitive basis for material performance evaluation. The final material combination is determined based on the peak pressure decay rate index to ensure that the selected materials can effectively respond to pressure changes in actual operation, significantly improve the wear resistance and service life of the turbine runner blades, and ensure the long-term stable and efficient operation of the turbine.

[0074] In one embodiment, using a clustering algorithm to classify the locations of high-pressure areas in a dynamic load spectrum and using a formula to calculate stress gradients in different areas to obtain a stress gradient distribution map associated with material wear characteristics may include the following steps:

[0075] Step S401 : Processing the data of the dynamic load spectrum using a clustering algorithm to obtain a position classification result of the high-pressure area.

[0076] Step S402 : extracting high-voltage area features from the position classification results to obtain area division boundaries.

[0077] Step S403 : Based on the region division boundary, the stress gradient value of each region is calculated using the stress gradient formula.

[0078] The stress gradient value is calculated using the following stress gradient formula:

[0079] ;

[0080] in, represents the stress gradient value, represents the spatial dimension, represents stress, and Represents the spatial coordinate components of calculated stress changes in different directions, represents the first-order stress derivative, represents the second-order stress derivative, and Indicates the change in the spatial coordinate components of the calculated stress changes in different directions.

[0081] Step S404: determining the correlation strength between the stress gradient value and the wear characteristic according to the stress gradient value, and obtaining characteristic correlation data.

[0082] Step S405 , based on the characteristic correlation data, the stress data of different regions are fitted and interpolated using the finite element method combined with a high-order interpolation function to generate a stress gradient distribution map associated with the material wear characteristics.

[0083] First, a clustering algorithm was used to systematically process the large amount of complex data contained in the dynamic load spectrum, clustering data points with similar characteristics to accurately classify the locations of high-pressure areas. Subsequently, based on this location classification, in-depth feature extraction of the high-pressure areas was carried out. Using specific algorithms and techniques, regional demarcation boundaries that clearly defined the high-pressure areas were extracted from the complex data. Next, based on the identified regional demarcation boundaries, a rigorous calculation was performed using a well-established stress gradient formula. This formula comprehensively considers factors such as spatial dimension, the spatial coordinate components for calculating stress changes in different directions, and first- and second-order stress derivatives. Through precise calculations, the stress gradient values ​​corresponding to each high-pressure area were accurately calculated. Based on the obtained stress gradient values, the correlation strength between them and wear characteristics was analyzed, and the likelihood and extent of blade wear under different stress gradients were carefully evaluated, thereby obtaining characteristic correlation data. Finally, based on this characteristic correlation data, the finite element method, combined with high-order interpolation functions, was used to perform precise fitting and interpolation operations on the stress data of different regions, transforming the discrete stress data into a continuous and intuitive graph. The resulting stress gradient distribution map was successfully generated, which is closely linked to the material's wear characteristics.

[0084] The clustering algorithm processes the dynamic load spectrum data, efficiently and accurately identifying the location of the high-pressure area, greatly improving the pertinence and efficiency of data processing. The feature extraction of the high-pressure area clearly divides the boundaries, providing a precise range definition for subsequent stress calculations. The stress gradient calculation based on professional formulas comprehensively considers multiple factors to ensure the scientificity and reliability of the obtained stress gradient value. The characteristic correlation data obtained by judging the correlation strength between stress gradient and wear characteristics provides a key basis for in-depth understanding of the blade wear mechanism. The stress gradient distribution map generated by the finite element method combined with the high-order interpolation function presents the intrinsic connection between stress distribution and material wear characteristics in an intuitive and visual way, providing an extremely important reference for the selection of wear-resistant materials, structural optimization design and operation and maintenance strategy formulation of turbine runner blades, effectively ensuring the stable operation of turbines under complex working conditions and effectively improving the wear resistance and service life of blades.

