Method for optimizing cavitation erosion resistance of water turbine runner based on control of blade outflow angle

Abstract

The application provides a water turbine runner anti-cavitation abrasion optimization method based on control of blade outflow angle, and the method comprises the following steps: determining runner basic geometric parameters according to water turbine operation conditions, and constructing an initial three-dimensional geometric model containing a blade profile and a corresponding flow passage calculation domain; dividing a blade spanwise into multiple design sections, and setting a target outflow angle distribution; the target outflow angle distribution is obtained by adjusting a flow velocity and pressure relationship of a blade back surface outlet area, so that the pressure of the blade back surface outlet area is higher than a saturated steam pressure of water and a safety margin is left; taking the target outflow angle distribution as a direct objective function, iteratively optimizing blade skeleton line curvature and thickness distribution until an error between a calculated outflow angle distribution and the target distribution meets a preset convergence standard; performing full three-dimensional cavitation CFD numerical simulation on the optimized blade model, dynamically adjusting according to a simulation result, and repeating the iterative optimization process of S2 to S3 until a cavitation performance index meets a preset requirement.

Description

Technical Field

[0001] This invention relates to the field of anti-cavitation erosion design technology for turbine runner blades, and particularly to an optimization method for anti-cavitation erosion of turbine runners based on controlling the blade outflow angle. Background Technology

[0002] Hydropower turbines, as core equipment for clean energy development, are widely used in hydropower stations operating in silty rivers and under varying conditions. Among related technologies, a traditional anti-cavitation design system has been constructed through the coordinated operation of blade airfoil optimization, corrosion-resistant material protection, and air injection systems. Specifically, this system covers the entire process from fluid dynamics analysis to material surface treatment, including key aspects such as negative curvature design, stainless steel selection, and air injection. Among these, the blade outlet flow angle... As a key parameter determining the flow state and pressure distribution at the turbine runner outlet, its proper control plays a fundamental role in suppressing cavitation. With the development of CFD technology, although existing design methods have incorporated numerical simulation, the blade outflow angle is still considered an indirect result of the direct problem design, lacking an active control mechanism and making it difficult to achieve precise optimization of the pressure field on the blade surface.

[0003] However, existing cavitation design methods have systemic limitations. Specifically, traditional airfoil optimization relies on empirical trial-and-error forward problem simulations, making it difficult to accurately control the exit region on the back of the blade. The distribution of sediment in the water results in excessively high flow velocities and low pressures, creating a high-risk zone for cavitation. While material protection solutions employ technologies such as tungsten carbide coatings, these are passive and costly measures that fail to address the root cause of cavitation. Injection methods can mitigate the intensity of cavitation collapse, but require additional equipment and have limited effectiveness. Consequently, the localized low-pressure area at the blade's exit region generates microjets and impact forces during cavitation collapse, leading to material fatigue and spalling. The scouring effect of sediment-laden water further exacerbates erosion damage. This technical deficiency directly results in a 30%-50% reduction in blade life and a significant increase in maintenance costs when turbines operate in high-silt-laden river basins such as the Yellow River and Jinsha River, hindering the efficient utilization of clean energy. Summary of the Invention

[0004] The main objective of this invention is to provide an optimization method for the anti-cavitation erosion of a turbine runner based on controlling the blade outflow angle.

[0005] Another objective of this invention is to propose an optimization device for anti-cavitation erosion of a turbine runner based on controlling the blade outflow angle.

[0006] The third objective of this invention is to provide a computer device.

[0007] The fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention proposes an optimization method for cavitation erosion resistance of a turbine runner based on controlling the blade outflow angle, comprising: S1. Determine the basic geometric parameters of the runner based on the turbine's operating conditions, and construct an initial three-dimensional geometric model including the blade profile and the corresponding flow channel computation domain. S2, the blade spanwise is divided into multiple design sections, and a target outflow angle distribution is set for each design section. The target outflow angle distribution is achieved by adjusting the velocity and pressure relationship in the outlet region on the back of the blade to ensure that the pressure in this region is higher than the saturated vapor pressure of water and leaves a safety margin. S3, using the target outflow angle distribution as the direct objective function, the three-dimensional inverse problem calculation method is used to iteratively optimize the blade rib line curvature and thickness distribution until the error between the calculated outflow angle distribution and the target distribution meets the preset convergence criterion; S4. Perform a full three-dimensional cavitation CFD numerical simulation on the optimized blade model to obtain the pressure distribution and cavitation performance index on the blade surface. Based on the simulation results, dynamically adjust the target outflow angle distribution and repeat the iterative optimization process from S2 to S3 until the cavitation performance index meets the preset requirements.

[0009] In one embodiment of the present invention, S2 includes: S21, the blade spanwise is divided into at least 5 design sections, and the spacing of each section is dynamically adjusted according to the blade curvature change rate to ensure that the section density in the high curvature region is higher than that in the low curvature region; S22, for each design section i, set the target outflow angle. The value is 1.1 to 1.5 times the value calculated based on the one-dimensional flow theory, and the minimum pressure value in the outlet region on the back of the blade meets the requirements. ,in This is the saturated vapor pressure of water. This is a preset safety margin.

[0010] In one embodiment of the present invention, S3 includes: S31, the three-dimensional inverse problem calculation method uses the least squares method to optimize the objective function, defined as follows: ,in Let i be the target outflow angle of the i-th cross section. The outflow angle is calculated in the current iteration; S32, during the iterative optimization process, the curvature of the blade exit edge section is adjusted first, followed by the thickness distribution. After each adjustment, the pressure distribution change is quickly predicted by CFD to avoid global recalculation.

[0011] In one embodiment of the present invention, S4 includes: S41, the full three-dimensional cavitation CFD numerical simulation adopts a multiphase flow model, wherein the governing equations of the cavitation model include a continuity equation. and momentum equation , For fluid density, It is a velocity vector. For pressure, For viscous stress tensor, It is the acceleration due to gravity; S42, Setting a non-steady time step in the simulation. satisfy ,in This is the minimum geometric feature size of the blade. This is the maximum flow velocity within the channel to ensure the accuracy of capturing the cavitation collapse process.

