A method for modifying the runner blades of a mixed-flow turbine

Abstract

This invention relates to the field of hydraulic turbine technology, and more particularly to a method for modifying the blade profile of a mixed-flow hydraulic turbine runner, comprising the following steps: S1, determining the optimization objective and required operating conditions for modifying the hydraulic turbine runner blade profile, identifying the blade region requiring modification, and setting the optimization objective; S2, using computer-aided design technology to establish a three-dimensional geometric model of the hydraulic turbine runner blade, and creating 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, performing flow field simulation, acquiring flow velocity, pressure, and eddy current data, and analyzing the simulation results to identify blade performance bottlenecks. In this invention, by determining the optimization objective and required operating conditions for modifying the hydraulic turbine runner blade profile, the problem that traditional hydraulic turbine blade modification mostly relies on empirical judgment and single-condition optimization, resulting in the difficulty of simultaneously optimizing blade efficiency and flow performance, is improved.

Description

Technical Field

[0001] This invention relates to the field of water turbine technology, and in particular to a method for modifying the runner blades of a mixed-flow water turbine. Background Technology

[0002] With the widespread construction and operation of hydropower stations, mixed-flow turbines have become widely used due to their high hydraulic efficiency under conditions of low to medium head and large flow variations. During turbine operation, the hydraulic performance of the runner blades directly affects the unit's efficiency, cavitation, and vibration and noise levels. Therefore, optimizing the runner blades to improve hydraulic performance and extend service life has become a crucial aspect of turbine design and maintenance.

[0003] Traditional turbine blade modification mostly relies on experience-based judgment and single-condition optimization. Because it cannot take into account the performance differences under multiple conditions, it is difficult to optimize blade efficiency and flow performance at the same time. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a method for modifying the blades of a mixed-flow turbine runner, aiming to improve the problem that traditional turbine blade modification mostly relies on experience-based judgment and single-condition optimization, which cannot take into account the performance differences under multiple conditions, thus making it difficult to simultaneously optimize blade efficiency and flow performance.

[0005] In a first aspect, the present invention provides the following technical solution: a method for modifying the blades of a mixed-flow turbine runner, comprising the following steps:

[0006] 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.

[0007] 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;

[0008] 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.

[0009] S4. Based on the simulation analysis results, adjust the geometry of the impeller blades using optimization algorithms;

[0010] S5. Perform a re-simulation based on the optimized blade geometry to verify the optimization results and evaluate the performance of the modified blade.

[0011] S6. Confirm the final optimized design, manufacture the optimized blades through actual processing, and conduct on-site testing and verification.

[0012] By adopting the above technical solution, the optimization target and required operating conditions for turbine runner blade modification are determined, the blade area that needs modification is identified, and optimization targets are set. This guides the entire modification process to achieve targeted adjustments, thereby improving the problem that traditional turbine blade modification mostly relies on experience-based judgment and single-condition optimization, which cannot take into account the performance differences of multiple operating conditions, thus making it difficult to optimize blade efficiency and flow performance simultaneously.

[0013] Further, S1 includes the following steps:

[0014] 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.

[0015] 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.

[0016] 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.

[0017] Furthermore, establishing the three-dimensional geometric model of the turbine runner blades includes the following steps:

[0018] 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.

[0019] The three-dimensional curved surface of the blade is generated by CAD software so that the geometry of each region meets the design requirements.

[0020] Verify the integrity of the model to ensure that the geometric model meets the requirements of fluid dynamics simulation.

[0021] Furthermore, creating a mesh for the fluid domain includes the following steps:

[0022] Define the fluid domain around the turbine runner blades and create an initial mesh using mesh generation software;

[0023] The mesh on the blade surface is refined so that the mesh density can capture flow characteristics, including eddies and pressure gradients.

[0024] Quality control is performed on the mesh to verify its orthogonality, non-distortion, and smoothness, making the mesh suitable for CFD simulation.

[0025] Furthermore, the dynamic adjustment of grid density includes the following steps:

[0026] 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;

[0027] 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;

[0028] By combining machine learning algorithms, flow field changes can be predicted in real time, and grid density can be dynamically adjusted.

[0029] Furthermore, the flow field simulation includes the following steps:

[0030] Import the established 3D geometric model and mesh into the CFD solver and perform a consistency check on the geometry and mesh;

[0031] Set the boundary conditions and fluid properties for the inlet, outlet, and blade surfaces;

[0032] Select the flow type and turbulence model, and set the solution algorithm, discretization scheme and convergence criteria;

[0033] Initialize the flow field and run the solver to perform steady-state or transient calculations until the residuals converge or the monitored quantities stabilize.

