Mixed-flow water turbine runner blade shaping method

Abstract

The invention relates to the technical field of water turbines, in particular to a mixed-flow water turbine runner blade modification method which comprises the following steps: S1, determining an optimization target and required working conditions of water turbine runner blade modification, determining a blade area needing to be modified, and setting the optimization target; s2, a computer aided design technology is used for establishing a three-dimensional geometric model of the turbine runner blade, grids are established for a fluid domain, and the density of the grids is dynamically adjusted in different areas according to flow characteristics; and S3, based on the established geometric model and grid, performing flow field simulation to obtain flow velocity, pressure and vortex data, and analyzing a simulation result to identify the performance bottleneck of the blade. According to the method, the optimization target and the required working condition of the turbine runner blade modification are determined, so that the problem that the blade efficiency and the flowing property are difficult to optimize at the same time due to the fact that empirical judgment and single-working-condition optimization are mostly adopted in traditional turbine blade modification is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic turbine, and particularly relates to a Francis turbine runner blade reshaping method. BACKGROUND

[0002] With the wide construction and operation of hydropower stations, Francis turbines are widely used due to their high hydraulic efficiency in the conditions of medium and low water head and large flow variation. In the operation process of the hydraulic turbine, the hydraulic performance of the runner blade directly affects the unit efficiency, cavitation condition and vibration and noise level. Therefore, the runner blade is optimized for reshaping to improve the hydraulic performance and prolong the service life, which becomes an important part of the design and maintenance of the hydraulic turbine.

[0003] The traditional runner blade reshaping mostly adopts experience judgment and single condition optimization, which cannot take into account the performance difference in multiple conditions, thereby causing the problems that the blade efficiency and flow performance are difficult to be optimized at the same time. SUMMARY

[0004] In order to make up for the above shortcomings, the present application provides a Francis turbine runner blade reshaping method, which aims at improving the problems that the traditional runner blade reshaping mostly adopts experience judgment and single condition optimization, which cannot take into account the performance difference in multiple conditions, thereby causing the problems that the blade efficiency and flow performance are difficult to be optimized at the same time.

[0005] In the first aspect, the present application provides the following technical scheme, a Francis turbine runner blade reshaping method, comprising the following steps: S1, determining the optimization target and required conditions of the runner blade reshaping, determining the blade area needing reshaping, and setting the optimization target; S2, using computer-aided design technology to establish a three-dimensional geometric model of the runner blade, and creating a grid for the fluid domain, wherein the density of the grid is dynamically adjusted according to the flow characteristics in different areas; S3, based on the established geometric model and grid, performing flow field simulation to obtain flow velocity, pressure and vortex data, and analyzing the simulation results to identify the blade performance bottleneck; S4, according to the simulation analysis results, adjusting the geometric shape of the runner blade by using an optimization algorithm; S5, according to the optimized blade geometric shape, performing re-simulation to verify the optimization results and evaluate the performance of the reshaped blade; S6, confirming the final optimization design, manufacturing the optimized blade through actual processing, and performing on-site test and verification.

[0006] By adopting the technical scheme, the optimization target of the water turbine runner blade modification and the required working condition are determined, the blade area needing modification is determined, and the optimization target is set, thereby guiding the whole modification process to realize targeted adjustment, so that the problem that the blade efficiency and flow performance are difficult to optimize simultaneously due to the fact that the traditional water turbine blade modification mostly adopts experience judgment and single working condition optimization and the performance difference in multiple working conditions cannot be considered is improved.

[0007] Further, the S1 comprises the following steps: By collecting the working data of the water turbine, including flow, load, and rotating speed information, and based on the historical operation data or field test data, the full load, partial load, and start-stop working conditions are selected, CFD simulation is performed, the flow field performance of the blade in different working conditions is analyzed, and the working condition most needing modification is determined; Based on the simulation results of the working condition, the regions of the water turbine blade where cavitation, vortex, and low efficiency exist are analyzed, and the blade surface, boundary layer, tip region, and wake region are determined as the key modification regions; According to different optimization requirements, at least one optimization target is selected, the optimization target includes changing the hydraulic efficiency, cavitation performance, vibration noise, or blade fatigue resistance, and according to the optimization target, a corresponding objective function and algorithm are selected for optimization calculation.

[0008] Further, the establishment of the three-dimensional geometric model of the water turbine runner blade comprises the following steps: Using CAD software, the basic geometric characteristics of the blade are defined according to the design requirements of the blade, and a three-dimensional geometric model of the blade is established; The three-dimensional surface of the blade surface is generated by the CAD software, so that the geometric shape of each region meets the design requirements; The integrity of the model is checked to make the geometric model meet the requirements of fluid mechanics simulation.

[0009] Further, the grid creation for the fluid domain comprises the following steps: The fluid domain around the water turbine runner blade is defined, and an initial grid is created using a grid generation software; The grid is refined on the blade surface, so that the grid density can capture the flow characteristics, including vortex and pressure gradient; The grid quality is controlled to verify the orthogonality, non-distortion degree, and smoothness, so that the grid is suitable for CFD simulation.

