A method for predicting the fatigue life of turbine runners based on wear and stress mapping and a method for predicting the fatigue life of water turbines.

Abstract

This invention discloses a method for predicting the fatigue life of a turbine runner based on wear and stress mapping, and a corresponding turbine. The method includes determining relevant basic parameters through fatigue wear tests on the runner material; constructing an S-N degradation curve under silt wear based on the characteristics of the power station sediment; establishing the correlation between the fatigue resistance coefficient and the attenuation of wear; iteratively solving a design load condition table including the number of start-stop cycles and the duration of zoned operation based on the hydrological and unit output characteristics of the power station; constructing a stress mapping model; obtaining the full-condition real stress field of the appropriate condition table using CFD fluid-structure interaction calculations; performing mean stress correction using the Goodman formula; calculating steady-state high-cycle fatigue damage and start-stop low-cycle fatigue damage using the Miner criterion and the Morrow formula respectively; and superimposing the two types of damage to obtain the fatigue life of the runner. This method solves the problems of traditional fixed S-N curves not considering material wear degradation, large simulation stress deviations, and inaccurate condition assignments, effectively improving the accuracy of runner fatigue life prediction and ensuring the safe and stable operation of the turbine.

Description

Technical Field

[0001] This invention belongs to the field of turbine runner life prediction technology, specifically relating to a method for predicting runner fatigue life based on wear and stress mapping and a turbine. Background Technology

[0002] As a core component for energy conversion in power plants, the fatigue life of the turbine runner directly determines the operational safety and service life of the unit. Current fatigue life assessments of turbine runners largely rely on traditional fatigue theory, using simulations and empirical data for prediction during the design phase. Existing technologies often employ fixed SN curves for life estimation. These curves are based solely on initial material performance tests and do not consider material performance degradation caused by long-term silt wear. Static SN curves are insufficient to characterize the decay of fatigue strength with wear, easily leading to overly optimistic life predictions. Furthermore, current stress analyses rely solely on CFD simulations under idealized steady-state conditions, failing to reproduce the true stress state of the unit during unsteady multi-condition coupled operation, resulting in significant discrepancies between design and actual stresses. In addition, current operating condition durations and start-up / shutdown frequencies are often roughly assigned based on experience, without precise matching with power plant hydrology, unit output, and grid dispatch characteristics, leading to distorted basic data for fatigue damage calculations. Summary of the Invention

[0003] The purpose of this invention is to provide a method for predicting the fatigue life of a turbine runner based on wear and stress mapping, and a turbine in general, to solve the technical defects of existing technologies that use fixed SN curves to ignore the degradation of material properties caused by sediment wear, have large deviations between idealized CFD simulations and actual stresses of the actual machine, and rely on empirically assigned operating parameters with low accuracy.

[0004] To achieve the above objectives, this application provides the following technical solution: A first aspect of this application provides a method for predicting the fatigue life of a turbine runner based on wear-stress mapping, comprising: The initial fatigue resistance coefficient, wear rate, and fitting constant were determined based on the fatigue wear test of the turbine material. The SN degradation curve under silt wear was constructed by combining the initial fatigue characteristics of the material with the characteristics of the power station sediment. The wear amount was calculated based on the wear rate, and the decay correlation between the fatigue resistance coefficient and the wear amount was established. Based on the SN degradation curve, combined with the hydrological characteristics of the power station and the power output characteristics of the unit, the design load condition table including the number of start-stop times and the operating time of the zone is obtained by iterative solution. A stress mapping model is constructed based on the design stress and actual measured stress of the same type of unit. The full-condition real stress field that is adapted to the operating condition table is obtained by combining CFD fluid-structure interaction calculation. The average stress is corrected using the Goodman formula for the actual stress field. The Miner criterion is used to calculate high-cycle fatigue damage for steady-state conditions, and the Morrow formula is used to calculate low-cycle fatigue damage for start-stop conditions. The fatigue life of the runner is obtained by superimposing the two types of damage.

[0005] Furthermore, when constructing the SN degradation curve under the aforementioned sediment abrasion, the following is also included: Establish the correlation between fatigue resistance index and wear amount; The fatigue resistance index is determined by the initial fatigue resistance index, the saturated fatigue resistance index, and the fitting constant determined by the fatigue wear test of the wheel material.

[0006] Furthermore, a preset wear database is retrieved, and regression fitting calculation is performed on the wear rate obtained through the fatigue wear test of the wheel material to solve for the wear amount of the wheel under the corresponding actual working conditions; The wear database establishes a correlation and matching relationship with various parameters such as operating head, turbine generator capacity, sediment particle size, and sediment concentration, and collects and stores the basic data of measured wear rate corresponding to each parameter combination under the operating conditions.

[0007] Furthermore, the design load condition table is iteratively solved using a predictive correction iterative algorithm; wherein, during the iterative solution process, the actual net head of the power station is used as the convergence criterion, and the head parameter correction and dynamic allocation of unit load are completed sequentially. After the iterative calculation meets the convergence condition, the solidified design load condition table is output.

[0008] Furthermore, the design load condition table obtained through iterative solution is divided into a low-load zone, a hydraulic vibration zone, and a steady-state operation zone according to the operating characteristics of the hydro-generator unit; The design load condition table corresponds to the collection of the continuous operating time of the units under each operating zone, and at the same time matches and counts the number of annual start-up and shutdown cycles of the units.

[0009] Furthermore, the construction of the stress mapping model specifically includes: Normalization preprocessing was performed on the design stress parameters and the actual measured stress parameters of the runner structure, respectively. The preprocessed design stress parameters and the measured stress parameters of the actual machine are trained and modeled using parameter fitting or machine learning algorithms to establish a quantitative mapping relationship between the design stress and the measured stress, thus completing the construction of the stress mapping model.

[0010] Furthermore, the CFD fluid-structure interaction calculation uses the turbine runner as the calculation component, and matches the low-load operation condition, hydraulic vibration zone condition, steady-state operation condition and start-up and shutdown transition operation condition of the unit to perform unsteady transient fluid-structure interaction numerical calculation on the flow structure domain and fluid domain of the runner.

[0011] Furthermore, the true stress field under all working conditions, adapted to the aforementioned working condition table, is obtained, specifically including: The stress coefficients of the impeller obtained by CFD fluid-structure interaction solution are normalized and preprocessed, and the stress coefficient mapping is corrected by stress mapping model. The corrected stress coefficients are then denormalized and restored to their original values, and a true stress field under all working conditions is generated by fitting the data.