[0012] In one embodiment of the present invention, it further includes: S5, based on the steam volume fraction on the blade surface in the CFD simulation. Based on the distribution characteristics, a non-uniform incremental adjustment strategy is adopted for the spanwise section i corresponding to the high-risk cavitation region, i.e. ,in As the baseline increment, This is a risk-weighted coefficient. This represents the maximum steam volume fraction in the simulation.

[0013] To achieve the above objectives, a second aspect of the present invention provides an optimization device for cavitation erosion resistance of a turbine runner based on controlling the blade outflow angle, comprising: The geometric parameter acquisition and model building module is used to determine the basic geometric parameters of the runner based on the turbine's operating conditions, and to build an initial three-dimensional geometric model including the blade profile and the corresponding flow channel calculation domain. The design section division and outflow angle setting module is used to divide the blade spanwise into multiple design sections and set a target outflow angle distribution for each design section. The target outflow angle distribution is achieved by adjusting the velocity and pressure relationship in the outlet region on the back of the blade to ensure that the pressure in this region is higher than the saturated vapor pressure of water and leaves a safety margin. The blade curvature and thickness optimization module is used to iteratively optimize the blade curvature and thickness distribution using a three-dimensional inverse problem calculation method with the target outflow angle distribution as the direct objective function, until the error between the calculated outflow angle distribution and the target distribution meets the preset convergence criterion. The cavitation simulation and adjustment module is used to perform full three-dimensional cavitation CFD numerical simulation on the optimized blade model, obtain the pressure distribution and cavitation performance index on the blade surface, and dynamically adjust the target outflow angle distribution based on the simulation results. The iterative optimization process from S2 to S3 is repeated until the cavitation performance index meets the preset requirements.

[0014] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing the turbine runner anti-cavitation erosion optimization method based on controlling the blade outflow angle as described in the first aspect embodiment.

[0015] To achieve the above objectives, the fourth aspect of this application proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the turbine runner anti-cavitation erosion optimization method based on controlling the blade outflow angle as described in the first aspect embodiment.

[0016] The embodiments of the present invention have the following beneficial effects: This invention optimizes the pressure field on the blade surface by actively controlling the blade outflow angle distribution and combining three-dimensional inverse problem design with CFD verification. This effectively suppresses cavitation in the blade outlet region, reduces the risk of cavitation erosion, and extends the service life of turbine blades. Attached Figure Description

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A schematic flowchart illustrating an optimization method for cavitation erosion resistance of a turbine runner based on controlling the blade outflow angle, provided in an embodiment of the present invention. Figure 2 A flowchart illustrating an optimization method for cavitation erosion resistance of a turbine runner based on controlling the blade outflow angle, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the spanwise cross-section of a turbine runner blade provided in an embodiment of the present invention; Figure 4 This is a skeleton diagram of optimized control of turbine runner blades provided in an embodiment of the present invention; Figure 5 This is a schematic diagram showing the comparison of pressure distribution on the blade surface before and after optimization of a turbine runner anti-cavitation erosion optimization method based on controlling the blade outflow angle, provided in an embodiment of the present invention. Figure 6 This is a schematic diagram showing the comparison of blade surface wear distribution before and after optimization of a turbine runner anti-cavitation erosion optimization method based on controlling the blade outflow angle, provided in an embodiment of the present invention. Figure 7 This is a schematic diagram of a turbine runner anti-cavitation erosion optimization device based on controlling the blade outflow angle, provided in an embodiment of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0020] The following describes, with reference to the accompanying drawings, an optimization method and apparatus for anti-cavitation erosion of turbine runners based on controlling the blade outflow angle, according to an embodiment of the present invention.

[0021] Example 1 This embodiment provides an optimization method for cavitation erosion resistance of a turbine runner based on controlling the blade outflow angle. For example... Figure 1 As shown, the method includes the following steps: S1. Determine the basic geometric parameters of the runner based on the turbine's operating conditions, and construct an initial three-dimensional geometric model including the blade profile and the corresponding flow channel calculation domain.

[0022] Specifically, in some implementations, establishing the initial geometric model and computational domain is a crucial foundational step in the design process of this invention. Its technical implementation relies on the combination of basic hydraulic parameters of the turbine design and three-dimensional modeling methods. The core of this step lies in determining the design head... Rated flow rate Rotation speed Specific speed Based on the operating conditions and parameters, calculate and determine the basic geometric parameters of the runner, including the inlet diameter. outlet diameter and the number of leaves The selection of these parameters must comply with turbine design specifications, such as IEC 60193 or GB / T 15469, to ensure the hydraulic efficiency and structural rationality of the runner under design conditions.

[0023] Furthermore, based on the aforementioned basic parameters, a preliminary three-dimensional geometric model of the runner is generated using conventional hydraulic design methods (such as the velocity triangle method and one-dimensional flow theory). This model typically consists of three parts: the upper crown, the lower ring, and the blades, where the blade profile is defined by the rib line (centerline) and thickness distribution. During the modeling process, parametric modeling tools (such as CAD software or dedicated turbine design software) can optionally be used to perform preliminary blade design to ensure its geometric continuity and hydrodynamic feasibility.

[0024] When constructing the computational domain for the flow channel, the turbine runner model needs to be embedded into the complete turbine flow channel, including the volute, guide vanes, runner cavity, and draft tube. The boundary conditions of the computational domain should be set according to actual operating conditions, such as inlet velocity boundaries and outlet pressure boundaries. Furthermore, to ensure the accuracy of subsequent CFD simulations, the mesh generation of the computational domain must meet certain quality standards, such as a non-orthogonal angle of less than [value missing]. Mesh expansion ratio less than 1.2, etc.

[0025] The technical advantage of this step lies in providing an accurate geometric basis and fluid computational environment for subsequent inverse problem design and cavitation performance verification. By constructing an initial 3D model that includes the blade profile, an operable geometric framework can be provided for the active optimization design of the blade, while ensuring the integrity and physical consistency of the flow channel computational domain, thereby improving the reliability and efficiency of the entire design process.

[0026] S2, the blade spanwise is divided into multiple design sections, and a target outflow angle distribution is set for each design section. The target outflow angle distribution is achieved by adjusting the velocity and pressure relationship in the outlet region on the back of the blade to ensure that the pressure in this region is higher than the saturated vapor pressure of water and that a safety margin is left.