[0085] In one embodiment, Figure 3 As shown, the final material combination data is input into the wear prediction model and the blade surface stress distribution and wear rate are analyzed by numerical simulation to obtain an estimated wear distribution value of the blade during long-term operation. The following steps may be included:

[0086] Step S501: input the final material combination data into the wear prediction model to obtain material attribute parameters; the material attribute parameters include material composition and mechanical property data.

[0087] Step S502 : obtaining blade surface stress field data by finite element simulation according to material property parameters.

[0088] Step S503 , coupling the surface stress field data with the dynamic wear coefficient and updating the parameters of the wear prediction model to obtain a wear spatial distribution map; the wear spatial distribution map includes predicted values ​​of wear depth in different regions.

[0089] Step S504 : matching and verifying the wear spatial distribution map with the real-time monitoring data and performing iterative calculations based on the verification results to obtain optimized stress field distribution characteristics.

[0090] Step S505 : constructing wear accumulation curves of multiple time nodes based on the stress field distribution characteristics, and calculating an estimated wear distribution value of the blade during long-term operation.

[0091] The final material combination data, determined through multiple rounds of screening, is accurately input into a pre-built wear prediction model. Based on material-related algorithms and data reserves, the model outputs material property parameters, including material composition and mechanical properties. These material property parameters are then used to simulate the mechanical behavior of the blade under actual operating conditions using finite element simulation techniques, resulting in accurate calculations of the blade surface stress field. This surface stress field data is then deeply coupled with the dynamic wear coefficient, comprehensively considering material properties and actual wear influencing factors. This update of the wear prediction model parameters results in a detailed wear spatial distribution map, clearly displaying predicted wear depth values ​​for different regions. The wear spatial distribution map is rigorously matched and verified with real-time monitoring data, and the differences between the two are analyzed. Based on the verification results, iterative calculations are performed to continuously optimize the model's simulation accuracy of the blade stress field, resulting in an optimized stress field distribution. Finally, based on this optimized stress field distribution, wear accumulation curves are constructed at multiple time points. Through simulation and calculation of wear conditions at different time points, an estimate of the blade's wear distribution over long-term operation is derived.

[0092] By inputting the final material combination data into the wear prediction model to obtain material property parameters, accurate basic material information is provided for subsequent analysis. Finite element simulation calculates surface stress field data to realistically simulate the actual force conditions of the blades. The surface stress field data is coupled with the dynamic wear coefficient to update the model parameters and generate a wear spatial distribution map, which comprehensively considers a variety of wear-related factors and improves the accuracy of wear prediction. Matching verification and iterative calculations with real-time monitoring data ensure that the model can continuously adapt to actual operating changes and optimize the stress field distribution characteristics. Constructing a multi-time node wear accumulation curve to derive a long-term wear distribution estimate provides a key basis for preventive maintenance, replacement cycle determination, and wear-resistant design optimization of turbine runner blades, effectively ensuring the long-term stable operation of the turbine, reducing maintenance costs, and extending the service life of the blades.

[0093] In one embodiment, Figure 4 As shown, the strength and wear resistance balance parameters in the wear prediction model are adjusted using an iterative optimization algorithm based on the wear distribution estimate to obtain the final wear resistance design solution, which may include the following steps:

[0094] Step S601: Obtain material performance indicators of wear distribution estimation values.

[0095] Step S602 : adjusting the wear prediction model according to the material performance index to obtain a parameter adjustment strategy; the parameter adjustment strategy is used to balance the weight ratio of strength and wear resistance.

[0096] Step S603: Process the parameter adjustment strategy using an iterative optimization algorithm to obtain a dynamic feedback parameter update sequence.

[0097] Step S604: input the parameter update sequence into the wear prediction model, and output multiple sets of anti-wear performance simulation results.

[0098] Step S605 , extracting the environmental factor variable correction update sequence according to the anti-wear performance simulation result, and generating an optimized anti-wear parameter combination.

[0099] Step S606 , comparing the anti-wear parameter combination with the preset model convergence condition, and outputting a final anti-wear design solution if the model convergence condition is met; the final anti-wear design solution includes a matching relationship between the strength threshold and the anti-wear coefficient.