[0012] Furthermore, the Miner criterion is used to calculate high-cycle fatigue damage under steady-state conditions, and the Morrow formula is used to calculate low-cycle fatigue damage under start-up and shutdown conditions. Specifically, this includes: The small-amplitude high-frequency cyclic stress damage generated by the low-load operation, hydraulic vibration zone, and steady-state operation of the unit on the runner structure is uniformly classified as the high-cycle fatigue cumulative damage of the runner. The large-amplitude low-frequency alternating stress damage generated by the unit start-up and shutdown transition conditions on the runner structure is defined as the low-cycle fatigue cumulative damage of the runner.

[0013] A second aspect of this application provides a water turbine, comprising: Turbine runner, flow channel assembly, unit operation monitoring module and operating condition control module; The turbine runner is predicted to have its full life cycle fatigue life under the coupled stress of silt wear using the runner fatigue life prediction method based on wear and stress mapping as described above. The unit operation monitoring module collects real-time data on the power station's operating head, sediment particle size, sediment concentration, unit capacity, and turbine runner operating stress parameters. The operating condition control module matches and defines the operating time of the unit in the low load zone, hydraulic vibration zone, and steady-state operation zone based on the fatigue life prediction results, and optimizes the annual start-up and shutdown cycle allocation of the unit to suppress the high-cycle and low-cycle fatigue cumulative damage of the runner caused by the combined action of silt wear and alternating stress.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. By constructing the SN degradation curve under silt abrasion, the shortcomings of existing technologies that use fixed SN curves and do not consider the degradation of turbine material performance caused by silt abrasion are overcome. This can accurately characterize the fatigue strength decay law and avoid the problem of overly optimistic life prediction. By combining the hydrology of the power station and the output characteristics of the unit to iteratively solve the design load condition table, the experience-based rough assignment method is abandoned, and the partitioned operating time and start-stop frequency are accurately matched to correct the distortion of the basic data for fatigue damage calculation. By building a stress mapping model and combining it with CFD fluid-structure interaction to solve the real stress field under all operating conditions, the drawbacks of traditional single idealized steady-state simulation and large deviation between actual machine stress are overcome. After average stress correction and sub-criteria calculation to superimpose two types of fatigue damage, the accuracy of turbine fatigue life prediction is greatly improved, providing a reliable technical basis for the safe and stable operation and structural optimization design of the turbine.

[0015] 2. When constructing the SN degradation curve under silt abrasion, the correlation between the fatigue resistance index and the wear amount is established. The initial fatigue resistance index, saturated fatigue resistance index and fitting constant determined by experiments are used for calculation. This can more accurately characterize the decay law of fatigue performance of the turbine material under long-term silt erosion, making the SN curve more consistent with the actual material degradation process. This effectively makes up for the defect that the fixed SN curve cannot reflect the dynamic performance deterioration, improves the authenticity and reliability of fatigue life prediction, and provides a more accurate material performance benchmark for subsequent damage calculation.

[0016] 3. By retrieving the preset wear database and performing regression fitting on the test wear rate, the wear amount of the runner is obtained. This makes the wear calculation strongly correlated with actual operating parameters such as operating head, unit capacity, sediment particle size, and sediment concentration. This eliminates the problem of large deviations in traditional theoretical estimation, significantly improves the accuracy of wear amount calculation, ensures that the SN degradation curve matches the actual operating environment of the power station, enhances the applicability of fatigue life prediction to hydropower stations in rivers with high sediment content, and strengthens the engineering reference value of the prediction results.

[0017] 4. The predictive correction iterative algorithm is adopted to solve the design load condition table based on the actual net head of the power station. This enables dynamic correction of head parameters and automatic allocation of unit load, solving the distortion problem caused by the traditional reliance on empirical assignment of operating parameters. This makes the design load condition table more consistent with the hydrological and power output characteristics of the power station, ensuring the accuracy and reliability of basic data such as operating duration and start-up / shutdown frequency, and providing accurate boundary conditions for subsequent stress field calculation and fatigue damage assessment.

[0018] 5. The design load condition table is divided into low load zone, hydraulic vibration zone and steady-state operation zone according to operating characteristics. The running time and annual start-stop cycle number of each zone are statistically analyzed to achieve a refined classification of unit operating conditions. This can distinguish the different impacts of different load characteristics on runner fatigue, provide a clear basis for calculating high-cycle and low-cycle fatigue damage separately, avoid calculation errors caused by confusion of operating conditions, and improve the accuracy of fatigue damage accumulation and life prediction.

[0019] 6. By normalizing and preprocessing the design stress and the actual measured stress, and by using parameter fitting or machine learning to construct a stress mapping model, the effects of dimensional differences, unit size and installation deviations can be effectively eliminated. A quantitative relationship between design stress and actual stress can be established, the systematic deviation between traditional CFD simulation and actual stress can be corrected, the accuracy of stress calculation can be greatly improved, and reliable stress data support can be provided for fatigue life prediction.

[0020] 7. Using the runner as the calculation component, unsteady transient CFD fluid-structure interaction calculations are carried out for low load, hydraulic vibration, steady state and start-stop transition conditions. It can realistically reproduce the stress state of the runner under multi-condition coupling and unsteady flow, overcome the shortcomings of traditional single steady-state simulation that is divorced from actual operation, obtain stress distribution that is closer to the real machine, provide a high-precision load basis for the generation of real stress field under all conditions, and improve the reliability of fatigue prediction.

[0021] 8. The CFD stress coefficient normalization, stress mapping model correction and denormalization process generate a real stress field under all working conditions. The simulated stress can be accurately converted into the equivalent stress of the real machine, which fully covers all operating conditions in the design load condition table, accurately reflects the actual stress state of the runner under complex working conditions, eliminates the deviation between the design stress and the measured stress, and provides a reliable stress input for subsequent average stress correction and fatigue damage calculation.

[0022] 9. Small-amplitude high-frequency stress is classified as high-cycle fatigue damage, and large-amplitude low-frequency stress is classified as low-cycle fatigue damage. The Miner criterion and Morrow formula are used for calculation respectively, which conforms to the actual load characteristics of the runner, takes into account both continuous operation and start-stop impact damage mechanisms, avoids the errors caused by a single calculation model, realizes accurate layered accounting of fatigue damage, and improves the scientificity and accuracy of total damage superposition and life prediction results. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart of a wheel fatigue life prediction method based on wear and stress mapping provided by the present invention; Figure 2 A schematic diagram of the SN degradation curve of a runner in a wear and stress mapping-based method for predicting the fatigue life of a runner provided by this invention; Figure 3 This invention provides a schematic diagram of the operating zones of a water turbine. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0028] To address the technical deficiencies mentioned in the background section, this embodiment provides a method for predicting the fatigue life of a turbine runner based on wear and stress mapping, and a water turbine.