[0034] The flow field data, including velocity field, pressure field, and vorticity distribution, are extracted for subsequent analysis.

[0035] Furthermore, the analysis of the simulation results includes the following steps:

[0036] Analyze the velocity distribution on the blade surface to identify regions of velocity variation or flow separation.

[0037] Analyze the pressure field to identify any pressure abrupt changes or abnormal pressure distributions, and predict regions that may trigger cavitation.

[0038] 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.

[0039] Furthermore, the step of adjusting the rotor blade geometry using an optimization algorithm includes the following steps:

[0040] 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.

[0041] 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.

[0042] 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;

[0043] Simulations are run based on the geometric schemes obtained in each iteration to calculate the objective function values ​​and update the design variables.

[0044] When the objective function converges or reaches the preset number of iterations, the optimized rotor blade geometry is output.

[0045] Furthermore, the verification of the optimization results and the evaluation of the modified blade performance include the following steps:

[0046] 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.

[0047] Run CFD simulation calculations to obtain data on flow velocity, pressure, turbulent kinetic energy, and eddy current distribution;

[0048] Extract hydraulic efficiency, flow-head curves, cavitation index, or pulsating pressure parameters from the simulation results;

[0049] The optimized performance parameters are compared with the corresponding parameters before optimization, and the performance difference is calculated.

[0050] Simulation verification was performed under rated operating conditions, partial load conditions, and overload conditions, and the performance parameter comparison results were output.

[0051] Secondly, the present invention provides the following technical solution: a mixed-flow turbine runner blade modification system, the system comprising:

[0052] 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.

[0053] 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;

[0054] 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.

[0055] The blade geometry optimization module is used to adjust the rotor blade geometry based on simulation analysis results and optimization algorithms.

[0056] 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.

[0057] 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.

[0058] The present invention has the following beneficial effects:

[0059] 1. In this invention, by determining the optimization target and required operating conditions for the modification of turbine runner blades, the blade area that needs modification is determined, and optimization targets are set, thereby guiding the entire modification process to achieve targeted adjustments. This improves the problem that traditional turbine blade modification mostly relies on experience judgment and single-condition optimization, which cannot take into account the performance differences of multiple operating conditions, thus making it difficult to optimize blade efficiency and flow performance at the same time.

[0060] 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.

[0061] 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

[0062] Figure 1 This is a flowchart of a method for modifying the runner blades of a mixed-flow turbine proposed in this invention;

[0063] Figure 2 This is a system architecture diagram of a mixed-flow turbine runner blade modification system proposed in this invention. Detailed Implementation

[0064] 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.

[0065] Example 1

[0066] 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:

[0067] 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.

[0068] Furthermore, S1 includes the following steps:

[0069] 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.

[0070] 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.

[0071] Based on different optimization requirements, at least one optimization objective is selected. The optimization objective may include changing hydraulic efficiency, cavitation performance, vibration and noise, or blade fatigue resistance. The corresponding objective function and algorithm are selected for optimization calculation based on the optimization objective.

[0072] Specifically, the optimization objectives and required operating conditions for turbine runner blade modification were determined by collecting turbine operating data, including parameters such as flow rate, load, and speed, and combining this data with historical operating records and field test data to form input conditions for CFD simulation. Based on these input conditions, full load, partial load, and start-stop conditions were selected as simulation conditions. Simulations were run using a computational fluid dynamics solver to obtain velocity field, pressure field, and vorticity distribution data of the blades under different operating conditions. These data serve as key indicators for evaluating blade performance. The simulation results were analyzed, and flow anomalies or performance bottlenecks on the blade surface, boundary layer, tip region, and wake region were identified through numerical comparison and flow field feature identification methods, thus clarifying the blade regions requiring modification. The optimization objectives were set using mathematical modeling, with hydraulic efficiency, cavitation performance, vibration noise, and blade fatigue resistance as objective functions or constraints, respectively, to construct a comprehensive optimization model. The model input was a vector of blade geometric parameters. ;in Indicates the degree of blade curvature. Indicates the leaf tip angle. Indicates the thickness distribution of the blade. This represents the rate of change of curvature, whose value is derived from CAD modeling and measurement data, and the output is the value of the optimization objective function. ,in , Where is the hydraulic efficiency, CI(x) is the cavitation index, V(x) is the vibration amplitude, and F(x) is the fatigue strength index. The weighting coefficients are determined based on design requirements. During optimization, a genetic algorithm can be used to iteratively search the blade geometric parameter space. Simulations are run to calculate the objective function value for each generation of individuals using their corresponding geometric schemes. The parameter set for the next generation is updated based on selection, crossover, and mutation operations until the objective function converges or the preset number of iterations is reached. After optimization, the optimal set of blade geometric parameters is output. The results are used to guide subsequent blade geometry modeling and mesh generation steps, thereby enabling flow field simulation and performance verification to achieve the goals of improving the efficiency of modified blades and controlling cavitation and vibration.