[0027] Specifically, in the steps of dividing the blade spanwise into design sections and setting the target outflow angle distribution, this invention employs a discretization method based on spanwise position to divide the spanwise direction of the blade from the hub to the rim into... Each design section. ( This corresponds to the local geometric features at a certain spanwise position of the blade, and is used for subsequent inverse problem design. This division method ensures that the blade geometry has sufficient degrees of freedom in the spanwise direction, thereby enabling fine-grained control of the pressure distribution on the blade surface.

[0028] In each design section Above, set the target outflow angle. It is defined as the angle between the flow direction in the outlet region on the back of the blade and the tangential direction of the blade. This angle must satisfy the following condition: the minimum pressure in the outlet region on the back of the blade under design operating conditions. Higher than the saturated vapor pressure of water And leave a certain safety margin ,Right now Safety margin The value is usually taken as This is to ensure that, in actual operation, fluctuations in operating conditions or calculation errors will not cause local pressure to fall below the critical value, thereby triggering cavitation.

[0029] In practice, the target outflow angle The setting is based on the theoretical outflow angle calculated using one-dimensional flow theory. And on this basis, increase it appropriately, that is The magnitude of this increment depends on the level of cavitation risk in the blade exit area, typically within... Adjustments can be made within the specified range. Increasing the outflow angle can effectively reduce the flow velocity in this region. This increases local pressure. This improves the cavitation environment on the back of the blades.

[0030] In practical applications, this step typically combines CFD pre-analysis results to assign larger outflow angle increments to identified high-risk cavitation areas, thereby achieving targeted optimization. Its technical value lies in transforming the outflow angle from a passive result in traditional design into a design input variable, thus enabling active control of the pressure distribution in the blade outlet region. This provides a clear optimization objective for subsequent three-dimensional inverse problem design and is a crucial preliminary step in improving the cavitation and erosion resistance of hydraulic turbines.

[0031] Furthermore, S2 includes: S21 divides the blade spanwise into at least 5 design sections, and the spacing of each section is dynamically adjusted according to the blade curvature change rate to ensure that the section density in the high curvature region is higher than that in the low curvature region.

[0032] Specifically, dividing the blade into design sections along the spanwise direction is a key preliminary step in the design method of this invention to achieve blade geometry optimization. The core of this step is to discretize the blade along the spanwise direction from the hub to the rim into at least 5 design sections. The spacing between each section is not fixed, but dynamically adjusted according to the rate of change of blade curvature, thereby setting a higher section density in high curvature regions (such as the blade exit edge) to improve the control accuracy of local geometric parameters.

[0033] In some implementations, the rate of change of curvature in the spanwise direction of the blade can be expressed as a function of the curvature of the blade centerline (rib line). To quantify, among which This represents the spanwise position parameter. It is calculated by determining the curvature difference between adjacent spanwise positions. This can determine whether the region is a high curvature variation area. If In this case, the density of the design cross-sections is increased in that area, typically by setting the cross-section spacing to a fraction of the original average spacing. to This is to ensure more precise control in areas with dramatic geometric changes.

[0034] In one embodiment of the present invention, the total number of design cross-sections Should meet This ensures sufficient discretization of the blade geometry. The adjustment of the cross-sectional spacing can be set based on the curvature change rate threshold. Its typical value range is The specific values ​​are adapted based on the blade length and geometric complexity. Furthermore, the target outflow angle for each cross-section... Must meet ,in The theoretical outflow angle is calculated based on the one-dimensional flow theory to ensure that the pressure in the outlet region on the back of the blade is higher than the saturated vapor pressure of water and to allow for a safety margin.

[0035] In practical applications, this step is typically performed by combining CAD modeling with CFD pre-analysis. After the initial blade modeling, CFD simulation is used to predict high-risk cavitation areas, further guiding the setting of the spanwise section density and the target outflow angle. By dynamically adjusting the section spacing, the geometric control accuracy of the blade exit region can be significantly improved, providing more precise input conditions for subsequent 3D inverse problem design.

[0036] In this embodiment of the invention, this step, through a refined geometric discretization strategy, improves the outflow angle control capability in the blade exit region, thereby effectively improving the pressure distribution on the blade surface and reducing the probability of cavitation. Its innovation lies in combining geometric partitioning with hydrodynamic characteristics, laying the foundation for subsequent active optimization design and improving design efficiency and cavitation protection effectiveness.

[0037] S22, for each design section i, set the target outflow angle. The value is 1.1 to 1.5 times the value calculated based on the one-dimensional flow theory, and the minimum pressure value in the outlet region on the back of the blade meets the requirements. ,in This is the saturated vapor pressure of water. This is a preset safety margin.

[0038] Specifically, in the blade spanwise design process, for each design section... Set the target outflow angle The value is 1.1 to 1.5 times that calculated based on the one-dimensional flow theory, and is one of the key steps in realizing the anti-cavitation erosion design in this invention. This setting is based on an in-depth analysis of the flow state and pressure distribution in the blade exit region, and aims to effectively suppress cavitation by actively controlling the outflow angle and optimizing the pressure distribution in the exit region on the back side (suction side) of the blade.

[0039] This step first uses one-dimensional flow theory to calculate the theoretical outflow angle of each spanwise section under the design conditions. This theoretical value is usually based on the momentum equation and the principle of energy conservation, combined with the design head. ,flow Rotation speed Estimate basic parameters such as the flow rate. Based on this, determine the target outflow angle. Set as ,Right now:

[0040] in , where is the outflow angle magnification factor. The selection of this factor needs to be combined with the cavitation risk assessment results of the blade exit area. Generally, a larger magnification factor (such as 1.4 or 1.5) is used in high-cavitation risk areas to enhance the flow stability in that area.

[0041] In this embodiment of the invention, the minimum pressure value in the outlet region on the back of the blade is... Must meet:

[0042] in This is the saturated vapor pressure of water, typically approximately [value missing] at room temperature and pressure. (20°C) As a preset safety margin, it is generally taken as This is to ensure that, in actual operation, fluctuations in operating conditions or calculation errors will not cause the local pressure to fall below the cavitation threshold.