[0010] Further, the dynamic adjustment of the grid density comprises the following steps: The flow field data are monitored in real time by CFD simulation, the flow velocity, pressure, and turbulence are analyzed, and the regions needing grid refinement or coarsening are identified; Based on the changes in the flow field, an adaptive mesh refinement method is used to increase the grid density in the boundary layer and vortex region. Combined with machine learning algorithms, real-time prediction of flow field changes and dynamic adjustment of grid density.

[0011] Further, the flow field simulation includes the following steps: Import the established three-dimensional geometric model and grid into the CFD solver, and perform consistency check on the geometry and grid. Set the boundary conditions of the inlet, outlet and blade surface, as well as the fluid physical property parameters. Select the flow type and turbulence model, and set the solving algorithm, discrete format and convergence criterion. Initialize the flow field and run the solver for steady or transient calculation until the residual converges or the monitoring quantity stabilizes. Extract the flow field data of velocity field, pressure field and vorticity distribution for subsequent analysis.

[0012] Further, the analysis of the simulation results includes the following steps: Analyze the blade surface flow velocity distribution to identify the area of flow velocity change or flow separation. Analyze the pressure field to identify whether there is a pressure jump or abnormal pressure distribution, and predict the area that may cause cavitation. Analyze the vortex and turbulence intensity, identify the vortex or flow separation phenomenon, evaluate the influence of vortex or flow separation phenomenon on blade performance, and further determine the area that needs to be optimized.

[0013] Further, the adjustment of the runner blade geometry using optimization algorithms includes the following steps: Determine the optimization target and design variables according to the simulation results, the design variables including blade curvature, blade tip angle, thickness or curvature. Construct an optimization model containing objective function and constraint conditions, the objective function used to calculate the hydraulic efficiency, cavitation index or vibration amplitude of the blade under different working conditions. Use genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm to perform iterative calculation in the design variable space to obtain multiple candidate geometric schemes. Run the simulation based on the geometric scheme obtained in each iteration, calculate the objective function value and update the design variables. When the objective function converges or reaches the preset number of iterations, output the optimized runner blade geometry.

[0014] Further, the verification of the optimization results and the evaluation of the performance of the modified blade include the following steps: Based on the optimized blade geometry model and the same grid generation strategy as before, establish a fluid domain model and set the same boundary conditions and turbulence model as before. Run CFD simulation to obtain flow velocity, pressure, turbulent kinetic energy and vortex distribution data; Extract hydraulic efficiency, flow head curve, cavitation index or fluctuating pressure parameters from simulation results; Compare the optimized performance parameters with the corresponding parameters before optimization to calculate the performance difference; Respectively simulate and verify under rated working condition, partial load working condition and overload working condition, and output performance parameter comparison results.

[0015] In the second aspect, the present application provides the following technical scheme, a Francis turbine runner blade reshaping system, the system comprises: An optimization target and working condition determination module is used to determine the optimization target of the Francis turbine runner blade reshaping and the required working condition, determine the blade area that needs to be reshaped, and set the optimization target; A three-dimensional geometric modeling and mesh generation module is used to establish a three-dimensional geometric model of the Francis turbine runner blade using computer-aided design technology, and to create a mesh for the fluid domain, wherein the density of the mesh is dynamically adjusted according to the flow characteristics in different areas; A CFD simulation module is used to perform flow field simulation based on the established geometric model and mesh, obtain flow velocity, pressure and vortex data, and analyze the simulation results to identify blade performance bottlenecks; A blade geometry optimization module is used to adjust the runner blade geometry according to the simulation analysis results using an optimization algorithm; A re-simulation and performance verification module is used to re-simulate according to the optimized blade geometry, verify the optimization results and evaluate the performance of the reshaped blade; A design confirmation and actual processing module is used to confirm the final optimized design, manufacture the optimized blade through actual processing, and perform on-site testing and verification.

[0016] The present application has the following beneficial effects: 1、In the present application, by determining the optimization target of the Francis turbine runner blade reshaping and the required working condition, determining the blade area that needs to be reshaped, and setting the optimization target, the entire reshaping process is guided to achieve targeted adjustment, thereby improving the problem that traditional Francis turbine blade reshaping mostly adopts experience judgment and single working condition optimization, which cannot take into account the performance difference of multiple working conditions, thereby causing the blade efficiency and flow performance to be difficult to optimize simultaneously.

[0017] 2. In this invention, a three-dimensional geometric model of the turbine runner blade is established by using computer-aided design technology, and a mesh is created for the fluid domain. The mesh density is dynamically adjusted according to the flow characteristics in different regions, thereby improving the accuracy of the simulation results. This improves the problem that traditional mesh generation methods mostly use uniform or static mesh division, which cannot adapt to complex flow changes and thus cannot effectively capture the flow characteristics in key areas.