[0073] This allows for the analysis of the flow field characteristics of the turbine under different operating conditions, identification of flow anomalies and performance bottlenecks on the blade surface, boundary layer, blade tip region, and wake region, thereby clarifying the blade regions that need modification and setting targeted optimization goals, including hydraulic efficiency, cavitation performance, vibration and noise, and blade fatigue resistance, thus improving the targeting and efficiency of blade modification.

[0074] 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;

[0075] Furthermore, establishing a three-dimensional geometric model of the turbine runner blades includes the following steps:

[0076] 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.

[0077] The three-dimensional curved surface of the blade is generated by CAD software so that the geometry of each region meets the design requirements.

[0078] Verify the integrity of the model to ensure that the geometric model meets the requirements of fluid dynamics simulation.

[0079] Specifically, computer-aided design technology is used to achieve accurate 3D modeling of turbine runner blades and generate meshes suitable for flow field simulation. First, CAD software is used to define the basic geometric features of the blades according to the blade design requirements, including blade length, curvature, thickness distribution, and tip angle. These geometric parameters are then input into the software in digital form to construct a 3D surface model of the blade. Subsequently, the continuity and smoothness of the generated 3D surface are verified to ensure that the surface meets the conditions for fluid dynamics simulation. Then, an initial mesh is generated on the blade surface and in the surrounding fluid domain. The mesh density in the boundary layer and vortex region is increased through local refinement methods, enabling the mesh to accurately capture flow characteristics. Input data includes blade geometric parameter vectors.

[0080] ;

[0081] in For the blade length, For the leaf tip angle, The thickness distribution along the blade chord, The coordinate distribution is for the chord lines;

[0082] These parameters are derived from design specifications and engineering measurements, and the output is a three-dimensional blade geometry model. The next step will be to connect the corresponding fluid domain mesh Q. The flow field simulation analysis was performed using a CFD solver and Q. The results were used to provide basic data for blade performance evaluation and as input conditions for subsequent optimization algorithms to adjust the blade geometry, ensuring that the hydraulic efficiency and cavitation performance of the blade under different operating conditions can be accurately predicted and improved.

[0083] By using computer-aided design technology to establish a three-dimensional geometric model of the turbine runner blade and create a mesh for the fluid domain, we can obtain an accurate blade geometry and a high-quality computational mesh that meet the design requirements. This ensures that the flow characteristics of the blade surface and key areas can be accurately captured. At the same time, the mesh density is dynamically adjusted according to the flow characteristics in different regions, thus providing a reliable data foundation for subsequent flow field simulation and helping to analyze the hydraulic performance and cavitation of the blade under different operating conditions.

[0084] Creating a mesh for a fluid domain involves the following steps:

[0085] Define the fluid domain around the turbine runner blades and create an initial mesh using mesh generation software;

[0086] The mesh on the blade surface is refined so that the mesh density can capture flow characteristics, including eddies and pressure gradients.

[0087] Quality control is performed on the mesh to verify its orthogonality, non-distortion, and smoothness, making the mesh suitable for CFD simulation.