[0029] The present invention will now be described in further detail with reference to the accompanying drawings: like Figures 1-2 As shown, in a first aspect of the present invention, a method for predicting the fatigue life of a wheel auger based on wear and stress mapping is provided, comprising: S101. Based on the fatigue wear test of the turbine material, determine the initial fatigue resistance coefficient, wear rate and fitting constant. Combine the initial fatigue characteristics of the material with the characteristics of the power station sediment to construct the SN degradation curve under sediment wear. Calculate the wear amount based on the wear rate and establish the decay correlation between the fatigue resistance coefficient and the wear amount. For example, fatigue wear test of the impeller material is first carried out. A stainless steel test block consistent with the actual impeller is selected and fatigue wear coupling test is carried out in a sandy water flow environment. The initial fatigue resistance coefficient, mud and sand wear rate and fatigue performance fitting constant of the material are measured. At the same time, the initial fatigue characteristic parameters of the material in the unworn state are obtained to form the original SN curve basic data.

[0030] The original SN curve characterizes the fatigue performance of the material when there is no wear, and only reflects the initial mechanical properties. It cannot reflect the performance degradation after long-term service. In the process of constructing the SN degradation curve under mud and sand wear, it is also necessary to establish the correlation between the fatigue resistance index and the wear amount. The fatigue resistance index is determined by the initial fatigue resistance index, the saturated fatigue resistance index and the fitting constant determined by the fatigue wear test of the wheel material.

[0031] By combining the characteristics of the power station sediment, including sediment particle size, sediment concentration, operating head and unit capacity, a SN degradation curve that can reflect the erosion effect of sediment is constructed. In order to characterize the weakening effect of wear on fatigue performance, the relationship between the fatigue resistance coefficient and the amount of wear is established. The fatigue resistance coefficient shows a monotonically decreasing trend as the wear rate increases.

[0032] Simultaneously, the relationship between the fatigue resistance index and wear was established. The absolute value of the fatigue resistance index gradually decreases as wear intensifies. The wear of the runner was calculated by retrieving a pre-set wear database and using regression fitting. The wear database was correlated with various parameters, including operating head, turbine generator capacity, sediment particle size, and sediment concentration, and the basic data of measured wear rates under various parameter combinations were collected and stored. In practical applications, the pre-set wear database was retrieved, and regression fitting calculations were performed on the wear rates obtained from fatigue wear tests of the runner material. Combined with the design head, rated capacity, and sediment conditions of the target power station, the runner wear under the corresponding actual operating conditions was calculated.

[0033] The fatigue resistance coefficient and fatigue resistance index are updated generation by generation to generate a family of SN curves that continuously degrade with the length of service life. For example... Figure 2 As shown, the horizontal axis represents the number of fatigue cycles, and the vertical axis represents the stress amplitude. The curves sequentially show the SN degradation trend after the initial state (t=0), 1 year of operation, 2 years of operation, 3 years of operation, 4 years of operation, and 5 years of operation.

[0034] As wear accumulates, the number of fatigue cycles corresponding to the same stress amplitude gradually decreases, and the curve shifts to the lower left, which intuitively reflects the law that the fatigue strength of the impeller material continuously decreases due to mud and sand wear. The construction of the SN degradation curve and the establishment of the correlation between fatigue resistance coefficient decay are completed, providing a dynamic material performance basis for subsequent fatigue damage calculation.

[0035] In this embodiment, the original SN curve of the turbine runner, , The fatigue resistance coefficient, The initial fatigue resistance index is used; further, the degradation SN curve after wear is constructed, characterized by a decrease in the number of cycles under the same stress amplitude, mainly affected by surface morphology, material, etc., and the dynamic fatigue coefficient under mud and sand wear is constructed. As the silt wear rate decreases, the fatigue resistance coefficient decay function is established as follows:

[0036] In the formula The initial fatigue resistance coefficient was determined experimentally. For wear rate, It is a constant, determined through experiments.

[0037] Furthermore, the dynamic fatigue coefficient under sediment abrasion was constructed. The absolute value decreases as the silt abrasion rate decreases.

[0038]

[0039] In the formula This refers to the amount of wear. This represents the initial fatigue resistance index. The saturation fatigue resistance index. It is a constant. , , Determined through experiments.

[0040] Furthermore, a method for calculating power plant wear was established, including the wear of the runner. Wear rates can be determined through a wear database or through theoretical calculations. The steps for determining wear rates through a wear database are as follows: First, statistically analyze the changes in wear rates over time for the same type of generator unit with different heads and capacities. Simultaneously, statistically analyze the corresponding power station's sediment characteristic parameters, including sediment particle size and sediment concentration, to obtain a typical wear rate database. , For different power plants, the target head H, unit capacity M, sediment particle size d, and sediment concentration c were obtained based on the design data. The wear and tear of the target power plant was then obtained through data regression.

[0041] Furthermore, the SN curves for wear degradation are obtained:

[0042] S102. Based on the SN degradation curve, combined with the hydrological characteristics of the power station and the output characteristics of the unit, the design load condition table including the number of start-stop times and the operating time of the zone is obtained by iterative solution. For example, after obtaining the SN degradation curve under silt abrasion, the curve is used as the benchmark for the fatigue performance of the runner. The hydrological characteristic parameters of the power station and the output characteristic parameters of the turbine generator unit are integrated, and the design load condition table is iteratively solved using a predictive correction iterative algorithm.

[0043] Among them, the hydrological characteristics of the power station mainly include the inflow process, reservoir water level and capacity characteristics, upstream and downstream water level correlation and head loss parameters of the water diversion system; the unit output characteristics cover the coupling mapping relationship of unit flow, head, and power, maximum and minimum output limits, load regulation constraints and grid dispatch load demand characteristics.

[0044] In the iterative solution process, the actual net head of the power plant is used as the convergence criterion. During the calculation, the head parameters are corrected in real time and the unit load is dynamically allocated. After the iterative calculation starts, the net head for each time period is estimated based on the initial hydrological boundary and load allocation strategy. Then, the load of the entire plant is broken down and the load of each unit is allocated according to the unit output characteristics. At the same time, the unit's operating or shutdown status is determined, and the frequency of unit start-up and shutdown is preliminarily counted. Subsequently, hydrological calculations are carried out in combination with the water balance principle to correct the actual water level and the true net head of the upstream and downstream. The difference between the corrected net head and the iteratively estimated head is compared. When the difference is less than the preset allowable error threshold, the iterative convergence is determined. If the convergence condition is not met, the head parameters are updated and the load allocation and status determination are carried out again. The cycle is iterated until the convergence requirements are met.