[0100] Specifically, the wear prediction model is adjusted based on material performance indicators. A parameter adjustment strategy is developed by comprehensively considering the relationship between multiple material properties, such as strength, toughness, and hardness, and the blade's wear resistance requirements. The core of this strategy is to rationally balance the weights of strength and wear resistance in the model, ensuring that the model more accurately reflects actual operating conditions. Subsequently, an iterative optimization algorithm is introduced to further refine the established parameter adjustment strategy. This iterative optimization algorithm dynamically adjusts parameters based on the results of the previous calculation cycle through repeated calculations, resulting in a series of parameter update sequences with dynamic feedback. This sequence gradually approaches the optimal solution with increasing iterations. The resulting parameter update sequences are sequentially input into the wear prediction model. Based on these updated parameters, the model simulates the blade's wear resistance under different parameter combinations, outputting multiple sets of wear performance simulation results. From these simulation results, environmental variables, such as water velocity, sediment content, and water temperature, that have a significant impact on blade wear resistance, are carefully extracted. These environmental variables are then modified to produce optimized wear parameter combinations. Finally, the optimized anti-wear parameter combination is strictly compared with the pre-set model convergence conditions. If the convergence conditions are met, it means that the model has reached a stable and accurate state. At this time, the final anti-wear design scheme is output, which clearly shows the precise matching relationship between the strength threshold and the anti-wear coefficient.

[0101] This embodiment adjusts the wear prediction model based on material performance indicators to make the model more suitable for actual application scenarios and improve prediction accuracy. The iterative optimization algorithm generates a parameter update sequence with dynamic feedback, effectively exploring the parameter space and improving optimization efficiency. Multiple sets of anti-wear performance simulation results provide rich data for subsequent analysis, which is convenient for comprehensively evaluating the effects of different parameter combinations. Based on the environmental factor variable correction update sequence, the impact of the actual operating environment on blade wear is fully considered to enhance the practicality of the solution. The anti-wear parameter combination is compared with the model convergence condition to ensure that the final anti-wear design scheme output is stable and reliable. The matching relationship between the strength threshold and the anti-wear coefficient provides key guidance for the material selection, structural design and operation and maintenance of the turbine runner blades, effectively ensuring that the blades have good wear resistance in complex environments, extending the service life, and improving the stability and economy of the turbine operation.

[0102] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0103] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the aforementioned method for optimizing the wear resistance of a turbine runner blade are implemented.

[0104] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0105] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0106] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A wear-resistant optimization design method for turbine runner blades, characterized in that: The method comprises: Acquiring environmental characteristic information of turbine runner blades during operation; preprocessing and feature extraction of the environmental characteristic information to obtain blade surface force and initial distribution characteristics; the environmental characteristic information including at least one of water flow impact data, particle erosion parameters, flow field velocity, pressure distribution, and sediment content information; The stress distribution of the blade under different working conditions is calculated based on the force and initial distribution characteristic data of the blade surface to obtain a dynamic load spectrum of the blade surface; the high-pressure area position in the dynamic load spectrum is classified using a clustering algorithm and the stress gradient of different areas is calculated using a formula to obtain a stress gradient distribution map associated with the material wear characteristics; Matching and screening the stress gradient distribution diagrams based on preset fatigue threshold parameters for different material types to obtain candidate material combinations; performing multi-axial stress simulation tests on the candidate material combinations to generate stress-strain response curves of the mechanical behavior of each combination material under different stress states; extracting peak pressure decay rate index data based on the stress-strain response curves to obtain a final material combination that meets the peak pressure decay rate index; Inputting the final material combination data into a wear prediction model and analyzing the blade surface stress distribution and wear rate using numerical simulation to obtain an estimated wear distribution value of the blade during long-term operation; The strength and wear resistance balance parameters in the wear prediction model are adjusted using an iterative optimization algorithm according to the wear distribution estimated value to obtain a final wear resistance design solution.