[0088] Specifically, the fluid domain surrounding the turbine runner blades is discretized using mesh generation software, and local mesh refinement is performed in key areas to ensure accurate capture of flow characteristics. The specific steps include first defining the boundary of the fluid domain based on the three-dimensional geometric model of the blades and generating an initial mesh. This initial mesh contains the basic spatial discretization information of the fluid domain, and its input data is the coordinates of points on the blade geometric model. The fluid domain boundary conditions were obtained through CAD modeling, and the boundary conditions were set according to the operating parameters. Next, the mesh was refined on the blade surface and in areas with significant eddies and pressure gradients, with the initial mesh node positions as input data. and flow field characteristic indicators The indicators include the local velocity gradient. and pressure gradient The mesh refinement, obtained through preliminary simulations or empirical formula calculations, can be expressed by the adaptive refinement algorithm formula as follows: ,in The original grid cell length, For adjustment coefficients, To determine the flow characteristic intensity, a new mesh cell size is calculated. The mesh nodes are then regenerated, and finally, the mesh is verified for orthogonality, non-distortion, and smoothness using a quality control algorithm. The output is a high-quality mesh that meets the simulation requirements.

[0089] This grid, as input for subsequent CFD simulations, ensures the accuracy of flow field calculations and provides a reliable data foundation for evaluating blade performance and optimizing designs.

[0090] By using computer-aided design technology to establish a three-dimensional geometric model and create a high-quality mesh for the fluid domain, mesh refinement can be achieved in key areas such as blade surfaces, vortex regions, and areas with significant pressure gradients. This allows for accurate capture of flow characteristics and improves the accuracy of flow field simulation calculations. After verification of orthogonality, non-distortion, and smoothness, the mesh can stably support the operation of the CFD solver.

[0091] Dynamically adjusting the grid density includes the following steps:

[0092] 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;

[0093] 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;

[0094] By combining machine learning algorithms, flow field changes can be predicted in real time, and grid density can be dynamically adjusted.

[0095] Specifically, firstly, the established three-dimensional geometric model of the turbine runner blades and the initial fluid domain mesh are input into the CFD solver, and flow field data, including the velocity field, is obtained through real-time simulation. Pressure field and turbulent kinetic energy These data reflect different locations. and time The flow state is then assessed; next, based on the monitored flow field data, the flow characteristics of the region are analyzed to identify areas requiring mesh refinement or coarsening, typically by defining local error indices. To quantify the changes in the flow field, the formula is: ;in These are the weighting coefficients for flow velocity, pressure, and turbulent kinetic energy gradients, determined from historical simulation data and engineering experience. The reference values ​​are used for normalization; subsequently, an adaptive mesh refinement algorithm is employed to increase the mesh density in regions where the error index exceeds a preset threshold and to coarsen the mesh in regions with lower error indices, thereby reducing computational load while maintaining computational accuracy; simultaneously, a flow field prediction model is established using machine learning algorithms, taking past simulation data and current real-time flow field data as input.

[0096] Predict the flow field changes at the next time step. The grid layout is then dynamically adjusted based on the prediction results; the final output is a high-quality grid that has undergone adaptive optimization. This mesh can be used for subsequent accurate CFD simulations, thereby more accurately analyzing blade performance bottlenecks and guiding geometry optimization. The values ​​of each parameter are derived from previous experimental data, historical simulation results, and design requirements. Error indices and thresholds can be reliably verified through multiple simulations. This result is mainly used to provide high-precision flow field data, providing a basis for blade optimization design.

[0097] By using computer-aided design technology to establish a three-dimensional geometric model and generate a fluid domain mesh, and combining CFD simulation to monitor flow field data in real time, analyze flow velocity, pressure and turbulence, it is possible to identify regions with significant changes in flow characteristics and refine the mesh in the boundary layer and eddy region. At the same time, machine learning algorithms are used to predict flow field changes and dynamically adjust the mesh density, thereby obtaining a high-precision computational mesh that can adapt to complex flows, improving the accuracy and efficiency of simulation calculations.

[0098] 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.

[0099] Furthermore, the flow field simulation includes the following steps:

[0100] Import the established 3D geometric model and mesh into the CFD solver and perform a consistency check on the geometry and mesh;

[0101] Set the boundary conditions and fluid properties for the inlet, outlet, and blade surfaces;

[0102] Select the flow type and turbulence model, and set the solution algorithm, discretization scheme and convergence criteria;

[0103] Initialize the flow field and run the solver to perform steady-state or transient calculations until the residuals converge or the monitored quantities stabilize.

[0104] The flow field data, including velocity field, pressure field, and vorticity distribution, are extracted for subsequent analysis.