[0045] After the iterative calculation meets the convergence condition, based on the operating characteristics of the hydro-generator unit, the unit's operating range is automatically divided into three major characteristic ranges: low-load operating range, hydraulic vibration range, and steady-state operating range. The continuous operating time of the unit corresponding to each operating zone is collected and statistically analyzed in time period, and the annual start-up and shutdown cycle number of the unit is calculated simultaneously. The design load condition table, which is solidified, structurally complete, and whose data matches the actual operating law of the power station, is output, providing standardized operating condition boundary conditions for subsequent full-condition real stress field solution and fatigue damage layered calculation.

[0046] In this embodiment, basic data models such as the hydrological characteristics, load commands, and unit output characteristics of the designed power station are acquired and established. The specific operational constraints of the hydropower units are clarified, including hydrological characteristic modeling, unit output characteristic modeling, and determination of input boundary conditions. The reservoir water level-storage capacity relationship and the tailrace water level-discharge flow relationship are established. The reservoir flow change at each time period is equal to the reservoir inflow minus the total power generation flow minus the ecological and wastewater discharge.

[0047] Based on the number of generating units, a three-dimensional relationship surface of flow-head-power for each unit is constructed. Boundary conditions are input to obtain the total load curve of the power plant throughout the year, as well as the inflow process and load allocation strategy of each unit throughout the year. Based on this, unit constraints such as output-load matching constraints, maximum and minimum output constraints, and vibration zone constraints, as well as hydrological constraints such as water level constraints, reservoir capacity constraints, and water balance constraints are determined.

[0048] Furthermore, load allocation and operating condition analysis are performed based on a data model of hydrological characteristics, load commands, and unit output characteristics. Since the head is affected by the discharge flow, and the discharge flow depends on both the head and load allocation, the two are strongly coupled. Further, load allocation is performed based on a preset load allocation strategy, distributing the total load to n units and calculating the power and start / stop status variables for each unit, where 1 represents operation and 0 represents shutdown.

[0049] Furthermore, power balance constraints are established:

[0050] Furthermore, by calling the flow-head-power relationship model, the flow rate of a single unit is calculated and summed to obtain the total discharge flow rate of the entire plant.

[0051] Further, hydrological calculations and head corrections are performed to calculate the current reservoir capacity, look up tables to calculate upstream and downstream water levels, and calculate the corrected net head.

[0052] in Let t be the upstream water level of the reservoir. Let t be the downstream water level. This represents the head loss in the water diversion system. Finally, a convergence criterion is performed:

[0053] If ε is the set allowable error limit, then the iteration ends, and the current value is recorded. , u and Otherwise, let k = k +1, return to reallocation.

[0054] Furthermore, throughout the year, the number of unit start-ups and shutdowns is statistically analyzed, and the operating area and duration are determined and accumulated. By comparing the start-up and shutdown status variables ui,t at adjacent times, the annual start-up count of the i-th unit is calculated. and number of downtime :

[0055]

[0056] Furthermore, a division of the power plant operation area is established, dividing the operation area into a low-load zone, a vibration zone, and a stable zone. The low-load zone, vibration zone, and stable zone are determined based on historical power plant statistical data.

[0057] Furthermore, the system outputs an annual operating condition data table for the hydropower units. After completing the calculations for T time periods throughout the year, the system outputs the following data, including the annual power generation, total operating hours, number of starts, number of shutdowns, and the operating hours and percentages of each unit in the low-efficiency zone, vibration zone, and stable zone.

[0058] S103. Based on the design stress and actual measured stress of the same type of unit, a stress mapping model is constructed, and the full-condition real stress field that is adapted to the operating condition table is obtained by CFD fluid-structure interaction calculation. For example, after obtaining the solidified design load condition table, the design load condition table is obtained by iterative solution and can be divided into low load zone, hydraulic vibration zone and steady-state operation zone according to the operating characteristics of the hydro-generator unit. At the same time, the continuous running time of the unit under each operating zone is collected and the annual start-up and shutdown cycle number of the unit is matched and counted, providing a complete working condition boundary basis for subsequent multi-condition stress calculation.

[0059] Select similar hydro-turbine generator units with similar structural form, capacity level, and operating head as reference samples, collect the design stress parameters and actual measured stress parameters of key parts of the turbine runner of similar units, and build a stress mapping model based on this.

[0060] When constructing the stress mapping model, the process includes normalizing the design stress parameters and the actual measured stress parameters of the turbine runner structure to eliminate the differences in dimensions and amplitudes caused by unit size, operating condition boundaries, and installation deviations. Then, parameter fitting or machine learning algorithms are used to train and fit the preprocessed design stress parameters and the actual measured stress parameters to establish a quantitative mapping relationship between the design stress and the measured stress, thus completing the construction of the stress mapping model to correct the systematic deviation between the traditional design stress and the actual stress of the turbine.

[0061] Meanwhile, CFD fluid-structure interaction calculations use the turbine runner as the calculation component, corresponding to the low-load operation condition, hydraulic vibration zone condition, steady-state operation condition, and start-up and shutdown transition operation condition of the unit. Unsteady transient fluid-structure interaction numerical calculations are performed on the flow structure domain and fluid domain of the runner to solve the original stress coefficient distribution of the runner under different operating conditions.

[0062] When obtaining the full-condition true stress field that matches the working condition table, the specific steps include normalizing the stress coefficients of the runner obtained from the CFD fluid-structure interaction solution, and mapping and correcting the stress coefficients using the constructed stress mapping model. Then, the corrected stress coefficients are inversely normalized and restored to fit and generate the full-condition true stress field. This can accurately reproduce the actual stress distribution characteristics of the runner under multiple working conditions and unsteady coupled operation, providing accurate stress field basic data for subsequent average stress correction and graded fatigue damage calculation.

[0063] Specifically, historical design stress data collection was conducted, extensively gathering original data such as feasibility study reports, design calculation sheets, and model test stress reports from operational power plants of the same type but different capacities and heads. Design stress values ​​for key locations on the runner blades were extracted at different design operating conditions. Record the unit operating parameters corresponding to each design value, including the water head. , contribute ,flow etc., to form a design parameter vector .