[0018] 3. In this invention, the geometry of the impeller blades is adjusted by using optimization algorithms based on simulation analysis results, thereby optimizing the hydraulic performance of the blades under different working conditions. This improves the problem that traditional blade optimization mostly relies on manual experience or single-factor adjustments, which makes it difficult to systematically process performance indicators under multiple working conditions, resulting in limited improvement in blade performance. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for modifying the runner blades of a mixed-flow turbine proposed in this invention; Figure 2 This is a system architecture diagram of a mixed-flow turbine runner blade modification system proposed in this invention. Detailed Implementation

[0020] The technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 In a first embodiment of the present invention, the present invention provides a method for modifying the runner blades of a mixed-flow turbine, such as... Figure 1 As shown, it includes the following steps: S1. Determine the optimization objectives and required operating conditions for the modification of turbine runner blades, identify the blade areas that need modification, and set optimization objectives. Furthermore, S1 includes the following steps: By collecting the turbine's operating data, including flow rate, load, and speed information, and based on historical operating data or field test data, CFD simulations are performed under full load, partial load, and start-up / shutdown conditions to analyze the flow field performance of the blades under different conditions and determine the conditions that require the most modification. Based on the simulation results, we analyzed the regions on the turbine blades that have cavitation, eddies, and low efficiency, and focused on identifying the blade surface, boundary layer, blade tip region, and wake region as key areas for blade modification. According to different optimization requirements, at least one optimization target is selected, the optimization target including changing hydraulic efficiency, cavitation performance, vibration noise or blade fatigue resistance, and an optimization calculation is performed according to a corresponding objective function and an algorithm selected according to the optimization target.

[0022] Specifically, the determination of the optimization target of the water turbine runner blade modification and the implementation of the required working condition are achieved by collecting water turbine operation data, including flow, load, speed and other parameters, and combining historical operation records and field test data to form input conditions for CFD simulation. Based on these input conditions, full load, partial load and start-stop working conditions are selected as simulation working conditions, and simulation is performed through a computational fluid dynamics solver to obtain flow velocity field, pressure field and vorticity distribution data of the blade under different working conditions, which are used as key indicators for evaluating the performance of the blade. The simulation results are analyzed, and the flow abnormalities or performance bottleneck positions of the blade surface, boundary layer, tip region and wake region are determined through numerical comparison and flow field characteristic identification methods, so as to determine the blade regions that need to be modified. The optimization target is set by using a mathematical modeling method, and the hydraulic efficiency, cavitation performance, vibration noise and blade fatigue resistance are respectively taken as objective functions or constraint conditions to construct a comprehensive optimization model, the model input is a blade geometric parameter vector ; wherein represents the blade curvature, represents the blade tip angle, represents the blade thickness distribution, represents the curvature change rate, and the numerical value is derived from CAD modeling and measurement data, and the output is an optimization objective function value , wherein , is the hydraulic efficiency, CI(x) is the cavitation index, V(x) is the vibration amplitude, and F(x) is the fatigue strength index, is a weight coefficient determined according to design requirements. In the optimization process, a genetic algorithm can be used to iteratively search the blade geometric parameter space, the target function value is calculated by running simulation for each generation of individuals corresponding to the geometric scheme, and the next generation of parameter sets is updated according to selection, crossover and mutation operations until the target function converges or the preset number of iterations is reached. After optimization, the best blade geometric parameter set is output, which is used to guide subsequent blade geometric modeling and mesh generation steps to perform flow field simulation and performance verification, so as to achieve the efficiency improvement and cavitation, vibration control targets of the modified blade.

[0023] Therefore, the flow field characteristics of the water turbine under different working conditions can be analyzed, the flow abnormalities and performance bottlenecks of the blade surface, boundary layer, tip region and wake region are identified, the blade regions that need to be modified are determined, and the corresponding optimization targets are set, including hydraulic efficiency, cavitation performance, vibration noise and blade fatigue resistance, so as to improve the pertinence and efficiency of blade modification.

[0024] S2, using computer aided design technology to establish a three-dimensional geometric model of the runner blade of the water turbine, and creating a grid for the fluid domain, wherein the density of the grid is dynamically adjusted according to the flow characteristics in different areas; Further, the three-dimensional geometric model of the runner blade of the water turbine comprises the following steps: Using CAD software, defining the basic geometric characteristics of the blade according to the design requirements of the blade, and establishing a three-dimensional geometric model of the blade; Generating a three-dimensional surface of the blade surface through CAD software, so that the geometric shape of each area meets the design requirements; Checking the integrity of the model to ensure that the geometric model meets the requirements of fluid mechanics simulation.

[0025] Specifically, the precise three-dimensional modeling of the runner blade of the water turbine and the generation of the grid suitable for flow field simulation are realized through computer aided design technology. First, using CAD software, the basic geometric characteristics of the blade are defined according to the design requirements of the blade, including the length of the blade, the bending degree, the thickness distribution and the blade tip angle. These geometric parameters are input into the software in digital form to construct a three-dimensional curved surface model of the blade. Then the continuity and smoothness of the generated three-dimensional curved surface are checked to ensure that the curved surface meets the conditions of fluid mechanics simulation. Then the initial grid is generated on the surface of the blade and the surrounding fluid domain. The grid density of the boundary layer and vortex area is improved through local refinement method, so that the grid can accurately capture the flow characteristics. The input data includes the vector of blade geometric parameters: ; wherein is the length of the blade, is the blade tip angle, is the thickness distribution along the chord line of the blade, is the chord line coordinate distribution; These parameters come from design specifications and engineering measurements, and the output result is a three-dimensional blade geometric model and the corresponding fluid domain grid Q. In the next step, and Q are imported into the CFD solver for flow field simulation analysis. The results are used to provide basic data for evaluating the performance of the blade, and are used as input conditions for adjusting the geometric shape of the blade in subsequent optimization algorithms, to ensure that the hydraulic efficiency and cavitation performance of the blade under different working conditions can be accurately predicted and improved.