[0105] Specifically, the 3D geometric model and mesh generated by CAD are imported into the CFD solver, and a consistency check is performed on the geometry and mesh to ensure the integrity of the model and the applicability of the mesh. Then, boundary conditions for the inlet, outlet, and blade surfaces, as well as fluid properties such as density and viscosity, are set. A suitable flow type and turbulence model are selected, such as the standard k-ε model or the SSTK-ω model, and the solution algorithm, discretization scheme, and convergence criteria are set. After initializing the flow field, steady-state or transient calculations are performed until the residuals converge or the monitoring star stabilizes to obtain a stable numerical solution. The simulation calculation is based on the discretized Navier-Stokes equations, which can be expressed in the following discretized form:

[0106] ;in Fluid density is represented by material properties or experimental data. The velocity vector is obtained through the solver. For pressure field, The viscous stress tensor is calculated using viscosity and velocity gradient. This is the acceleration due to gravity, which is usually a known constant. The simulation results output includes the velocity field. Pressure field and vorticity distribution These data are used for subsequent blade performance analysis. By performing spatial and temporal analysis on the distribution of velocity, pressure, and eddies, regions of flow separation and eddy concentration on the blade surface, as well as locations that may lead to reduced efficiency or cavitation, can be identified. This provides target areas and design basis for geometry optimization in step S4, and can also be used to verify the rationality of the mesh and boundary conditions, thereby guiding blade modification and design improvement.

[0107] By performing flow field simulations based on established geometric models and meshes, detailed flow information such as velocity fields, pressure fields, and vorticity distributions around turbine runner blades can be obtained. Analyzing these data can identify areas such as flow separation, vortex concentration, and local pressure abrupt changes on the blade surface, thereby determining the locations that may affect the blade's hydraulic efficiency, cavitation performance, and vibration characteristics. These analytical results provide quantitative basis for subsequent blade geometry optimization, enabling the optimization design to adjust key performance bottlenecks, thereby improving the overall flow performance and operational stability of the blade and enhancing the accuracy of simulation-guided modification.

[0108] The analysis of simulation results includes the following steps:

[0109] Analyze the velocity distribution on the blade surface to identify regions of velocity variation or flow separation.

[0110] Analyze the pressure field to identify any pressure abrupt changes or abnormal pressure distributions, and predict regions that may trigger cavitation.

[0111] 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.

[0112] Specifically, by analyzing the CFD simulation results based on the established geometric model and mesh, abnormal regions of velocity distribution, pressure field, and eddy and turbulence intensity on the surface of turbine runner blades can be identified, thereby determining the location of blade performance bottlenecks. The velocity field is extracted first. Pressure field and vorticity field Data, including This represents the velocity vector at any point in the fluid domain. Indicates the pressure value. This represents the local vortex intensity. These data are obtained by the CFD solver by solving the Navier-Stokes equations, which are: ;in For fluid density, For dynamic viscosity, As a force source term, the gradients of the velocity and pressure fields are analyzed. and It can identify flow separation and pressure change regions, determine vortex concentration areas through vorticity field and evaluate their impact on blade performance. The output includes velocity distribution curves, pressure distribution curves and vorticity intensity distribution of each region, which are used to determine the blade regions that need to be optimized. Based on these analysis results, design variables are selected for blade geometry optimization, thereby improving hydraulic efficiency, reducing cavitation risk and reducing vibration.

[0113] By performing flow field simulations based on established geometric models and meshes, and analyzing the velocity, pressure, and vorticity fields, regions with significant velocity variations or flow separation on the blade surface can be identified. Simultaneously, regions with abrupt pressure changes or abnormal distributions can be discovered, and potential cavitation locations can be predicted. By evaluating eddy and turbulence intensity, regions with significant impacts on blade performance can be determined, thus identifying blade components requiring optimization. This provides a basis for subsequent geometric adjustments and performance improvements, contributing to the enhancement of hydraulic efficiency, cavitation performance, and operational stability of turbine runner blades under different operating conditions.

[0114] S4. Based on the simulation analysis results, adjust the geometry of the impeller blades using optimization algorithms;

[0115] Furthermore, adjusting the rotor blade geometry using optimization algorithms includes the following steps:

[0116] Based on the simulation results, the optimization objectives and design variables are determined. The design variables include blade curvature, blade tip angle, thickness or curvature.

[0117] 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.

[0118] 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;

[0119] Simulations are run based on the geometric schemes obtained in each iteration to calculate the objective function values ​​and update the design variables.

[0120] When the objective function converges or reaches the preset number of iterations, the optimized rotor blade geometry is output.

