Optimization design method and computer equipment of water pump water turbine

Abstract

This application provides an optimization design method and computer equipment for a pump-turbine. The method includes: selecting a set of values ​​for the runner's parameters to be optimized based on the overall hydraulic design requirements of the pump-turbine; iteratively optimizing the movable guide vanes given the determined values ​​of the runner's parameters; establishing a three-dimensional geometric model of the pump-turbine based on the runner with the determined parameters and the optimized movable guide vanes; dividing the three-dimensional geometric model into a fluid domain mesh; importing the divided fluid domain mesh into computational fluid dynamics software for numerical simulation of the flow channel, calculating the flow field parameters and entropy production distribution under different operating conditions; evaluating the entropy production and hydraulic performance before and after optimization; adjusting the values ​​of the runner's parameters to be optimized based on the evaluation results; repeating the above steps of selecting runner parameters up to the evaluation step until the hydraulic performance meets the predetermined design requirements and minimizes the entropy production.

Description

Technical Field

[0001] This application relates to the technical field of high-head pumped storage units, and in particular to an optimization design method for a pump-turbine and a computer device. Background Technology

[0002] Against the backdrop of a global push for clean energy development, pumped storage power stations, with their crucial roles in peak shaving, valley filling, frequency regulation, phase regulation, and emergency backup in the power system, have become an important means of achieving efficient renewable energy consumption and stable grid operation. The high-head pump-turbine, as the core equipment of a pumped storage power station, directly affects the overall efficiency and operational stability of the station.

[0003] High-head pump-turbines operate under extremely complex conditions, facing numerous severe challenges in actual operation. From a flow characteristics perspective, the guide vane and runner region exhibits complex three-dimensional unsteady turbulent flow. During acceleration, deceleration, and turning of the water flow between the guide vanes, vortices and flow separation are easily generated. Under high-head conditions, the high-speed impact of the water flow on the guide vanes and runner causes rapid local pressure changes, leading to intense pressure pulsations. For example, in some high-head pump-turbines, the pressure pulsation amplitude at the guide vane outlet can reach tens or even hundreds of kilopascals. This not only severely affects the unit's hydraulic efficiency but can also cause severe vibration and noise, threatening the safe and stable operation of the unit.

[0004] Therefore, optimizing the design of water pump turbines is a major problem that the industry urgently needs to solve. Summary of the Invention

[0005] The purpose of this application is to provide an optimized design method and computer equipment for a water pump turbine, which can solve at least one of the technical problems mentioned in the prior art.

[0006] One aspect of this application provides an optimization design method for a pump-turbine. The method includes: a runner parameter selection step: selecting a set of values ​​for the runner parameters to be optimized based on the overall hydraulic design requirements of the pump-turbine; a movable guide vane optimization step: iteratively optimizing the movable guide vanes of the pump-turbine when the values ​​for the runner parameters to be optimized are determined; a model establishment step: establishing a three-dimensional geometric model of the pump-turbine based on the runner with the determined values ​​for the parameters to be optimized and the optimized movable guide vanes; a mesh generation step: dividing the three-dimensional geometric model into a fluid domain mesh; a flow channel simulation step: importing the divided fluid domain mesh into computational fluid dynamics software to perform flow channel numerical simulation, calculating the flow field parameters and entropy production distribution under different operating conditions; and an evaluation step: evaluating the entropy production and hydraulic performance before and after optimization, adjusting the values ​​of the runner parameters to be optimized based on the evaluation results, and repeating the runner parameter selection step up to the evaluation step until the hydraulic performance meets the predetermined design requirements and minimizes the entropy production.

[0007] Furthermore, the iterative optimization of the movable guide vanes of the pump-turbine includes: optimizing the blade profile of the movable guide vanes of the pump-turbine; optimizing the guide vane inlet angle and guide vane outlet angle when the optimal blade profile parameters of the movable guide vanes are determined; and determining the optimal guide vane height and number of guide vanes when the guide vane inlet angle and guide vane outlet angle are determined.