[0026] By using computer-aided design technology to establish a three-dimensional geometric model of the runner blade of the water turbine and create a grid for the fluid domain, an accurate blade geometry conforming to the design requirements and a high-quality calculation grid can be obtained, ensuring that the flow characteristics of the blade surface and key areas can be accurately captured, and the grid density is dynamically adjusted in different areas according to the flow characteristics, thereby providing a reliable data basis for subsequent flow field simulation, which helps to analyze the hydraulic performance and cavitation condition of the blade under different working conditions.

[0027] Creating a grid for the fluid domain includes the following steps: Defining the fluid domain around the runner blade of the water turbine and creating an initial grid using grid generation software; Refining the grid on the blade surface to enable the grid density to capture flow characteristics, including vortex and pressure gradient; Performing quality control on the grid to verify orthogonality, non-distortion, and smoothness, so that the grid is suitable for CFD simulation.

[0028] Specifically, by using grid generation software to discretize the fluid domain around the runner blade of the water turbine and locally encrypt the grid in key areas to ensure that the flow characteristics can be accurately captured, the specific steps include first defining the boundary of the fluid domain according to the three-dimensional geometric model of the blade and generating an initial grid, which contains the basic spatial discrete information of the fluid domain, the input data of which are the point coordinates of the blade geometric model and the boundary conditions of the fluid domain, the point coordinates are obtained by CAD modeling, and the boundary conditions are set according to the working condition parameters; then the grid is refined on the blade surface and in areas where vortex and pressure gradient are obvious, the input data of which are the node positions of the initial grid and the flow field characteristic indicators , the indicators include local flow velocity gradient and pressure gradient , which are calculated by preliminary simulation or empirical formula, the grid refinement can be represented by an adaptive encryption algorithm formula as , where is the original grid element length, is the adjustment coefficient, is the flow characteristic intensity, and the new grid element size is calculated After regenerating the grid nodes, the grid is verified for orthogonality, non-distortion, and smoothness by a quality control algorithm, and the output result is a high-quality grid that meets the simulation requirements: This grid serves as the input for subsequent CFD simulation, ensuring the accuracy of flow field calculation and providing a reliable data basis for evaluating blade performance and optimizing design.

[0029] By using computer-aided design techniques to establish a three-dimensional geometric model and create high-quality grids for the fluid domain, grid refinement can be achieved in key areas such as blade surfaces, vortex regions, and areas with significant pressure gradients, thereby accurately capturing flow characteristics and improving the accuracy of flow field simulation calculations. The grid is verified for orthogonality, non-distortion, and smoothness to support the operation of the CFD solver stably.

[0030] Dynamic adjustment of grid density includes the following steps: Real-time monitoring of flow field data through CFD simulation, analysis of flow velocity, pressure, and turbulence, identification of areas requiring grid refinement or coarsening; Based on flow field changes, using adaptive grid refinement methods to increase grid density in boundary layers and vortex regions; Combining machine learning algorithms, real-time prediction of flow field changes, and dynamic adjustment of grid density.

[0031] Specifically, first, input the established three-dimensional geometric model of the turbine runner blade and the initial fluid domain grid into the CFD solver, obtain flow field data through real-time simulation, including flow velocity field , pressure field , and turbulent kinetic energy , which reflect the flow state at different positions and times ; then, analyze the regional flow characteristics based on the monitored flow field data, identify areas requiring grid refinement or coarsening, usually by defining a local error indicator to quantify flow field changes, the formula is: ; where are the flow velocity, pressure, and turbulent kinetic energy gradient weight coefficients, determined by historical simulation data and engineering experience, is the reference value for normalization; subsequently, using adaptive grid refinement algorithms, increase the grid density in areas where the error indicator exceeds the preset threshold, and coarsen the grid in areas with lower error indicators, thereby reducing computational load while ensuring calculation accuracy; at the same time, use machine learning algorithms to establish a flow field prediction model, input past simulation data and current real-time flow field data: , predict the flow field changes at the next time step, and further dynamically adjust the grid layout based on the prediction results; finally, output the high-quality grid optimized adaptively , which can be used for subsequent accurate CFD simulation calculations, thereby more accurately analyzing blade performance bottlenecks and guiding geometric optimization. The numerical values of each parameter are derived from previous experimental data, historical simulation results, and design requirements, and the error indicator and threshold can be obtained within a reliable range through multiple simulation verifications. The results are mainly used to provide high-precision flow field data and provide a basis for blade optimization design.