[0121] Specifically, by establishing an optimization model and performing iterative calculations within the design variable space, performance indicators such as hydraulic efficiency, cavitation index, and vibration amplitude are first extracted from the flow field simulation results as optimization objectives. Design variables include blade curvature, tip angle, thickness, and deflection. These variables are defined by the initial CAD geometric model and obtained through discretization. The optimization model takes the form of a mathematical expression combining the objective function and constraints. The objective function can be expressed as:

[0122] ;

[0123] in These correspond to the blade curvature, tip angle, thickness, and radius of curvature, respectively. For hydraulic efficiency, NI cav The cavitation index, The amplitude of vibration. The weighting coefficients are determined by the performance evaluation results of the simulation analysis. The input data comes from the performance parameters of each working condition obtained from the previous CFD simulation. The optimization process iteratively generates candidate geometric schemes in the design variable space through genetic algorithms, particle swarm optimization, or simulated annealing algorithms. For each candidate scheme, CFD simulation is run again to calculate the objective function value and update the design variables. The iteration continues until the objective function converges or the preset number of iterations is reached. The optimized rotor blade geometric parameters are output. These parameters are used to update the CAD model and enter the next simulation verification stage, thereby improving the hydraulic performance and operational stability of the blade under different working conditions and providing geometric basis for subsequent processing.

[0124] By adjusting the rotor blade geometry based on simulation analysis results, blade performance can be optimized in a targeted manner within the design variable space. By selecting blade curvature, tip angle, thickness, and curvature as design variables, and constructing an objective function to calculate hydraulic efficiency, cavitation index, and vibration amplitude, an iterative optimization algorithm is used to generate candidate geometric schemes and continuously update the design variables. This improves the hydraulic performance of the blade under different operating conditions, reduces cavitation risk and vibration amplitude, and provides accurate geometric basis for subsequent re-simulation verification and actual manufacturing, thereby improving the overall operating efficiency and stability of the blade.

[0125] S5. Perform a re-simulation based on the optimized blade geometry to verify the optimization results and evaluate the performance of the modified blade.

[0126] Furthermore, verifying the optimization results and evaluating the performance of the modified blades includes the following steps:

[0127] 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.

[0128] Run CFD simulation calculations to obtain data on flow velocity, pressure, turbulent kinetic energy, and eddy current distribution;

[0129] Extract hydraulic efficiency, flow-head curves, cavitation index, or pulsating pressure parameters from the simulation results;

[0130] The optimized performance parameters are compared with the corresponding parameters before optimization, and the performance difference is calculated.

[0131] Simulation verification was performed under rated operating conditions, partial load conditions, and overload conditions, and the performance parameter comparison results were output.

[0132] Specifically, by re-simulating based on the optimized blade geometry, the effect of the modification can be fully verified, ensuring that the optimization adjustment has indeed improved the hydraulic performance of the blade. First, the same mesh generation strategy and fluid domain model as before optimization are used, maintaining consistent boundary conditions and turbulence models to ensure comparability. The optimized 3D geometric model is then imported into a CFD solver to run steady-state or transient simulations, calculating flow field parameters such as velocity, pressure, turbulent kinetic energy, and eddy current distribution. By extracting indicators such as hydraulic efficiency, flow-head curves, cavitation index, and fluctuating pressure, the performance differences before and after optimization are compared. The formula can be expressed as:

[0133] ;

[0134] in Indicates hydraulic efficiency. Indicates the cavitation index, The parameters represent pulsating pressure. The sub-labels opt and pre correspond to the blade data before and after optimization, respectively. The input data is obtained by CFD solver through simulation calculation. The above comparison can quantify the optimization effect and identify potential performance improvement space. The next step will be to further adjust the blade design or directly enter the actual processing stage based on the verification results to ensure that the blade achieves the expected performance under rated conditions, partial load and overload conditions, and at the same time provide a basis for subsequent system operation.

[0135] By resimulating the optimized blade geometry, the effect of the modification can be systematically evaluated, ensuring that the optimization adjustment effectively improves the hydraulic performance and operational stability of the blade. In the implementation process, the same mesh generation strategy and fluid domain model as before optimization are used, and the same boundary conditions and turbulence model are set for CFD simulation to calculate flow velocity, pressure, turbulent kinetic energy and eddy distribution. Key performance indicators such as hydraulic efficiency, flow-head curve, cavitation index and pulsating pressure are extracted from the simulation results. The data after optimization are compared with those before optimization to quantify the performance improvement. The blade performance under various operating conditions is further analyzed. The results can provide a basis for the final blade design confirmation and guide subsequent actual processing and on-site operation adjustments, realizing the performance optimization and reliability improvement of the blade under rated operating conditions, partial load and overload conditions.