[0008] Furthermore, the optimization design of the airfoil of the movable guide vane of the water pump turbine includes: writing a non-uniform Bezier spline curve program based on the original airfoil data of the movable guide vane; parameterizing the upper and lower contours of the movable guide vane using non-uniform Bezier spline curves to generate control points and their control point lines of the upper and lower contours of the original airfoil of the movable guide vane; determining the coordinates of the control points that fit the airfoil of the movable guide vane; changing the airfoil shape by adjusting the coordinates of the control points; and searching for the optimal airfoil parameters of the movable guide vane using entropy minimization as the objective function and in conjunction with an optimization algorithm.

[0009] Furthermore, the optimization design of the blade profile of the movable guide vane of the water pump turbine also includes: selecting the minimum guide vane thickness, and when adjusting the blade profile using control points, keeping the coordinates of the thickest area in the middle of the movable guide vane and the control points at the front and rear ends of the movable guide vane unchanged.

[0010] Furthermore, the parameters to be optimized include the runner inlet width, runner outlet width, runner blade inlet angle, runner blade outlet angle, and number of runner blades.

[0011] Furthermore, the constraint range for the value of the parameter to be optimized is: , , , , in, Indicates the width of the runner inlet; Indicates the width of the runner outlet; Indicates the blade inlet angle; Indicates the blade exit angle; This represents the impeller inlet shaft surface coefficient, which is derived from design conditions. Indicates the number of revolutions; Indicates flow rate; Indicates head; Indicates rotational speed; It represents the acceleration due to gravity.

[0012] Furthermore, the method includes: calculating the entropy production based on the entropy production rate of fluid particles inside the water pump turbine, wherein the entropy production rate of fluid particles includes turbulent dissipation caused by time-averaged motion and instantaneous velocity fluctuations.

[0013] Furthermore, the entropy production rate of the fluid particles and the turbulent dissipation caused by the time-averaged motion and instantaneous velocity fluctuations are calculated using the following formulas: , , , in, This represents the entropy yield of fluid particles; This indicates turbulent dissipation caused by instantaneous velocity fluctuations; This represents the turbulent dissipation caused by time-averaged motion; Indicates the effective viscosity; Represents thermodynamic temperature; , , These represent the three spatial dimensions of the Cartesian coordinate system; , , These represent the three pulsating velocity components; , , These represent three time-averaged velocities.

[0014] Furthermore, the fluid domain meshing of the three-dimensional geometric model includes: performing structured meshing on the impeller and performing unstructured meshing on other parts of the pump turbine.

[0015] Another aspect of this application provides a computer device. The computer device includes a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the steps of the pump-turbine optimization design method described above.

[0016] The optimized design method and computer equipment for water pump turbines according to one or more embodiments of this application can have the following beneficial effects: (1) Through entropy production analysis, the key areas and factors of energy loss inside the guide vanes and impeller can be accurately located, changing the blindness of traditional optimization. Based on this, the structural parameters and flow channel shape can be optimized in a targeted manner, effectively reducing irreversible losses in the fluid flow process and significantly improving the energy conversion efficiency of the pump turbine. Under high head conditions, the overall efficiency of the unit can be improved, energy loss can be reduced, and the power generation efficiency of the power plant can be increased.

[0017] (2) Understand the flow characteristics of the flow field after the airfoil and runner are changed. Based on this, optimize the structural design, effectively suppress unstable factors such as eddies, cavitation, and pressure pulsation, reduce unit vibration and noise, improve the stability and reliability of unit operation, reduce the risk of equipment failure, extend the service life of equipment, and reduce maintenance costs. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an optimized design method for a water pump turbine according to an embodiment of this application.

[0019] Figure 2 This is a flowchart illustrating the optimized design method for the water pump turbine of this application.

[0020] Figure 3 This is a schematic block diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses consistent with some aspects of this application as detailed in the appended claims.