[0043] This step is applicable to the cavitation design of turbine runner blades in mixed-flow, axial-flow, or oblique-flow turbines. By using the outflow angle as a design input variable and combining it with a three-dimensional inverse problem design method, the blade's rib curvature, placement angle, and thickness distribution can be iteratively optimized, thereby creating a more reasonable pressure gradient in the blade outlet region and avoiding the formation of local low-pressure areas.

[0044] This step, by actively controlling the outflow angle, significantly increases the pressure level in the blade exit region, enabling... By maintaining the blade above a safe threshold, the generation and collapse of cavitation bubbles are effectively suppressed, reducing the coupling effect of cavitation erosion and abrasion. This method not only improves the blade's cavitation resistance but also enhances the controllability and efficiency of the design, demonstrating significant engineering practical value.

[0045] S3, using the target outflow angle distribution as the direct objective function, the three-dimensional inverse problem calculation method is used to iteratively optimize the blade rib curvature and thickness distribution until the error between the calculated outflow angle distribution and the target distribution meets the preset convergence criterion.

[0046] Specifically, based on the target outflow angle distribution As the direct objective function, a three-dimensional inverse problem calculation method is used to iteratively optimize the blade rib curvature and thickness distribution until the error between the calculated outflow angle distribution and the target distribution meets the preset convergence criterion. This step is the key to the present invention's ability to actively control the blade outlet flow state, optimize pressure distribution, and suppress cavitation and erosion.

[0047] In some implementations, the three-dimensional inverse problem design method uses the target outflow angle distribution as the optimization objective to solve inversely for the blade geometry parameters that satisfy the flow state. Specifically, this method is based on the blade spanwise division. One cross section ( ), in each cross section Set the target outflow angle This is used as a constraint in the optimization process. Optimization variables include the blade's rib line curvature. and thickness distribution By adjusting these geometric parameters, the actual outflow angle of the blade under design conditions can be achieved. Error between the target value Less than the preset convergence threshold , usually take .

[0048] This optimization process typically employs numerical optimization strategies such as gradient descent or genetic algorithms, combined with a CFD forward problem solver for flow field prediction. In each iteration, the optimization algorithm generates a CFD model based on the current blade geometry, solves for the outlet flow angle distribution, and calculates the deviation from the target distribution. The deviation function can be defined as follows: This loss function is used to evaluate how well the current design matches the objective. The optimization algorithm adjusts the blade geometry parameters based on this loss function until the convergence criterion is met.

[0049] In practical applications, this step typically involves establishing a parametric interface between the CAD modeling platform and the CFD solver to achieve automatic updates of blade geometry and closed-loop iteration of flow calculations. Its technical value lies in transforming the outflow angle from a passive result into an active design input, enabling precise control of the pressure distribution in the blade outlet region, thereby effectively improving cavitation and erosion resistance. This method is applicable to the optimization design of runner blades for mixed-flow, axial-flow, and oblique-flow turbines, and has broad engineering applicability.

[0050] Furthermore, S3 includes: S31, the three-dimensional inverse problem calculation method uses the least squares method to optimize the objective function, defined as follows: ,in Let i be the target outflow angle of the i-th cross section. The outflow angle is calculated in the current iteration.

[0051] Specifically, the three-dimensional inverse problem calculation method uses the least squares method to optimize the objective function, and its mathematical expression is as follows: ,in Indicates the first Target outflow angle of spanwise section This represents the outflow angle calculated in the current iteration. This objective function is used to quantify the deviation between the actually calculated outflow angle and the design expectation value. By minimizing this deviation, the blade geometry can be optimized.

[0052] In one embodiment of the present invention, this step is based on the coupling of a CFD forward problem solver and an inverse problem optimization algorithm, employing a parametric modeling approach to iteratively adjust the blade's rib line and thickness distribution. Specifically, the geometric parameters such as the blade placement angle, radius of curvature, and thickness of each spanwise section are defined as design variables. The optimization algorithm drives the CFD simulation, continuously correcting the blade profile so that the outflow angle distribution at the outlet section gradually approaches the target distribution. During the optimization process, the CFD solver needs to calculate the flow field in each iteration and extract the outflow angle data for each section, feeding it back to the optimization module for error calculation and parameter updates.

[0053] At the parameter level, the convergence criterion for the objective function is typically set as the sum of squared errors being less than 1 / 3. or This is to ensure that the accuracy of the outflow angle distribution meets engineering design requirements. (Number of spanwise sections) The value is typically between 5 and 10, depending on the geometric complexity of the blades and the required design precision. Target outflow angle. The setting needs to be combined with the calculation results of the one-dimensional flow theory, and on this basis, additional... to A safety increment to ensure that the pressure in the outlet region on the back of the blade is higher than the saturated vapor pressure of water. This effectively suppresses cavitation.

[0054] In application scenarios, this step is mainly used for the anti-cavitation design of turbine runner blades, especially in rivers with high sediment loads or under varying operating conditions, where low-pressure zones easily appear in the outlet area on the back of the blade, leading to intensified coupling effects of cavitation and erosion. By actively controlling the outflow angle distribution, the pressure gradient in this region can be significantly improved, reducing the intensity of cavitation generation and collapse, thereby enhancing the blade's durability and operational stability.

[0055] The technical advantage of this step lies in achieving precise control of the flow state at the blade exit by optimizing the objective function using the least squares method. This results in a more rational pressure distribution on the blade surface, avoiding the formation of local low-pressure zones and thus suppressing cavitation at its source in the fluid dynamics. This method offers advantages such as high design efficiency, good optimization accuracy, and strong adaptability, making it a key technical step in achieving cavitation-resistant erosion design for turbine blades.

[0056] S32, during the iterative optimization process, the curvature of the blade exit edge section is adjusted first, followed by the thickness distribution. After each adjustment, the pressure distribution change is quickly predicted by CFD to avoid global recalculation.

[0057] Specifically, in the three-dimensional inverse problem iterative design process, prioritizing the adjustment of the rib curvature of the blade exit edge section, followed by adjusting the thickness distribution, is a highly efficient optimization strategy based on the principle of fluid dynamics inversion. The core of this step lies in rapidly responding to changes in the pressure distribution on the blade surface through the adjustment of local geometric parameters, thereby achieving targeted optimization of the cavitation risk region without global recalculation.