[0136] S6. Confirm the final optimized design, manufacture the optimized blades through actual processing, and conduct on-site testing and verification.

[0137] Specifically, by confirming the final optimized design and carrying out actual processing and manufacturing, the simulation optimization results can be transformed into usable blade entities. During the implementation process, the blades are processed according to the optimized geometry and the processing accuracy is strictly controlled. Subsequently, they are installed on-site and operational tests are carried out to collect actual operating data such as flow rate, head, hydraulic efficiency, and vibration and noise. The test results are compared and analyzed with the simulation predictions to verify the applicability of the optimized design under real working conditions and the performance improvement effect.

[0138] Example 2:

[0139] During the low-water-level season operation of hydropower stations, mixed-flow turbines exhibit problems such as low blade hydraulic efficiency, high eddy current intensity, and abnormal vibration and noise under partial load conditions. To address these issues, the mixed-flow turbine runner blade modification system provided by this invention is adopted, the structure of which is as follows: Figure 2 As shown. The specific implementation process of this system is as follows:

[0140] First, the optimization target and operating condition determination module collects turbine flow, load and speed data, selects full load, partial load and start-up and shutdown conditions, analyzes blade cavitation, eddy current and low efficiency areas, and sets optimization targets to ensure that the key areas for modification are clearly defined and optimized in a targeted manner.

[0141] Secondly, the 3D geometric modeling and mesh generation module uses computer-aided design technology to establish a 3D geometric model of the runner blades and generate refined meshes on the blade surface and key flow regions. At the same time, it dynamically adjusts the mesh density to accurately capture eddies and pressure gradients, thus achieving a high-precision simulation foundation.

[0142] Subsequently, the CFD simulation module imports the established geometric model and mesh into the solver to perform steady-state or transient flow field calculations, obtain and analyze data such as flow velocity, pressure, and eddy currents, identify bottleneck areas in blade performance, and provide a basis for geometric optimization.

[0143] Then, the blade geometry optimization module sets design variables such as blade curvature, blade tip angle and thickness according to the simulation analysis results, constructs objective function to calculate hydraulic efficiency and cavitation index, and uses genetic algorithm or particle swarm optimization algorithm to iteratively solve the problem, outputs multiple candidate schemes and selects the optimized geometry to improve the hydraulic performance of the blade.

[0144] Next, the simulation and performance verification module performs CFD simulation on the optimized blade geometry to obtain hydraulic efficiency, eddy current distribution and pressure parameters, and compares them with the data before optimization to verify the optimization effect and ensure that the blade performance is improved under rated and partial load conditions.

[0145] Finally, the design verification and actual processing module will optimize the geometry for blade manufacturing, install it on the turbine for on-site testing, collect actual operating data and confirm the performance improvement, thereby achieving the goals of improving turbine efficiency, reducing vibration and noise, and enhancing operational stability during low water level operation.

[0146] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of modifying a Francis turbine runner blade, characterized by, The method comprises the following steps: S1, determining the optimization target of the water turbine runner blade modification and the required working condition, determining the blade area that needs to be modified, and setting the optimization target; By collecting the working data of the water turbine, including flow rate, load, and rotating speed information, and based on historical operation data or field test data, selecting full load, partial load, and start-stop working conditions, performing CFD simulation, analyzing the flow field performance of the blade under different working conditions, and determining the working condition that needs to be modified most; Based on the simulation results of the working condition, analyze the areas of cavitation, vortex, and low efficiency on the water turbine blade, and focus on determining the blade surface, boundary layer, tip region, and wake area as the key modification areas; S2, using computer-aided design technology to establish a three-dimensional geometric model of the water turbine runner blade, and creating a grid for the fluid domain, wherein the density of the grid is dynamically adjusted in different areas according to the flow characteristics, and the dynamic adjustment includes real-time prediction of flow field changes by combining machine learning algorithms; 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 blade performance bottlenecks; S4, according to the simulation analysis results, adjusting the geometric shape of the runner blade using an optimization algorithm; S5, according to the optimized blade geometry, performing re-simulation to verify the optimization results and evaluate the performance of the modified blade; the re-simulation needs to be performed under rated working conditions, partial load working conditions, and overload working conditions, respectively, and the hydraulic efficiency, flow head curve, cavitation index, or fluctuating pressure parameters are compared with those before optimization; S6, confirming the final optimization design, manufacturing the optimized blade through actual processing, and performing field testing and verification, and comparing the actual operation data with the simulation prediction results.