[0022] From an energy loss perspective, the guide vane and runner designs of high-head pump-turbines in related technologies suffer from significant irreversible losses during energy conversion. In the guide vane region, friction between the water flow and the guide vane wall, boundary layer separation, and vortex generation lead to a substantial amount of mechanical energy being converted into heat and dissipated, making effective recovery of this energy loss difficult. In the runner region, in addition to the aforementioned losses, impact losses at the blade inlet and outlet, and leakage losses at the blade tip clearance are also significant. According to relevant research, under certain operating conditions, the energy loss of traditionally designed high-head pump-turbines can reach 10%-20% of the input energy, which greatly reduces the overall efficiency of the unit.

[0023] Currently, there are many shortcomings in the optimized design of guide vanes and runners for high-head pump-turbines that urgently need to be addressed, which limits the further improvement of pump-turbine performance.

[0024] On the one hand, most related technologies optimize based on a single factor, focusing on the optimization of individual components such as guide vanes or runners, without fully considering the complex interactions between them. For example, simply adjusting the geometry of the guide vanes or the blade angle of the runners fails to adequately consider the interaction between the guide vanes and the runners, as well as the comprehensive impact of multi-physics coupling on flow and energy loss. Similarly, adjusting only the placement angle of the guide vanes ignores the mutual influence of this change on the inlet and outlet flow states and energy conversion of the runners. This independent and one-sided optimization approach often fails to achieve optimal overall unit performance and cannot cope with the complex and variable flow conditions under high head conditions. Moreover, most optimizations only focus on geometry or a few operating parameters, without comprehensively considering multi-physics coupling, such as the thermal-fluid-solid multi-field interaction between the guide vanes and the runners under high-speed water flow impact. This results in the optimized design failing to adapt to complex operating conditions in actual operation, leading to a significant performance reduction.

[0025] On the other hand, the optimization design methods of related technologies lack a deep understanding and precise quantification of energy loss mechanisms when assessing energy loss. High-head pump turbines exhibit complex flow phenomena such as intense three-dimensional unsteady turbulence, vortices, flow separation, and cavitation. The research on the generation mechanisms and interactions of these phenomena is insufficient. In the guide vane region, there is a lack of clear understanding of the boundary layer separation and vortex generation mechanisms caused by high-speed water flow turning, making it impossible to accurately predict their impact on energy loss and pressure pulsation. In the runner region, key issues such as complex flow on the blade surface and leakage flow at the blade tip clearance have not been fully studied, making it difficult to establish accurate flow models. This results in a lack of solid theoretical support for optimization design, hindering effective improvement of internal flow conditions and reduction of energy loss and instability factors.

[0026] Traditional evaluation methods primarily rely on macroscopic performance indicators such as efficiency and head, making it difficult to deeply reveal the specific locations and distribution of energy losses within the guide vanes and runners. When analyzing energy losses in the guide vanes and runners, it's impossible to precisely pinpoint localized areas, such as high-speed flow losses in the bladeless zone of the guide vanes, impact losses at the runner blade inlets and outlets, and leakage losses at the blade tip clearance. This lack of clear direction and focus in optimization efforts makes it difficult to fundamentally reduce energy losses and improve unit efficiency. Furthermore, related technologies cannot accurately quantify the proportion of each loss factor under different operating conditions, hindering targeted optimization of key energy loss areas and resulting in poor optimization effects that fail to significantly improve the energy conversion efficiency of the pump-turbine.

[0027] In general, the optimization design methods for the movable guide vanes of water pump turbines have certain limitations in related technologies. Some methods optimize from only a single perspective, such as simply changing the shape of the guide vane, without comprehensively considering the influence of multiple factors on the guide vane performance. Some optimization design methods lack in-depth analysis of the internal flow characteristics of the guide vane under complex flow conditions, resulting in the optimized guide vane failing to effectively improve the unit performance in actual operation.

[0028] Pumped storage pump-turbines need to take into account both pumping and power generation conditions, and achieve stable operation under bidirectional variable loads. The optimization design is extremely difficult, and traditional design methods are hard to balance the performance optimization under both conditions.

[0029] In view of this, this application provides an optimized design method for a water pump turbine, which can effectively solve at least one of the technical problems mentioned in the above-mentioned related technologies.