[0032] By using computer-aided design techniques to establish a three-dimensional geometric model and generate a fluid domain mesh, while combining CFD simulation to monitor flow field data in real time, analyze flow velocity, pressure and turbulence, it can identify areas with significant changes in flow characteristics and perform mesh refinement in boundary layers and vortex regions, while using machine learning algorithms to predict flow field changes and dynamically adjust mesh density, thereby obtaining a high-precision and adaptive complex flow calculation mesh, improving the accuracy and efficiency of simulation calculations.

[0033] S3, based on the established geometric model and mesh, perform flow field simulation to obtain flow velocity, pressure and vortex data, and analyze the simulation results to identify blade performance bottlenecks; Further, the flow field simulation includes the following steps: Import the established three-dimensional geometric model and mesh into the CFD solver, and perform consistency checks on the geometry and mesh; Set the boundary conditions of the inlet, outlet and blade surface, as well as the fluid physical property parameters; Select the flow type and turbulence model, and set the solution algorithm, discretization format and convergence criteria; Initialize the flow field and run the solver for steady-state or transient calculation until the residual converges or the monitoring quantity stabilizes; Extract the flow field data of the velocity field, pressure field and vorticity distribution for subsequent analysis.

[0034] Specifically, the three-dimensional geometric model generated by CAD and the generated mesh are imported into the CFD solver, and consistency checks are performed on the geometry and mesh to ensure the integrity of the model and the suitability of the mesh. Then, the boundary conditions of the inlet, outlet and blade surface are set, as well as the density, viscosity and other physical property parameters of the fluid. The appropriate flow type and turbulence model are selected, such as the standard k-ε model or SST K-ω model, and the solution algorithm, discretization format and convergence criteria are set. After initializing the flow field, steady-state or transient calculation is performed until the residual converges or the monitoring quantity stabilizes to obtain a stable numerical solution. The simulation calculation is based on the discretized Navier-Stokes equation, which can be expressed as: ; where ρ represents the fluid density, obtained from material properties or experimental data, u is the velocity vector, calculated by the solver, p is the pressure field, τ is the viscous stress tensor, calculated by the viscosity and velocity gradient, g is the gravitational acceleration, usually a known constant. The simulation results include the velocity field , pressure field and vorticity distribution These data are used for subsequent blade performance analysis. By spatial and temporal analysis of the distribution of velocity, pressure and vortex, the regions of flow separation, vortex concentration on the blade surface and the positions that may cause efficiency reduction or cavitation can be identified, providing target areas and design basis for geometric optimization in step S4, and at the same time, the reasonableness of the grid and boundary conditions can be verified, thereby guiding the blade modification and improving the design.

[0035] By simulating the flow field based on the established geometric model and grid, detailed flow information such as velocity field, pressure field and vorticity distribution around the runner blade of the water turbine can be obtained. By analyzing these data, regions such as flow separation, vortex concentration and local pressure jump on the blade surface can be identified, so as to judge the positions that may affect the hydraulic efficiency, cavitation performance and vibration characteristics of the blade. These analysis results provide quantitative basis for subsequent blade geometric optimization, so that the optimization design can adjust to the key performance bottlenecks, thereby improving the overall flow performance and operation stability of the blade and enhancing the accuracy of simulation guidance for modification.

[0036] The analysis of the simulation results includes the following steps: Analyze the blade surface flow velocity distribution to identify regions of flow velocity change or flow separation; Analyze the pressure field to identify whether there are pressure jumps or abnormal pressure distributions, and predict the regions that may cause cavitation; Analyze the vortex and turbulence intensity to identify vortex or flow separation phenomena, evaluate the influence of vortex or flow separation phenomena on blade performance, and further determine the regions that need to be optimized.

[0037] Specifically, by analyzing the CFD simulation results based on the established geometric model and grid, abnormal regions of the blade surface flow velocity distribution, pressure field, vortex and turbulence intensity can be identified, so as to judge the positions of the blade performance bottlenecks. First, the velocity field , pressure field and vorticity field data are extracted, where represents the flow velocity vector at any point in the fluid domain, represents the pressure value, represents the local vortex intensity. These data are obtained by the CFD solver by solving the Navier-Stokes equation, which is: ; where is the fluid density, is the dynamic viscosity, is the body force source term. By analyzing the gradients and of the velocity field and pressure field, the regions of flow separation, vortex concentration and local pressure jump on the blade surface can be identified.,flow separation and pressure jump regions can be identified, vortex concentration regions can be determined by vorticity field and their impact on blade performance can be evaluated, and the output includes flow velocity distribution curve, pressure distribution curve and vorticity intensity distribution of each region, which are used to determine the blade regions that need to be optimized, and design variables are selected based on these analysis results to optimize the blade geometry, thereby improving hydraulic efficiency, reducing cavitation risk and reducing vibration.

[0038] By simulating the flow field based on the established geometric model and mesh, and analyzing the velocity field, pressure field and vorticity field, regions with significant changes in flow velocity or flow separation on the blade surface can be identified, and regions with pressure jumps or abnormal distribution can be found and the positions that may cause cavitation can be predicted, and regions that have a significant impact on blade performance can be determined by evaluating the intensity of vortex and turbulence, thereby determining the blade parts that need to be optimized, providing a basis for subsequent geometric adjustment and performance improvement, and helping to improve the hydraulic efficiency, cavitation performance and operating stability of the runner blade of the hydraulic turbine under different operating conditions.