[0064] Furthermore, normalization is performed to eliminate the influence of unit size, and all design stress values ​​are normalized to the rated stress of the unit. After normalization, the dimensionless design stress coefficient is obtained. The design parameter vector, after normalization, is .

[0065] Furthermore, statistical analysis of on-site dynamic stress data of the unit is conducted, and dynamic stress test reports are reviewed or dynamic stress tests are carried out to determine the actual dynamic stress value of the actual unit, thereby obtaining the dynamic stress time history of each measuring point during the stable phase of the unit's transient operating conditions. and the corresponding real-time operating parameter vector of the unit. .

[0066] Furthermore, statistical analysis was performed on the dynamic stress time history under each working condition to extract characteristic stress values. In this invention, to compare with CFD results, the equivalent stress was mainly extracted. Similarly, for The dimensionless measured stress coefficient was obtained by normalizing the measured stress under rated operating conditions during the unit's test. The normalized runtime parameter vector is .

[0067] A design stress-actual stress mapping model is established, characterized by the creation of a mapping model between design stress and actual stress. The actual stress of the unit under all operating conditions can be directly obtained using the design stress, avoiding the need for complex dynamic stress tests. The specific implementation steps are as follows: Furthermore, a mapping function is established, assuming a functional relationship exists between the design value and the measured value, such that the design stress coefficient can be obtained by correcting the measured stress coefficient. The mapping function is defined as follows. F ,satisfy:

[0068] function F This characterizes the systematic deviations caused by a combination of factors, including actual manufacturing errors, differences in material properties, simplified boundary conditions, and approximate computational models. Generally, machine learning or parameter fitting methods are used to map the function. F For more complex nonlinear relationships, machine learning models such as neural networks and Gaussian processes can be used for training.

[0069] Furthermore, high-fidelity CFD stress field calculations were performed on the foundation under the designed power plant operating conditions. The most representative characteristic operating conditions were selected: low-load condition, vibration zone condition, steady-state condition, and start-up / shutdown condition. Transient CFD-FSI coupled calculations were conducted for these three conditions. Detailed unsteady CFD calculations were performed on these three conditions, and the calculated flow field pressure loads were dynamically transferred to the structural finite element mesh for transient structural response analysis. Finally, the stress eigenvalues ​​of the target component were extracted throughout the entire steady-state period of the fluid-structure interaction calculation. , , , This includes the average dynamic stress amplitude and average stress. The dynamic stress location is selected at the water outlet edge of the wheel, near the upper crown and lower ring. The load frequency is statistically obtained. , , .

[0070] Furthermore, the foundation field is normalized by dividing the average stress fields obtained from the four CFD calculations by the spatial average value of the maximum principal stress or equivalent stress at the corresponding location under rated operating conditions, resulting in three normalized foundation CFD stress coefficients. , , , .

[0071] Furthermore, stress calculations were performed for different zones of the power plant under design operating conditions. The stress coefficients calculated by CFD were substituted into the "design stress-actual stress mapping" model to obtain the measured stress values. , , , .

[0072] Furthermore, by performing inverse normalization, the final dimensionless stress coefficient field is multiplied by the actual physical stress reference value obtained from the CFD calculation of the target unit under rated operating conditions, thus yielding the true physical stress field amplitude under each operating condition. , , , And the corresponding average stress.

[0073] S104. The average stress is corrected by the Goodman formula for the real stress field. The high-cycle fatigue damage is calculated by the Miner criterion for steady-state conditions. The low-cycle fatigue damage is calculated by the Morrow formula for start-stop conditions. The fatigue life of the runner is obtained by superimposing the two types of damage.

[0074] For example, after fitting and generating the real stress field of the runner under all working conditions that matches the design load condition table, the mean stress correction process is first carried out using the Goodman formula for the asymmetric cyclic stress characteristics at various points in the real stress field. The alternating stress that actually has the influence of mean stress is equivalently transformed into the amplitude of symmetric cyclic stress, eliminating the weakening effect of mean stress on the fatigue strength of the runner and providing an equivalent stress benchmark for subsequent fatigue damage quantification calculation.

[0075] In the fatigue damage calculation process, the Miner criterion is used to calculate high-cycle fatigue damage for steady-state conditions, and the Morrow formula is used to calculate low-cycle fatigue damage for start-up and shutdown conditions. Specifically, the small-amplitude high-frequency cyclic stress damage generated by the low-load operation, hydraulic vibration zone, and steady-state operation conditions of the unit on the runner structure is uniformly collected as the runner high-cycle fatigue cumulative damage; the large-amplitude low-frequency alternating stress damage generated by the start-up and shutdown transition conditions of the unit on the runner structure is defined as the runner low-cycle fatigue cumulative damage.

[0076] For the collected steady-state continuous operating conditions, the Miner linear cumulative damage criterion is used to calculate high-cycle fatigue damage. The stress cycle number is calculated based on the operating time of each zone, and the fatigue damage of a single operating condition is calculated by combining the SN degradation curve. For the low-cycle fatigue load corresponding to the unit start-up and shutdown transition conditions, the Morrow formula is used to calculate low-cycle fatigue damage. The coupling effect of plastic strain and stress amplitude is considered to solve the fatigue damage value corresponding to a single start-up and shutdown cycle. The total low-cycle fatigue damage is obtained by summing the annual start-up and shutdown cycle count.

[0077] After independently calculating high-cycle fatigue damage and low-cycle fatigue damage respectively, the cumulative fatigue damage of the two different mechanisms is superimposed and summed according to the principle of linear superposition of fatigue damage to obtain the total annual fatigue damage of the turbine runner. The total damage is accumulated year by year until the fatigue failure critical value is reached. Finally, the overall fatigue life of the turbine runner under the coupling effect of silt wear and multi-condition alternating stress is accurately deduced, providing a reliable theoretical basis for runner structure optimization, maintenance cycle formulation and unit operating condition control.

[0078] During implementation, fatigue life damage calculations were performed under different operating conditions. The annual SN degradation curves and annual operating condition tables for the turbine were obtained, and the actual physical stress field values ​​under each operating condition were input. The calculation steps are as follows: High-cycle fatigue calculations were used for low-load, vibration zone, and stable operating conditions. Miner's method was employed to calculate cumulative damage. Since the actual stress is asymmetric cyclic, an average stress correction was required. The Goodman formula was used to convert the actual stress amplitude into a symmetric cyclic stress amplitude. For year t, the number of cycles for the low-load, vibration zone, and stable operating conditions were respectively... , , ,in , Operating hours under various conditions, and annual high-cycle damage:

[0079] Low-cycle fatigue calculations were used for start-up and shutdown conditions, based on local stress. Calculate the strain Local stress and strain Substituting into the strain-life Morrow formula, we can obtain the stress amplitude as follows: Low-cycle loading loop failure count The low-cycle fatigue damage over one year is , Total damage to the unit (number of start-ups and shutdowns) .