[0030] This application proposes an optimization design method for pump-turbines based on entropy production analysis. Entropy production analysis, as a thermodynamic method capable of quantitatively assessing irreversible losses within a system, provides a novel approach for the optimization design of guide vanes and runners in high-head pump-turbines. Through entropy production analysis, the optimization design method of this application can deeply reveal the energy loss mechanism in the internal flow process of the guide vanes and runners, identify key areas and factors of energy loss, provide a scientific and accurate basis for optimization design, overcome related technical bottlenecks, and achieve a significant improvement in the performance of high-head pump-turbines.

[0031] The optimized design method for the water pump turbine and the computer equipment of this application will be described in detail below with reference to the accompanying drawings. Unless otherwise specified, the features in the following embodiments and implementation methods can be combined with each other.

[0032] Figure 1 A flowchart illustrating an embodiment of the optimized design method for a water pump turbine according to this application is provided. Figure 1As shown, the optimized design method of a water pump turbine in one embodiment of this application may include steps S1 to S7.

[0033] Step S1, Selection of runner parameters: Based on the overall hydraulic design requirements of the pump turbine, a set of parameters to be optimized for the runner of the pump turbine can be selected based on design experience.

[0034] In some embodiments, the parameters to be optimized for the runner may include the runner inlet width, runner outlet width, runner blade inlet angle, runner blade outlet angle, and number of runner blades.

[0035] The constraints for the parameters to be optimized on the impeller are given below, while other impeller parameters are treated as design constants. The constraint ranges for the values ​​of the impeller's parameters to be optimized are: , , , , in, Indicates the width of the runner inlet; Indicates the width of the runner outlet; Indicates the blade inlet angle; Indicates the blade exit angle; The impeller inlet axial surface coefficient can be obtained from design conditions; Indicates the number of revolutions; Indicates flow rate; Indicates head; Indicates rotational speed; It represents the acceleration due to gravity.

[0036] Step S2, Optimization of the moving guide vane: Given that the values ​​of the parameters to be optimized for the impeller are determined, the moving guide vane of the pump turbine is iteratively optimized.

[0037] In some embodiments, step S2, which iteratively optimizes the movable guide vanes of the water pump turbine, may further include steps S21 to S23.

[0038] In step S21, the blade shape of the movable guide vane of the water pump turbine is optimized.

[0039] For example, a non-uniform Bezier spline curve program can be written based on the original airfoil data of the active guide vane.

[0040] The upper and lower contours of the movable guide vane are parameterized using non-uniform Bezier spline curves to generate control points and control point lines for the upper and lower contours of the original airfoil of the movable guide vane.

[0041] Determine the coordinates of the control points for fitting the active guide vane airfoil. The specific number of control points is determined based on the matching accuracy between the Bezier curve fitted to the original airfoil curve. To accurately fit the active guide vane airfoil, for example, the coordinates of 12 control points can be ultimately determined.

[0042] To ensure the structural strength of the movable guide vane and address apex positioning, a minimum blade thickness needs to be selected. This minimum thickness is necessary to guarantee sufficient structural strength under water pressure, preventing breakage or excessive deformation; the movable blades must have a certain thickness. However, to achieve optimal hydraulic performance and reduce flow separation and losses, the blades of the movable guide vane should be as thin as possible. Therefore, the selected minimum blade thickness is a trade-off—the thinnest blade thickness allowed while meeting minimum structural strength requirements.

[0043] Furthermore, the thickest region in the middle of the movable guide vane, as well as the positions of the front and rear ends of the movable guide vane, are not set as independent variables. That is, when adjusting the blade shape using control points, the coordinates of the control points for the thickest region in the middle of the movable guide vane, as well as the front and rear ends of the movable guide vane, remain unchanged.

[0044] The shape of the guide vane changes as the control point position changes. The vane shape is altered by adjusting the coordinates of the control point.

[0045] Using entropy minimization as the objective function, and combining optimization algorithms such as genetic algorithms, particle swarm optimization, or simulated annealing, the optimal airfoil parameters for the movable guide vane are searched. The optimal airfoil parameters refer to the best coordinate values ​​(x, y) of the control points that are allowed to be adjusted among the 12 control points. These coordinate values ​​determine the precise shape of the upper and lower contours of the movable guide vane.