[0039] S4、According to the simulation analysis results, the runner blade geometry is adjusted using an optimization algorithm; Further, adjusting the runner blade geometry using an optimization algorithm includes the following steps: According to the simulation results, determine the optimization target and design variables, including blade curvature, blade tip angle, thickness or curvature; Construct an optimization model containing objective function and constraint conditions, the objective function is used to calculate the hydraulic efficiency, cavitation index or vibration amplitude of the blade under different operating conditions; Use genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm to perform iterative calculation in the design variable space to obtain multiple candidate geometric schemes; Based on the geometric scheme obtained by each iteration, run the simulation, calculate the objective function value and update the design variables; When the objective function converges or reaches the preset number of iterations, output the optimized runner blade geometry.

[0040] Specifically, by establishing an optimization model and performing iterative calculation in the design variable space, first, extract the performance indicators such as hydraulic efficiency, cavitation index and vibration amplitude from the flow field simulation results as optimization targets, the design variables include blade curvature, blade tip angle, thickness and curvature, these variables are defined by the initial CAD geometric model and are obtained after discretization, the optimization model is a mathematical expression combined by objective function and constraint conditions, the objective function can be expressed as: ; Wherein corresponding to blade curvature, blade tip angle, thickness and curvature, is the hydraulic efficiency, NI cavcavitation index, vibration amplitude, weight coefficient, determined by the performance evaluation results of simulation analysis, input data comes from performance parameters of each working condition obtained by CFD simulation in the previous step, optimization process generates candidate geometry schemes in design variable space through genetic algorithm, particle swarm optimization or simulated annealing algorithm, calculates target function value again for each candidate scheme, updates design variables, iterates until target function converges or reaches preset iteration number, outputs optimized runner blade geometry parameters, these parameters are used to update CAD model and enter the next step of re-simulation verification stage, thereby improving the hydraulic performance and operating stability of the blade under different working conditions, and providing geometric basis for subsequent processing.

[0041] By adjusting the runner blade geometry based on simulation analysis results, the blade performance can be optimized in the design variable space. By selecting blade curvature, blade tip angle, thickness and curvature as design variables, and constructing a target function to calculate hydraulic efficiency, cavitation index and vibration amplitude, an iterative optimization algorithm is used to generate candidate geometry schemes and continuously update design variables, so that the hydraulic performance of the blade under different working conditions is improved, the cavitation risk and vibration amplitude are reduced, and accurate geometric basis is provided for subsequent re-simulation verification and actual processing, thereby improving the overall operating efficiency and stability of the blade.

[0042] S5. Re-simulate according to the optimized blade geometry, verify the optimization results and evaluate the performance of the modified blade; Further, verifying the optimization results and evaluating the performance of the modified blade includes the following steps: Based on the optimized blade geometry model and the same grid generation strategy as before the optimization, a fluid domain model is established, and the same boundary conditions and turbulence model as before the optimization are set; Run CFD simulation to obtain flow velocity, pressure, turbulent kinetic energy and vortex distribution data; Extract hydraulic efficiency, flow-head curve, cavitation index or fluctuating pressure parameters from simulation results; Compare the optimized performance parameters with the corresponding parameters before optimization, and calculate the performance difference; Simulate and verify under rated working condition, partial load working condition and overload working condition respectively, and output performance parameter comparison results.

[0043] Specifically, by re-simulating based on the optimized blade geometry, the modification effect can be comprehensively verified to ensure that the optimization adjustment indeed improves the hydraulic performance of the blade. First, the same grid generation strategy and fluid domain model as before optimization are used to maintain the consistency of boundary conditions and turbulence models to ensure comparability. The optimized three-dimensional geometric model is imported into the CFD solver to run steady-state or transient simulation, and the flow velocity, pressure, turbulent kinetic energy and vortex distribution, etc. are calculated. By extracting the hydraulic efficiency, flow-head curve, cavitation index and fluctuating pressure, etc. indicators, the performance difference before and after optimization is compared, which can be expressed as: ; Wherein represents the hydraulic efficiency, represents the cavitation index, represents the fluctuating pressure parameter, and the subscripts opt and pre correspond to the blade data after and before optimization, respectively. The input data is obtained by simulation calculation by the CFD solver. Through the above comparison, the optimization effect can be quantified and the potential performance improvement space can be identified. In the next step, the blade design will be further adjusted according to the verification results or directly enter the actual processing stage to ensure that the blade meets the expected performance under the rated operating condition, partial load and overload condition, and at the same time provides a basis for subsequent system operation.

[0044] By re-simulating the optimized blade geometry, the modification effect can be systematically evaluated to ensure that the optimization adjustment effectively improves the hydraulic performance and operational stability of the blade. During implementation, the same grid generation strategy and fluid domain model as before optimization are used, the same boundary conditions and turbulence models are set for CFD simulation, the flow velocity, pressure, turbulent kinetic energy and vortex distribution are calculated, and the hydraulic efficiency, flow-head curve, cavitation index and fluctuating pressure, etc. key performance indicators are extracted from the simulation results. The data after optimization is compared with the data before optimization to quantify the performance improvement, further analyze the performance of the blade under various operating conditions, and provide a basis for final blade design confirmation and guide subsequent actual processing and on-site operation adjustment to achieve performance optimization and reliability improvement of the blade under the rated operating condition, partial load and overload condition.

