Method, device and equipment for optimizing runner parameters of a pump-turbine and storage medium

Abstract

The present application relates to the technical field of hydroelectric power generation, and discloses a runner parameter optimization method, device, equipment and storage medium of a pump-turbine, wherein the method adjusts initial geometric parameters, inputs the adjusted parameters into a pre-constructed parameter optimization model, and outputs the pressure fluctuation amplitude of the runner in the no blade area corresponding to the adjusted geometric parameters until the pressure fluctuation amplitude of the runner in the no blade area corresponding to the adjusted geometric parameters is less than a preset threshold value, and the geometric parameter is stored as an optimized target parameter, so that the best geometric parameter for suppressing the pressure fluctuation of the runner in the no blade area can be quickly and efficiently found based on an artificial intelligence optimization algorithm, the optimization efficiency and precision of the geometric parameter are greatly improved, and the time and cost required by the traditional trial-and-error method are reduced.

Description

Technical Field

[0001] This invention relates to the field of hydropower technology, specifically to a method, apparatus, equipment, and storage medium for optimizing the runner parameters of a water pump turbine. Background Technology

[0002] The stable operation of pump-turbines is crucial to the stability and reliability of hydropower stations. The runner geometry of a pump-turbine significantly impacts its stable operation, making its optimization essential. In related technologies, under off-design conditions and during transient processes, the pressure pulsation amplitude in the bladeless region of the pump-turbine is large, causing unit vibration and affecting stability. Traditional solutions often rely on experience to optimize runner geometry, which is inefficient, prone to errors, and struggles to find the optimal design solution. Summary of the Invention

[0003] In view of this, the present invention provides a method, apparatus, equipment and storage medium for optimizing the runner parameters of a water pump turbine, in order to solve the problems of low efficiency, large error and difficulty in finding the optimal design scheme in the related art of optimizing the runner geometric parameters based on human experience.

[0004] In a first aspect, the present invention provides a method for optimizing the runner parameters of a pump-turbine. The method includes: obtaining initial geometric parameters of the runner of the pump-turbine to be optimized; adjusting the initial geometric parameters according to a preset adjustment step size to obtain adjusted geometric parameters; inputting the adjusted geometric parameters into a pre-constructed parameter optimization model so that the parameter optimization model outputs the amplitude of pressure pulsation in the bladeless region corresponding to the adjusted geometric parameters, wherein the parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the amplitude of pressure pulsation in the bladeless region of the pump-turbine; if the amplitude of pressure pulsation in the bladeless region is greater than or equal to a preset threshold, using the adjusted geometric parameters as initial geometric parameters, returning to the step of adjusting the initial geometric parameters according to a preset adjustment step size, until the amplitude of pressure pulsation in the bladeless region is less than a first preset threshold, and storing the adjusted geometric parameters as optimized target parameters.

[0005] The method for optimizing the runner parameters of a pump-turbine provided by this invention involves adjusting the initial geometric parameters according to a preset adjustment step size to obtain the adjusted geometric parameters; inputting the adjusted geometric parameters into a pre-constructed parameter optimization model so that the parameter optimization model outputs the amplitude of the pressure pulsation in the bladeless region corresponding to the adjusted geometric parameters. The parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the amplitude of the pressure pulsation in the bladeless region of the pump-turbine; if the amplitude of the pressure pulsation in the bladeless region is greater than or equal to a preset threshold, the adjusted geometric parameters are used as the initial geometric parameters, and the process returns to the step of adjusting the initial geometric parameters according to the preset adjustment step size until the amplitude of the pressure pulsation in the bladeless region is less than a first preset threshold. The adjusted geometric parameters are then stored as the optimized target parameters. The method provided by this invention adjusts the initial geometric parameters and inputs the adjusted parameters into a pre-constructed parameter optimization model. The parameter optimization model outputs the amplitude of pressure pulsation in the bladeless region corresponding to the adjusted geometric parameters until the amplitude of pressure pulsation in the bladeless region corresponding to an adjusted geometric parameter is less than a preset threshold. This geometric parameter is then stored as the optimized target parameter. Based on an artificial intelligence optimization algorithm, the optimal geometric parameters for suppressing pressure pulsation in the bladeless region can be found quickly and efficiently, greatly improving the optimization efficiency and accuracy of geometric parameters and reducing the time and cost required by traditional trial-and-error methods.

[0006] In one optional implementation, the parameter optimization model is constructed through the following steps: obtaining multiple geometric parameters and the pressure pulsation amplitude of the bladeless region corresponding to each geometric parameter; associating each geometric parameter with the corresponding pressure pulsation amplitude of the bladeless region to obtain an associated dataset; and training a preset model using the associated dataset until the model's accuracy meets the preset conditions to obtain the parameter optimization model.

[0007] The method provided in this optional implementation improves the accuracy of prediction by training a preset model to better predict the influence of the wheel's geometric parameters on pressure pulsations.