[0046] In step S22, given that the optimal blade profile parameters of the movable guide vane are determined, the guide vane inlet angle and guide vane outlet angle can be optimized according to the characteristics of different operating conditions of the pump turbine (pumping condition and power generation condition).

[0047] In step S23, given that the guide vane inlet angle and guide vane outlet angle are determined, the optimal guide vane height and number of guide vanes for the movable guide vane are determined.

[0048] By following the steps described above, the overall optimization of the movable guide vane structure can be achieved.

[0049] Step S3, Model Establishment Step: Based on the determined values ​​of the parameters to be optimized, a three-dimensional geometric model of the water pump turbine is established.

[0050] Step S4, Mesh Generation Step: Perform fluid domain mesh generation on the 3D geometric model.

[0051] The computational domain of the pump turbine runner can be modeled in a defined coordinate system using 3D modeling software; the 3D model can be meshed using ANSYS ICEM (Integrated Computational Engineering and Manufacturing) software.

[0052] In some embodiments, the runner can be divided into structured grids and other parts of the pump turbine can be divided into unstructured grids.

[0053] Step S5, Flow channel simulation step: Import the divided fluid domain mesh into Computational Fluid Dynamics (CFD) software to perform flow channel numerical simulation and calculate the flow field parameters and entropy production distribution under different operating conditions.

[0054] Optionally, the divided fluid domain mesh can be imported into CFD software for full-channel numerical simulation.

[0055] Step S6, Evaluation Step: Evaluate the entropy production and hydraulic performance before and after optimization.

[0056] For example, the optimized results can be compared with the unoptimized results to evaluate the effectiveness of the optimized scheme in reducing entropy production and improving hydraulic performance. The most direct measure of hydraulic performance is efficiency, which is the ratio of output power to input power. Optimization aims to achieve higher efficiency.

[0057] Step S7: Determine whether the hydraulic performance meets the predetermined design requirements and minimizes entropy production. If the result is "yes", the optimization stops, and the parameter values ​​of the runner and movable guide vanes corresponding to meeting the predetermined design requirements and minimizing entropy production are taken as the optimal design parameter values ​​for the pump-turbine. Otherwise, return to the runner parameter selection step in Step S1, adjust the values ​​of the runner's parameters to be optimized according to the evaluation results, reselect a set of values ​​for the runner's parameters to be optimized, and repeat the runner parameter selection step in Step S1 up to the evaluation step in Step S6 until the hydraulic performance meets the predetermined design requirements and minimizes entropy production.

[0058] The optimization design method for the pump-turbine in this application is based on entropy production analysis. By analyzing the irreversible losses of the internal flow of the guide vanes and runner under different operating conditions, the key areas and influencing factors of energy loss in the bidirectional operation process are identified, thereby achieving the purpose of optimizing the geometric parameters and flow channel structure of the guide vanes and runner. This method can significantly improve the efficiency and operational stability of the pump-turbine under different operating conditions.

[0059] The pump-turbine optimization design method of this application combines entropy production analysis to optimize the shape of the guide vanes and runner, which significantly improves the flow pattern in the bladeless region. On the one hand, it evaluates the impact of different airfoil parameters and runner structure combinations on the unit efficiency and finally selects the optimal parameter combination to ensure stability. On the other hand, it specifically optimizes the guide vanes and runner of the pump-turbine to reduce irreversible losses in the flow process, improve energy conversion efficiency, and enhance the stability and reliability of the unit.

[0060] Entropy production can be calculated based on the entropy production rate of fluid particles inside the pump turbine. The entropy production rate of fluid particles includes two parts: time-averaged motion and turbulent dissipation caused by instantaneous velocity fluctuations. These two parts are called turbulent dissipation terms.