[0045] S6, confirm the final optimized design, and manufacture the optimized blade through actual processing, and carry out on-site test and verification.

[0046] Specifically, by confirming the final optimized design and actual processing and manufacturing, the simulation optimization results can be converted into usable blade entities. During implementation, the blade is processed according to the optimized geometry and the processing precision is strictly controlled. Then, the blade is installed on site and the running test is carried out. The actual running data such as flow, head, hydraulic efficiency and vibration noise are collected. The test results are compared and analyzed with the simulation prediction to verify the applicability and performance improvement effect of the optimized design under the real operating condition.

[0047] Embodiment two: In the low water level season operation process of the hydropower station, the mixed flow water turbine has the problems of low blade hydraulic efficiency, strong vortex, abnormal vibration and noise under partial load conditions. In order to solve the above problems, the mixed flow water turbine runner blade modification system provided by the application is adopted, and its structure is shown in Figure 2 The specific implementation process of the system is as follows: Firstly, the optimization target and working condition determination module collects the water turbine flow, load and speed data, selects full load, partial load and start-stop working conditions, analyzes the blade cavitation, vortex and low efficiency area, and sets the optimization target, so as to ensure that the modification key area is clear and optimized; Secondly, the three-dimensional geometric modeling and grid generation module uses computer aided design technology to establish a three-dimensional geometric model of the runner blade, and generates refined grids on the blade surface and key flow areas, and dynamically adjusts the grid density to accurately capture the vortex and pressure gradient, and realizes high-precision simulation foundation; Subsequently, the CFD simulation module imports the established geometric model and grid into the solver to calculate the steady or transient flow field, obtains the flow velocity, pressure, vortex and other data and analyzes them, identifies the blade performance bottleneck area, and provides the basis for geometric optimization; Then, the blade geometric optimization module sets design variables such as blade curvature, blade tip angle and thickness according to the simulation analysis results, constructs an objective function to calculate the hydraulic efficiency and cavitation index, uses genetic algorithm or particle swarm optimization algorithm to iteratively solve, outputs multiple candidate schemes and selects the optimized geometric shape, and realizes the improvement of the blade hydraulic performance; Next, the re-simulation and performance verification module performs CFD simulation on the optimized blade geometric shape, obtains the hydraulic efficiency, vortex distribution and pressure parameters, and compares them with the data before optimization, verifies the optimization effect, and ensures the performance improvement of the blade under rated and partial load conditions; Finally, the design confirmation and actual processing module uses the optimized geometry for blade processing and manufacturing, installs it to the water turbine for on-site testing, collects actual operation data and confirms the performance improvement, so as to realize the goals of improving the efficiency of the water turbine, reducing the vibration and noise and enhancing the running stability during the low water level operation.

[0048] Finally, it should be pointed out that: the above only for the preferred embodiments of the application, and not for limiting the application, although the application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application, should be included in the protection scope of the application.

Claims

1. A method for modifying the runner blades of a mixed-flow turbine, characterized in that, Includes the following steps: S1. Determine the optimization objectives and required operating conditions for the modification of turbine runner blades, identify the blade areas that need modification, and set optimization objectives. S2. Use computer-aided design technology to establish a three-dimensional geometric model of the turbine runner blades and create a mesh for the fluid domain, wherein the mesh density is dynamically adjusted according to the flow characteristics in different regions; S3. Based on the established geometric model and mesh, perform flow field simulation to obtain flow velocity, pressure and eddy current data, and analyze the simulation results to identify blade performance bottlenecks. S4. Based on the simulation analysis results, adjust the geometry of the impeller blades using optimization algorithms; S5. Perform a re-simulation based on the optimized blade geometry to verify the optimization results and evaluate the performance of the modified blade. S6. Confirm the final optimized design, manufacture the optimized blades through actual processing, and conduct on-site testing and verification.

2. The method for modifying the runner blades of a mixed-flow turbine according to claim 1, characterized in that, S1 includes the following steps: By collecting the turbine's operating data, including flow rate, load, and speed information, and based on historical operating data or field test data, CFD simulations are performed under full load, partial load, and start-up / shutdown conditions to analyze the flow field performance of the blades under different conditions and determine the conditions that require the most modification. Based on the simulation results of the above operating conditions, the regions on the turbine blades with cavitation, eddies and low efficiency are analyzed. The blade surface, boundary layer, blade tip region and wake region are identified as key areas for modification. Based on different optimization requirements, at least one optimization objective is selected. The optimization objective may include changing hydraulic efficiency, cavitation performance, vibration noise, or blade fatigue resistance. The corresponding objective function and algorithm are selected for optimization calculation based on the optimization objective.