[0058] In some implementations, the curvature of the blade lines is adjusted using parametric modeling. Specifically, the spanwise direction of the blade is divided into... Each design section has a bone line parametrically represented using spline curves (such as B-spline or NURBS curves), and its curvature is determined by the location of control points. During the iteration process, the bone line control points of key sections near the outlet edge are first fine-tuned to alter the flow separation trend and pressure gradient in that region. The adjustment range is typically controlled within... Within the range of placement angle variations, to ensure the continuity and convergence of geometric changes.

[0059] Furthermore, after adjusting the blade curvature, thickness distribution optimization serves as a secondary adjustment method. Blade thickness distribution is typically expressed as a function of the spanwise thickness. It means that, among them Let the coordinates be dimensionless coordinates along the chord direction. For the first The thickness value of each cross-section. In this step, the thickness distribution adjustment mainly targets the area near the outlet edge to improve flow adhesion and pressure recovery capability in this region. The adjustment strategy is usually to locally increase or decrease the thickness, with the magnitude controlled within... Within the average thickness range of the blades.

[0060] After each adjustment, the pressure distribution changes on the blade surface are rapidly predicted using computational fluid dynamics (CFD) methods. This prediction process employs a steady-state, incompressible RANS (Reynolds-averaged Navier-Stokes) equation solver, combined with... or The SST turbulence model obtains the pressure gradient and minimum pressure point location in key regions with low computational cost. By comparing the pressure distribution before and after adjustment, it can be determined whether the target outflow angle is approached. The corresponding ideal pressure environment determines whether to continue iteration.

[0061] In practical applications, this step is typically deployed in the initial optimization stage of turbine blades, especially in rivers with high sediment loads or under varying operating conditions. It is used to quickly identify and correct high-risk cavitation areas at the blade exit edge. Its technical value lies in significantly improving design efficiency, avoiding frequent global remodeling and calculations in traditional forward problem design, and ensuring the continuity and stability of pressure distribution in the blade exit region, thus laying a reliable foundation for subsequent full-three-dimensional unsteady cavitation simulation.

[0062] S4. Perform a full three-dimensional cavitation CFD numerical simulation on the optimized blade model to obtain the pressure distribution and cavitation performance index on the blade surface. Based on the simulation results, dynamically adjust the target outflow angle distribution and repeat the iterative optimization process from S2 to S3 until the cavitation performance index meets the preset requirements.

[0063] Specifically, this step is a key verification and optimization step in the design method for cavitation erosion resistance of turbine runner blades. Its technical implementation principle is based on full three-dimensional cavitation simulation using computational fluid dynamics (CFD). By quantitatively analyzing the pressure distribution and cavitation performance indicators on the blade surface, dynamic feedback optimization of the blade's geometric parameters is achieved. In some implementations, this step uses a multiphase flow model (such as the VOF model) combined with a cavitation model (such as the Schnerr-Sauer model or the Zwart-Gerber-Belamri model) for unsteady numerical simulation to accurately capture the generation, development, and collapse processes of cavitation bubbles.

[0064] At the parameter level, key cavitation performance evaluation parameters need to be set during the simulation process, including the initial cavitation coefficient. and critical cavitation coefficient The primary cavitation coefficient is defined as follows: Critical cavitation coefficient is used to determine whether cavitation has occurred. This is used to evaluate the stability of the blade under critical cavitation conditions. The pressure distribution on the blade surface needs to be... As a basic requirement, and to ensure and There should be sufficient safety margin between them; it is generally recommended that the margin be no less than 5%.

[0065] In the specific implementation, the simulation computation domain should include the complete turbine flow channel, and the boundary conditions should be set to the head under the design conditions. ,flow and rotational speed The pressure contour map and vapor volume fraction contour map on the blade surface will be the main output results, used to identify high-risk cavitation areas. If the simulation results show that there is a local low-pressure area in the exit region on the back of the blade or the cavitation volume fraction exceeds the set threshold (e.g., ... If so, return to S3 and set the target outflow angle for the corresponding spanwise section. Fine-tuning is typically done by appropriately increasing the pressure level in the area, based on the original values.

[0066] At the application level, this step is widely applicable to the blade optimization design of mixed-flow, axial-flow, and oblique-flow turbines, especially in rivers with high sediment content or under varying operating conditions, effectively improving the blades' resistance to cavitation and erosion. Through iterative optimization of this step, the flow stability in the blade outlet region can be significantly improved, reducing the probability of cavitation damage, thereby extending equipment life and improving operating efficiency.

[0067] In terms of technical effectiveness, this step achieves a shift from passive verification to active optimization. Through closed-loop iteration of CFD simulation and inverse problem design, it ensures a strong correlation between blade geometric parameters and cavitation performance indicators, thereby improving the accuracy and reliability of the design.

[0068] The turbine runner anti-cavitation erosion optimization method based on controlling the blade outflow angle of this invention optimizes the pressure field on the blade surface by actively controlling the blade outflow angle distribution, effectively suppressing cavitation in the outlet region on the back of the blade, thereby significantly improving the anti-cavitation and anti-erosion performance of the turbine runner blades.

[0069] Furthermore, S4 includes: S41, the full three-dimensional cavitation CFD numerical simulation adopts a multiphase flow model, wherein the governing equations of the cavitation model include a continuity equation. and momentum equation , For fluid density, It is a velocity vector. For pressure, For viscous stress tensor, This is the acceleration due to gravity.

[0070] Specifically, the "full three-dimensional cavitation CFD numerical simulation" described in this invention is a core step in verifying the cavitation performance of optimized turbine runner blades based on computational fluid dynamics (CFD) technology. This step employs a multiphase flow model, solving the continuity and momentum equations of the fluid to simulate the pressure distribution and cavitation generation and collapse processes on the blade surface under design conditions, thereby evaluating its cavitation resistance.

[0071] At the technical implementation level, this simulation is based on the Navier-Stokes equations, combined with cavitation models (such as the Schnerr-Sauer model or the Zwart-Gerber-Belamri model) for solution. Specifically, the continuity equation is:

[0072] The momentum equation is:

[0073] in, Indicates fluid density, It is a velocity vector. For pressure, For viscous stress tensor, Let gravitational acceleration be the acceleration due to gravity. By solving the above governing equations, pressure distribution contour maps and steam volume fraction contour maps on the blade surface can be obtained, thereby identifying the cavitation occurrence region and its intensity.