[0080] A second aspect of the present invention provides a water turbine, comprising: Turbine runner, flow channel assembly, unit operation monitoring module and operating condition control module; The turbine runner is predicted to have its full life cycle fatigue life under the coupled stress of silt wear using the runner fatigue life prediction method based on wear and stress mapping as described above. The unit operation monitoring module collects real-time data on the power station's operating head, sediment particle size, sediment concentration, unit capacity, and turbine runner operating stress parameters. The operating condition control module matches and defines the operating time of the unit in the low load zone, hydraulic vibration zone, and steady-state operation zone based on the fatigue life prediction results, and optimizes the annual start-up and shutdown cycle allocation of the unit to suppress the high-cycle and low-cycle fatigue cumulative damage of the runner caused by the combined action of silt wear and alternating stress.

[0081] Example 1 Taking a power plant as an example, the turbine runner material is ZG00Cr13Ni4Mo. The original SN curve of the material is shown below in this case:

[0082] in The stress amplitude, The number of loops. , These are the fatigue resistance coefficient and fatigue resistance coefficient, respectively, which are related to material properties. Degradation SN curves after wear are constructed. A characteristic of degradation SN curves is that the number of cycles decreases under the same stress amplitude. Dynamic fatigue coefficients under silt abrasion are also constructed. As the silt wear rate decreases, the fatigue resistance coefficient decay function is established as follows:

[0083] In the formula The initial fatigue resistance coefficient was determined experimentally. For wear rate, It is a constant, determined through experiments.

[0084] Construct the dynamic fatigue coefficient under silt abrasion. The absolute value decreases as the silt abrasion rate decreases.

[0085]

[0086] In the formula This refers to the amount of wear. This represents the initial fatigue resistance index. The saturation fatigue resistance index. It is a constant. , , Determined through experiments.

[0087] The wear rate of the turbine runner can be determined through a wear database or through theoretical calculation. The steps for determining the wear rate through a wear database are as follows: First, statistically analyze the wear rate changes over time for the same type of unit with different heads and capacities (M). Simultaneously, statistically analyze the sediment characteristic parameters of the corresponding power station, including sediment particle size and sediment concentration, to obtain a typical wear rate database. , For different power plants, the target heads H, M, d, and c were then obtained based on the design data, and the wear and tear of the target power plant was obtained through data regression.

[0088] The original SN curve of a certain power station is:

[0089] Pick It is 0.05. It is 0.5. The SN curve for the next 5 years is obtained with a value of -0.2, as shown in Table 1: Table 1 SN Degradation Curve

[0090] Next, basic data models of the hydrological characteristics, load commands, and unit output characteristics of the designed power station will be acquired and established to clarify the operating constraints of the hydropower units. This includes hydrological characteristic modeling, unit output characteristic modeling, and determination of input boundary conditions.

[0091] Hydrological characteristic modeling: Establishing the reservoir water level-capacity relationship curve and the tailwater level-discharge curve The change in reservoir flow at different times is equal to the reservoir inflow minus the total power generation flow minus the ecological and wastewater flow. The net head of the generating unit is equal to the difference between the upstream and downstream water levels minus the head loss.

[0092] Modeling the power output characteristics of the generating units involves inputting the number of generating units and constructing a three-dimensional surface representing the flow rate, head, and power relationship for each unit. ,in Q i For the first i The flow rate of the Taiwanese unit H For the water purification head, P i This refers to the unit's power output.

[0093] The system also inputs the unit operating zones, dividing the operating range of each unit into three typical areas based on different water heads, including a low-load operating zone:

[0094] Vibration zone:

[0095] For impulse turbine units, there is no typical vibration zone, so they do not need to be counted separately.

[0096] Stable operating area:

[0097] An example of hydropower unit operation zoning is attached. Figure 3 As shown.

[0098] Input the boundary conditions to obtain the total load curve of the power plant for the whole year:

[0099] And the entire year's inbound flow process The load allocation strategy for each unit is determined accordingly, as well as unit constraints such as output-load matching constraints, maximum and minimum output constraints, vibration zone constraints, and hydrological constraints such as water level constraints, reservoir capacity constraints, and water balance constraints.

[0100] Furthermore, load allocation and operating condition analysis are performed based on a data model of hydrological characteristics, load commands, and unit output characteristics. Since the head is affected by the discharge flow, and the discharge flow depends on both the head and load allocation, the two are strongly coupled. This step uses a prediction-correction iterative algorithm for solution: For the t-th time step: the net head at the previous time step... As the initial predicted head at the current moment .

[0101] Furthermore, load allocation is performed based on a preset load allocation strategy, distributing the total load... P t Assign power to n generating units and calculate the power of each unit. and start / stop state variables 1 represents running, and 0 represents stopped. Power balance constraints must be met:

[0102] Furthermore, the flow-head-power relationship model is invoked to calculate the flow rate of a single unit and sum them to obtain the total discharge flow rate of the entire plant. Hydrological calculations and head correction are then performed: the current reservoir capacity is calculated, upstream and downstream water levels are calculated from tables, and the corrected net head is calculated.

[0103] in Let t be the upstream water level of the reservoir. Let t be the downstream water level. This represents the head loss in the water diversion system. Finally, a convergence criterion is performed:

[0104] If ε is the set allowable error limit, then the iteration ends, and the current value is recorded. , u and Otherwise, let k = k +1, return to reallocation.

[0105] Furthermore, throughout the year, the number of unit start-ups and shutdowns is statistically analyzed, and the operating area and duration are determined and accumulated. By comparing the start-up and shutdown status variables ui,t at adjacent times, the annual start-up count of the i-th unit is calculated. and number of downtime :

[0106]

[0107] For the i-th unit during time period t, if Then its actual power With the current water head Compare the boundaries of the running area below, if:

[0108] Then it is determined to be in the low load zone, low efficiency zone, and the cumulative variable of the running time is:

[0109] like:

[0110] It is then determined to be in the vibration zone, and the cumulative variable of the vibration zone running time is:

[0111] like:

[0112] Then it is determined to be in the stable region, and the cumulative variable of the running time in the stable region is:

[0113] Where Δt is the step size for a time interval, in hours (h).