2. A method of modifying a Francis turbine runner blade according to claim 1, characterized in that The S1 comprises the following steps: 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.

3. A method of modifying a Francis turbine runner blade according to claim 1, wherein The establishment of the three-dimensional geometric model of the water turbine runner blade 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; Verify the integrity of the model to ensure that the geometric model meets the requirements of fluid mechanics simulation.

4. A method of modifying a Francis turbine runner blade according to claim 1, wherein The grid creation for the fluid domain comprises the following steps: Defining the fluid domain around the water turbine runner blade, and using grid generation software to create an initial grid; Refining the grid on the blade surface to capture the flow characteristics, including vortex and pressure gradient; Quality control of the grid to verify orthogonality, non-distortion, and smoothness to make the grid suitable for CFD simulation.

5. A method of modifying a Francis turbine runner blade according to claim 1, wherein The dynamic adjustment of the grid density comprises the following steps: Real-time monitoring of flow field data through CFD simulation, analyzing flow velocity, pressure, and turbulence, and identifying areas that need to be refined or coarsened; Based on the flow field changes, using adaptive grid refinement method to increase the grid density in the boundary layer and vortex area; In combination with machine learning algorithms, real-time prediction of flow field changes and dynamic adjustment of grid density.

6. A method of modifying a Francis turbine runner blade according to claim 1, wherein The flow field simulation includes the following steps: Import the established three-dimensional geometric model and grid into the CFD solver, and perform consistency checks 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-state 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.

7. A method of modifying a Francis turbine runner blade according to claim 1, wherein The analysis of the simulation results includes the following steps: Analyze the blade surface flow velocity distribution to identify areas of flow velocity variation or flow separation. Analyze the pressure field to identify whether there are pressure jumps or abnormal pressure distributions, and predict areas that may cause cavitation. Analyze the vortex and turbulence intensity to identify vortex or flow separation phenomena, assess the impact of vortex or flow separation phenomena on blade performance, and further determine the areas that need to be optimized.

8. A method of modifying a Francis turbine runner blade according to claim 1, wherein The optimization algorithm for adjusting the runner blade geometry includes the following steps: Determine the optimization target and design variables based on the simulation results, including blade curvature, blade tip angle, thickness or curvature. Construct an optimization model containing objective functions and constraints, with the objective functions used to calculate the hydraulic efficiency, cavitation index or vibration amplitude of the blade under different operating conditions. Use genetic algorithms, particle swarm optimization algorithms or simulated annealing algorithms to perform iterative calculations in the design variable space to obtain multiple candidate geometric schemes. Run the simulation based on the geometric scheme obtained from each iteration to 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.

9. A method of modifying a Francis turbine runner blade according to claim 1, wherein 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 the CFD simulation to obtain flow velocity, pressure, turbulent kinetic energy and vortex distribution data. Extract the hydraulic efficiency, flow-head curve, cavitation index or fluctuating pressure parameters from the simulation results. Compare the performance parameters after optimization with the corresponding parameters before optimization to calculate the performance difference. Perform simulation verification under rated operating conditions, partial load operating conditions and overload operating conditions, and output the performance parameter comparison results.

10. A Francis turbine runner blade reshaping system, characterized by, A mixed-flow water turbine runner blade modification method according to any one of claims 1-9, the system comprising: An optimization target and operating condition determination module for determining the optimization target of the water turbine runner blade modification and the required operating conditions, determining the blade area that needs to be modified, and setting the optimization target. A three-dimensional geometric modeling and grid generation module for establishing a three-dimensional geometric model of the water turbine runner blade using computer-aided design technology, 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. CFD simulation module, for simulating flow field based on established geometry model and mesh, obtaining flow velocity, pressure and vortex data, and analyzing simulation results to identify blade performance bottleneck; Blade geometry optimization module, for adjusting runner blade geometry shape according to simulation analysis results by using optimization algorithm; Resimulation and performance verification module, for resimulating according to optimized blade geometry shape, verifying optimization results and evaluating performance of modified blade; Design confirmation and actual processing module, for confirming final optimization design, manufacturing optimized blade through actual processing, and performing on-site test and verification.

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