[0074] At the parameter level, the simulation needs to set reasonable cavitation model parameters, such as cavitation generation rate, collapse rate, and initial cavitation volume fraction. Simultaneously, the mesh generation of the computational domain should meet the accuracy requirements of local high-gradient regions (such as the blade back outlet), typically using a hybrid unstructured hexahedral and tetrahedral mesh with a mesh size of at least 10^6, and a mesh size smaller than [missing information] in locally refined regions. To ensure the accuracy of cavitation detection.

[0075] At the application level, this step is usually performed after the three-dimensional inverse problem design is completed. It is used to verify whether the blade optimization effectively improves the pressure distribution in the outlet region and avoids local pressures falling below the saturated vapor pressure of water. The simulated environment must be consistent with actual operating conditions, including water head. ,flow Rotation speed Isostatic boundary conditions are used, and turbulence models (such as the k-ε or k-ω SST model in RANS) are employed to improve the simulation accuracy of unsteady flows.

[0076] From a technical perspective, this step, through high-precision CFD simulation, can visually reflect the cavitation risk areas on the blade surface, providing a reliable basis for subsequent cavitation performance evaluation. Its key value lies in verifying whether the design objective has been achieved, namely, whether the pressure in the exit region on the back of the blade is higher than... It also has sufficient safety margin, thereby suppressing cavitation and erosion from the source and improving the operational stability and lifespan of the turbine.

[0077] S42, Setting a non-steady time step in the simulation. satisfy ,in This is the minimum geometric feature size of the blade. This is the maximum flow velocity within the channel to ensure the accuracy of capturing the cavitation collapse process.

[0078] Specifically, in some implementations, the full three-dimensional CFD cavitation performance verification is a key numerical simulation step in the design method of this invention. Its technical implementation is based on computational fluid dynamics (CFD) to perform unsteady, viscous, multiphase flow cavitation simulations on the optimized three-dimensional geometric model of the blade, in order to evaluate the pressure distribution and cavitation development on the blade surface. The core of this step lies in capturing the generation and collapse process of cavitation bubbles using a high-precision numerical solver, thereby verifying whether the blade possesses good anti-cavitation performance.

[0079] At the technical implementation level, CFD simulations typically employ turbulence models based on the Navier-Stokes equations (such as RANS or LES) combined with cavitation models (such as the Schnerr-Sauer model or the Zwart-Gerber-Belamri model) for solution. The computational domain includes the entire runner flow path, and the boundary conditions are set to the design conditions: total inlet pressure, static outlet pressure, rotational speed, and rotating reference frame. The pressure distribution on the blade surface is visualized and analyzed using pressure contour maps, while steam volume fraction contour maps are calculated to identify cavitation generation regions and collapse intensities. Unsteady time steps also need to be set in the simulation. Its value must satisfy ,in This is the minimum geometric feature size of the blade. This represents the maximum flow velocity within the channel. This time step limit ensures high temporal resolution capture of the cavitation dynamics, thereby improving the physical realism of the simulation results.

[0080] At the parameter level, The minimum radius of curvature or minimum thickness of the blade is usually taken as the value, and the unit is meters (m). The maximum instantaneous velocity within the flow channel, expressed in meters per second (m / s). Time step. The time step size directly affects the computational efficiency and accuracy of the simulation. An excessively large time step may distort the cavitation collapse process, while an excessively small time step will significantly increase computational resource consumption. Therefore, this formula provides a scientific basis for setting the time step size, ensuring high-fidelity results are obtained at a reasonable computational cost.

[0081] At the application level, this step is widely used in the cavitation resistance design verification of turbine runner blades, especially in rivers with high sediment loads or under varying operating conditions, where the cavitation risk in the outlet region on the back of the blade is high. Through unsteady CFD simulation, designers can intuitively assess whether the pressure distribution on the blade surface meets the cavitation resistance requirements and further optimize the blade profile.

[0082] In terms of technical effectiveness, this step significantly improves the capture accuracy of the cavitation collapse process through strict time step control, providing a reliable basis for the evaluation of the blade's cavitation performance, thereby ensuring the effectiveness of design iteration and the final blade's anti-cavitation and anti-erosion performance.

[0083] The turbine runner anti-cavitation erosion optimization method based on controlling the blade outflow angle of this invention optimizes the pressure field on the blade surface by actively controlling the blade outflow angle distribution, effectively suppressing cavitation in the outlet region on the back of the blade, thereby significantly improving the anti-cavitation and anti-erosion performance of the turbine runner blades.

[0084] S5, based on the steam volume fraction on the blade surface in the CFD simulation. Based on the distribution characteristics, a non-uniform incremental adjustment strategy is adopted for the spanwise section i corresponding to the high-risk cavitation region, i.e. ,in As the baseline increment, This is a risk-weighted coefficient. This represents the maximum steam volume fraction in the simulation.

[0085] Specifically, in some implementations, based on the vapor volume fraction on the blade surface in the CFD simulation... The distribution characteristics of the spanwise cross section corresponding to high-risk cavitation areas. A non-uniform incremental adjustment strategy is employed to optimize the outflow angle distribution in the blade exit region, thereby improving cavitation resistance. The core of this strategy lies in quantifying the degree of cavitation risk and adjusting the target outflow angle accordingly. Dynamic adjustments are made, and the adjustment formula is as follows:

[0086] in, The base increment is typically set to a value of [value to be filled in]. This is used to control the magnitude of the overall outflow angle adjustment; Indicates the spanwise section The vapor volume fraction at a given location reflects the cavitation intensity in that region. The maximum steam volume fraction on the blade surface during the entire CFD simulation is used for normalization. This is the risk weighting coefficient, and its typical value range is... It is used to adjust the incremental sensitivity of high-risk areas.