[0114] Furthermore, the system outputs the annual operating condition data table for the hydropower units. After completing the calculation for T time periods throughout the year, the system outputs the following data, including the annual power generation, total operating hours, number of starts, number of shutdowns, and the operating hours and percentages of each unit in the low-efficiency zone, vibration zone, and stable zone. In this case, the power station uses the rated head calculation, the typical load table of hydropower stations in the region, and the average load distribution of the units to calculate the design operating condition table of the power station, as shown in Table 2.

[0115] Table 2. Design Operating Conditions of a Power Plant

[0116] The full-condition stress calculation is achieved through a design stress-actual stress mapping model. CFD calculations are performed on the designed power plant to obtain stresses under low load, rated load, and load shedding conditions, thus obtaining the design stress of the power plant. The original stress design values ​​of similar operational power plants are statistically analyzed, which can be obtained from feasibility study reports. The measured dynamic stress data of the power plant are also statistically analyzed, which can be obtained by conducting full-condition dynamic stress tests on the power plant. Subsequently, a design stress-measured stress mapping model is established. The design stress distribution of the designed power plant is then input into the design stress-actual stress mapping model to obtain the stress distribution under full-condition operation.

[0117] The steps for extracting the characteristics of design stress and measured stress are as follows: Historical design stress data collection was conducted, extensively gathering original data such as feasibility study reports, design calculation sheets, and model test stress reports from operational power plants of the same type but different capacities and heads. Design stress values ​​for key locations on the turbine runner blades were extracted at different design operating conditions. Record the unit operating parameters corresponding to each design value, including the water head. , contribute ,flow etc., to form a design parameter vector .

[0118] Further normalization was performed to eliminate the influence of unit size, and all design stress values ​​were normalized to the rated stress of their respective units. After normalization, the dimensionless design stress coefficient is obtained. The design parameter vector, after normalization, is .

[0119] Furthermore, statistical analysis of on-site dynamic stress data of the unit is conducted, and dynamic stress test reports are reviewed or dynamic stress tests are carried out to determine the actual dynamic stress value of the actual unit, thereby obtaining the dynamic stress time history of each measuring point during the stable phase of the unit's transient operating conditions. and the corresponding real-time operating parameter vector of the unit. .

[0120] Furthermore, statistical analysis was performed on the dynamic stress time history under each working condition to extract characteristic stress values. In this invention, to compare with CFD results, the equivalent stress was mainly extracted. Similarly, for The dimensionless measured stress coefficient was obtained by normalizing the measured stress under rated operating conditions during the unit's test. The normalized runtime parameter vector is .

[0121] A design stress-actual stress mapping model is established, characterized by the creation of a mapping model between design stress and actual stress. The actual stress of the unit under all operating conditions can be directly obtained using the design stress, avoiding the need for complex dynamic stress tests. The specific implementation steps are as follows: First, they are compared under the same or very similar working conditions. and The data is paired one by one to form multiple data pairs.

[0122] Next, a mapping function is established. It is assumed that a functional relationship exists between the design and measured values, such that the design stress coefficient can be obtained by correcting the measured stress coefficient. The mapping function is then defined. F ,satisfy:

[0123] function FThis characterizes the systematic deviations caused by a combination of factors, including actual manufacturing errors, differences in material properties, simplified boundary conditions, and approximate computational models. Machine learning or parameter fitting methods are used to learn the function. F This case study provides a method for establishing [the system] based on operating parameters. The entire operating area is divided into a low-load zone, a vibration zone, and a stable operating zone. Within each sub-region, the mapping relationship is assumed to be linear scaling.

[0124] Using the data pairs obtained through one-to-one pairing, the optimal scaling factor for each sub-region is fitted using optimization methods such as the least squares method. and offset For more complex nonlinear relationships, machine learning models such as neural networks and Gaussian processes can be used for training.

[0125] Furthermore, high-fidelity CFD stress field calculations were performed on the foundation under the design power station. The most representative characteristic operating conditions were selected, namely low-load condition, vibration zone condition, steady-state condition, and start-up / shutdown condition, for transient CFD-FSI coupled calculations. For the target power station, design condition A is the low-load condition, with a rated head of... 10% below Load; Design condition B is the rated load condition, rated head Rated output Design condition C is the vibration zone condition, with a rated head of [missing information]. 40% load.

[0126] Design condition D is the start-stop condition, rated head. From 100% Sudden load shedding was investigated, and detailed unsteady CFD calculations were performed for these three operating conditions. The calculated flow field pressure loads were dynamically transferred to the structural finite element mesh for transient structural response analysis. Finally, the stress eigenvalues ​​of the target component were extracted throughout the entire fluid-structure interaction calculation stabilization period. , , , This includes the average stress amplitude and average stress. The stress location is selected at the water outlet edge of the wheel, near the upper crown and lower ring. The load frequency is statistically obtained. , , .

[0127] Furthermore, the foundation field is normalized by dividing the average stress fields obtained from the four CFD calculations by the spatial average value of the maximum principal stress or equivalent stress at the corresponding location under rated operating conditions, resulting in three normalized foundation CFD stress coefficients. , , , .

[0128] Furthermore, stress calculations were performed for different zones of the power plant under design operating conditions. The stress coefficients calculated by CFD were substituted into the design stress-actual stress mapping model to obtain the measured stress values. , , , .

[0129] Furthermore, by performing inverse normalization, the final dimensionless stress coefficient field is multiplied by the actual physical stress reference value obtained from the CFD calculation of the target unit under rated operating conditions, thus yielding the true physical stress field amplitude under each operating condition. , , , And the corresponding average stress.

[0130] Taking a design life of 40 years as an example, by obtaining the annual SN degradation curve and annual operating condition table of the turbine during its 40-year operation, and substituting the actual physical stress field values ​​under each operating condition, the calculation steps are as follows: High-cycle fatigue calculations were used for low-load, vibration zone, and stable operating conditions. Miner's method was employed to calculate cumulative damage. Since the actual stress is asymmetric cyclic, an average stress correction was required. The Goodman formula was used to convert the actual stress amplitude into a symmetric cyclic stress amplitude. For year t, the number of cycles for the low-load, vibration zone, and stable operating conditions were respectively... , , ,in , Operating hours under various conditions, and annual high-cycle damage:

[0131] Low-cycle fatigue calculations were used for start-up and shutdown conditions, based on local stress. Calculate the strain Local stress and strain Substituting into the strain-life Morrow formula, we can obtain the stress amplitude as follows: Low-cycle loading loop failure count The low-cycle fatigue damage over one year is , This represents the number of times the device has been started and stopped.