3. The method for modifying the runner blades of a mixed-flow turbine according to claim 1, characterized in that, The process of establishing a three-dimensional geometric model of the turbine runner blades includes the following steps: Using CAD software, the basic geometric features of the blade are defined according to the design requirements, and a three-dimensional geometric model of the blade is established. The three-dimensional curved surface of the blade is generated by CAD software so that the geometry of each region meets the design requirements. Verify the integrity of the model to ensure that the geometric model meets the requirements of fluid dynamics simulation.

4. The method for modifying the runner blades of a mixed-flow turbine according to claim 1, characterized in that, Creating a mesh for the fluid domain includes the following steps: Define the fluid domain around the turbine runner blades and create an initial mesh using mesh generation software; The mesh on the blade surface is refined so that the mesh density can capture flow characteristics, including eddies and pressure gradients. Quality control is performed on the mesh to verify its orthogonality, non-distortion, and smoothness, making the mesh suitable for CFD simulation.

5. The method for modifying the runner blades of a mixed-flow turbine according to claim 1, characterized in that, The dynamic adjustment of grid density includes the following steps: Real-time monitoring of flow field data through CFD simulation, analysis of flow velocity, pressure and turbulence, and identification of areas where the mesh needs to be refined or coarsened; Based on the changes in the flow field, an adaptive mesh refinement method is adopted to increase the mesh density in the boundary layer and eddy region; By combining machine learning algorithms, flow field changes can be predicted in real time, and grid density can be dynamically adjusted.

6. The method for modifying the runner blades of a mixed-flow turbine according to claim 1, characterized in that, The flow field simulation includes the following steps: Import the established 3D geometric model and mesh into the CFD solver and perform a consistency check on the geometry and mesh; Set the boundary conditions and fluid properties for the inlet, outlet, and blade surfaces; Select the flow type and turbulence model, and set the solution algorithm, discretization scheme and convergence criteria; Initialize the flow field and run the solver to perform steady-state or transient calculations until the residuals converge or the monitored quantities stabilize. The flow field data, including velocity field, pressure field, and vorticity distribution, are extracted for subsequent analysis.

7. The method for modifying the runner blades of a mixed-flow turbine according to claim 1, characterized in that, The analysis of the simulation results includes the following steps: Analyze the velocity distribution on the blade surface to identify regions of velocity variation or flow separation. Analyze the pressure field to identify any pressure abrupt changes or abnormal pressure distributions, and predict regions that may trigger cavitation. Analyze the intensity of eddies and turbulence, identify eddies or flow separation phenomena, assess the impact of eddies or flow separation phenomena on blade performance, and further determine the areas that need optimization.

8. The method for modifying the runner blades of a mixed-flow turbine according to claim 1, characterized in that, The process of adjusting the rotor blade geometry using an optimization algorithm includes the following steps: The optimization objective and design variables are determined based on the simulation results. The design variables include blade curvature, blade tip angle, thickness or curvature. An optimization model is constructed that includes an objective function and constraints. The objective function is used to calculate the hydraulic efficiency, cavitation index, or vibration amplitude of the blade under different operating conditions. Genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm are used to perform iterative calculations in the design variable space to obtain multiple candidate geometric schemes; Simulations are run based on the geometric schemes obtained in each iteration to calculate the objective function values ​​and update the design variables. When the objective function converges or reaches the preset number of iterations, the optimized rotor blade geometry is output.

9. A method for modifying the runner blades of a mixed-flow turbine according to claim 1, characterized in that, The verification of optimization results and evaluation of the modified blade performance include the following steps: A fluid domain model is established based on the optimized blade geometry model and the same mesh generation strategy as before optimization, and the same boundary conditions and turbulence model are set as before optimization. Run CFD simulation calculations to obtain data on flow velocity, pressure, turbulent kinetic energy, and eddy current distribution; Extract hydraulic efficiency, flow-head curves, cavitation index, or pulsating pressure parameters from the simulation results; The optimized performance parameters are compared with the corresponding parameters before optimization, and the performance difference is calculated. Simulation verification was performed under rated operating conditions, partial load conditions, and overload conditions, and the performance parameter comparison results were output.

10. A mixed-flow turbine runner blade modification system, characterized in that, A method for modifying the runner blades of a mixed-flow turbine according to any one of claims 1-9, the system comprising: The optimization target and operating condition determination module is used to determine the optimization target and required operating conditions for the modification of turbine runner blades, determine the blade area that needs modification, and set the optimization target. The 3D geometric modeling and mesh generation module is used to create a 3D geometric model of the turbine runner blades using computer-aided design technology, and to create a mesh for the fluid domain, wherein the mesh density is dynamically adjusted according to the flow characteristics in different regions; The CFD simulation module is used to perform flow field simulation based on the established geometric model and mesh, acquire flow velocity, pressure and eddy current data, and analyze the simulation results to identify blade performance bottlenecks. The blade geometry optimization module is used to adjust the rotor blade geometry based on simulation analysis results and optimization algorithms. The re-simulation and performance verification module is used to re-simulate based on the optimized blade geometry, verify the optimization results and evaluate the performance of the modified blade. The design verification and actual manufacturing module is used to verify the final optimized design, manufacture the optimized blades through actual manufacturing, and conduct on-site testing and verification.

Images