[0061] The entropy production rate of fluid particles and the turbulent dissipation caused by time-averaged motion and instantaneous velocity fluctuations can be calculated using the following formula: , , , in, This represents the entropy yield of fluid particles; This indicates turbulent dissipation caused by instantaneous velocity fluctuations; This represents the turbulent dissipation caused by time-averaged motion; This represents the effective viscosity, which can be expressed as turbulent viscosity. and dynamic viscosity To indicate; Represents thermodynamic temperature; , , These represent the three spatial dimensions of the Cartesian coordinate system, corresponding to the x, y, and z directions, respectively; , , These represent the three fluctuating velocities (i.e., the difference between the instantaneous velocity component and the time-averaged velocity component in the corresponding direction). , , These represent the time-averaged velocities (i.e., the time averages of the velocity fluctuation components) in the x, y, and z directions, respectively.

[0062] Figure 2 A detailed flowchart of the optimized design method for the water pump turbine of this application is presented. (See attached flowchart.) Figure 2As shown, in step 201, the basic parameters of the pump-turbine runner are set; in step 202, based on the basic parameters of the pump-turbine runner, 3D modeling software is used to model and mesh the computational domain of the runner in a defined coordinate system; in step 203, the meshed fluid domain is imported into CFD software for simulation verification; in step 204, entropy production analysis is performed; in step 205, flow field parameters are calculated and hydraulic performance is evaluated; in step 206, it is determined whether the overall design goal has been achieved, i.e., whether the hydraulic performance meets the design requirements and minimizes entropy production; if the overall design goal has not been achieved, proceed to steps 207 and S208. In step 207, the optimized model of the movable guide vane can be reconstructed, and in step 208, optimization algorithms such as genetic algorithm, particle swarm optimization algorithm, or simulated annealing algorithm are used to search for the optimal blade profile parameters of the movable guide vane, and then the process returns to step 202 to re-model the 3D model. When the overall design goal is achieved, proceed to step 209. In step 209, the optimal design parameter values ​​for the runner and movable guide vanes of the water pump turbine can be obtained.

[0063] Compared with existing technologies, the optimized design method for water pump turbines in this application can have the following beneficial effects: (1) Through entropy production analysis, the key areas and factors of energy loss inside the guide vanes and impeller can be accurately located, changing the blindness of traditional optimization. Based on this, the structural parameters and flow channel shape can be optimized in a targeted manner, effectively reducing irreversible losses in the fluid flow process and significantly improving the energy conversion efficiency of the pump turbine. Under high head conditions, the overall efficiency of the unit can be improved, energy loss can be reduced, and the power generation efficiency of the power plant can be increased.

[0064] (2) Understand the flow characteristics of the flow field after the airfoil and runner are changed. Based on this, optimize the structural design, effectively suppress unstable factors such as eddies, cavitation, and pressure pulsation, reduce unit vibration and noise, improve the stability and reliability of unit operation, reduce the risk of equipment failure, extend the service life of equipment, and reduce maintenance costs.

[0065] This application also provides a computer device 300. Figure 3 A schematic block diagram of a computer device 300 according to one embodiment of this application is shown. Figure 3As shown, a computer device 300 according to one embodiment of this application includes a processor 301, an internal bus 302, a network interface 303, a memory 304, and a non-volatile memory 305. It may also include other hardware required for various operations. The processor 301 can read the corresponding computer program from the non-volatile memory 305 into the memory 304 and then run it to implement the steps of the optimized design method for the water pump turbine described above. Of course, besides software implementation, this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software. That is, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic components.

[0066] The computer device 300 of this application has similar beneficial technical effects to the optimized design method of the water pump turbine described above, therefore, it will not be described again here.

[0067] The above provides a detailed description of the optimized design method and computer equipment for the water pump turbine provided in the embodiments of this application. Specific examples have been used to illustrate the optimized design method and computer equipment for the water pump turbine in the embodiments of this application. The descriptions of the embodiments above are only for helping to understand the core ideas of this application and are not intended to limit this application. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the spirit and principles of this application, and these improvements and modifications should all fall within the protection scope of the appended claims.