[0087] The technical implementation of this step is based on a three-dimensional inverse problem design method, which involves adjusting the outflow angle... As a direct optimization variable, the blade profile is iteratively corrected based on CFD simulation results. Specifically, the blade surface is first meshed, and the cross-sections for each spanwise direction are extracted. Data identifies cavitation intensity exceeding a set threshold (e.g., The region is then targeted. Subsequently, the outflow angle of the corresponding cross-sections in these regions is adjusted non-uniformly according to the above formula. This results in a larger outflow angle increment in regions with higher cavitation risk, effectively increasing the surface pressure in these regions and preventing local pressures from falling below the saturated vapor pressure of water. This reduces the intensity of cavitation generation and collapse.

[0088] In practical applications, this step is often used in the anti-cavitation design process of runner blades for mixed-flow, axial-flow, or oblique-flow turbines, especially in rivers with high sediment loads or under varying operating conditions, where cavitation is prone to occur in the outlet region on the back of the blade. Through this non-uniform incremental strategy, designers can achieve fine-grained control over the blade geometry, improving the operational stability and lifespan of the blades under complex operating conditions.

[0089] Furthermore, the technical effect of this step is that by quantifying cavitation risk and introducing a nonlinear adjustment mechanism, the pressure distribution in the blade exit region becomes more uniform, avoiding the formation of local low-pressure areas, thereby significantly reducing the cavitation coefficient. This method enhances the cavitation and erosion resistance of blades. It exhibits good convergence and robustness in design iterations and is a key technical step in realizing active cavitation resistance design for blades.

[0090] The turbine runner anti-cavitation erosion optimization method based on controlling the blade outflow angle of this invention introduces a non-uniform incremental adjustment strategy based on CFD simulation of steam volume fraction distribution, and implements differentiated outflow angle correction for the spanwise section with high cavitation risk, further enhancing the optimization effect of the blade surface pressure field, and significantly improving the local anti-cavitation capability and overall operational stability of the turbine runner under complex operating conditions.

[0091] Example 2 This embodiment provides an optimization method for cavitation erosion resistance of a turbine runner based on controlling the blade outflow angle. For example... Figure 1 As shown, the method includes the following steps: S10, determine the design conditions and basic parameters.

[0092] Specifically, such as Figure 2 As shown, based on the design head of the water turbine H Rated flow rate Q Rotation speed n Specific speed n s Determine the basic geometric parameters of the runner, including the runner inlet diameter. D 1. Outlet diameter D 2. Number of leaves Z .

[0093] S20, Establish the initial geometric model and computational domain.

[0094] In this embodiment of the invention, based on the basic parameters determined in step S10, an initial three-dimensional model of the runner blade is generated using conventional hydraulic design methods. This model includes an upper crown, a lower ring, and blades, and a flow channel computational domain for CFD calculation is constructed on this basis.

[0095] S30, divide the spanwise design section and set the target outflow angle distribution.

[0096] In embodiments of the present invention, such as Figure 3 As shown, the spanwise direction of the blade from the hub (upper crown) to the rim (lower ring) is divided into... N One design section ( N ≥5), for each design section i (i=1, 2, ..., N), set a target outflow angle β2. The target outflow angle setting must satisfy: Under design conditions, the pressure distribution on the back of the blades should transition smoothly from inlet to outlet, ensuring that the minimum pressure in the outlet region on the back of the blades is higher than the saturated vapor pressure of water with sufficient safety margin. Specifically, the target outflow angle β2 should be greater than the theoretical outflow angle calculated based on the one-dimensional theory.

[0097] S40, based on the target outflow angle, performs a three-dimensional inverse problem iterative design.

[0098] In one embodiment of the present invention, such as Figure 4 As shown, the target outflow angle β of each spanwise section is set in step S30. 2ta r 9et (i) is the direct objective function, and a three-dimensional inverse problem calculation method is used for the iterative design of the blade rib line and thickness distribution. This method solves for the blade geometry that satisfies the given outflow angle distribution, and repeatedly adjusts the blade placement angle, rib line curvature and thickness of each section, so that the error between the outflow angle distribution obtained by the inverse problem calculation and the target outflow angle distribution is less than the preset convergence criterion.

[0099] S50, full 3D CFD cavitation performance verification.

[0100] Specifically, such as Figure 5 and Figure 6 As shown, a full three-dimensional, viscous, unsteady multiphase flow cavitation CFD numerical simulation is performed on the optimized three-dimensional blade model obtained in step S40. The calculation requires setting up a cavitation model, obtaining detailed pressure distribution cloud maps and steam volume fraction cloud maps on the blade surface, and calculating key cavitation performance indicators, including the primary cavitation coefficient and the critical cavitation coefficient.

[0101] S60, cavitation performance evaluation and iterative optimization.

[0102] Specifically, it is determined whether the CFD verification result of step S50 meets the preset cavitation performance requirements: If the conditions are met, the blade design is complete, and the final three-dimensional blade model is output.

[0103] If not, return to step S30 and fine-tune the target outflow angle β2 of the design section corresponding to the spanwise region that does not meet the cavitation performance requirements. Usually, the target outflow angle value is appropriately increased on the original basis. Then repeat steps S40 and S50 until the CFD verification results fully meet the cavitation performance requirements.

[0104] Compared with existing technologies, this invention elevates the outflow angle from a passive result to an active control design input, directly suppressing cavitation at its source in fluid dynamics, demonstrating an advanced design concept. Utilizing a three-dimensional inverse problem design method, it can directly target and optimize high-risk cavitation areas (blade exit edge), resulting in a shorter design cycle and significantly higher efficiency than traditional forward problem trial-and-error optimization. This method effectively increases the pressure in the exit region on the back of the blade, making the pressure distribution smoother, significantly reducing cavitation volume and cavitation erosion risk, thereby extending the blade's service life in cavitation and erosion environments. The core idea of ​​this design method is applicable to the design of various types of turbine runners, including mixed-flow, axial-flow, and oblique-flow turbines, and has broad engineering application prospects.