2. The method according to claim 1, characterized in that The preprocessing and feature extraction of the environmental feature information to obtain blade surface force and initial distribution characteristics includes: Based on the environmental feature information, noise is filtered using wavelet transform and sliding window filtering technology, and outliers are eliminated to obtain a stable feature data set; Performing feature mining on the feature data set using a convolutional neural network to obtain a potential feature subset related to the blade; the feature subset includes at least one of a water flow impact frequency feature, a particle size distribution feature, and a pressure fluctuation feature; The feature subsets are analyzed by combining the Pearson correlation coefficient and the grey correlation analysis method to obtain the key features reflecting the surface forces and initial distribution of the blades. Performing principal component analysis, dimensionality reduction and information fusion on the key features to obtain fusion feature parameters; The fused characteristic parameters are input into the trained fluid-solid coupling analysis model to obtain the blade surface force and initial distribution characteristics.

3. The method according to claim 1, characterized in that The clustering algorithm is used to classify the high-pressure area positions in the dynamic load spectrum and the stress gradients of different areas are calculated using a formula to obtain a stress gradient distribution diagram associated with the material wear characteristics, including: Processing the data of the dynamic load spectrum using a clustering algorithm to obtain a position classification result of the high-pressure area; Performing high-voltage area feature extraction on the position classification result to obtain area division boundaries; Based on the regional division boundaries, the stress gradient value of each region is calculated using the stress gradient formula; The stress gradient value is calculated using the following stress gradient formula: ； in, represents the stress gradient value, represents the spatial dimension, represents stress, and Represents the spatial coordinate components of calculated stress changes in different directions, represents the first-order stress derivative, represents the second-order stress derivative, and Indicates the change in the spatial coordinate components of the calculated stress changes in different directions; Determining the correlation strength between the stress gradient value and the wear characteristic according to the stress gradient value to obtain characteristic correlation data; Based on the characteristic correlation data, the stress data of different regions are fitted and interpolated using the finite element method combined with a high-order interpolation function to generate a stress gradient distribution associated with the material wear characteristics.

4. The method according to claim 1, wherein The method of inputting the final material combination data into a wear prediction model and analyzing the blade surface stress distribution and wear rate using numerical simulation to obtain an estimated wear distribution value of the blade during long-term operation includes: Inputting the final material combination data into a wear prediction model to obtain material property parameters; the material property parameters include material composition and mechanical property data; Obtaining blade surface stress field data using finite element simulation calculations based on the material property parameters; The surface stress field data is coupled with the dynamic wear coefficient and the parameters of the wear prediction model are updated to obtain a wear spatial distribution map; the wear spatial distribution map includes predicted values ​​of wear depth in different regions; Matching and verifying the wear spatial distribution map with the real-time monitoring data and performing iterative calculations based on the verification results to obtain optimized stress field distribution characteristics; Based on the stress field distribution characteristics, wear accumulation curves at multiple time nodes are constructed, and an estimated wear distribution value of the blade during long-term operation is calculated.

5. The method according to claim 1, wherein The method of adjusting the strength and wear resistance balance parameters in the wear prediction model using an iterative optimization algorithm according to the wear distribution estimate to obtain a final wear resistance design solution includes: Obtaining material performance indicators of the wear distribution estimate; The wear prediction model is adjusted according to the material performance index to obtain a parameter adjustment strategy; the parameter adjustment strategy is used to balance the weight ratio of strength and wear resistance; Processing the parameter adjustment strategy using an iterative optimization algorithm to obtain a parameter update sequence with dynamic feedback; Inputting the parameter update sequence into the wear prediction model to output multiple sets of anti-wear performance simulation results; Extracting environmental factor variables based on the anti-wear performance simulation results to modify and update the sequence, and generating an optimized anti-wear parameter combination; The anti-wear parameter combination is compared with a preset model convergence condition, and if the model convergence condition is met, a final anti-wear design solution is output; the final anti-wear design solution includes a matching relationship between a strength threshold and an anti-wear coefficient.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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