Claims

1. An optimized design method for a water pump turbine, characterized in that, include: Steps for selecting runner parameters: Based on the overall hydraulic design requirements of the pump turbine, select a set of values ​​for the runner parameters to be optimized. The optimization steps for the movable guide vane are as follows: given the values ​​of the parameters to be optimized for the impeller, the movable guide vane of the pump turbine is iteratively optimized. Model building steps: Based on the determined values ​​of the runner and the optimized movable guide vanes, a three-dimensional geometric model of the water pump turbine is established; Mesh generation steps: Perform fluid domain mesh generation on the three-dimensional geometric model; Flow channel simulation steps: Import the divided fluid domain mesh into computational fluid dynamics software to perform flow channel numerical simulation and calculate the flow field parameters and entropy production distribution under different working conditions; Evaluation steps: Evaluate the entropy production and hydraulic performance before and after optimization. Adjust the values ​​of the runner's parameters to be optimized based on the evaluation results, and repeat the above steps of selecting runner parameters up to the evaluation step until the hydraulic performance meets the predetermined design requirements and minimizes entropy production.

2. The optimization design method as described in claim 1, characterized in that, The iterative optimization of the movable guide vanes of the water pump turbine includes: The blade profile of the movable guide vanes of the water pump turbine is optimized. Given the optimal airfoil parameters of the active guide vane, optimize the guide vane inlet angle and guide vane outlet angle; Given that the guide vane inlet angle and the guide vane outlet angle are determined, the optimal guide vane height and number of guide vanes are determined.

3. The optimization design method as described in claim 2, characterized in that, The optimization design of the blade profile of the movable guide vanes of the water pump turbine includes: A non-uniform Bezier spline curve program was written based on the original airfoil data of the active guide vane; The upper and lower contour lines of the active guide vane are parameterized using non-uniform Bezier spline curves to generate control points and control point lines of the upper and lower contour lines of the original airfoil of the active guide vane. Determine the coordinates of the control points for fitting the active guide vane airfoil; The leaf shape can be changed by adjusting the coordinates of the control points; Using entropy minimization as the objective function and combining it with an optimization algorithm, the optimal airfoil parameters for the active guide vane are searched.

4. The optimization design method as described in claim 3, characterized in that, The optimization design of the blade profile of the movable guide vanes of the water pump turbine also includes: By selecting the minimum guide vane thickness, when adjusting the blade shape using control points, the coordinates of the thickest area in the middle of the movable guide vane and the control points at the front and rear ends of the movable guide vane remain unchanged.

5. The optimization design method as described in claim 1, characterized in that, The parameters to be optimized include the inlet width of the runner, the outlet width of the runner, the inlet angle of the runner blades, the outlet angle of the runner blades, and the number of runner blades.

6. The optimization design method as described in claim 5, characterized in that, The constraint range for the values ​​of the parameter to be optimized is: , , , , in, Indicates the width of the runner inlet; Indicates the width of the runner outlet; Indicates the blade inlet angle; Indicates the blade exit angle; This represents the impeller inlet shaft surface coefficient, which is derived from design conditions. Indicates the number of revolutions; Indicates flow rate; Indicates head; Indicates rotational speed; It represents the acceleration due to gravity.

7. The optimization design method as described in claim 1, characterized in that, The method includes: The entropy production is calculated based on the entropy production rate of the fluid particles inside the pump turbine, and the entropy production rate of the fluid particles includes the turbulent dissipation caused by the time-averaged motion and instantaneous velocity fluctuations.

8. The optimization design method as described in claim 7, characterized in that, The entropy yield of the fluid particles and the turbulent dissipation caused by the time-averaged motion and instantaneous velocity fluctuations are calculated using the following formulas: , , , in, This represents the entropy yield of fluid particles; This indicates turbulent dissipation caused by instantaneous velocity fluctuations; This represents the turbulent dissipation caused by time-averaged motion; Indicates the effective viscosity; Represents thermodynamic temperature; , , These represent the three spatial dimensions of the Cartesian coordinate system; , , These represent the three pulsating velocity components; , , These represent three time-averaged velocities.

9. The optimization design method as described in claim 1, characterized in that, The process of dividing the three-dimensional geometric model into fluid domain meshes includes: The impeller is divided into a structured mesh, and the other parts of the pump turbine are divided into unstructured meshes.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the optimized design method for a water pump turbine as described in any one of claims 1 to 9.

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