[0105] Example 3 This invention also provides an optimization device for anti-cavitation erosion of a turbine runner based on controlling the blade outflow angle. For example... Figure 7 As shown, the device 10 includes: The geometric parameter acquisition and model building module 100 is used to determine the basic geometric parameters of the runner based on the turbine's operating conditions, and to build an initial three-dimensional geometric model including the blade profile and the corresponding flow channel calculation domain. The design section division and outflow angle setting module 200 is used to divide the blade spanwise into multiple design sections and set a target outflow angle distribution for each design section. The target outflow angle distribution is achieved by adjusting the velocity and pressure relationship in the outlet region on the back of the blade to ensure that the pressure in this region is higher than the saturated vapor pressure of water and leaves a safety margin. The blade curvature and thickness optimization module 300 is used to iteratively optimize the blade curvature and thickness distribution using a three-dimensional inverse problem calculation method with the target outflow angle distribution as the direct objective function, until the error between the calculated outflow angle distribution and the target distribution meets the preset convergence criterion. The cavitation simulation and adjustment module 400 is used to perform full three-dimensional cavitation CFD numerical simulation on the optimized blade model, obtain the pressure distribution and cavitation performance index on the blade surface, and dynamically adjust the target outflow angle distribution according to the simulation results. The iterative optimization process from S2 to S3 is repeated until the cavitation performance index meets the preset requirements.

[0106] Example 4 To implement the methods of the above embodiments, the present invention also provides a computer device, which includes a memory and a processor; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, so as to implement the various steps of the methods described above.

[0107] Example 5 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0108] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0109] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0110] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. An optimization method for cavitation erosion resistance of a turbine runner based on controlling the blade outflow angle, characterized in that, Includes the following steps: S1. Determine the basic geometric parameters of the runner based on the turbine's operating conditions, and construct an initial three-dimensional geometric model including the blade profile and the corresponding flow channel computation domain. S2, the blade span is divided into multiple design sections, and a target outflow angle distribution is set for each design section. The target outflow angle distribution is achieved by adjusting the velocity and pressure relationship in the outlet region on the back of the blade. The target outflow angle is defined as the angle between the liquid flow direction in the outlet region on the back of the blade and the tangential direction of the blade. The setting of the angle ensures that the minimum pressure in the outlet region on the back of the blade is higher than the saturated vapor pressure of water and leaves a safety margin. S3, using the target outflow angle distribution as the direct objective function, the three-dimensional inverse problem calculation method is used to iteratively optimize the blade rib line curvature and thickness distribution until the error between the calculated outflow angle distribution and the target distribution meets the preset convergence criterion; S4. Perform a full three-dimensional cavitation CFD numerical simulation on the optimized blade model to obtain the pressure distribution and cavitation performance index on the blade surface. Based on the simulation results, dynamically adjust the target outflow angle distribution and repeat the iterative optimization process from S2 to S3 until the cavitation performance index meets the preset requirements.

2. The method according to claim 1, characterized in that, S2 includes: S21, the blade spanwise is divided into at least 5 design sections, and the spacing of each section is dynamically adjusted according to the blade curvature change rate to ensure that the section density in the high curvature region is higher than that in the low curvature region; S22, for each design section i, set the target outflow angle. The value is 1.1 to 1.5 times the value calculated based on the one-dimensional flow theory, and the minimum pressure value in the outlet region on the back of the blade meets the requirements. ,in This is the saturated vapor pressure of water. This is a preset safety margin.

3. The method according to claim 1, characterized in that, The S3 includes: S31, the three-dimensional inverse problem calculation method uses the least squares method to optimize the objective function, defined as follows: ,in Let i be the target outflow angle of the i-th cross section. The outflow angle is the one calculated in the current iteration, and N is the total number of design sections; S32, during the iterative optimization process, the curvature of the blade exit edge section is adjusted first, followed by the thickness distribution. After each adjustment, the pressure distribution change is quickly predicted by CFD to avoid global recalculation.

4. The method according to claim 1, characterized in that, The S4 includes: S41, the full three-dimensional cavitation CFD numerical simulation adopts a multiphase flow model, wherein the governing equations of the cavitation model include a continuity equation. and momentum equation , For fluid density, It is a velocity vector. For pressure, For viscous stress tensor, It is the acceleration due to gravity; S42, Setting a non-steady time step in the simulation. satisfy ,in This is the minimum geometric feature size of the blade. This is the maximum flow velocity within the channel to ensure the accuracy of capturing the cavitation collapse process.

5. The method according to claim 1, characterized in that, Also includes: S5, based on the steam volume fraction on the blade surface in the CFD simulation. Based on the distribution characteristics, a non-uniform incremental adjustment strategy is adopted for the spanwise section i corresponding to the high-risk cavitation region, i.e. ,in As the baseline increment, This is a risk-weighted coefficient. This represents the maximum steam volume fraction in the simulation.

6. A device for optimizing the anti-cavitation erosion of a turbine runner based on controlling the blade outflow angle, characterized in that, include: The geometric parameter acquisition and model building module is used to determine the basic geometric parameters of the runner based on the turbine's operating conditions, and to build an initial three-dimensional geometric model including the blade profile and the corresponding flow channel calculation domain. The design section division and outflow angle setting module is used to divide the blade spanwise into multiple design sections and set a target outflow angle distribution for each design section. The target outflow angle distribution is achieved by adjusting the velocity and pressure relationship in the outlet region on the back of the blade. The target outflow angle is defined as the angle between the liquid flow direction in the outlet region on the back of the blade and the tangential direction of the blade. The setting of the angle is used to ensure that the minimum pressure in the outlet region on the back of the blade is higher than the saturated vapor pressure of water and that a safety margin is provided. The blade curvature and thickness optimization module is used to iteratively optimize the blade curvature and thickness distribution using a three-dimensional inverse problem calculation method with the target outflow angle distribution as the direct objective function, until the error between the calculated outflow angle distribution and the target distribution meets the preset convergence criterion. The cavitation simulation and adjustment module is used to perform full three-dimensional cavitation CFD numerical simulation on the optimized blade model, obtain the pressure distribution and cavitation performance index of the blade surface, and dynamically adjust the target outflow angle distribution based on the simulation results. It iterative optimization process from the design section division and outflow angle setting module to the bone line curvature and thickness optimization module is repeated until the cavitation performance index meets the preset requirements.

7. A computer device, characterized in that, Including processor and memory; The processor reads the executable program code stored in the memory to run the program corresponding to the executable program code, so as to implement the turbine runner anti-cavitation erosion optimization method based on the control blade outflow angle as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the optimization method for anti-cavitation erosion of turbine runners based on the control of blade outflow angle as described in any one of claims 1-5.

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