[0132] This leads to the total damage of the unit over the next 40 years. Considering a certain safety margin, the total damage over the next 40 years is required. .

[0133] Example 2 The wear of the impeller is calculated using either the Archard wear model or the Finnie model. This case uses the Finnie model, as shown below: ‌

[0134] In the formula It is the amount of wear. These are model constants, which depend on the properties of the material; It is the impact velocity of the particles; It is an index related to materials; It is a function that describes the erosion rate as a function of the impact angle.

[0135] The dynamic SN curves of the material under sediment abrasion are established as follows:

[0136] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the fatigue life of a wheel based on wear and stress mapping, characterized in that, include: The initial fatigue resistance coefficient, wear rate, and fitting constant were determined based on the fatigue wear test of the turbine material. The SN degradation curve under silt wear was constructed by combining the initial fatigue characteristics of the material with the characteristics of the power station sediment. The wear amount was calculated based on the wear rate, and the decay correlation between the fatigue resistance coefficient and the wear amount was established. Based on the SN degradation curve, combined with the hydrological characteristics of the power station and the power output characteristics of the unit, the design load condition table including the number of start-stop times and the operating time of the zone is obtained by iterative solution. A stress mapping model is constructed based on the design stress and actual measured stress of the same type of unit. The full-condition real stress field that is adapted to the operating condition table is obtained by combining CFD fluid-structure interaction calculation. The average stress is corrected using the Goodman formula for the actual stress field. The Miner criterion is used to calculate high-cycle fatigue damage for steady-state conditions, and the Morrow formula is used to calculate low-cycle fatigue damage for start-stop conditions. The fatigue life of the runner is obtained by superimposing the two types of damage.

2. The method for predicting the fatigue life of a wheel based on wear and stress mapping according to claim 1, characterized in that, When constructing the SN degradation curve under the aforementioned sediment abrasion, the following is also included: Establish the correlation between fatigue resistance index and wear amount; The fatigue resistance index is determined by the initial fatigue resistance index, the saturated fatigue resistance index, and the fitting constant determined by the fatigue wear test of the wheel material.

3. The method for predicting the fatigue life of a wheel based on wear and stress mapping according to claim 1, characterized in that, The preset wear database is retrieved, and regression fitting calculation is performed on the wear rate obtained through the fatigue wear test of the wheel material to solve for the wear amount of the wheel under the corresponding actual working conditions. The wear database establishes a correlation and matching relationship with various parameters such as operating head, turbine generator capacity, sediment particle size, and sediment concentration, and collects and stores the basic data of measured wear rate corresponding to each parameter combination under the operating conditions.

4. The method for predicting the fatigue life of a wheel based on wear and stress mapping according to claim 1, characterized in that, The design load condition table is iteratively solved using a predictive correction iterative algorithm. During the iterative solution process, the actual net head of the power plant is used as the convergence criterion. The head parameter correction and dynamic load allocation of the units are completed sequentially. After the iterative calculation meets the convergence condition, the solidified design load condition table is output.

5. The method for predicting the fatigue life of a wheel based on wear and stress mapping according to claim 1, characterized in that, The design load condition table obtained through iterative solution is divided into low load zone, hydraulic vibration zone and steady-state operation zone according to the operating characteristics of the hydro-generator unit; The design load condition table corresponds to the collection of the continuous operating time of the units under each operating zone, and at the same time matches and counts the number of annual start-up and shutdown cycles of the units.

6. The method for predicting the fatigue life of a wheel based on wear and stress mapping according to claim 1, characterized in that, When constructing a stress mapping model, the specific steps include: Normalization preprocessing was performed on the design stress parameters and the actual measured stress parameters of the runner structure, respectively. The preprocessed design stress parameters and the measured stress parameters of the actual machine are trained and modeled using parameter fitting or machine learning algorithms to establish a quantitative mapping relationship between the design stress and the measured stress, thus completing the construction of the stress mapping model.

7. The method for predicting the fatigue life of a wheel based on wear and stress mapping according to claim 1, characterized in that, The CFD fluid-structure interaction calculation uses the turbine runner as the calculation component, and matches the low-load operation condition, hydraulic vibration zone condition, steady-state operation condition and start-up and shutdown transition operation condition of the unit to perform unsteady transient fluid-structure interaction numerical calculation on the flow structure domain and fluid domain of the runner.

8. The method for predicting the fatigue life of a wheel based on wear and stress mapping according to claim 1, characterized in that, Obtaining the full-condition true stress field adapted to the aforementioned working condition table specifically includes: The stress coefficients of the impeller obtained by CFD fluid-structure interaction solution are normalized and preprocessed, and the stress coefficient mapping is corrected by stress mapping model. The corrected stress coefficients are then denormalized and restored to their original values, and a true stress field under all working conditions is generated by fitting the data.

9. The method for predicting the fatigue life of a wheel based on wear and stress mapping according to claim 1, characterized in that, For steady-state conditions, the Miner criterion is used to calculate high-cycle fatigue damage, and for start-up and shutdown conditions, the Morrow formula is used to calculate low-cycle fatigue damage. Specifically, this includes: The small-amplitude high-frequency cyclic stress damage generated by the low-load operation, hydraulic vibration zone, and steady-state operation of the unit on the runner structure is uniformly classified as the high-cycle fatigue cumulative damage of the runner. The large-amplitude low-frequency alternating stress damage generated by the unit start-up and shutdown transition conditions on the runner structure is defined as the low-cycle fatigue cumulative damage of the runner.

10. A water turbine, characterized in that, include: Turbine runner, flow channel assembly, unit operation monitoring module and operating condition control module; The turbine runner uses the runner fatigue life prediction method based on wear and stress mapping as described in any one of claims 1 to 9 to complete the full life cycle fatigue life prediction under the action of silt wear coupled stress. The unit operation monitoring module collects real-time data on the power station's operating head, sediment particle size, sediment concentration, unit capacity, and turbine runner operating stress parameters. The operating condition control module matches and defines the operating time of the unit in the low load zone, hydraulic vibration zone, and steady-state operation zone based on the fatigue life prediction results, and optimizes the annual start-up and shutdown cycle allocation of the unit to suppress the high-cycle and low-cycle fatigue cumulative damage of the runner caused by the combined action of silt wear and alternating stress.

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