[0008] In one optional implementation, the step of obtaining the initial geometric parameters of the runner of the pump-turbine to be optimized includes: obtaining the initial geometric parameters of the runner of the target pump-turbine; inputting the initial geometric parameters of the runner of the target pump-turbine into a preset fluid dynamics calculation software for three-dimensional simulation to obtain the pressure pulsation amplitude in the bladeless region of the target pump-turbine; if the pressure pulsation amplitude in the bladeless region of the target pump-turbine is greater than or equal to a second preset threshold, the target pump-turbine is selected as the pump-turbine to be optimized.

[0009] In an optional implementation, the method further includes: inputting the target parameters into a preset fluid dynamics calculation software for three-dimensional simulation to obtain simulated values ​​of pressure pulsation amplitude in the bladeless region; comparing the simulated values ​​with a preset threshold, and evaluating the optimization effect of the target parameters based on the comparison results.

[0010] In one optional implementation, the initial geometric parameters include the rotor inlet diameter, the number of long and short blades, the blade wrap angle, the blade thickness, and the blade camber. The step of adjusting the initial geometric parameters according to a preset adjustment step size to obtain the adjusted geometric parameters includes: adjusting the rotor inlet diameter, the number of long and short blades, their length, thickness, camber, and wrap angle to obtain the adjusted geometric parameters.

[0011] In one optional implementation, the preset model is an adaptive gradient optimization model. The preset model is trained using an associated dataset until the model's accuracy meets the preset conditions to obtain a parameter-optimized model. This includes: dynamically adjusting the learning rate during adaptive training, updating different learning rates for different geometric parameters based on the cumulative sum of squared gradients, until the model parameters meet the preset conditions to obtain a parameter-optimized model.

[0012] The method provided in this optional implementation, with its mechanism for dynamically adjusting the learning rate, ensures the convergence of the algorithm and the accuracy of the optimization results.

[0013] Secondly, the present invention provides a device for optimizing the runner parameters of a water pump turbine. The device includes: an acquisition module for acquiring initial geometric parameters of the runner of the water pump turbine to be optimized; an adjustment module for adjusting the initial geometric parameters according to a preset adjustment step size to obtain adjusted geometric parameters; a first determination module for inputting the adjusted geometric parameters into a pre-constructed parameter optimization model, so that the parameter optimization model outputs the amplitude of the pressure pulsation in the bladeless region corresponding to the adjusted geometric parameters, the parameter optimization model being used to characterize the correlation between the geometric parameters of the runner and the amplitude of the pressure pulsation in the bladeless region of the water pump turbine; and a second determination module for, if the amplitude of the pressure pulsation in the bladeless region is greater than or equal to a preset threshold, using the adjusted geometric parameters as initial geometric parameters, returning to the step of adjusting the initial geometric parameters according to the preset adjustment step size, until the amplitude of the pressure pulsation in the bladeless region is less than the first preset threshold, and storing the adjusted geometric parameters as optimized target parameters.

[0014] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for optimizing the runner parameters of a water pump turbine as described in the first aspect or any corresponding embodiment.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the pump-turbine runner parameter optimization method described in the first aspect or any corresponding embodiment thereof.

[0016] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the method for optimizing the runner parameters of a water pump turbine as described in the first aspect or any corresponding embodiment. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for optimizing the runner parameters of a water pump turbine according to an embodiment of the present invention.

[0019] Figure 2 This is a flowchart illustrating another method for optimizing the runner parameters of a water pump turbine according to an embodiment of the present invention;

[0020] Figure 3 This is a flowchart illustrating another method for optimizing the runner parameters of a water pump turbine according to an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the structure of the water pump turbine in an embodiment of the present invention;

[0022] Figure 5 This is a cross-sectional view of the runner of the water pump turbine in an embodiment of the present invention;

[0023] Figure 6 This is a schematic diagram of the shape of the long and short blades of the water pump turbine in an embodiment of the present invention;

[0024] Figure 7 This is a structural block diagram of a pump-turbine runner parameter optimization device according to an embodiment of the present invention;

[0025] Figure 8 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] In related technologies, under non-design operating conditions and during transient processes, the pressure pulsation amplitude in the bladeless zone of a pump-turbine is relatively large, which can cause unit vibration and affect stability. Traditional solutions often rely on experience to optimize the runner geometry parameters, which is inefficient, prone to errors, and makes it difficult to find the optimal design solution.

[0028] In view of this, the method for optimizing the runner parameters of a water pump turbine provided in this application embodiment can be applied to a server to optimize the runner parameters of the water pump turbine. The method provided by this invention adjusts the initial geometric parameters and inputs the adjusted parameters into a pre-constructed parameter optimization model. This causes the parameter optimization model to output the amplitude of the pressure pulsation in the bladeless region corresponding to the adjusted geometric parameters, until the amplitude of the pressure pulsation in the bladeless region corresponding to an adjusted geometric parameter is less than a preset threshold. This geometric parameter is then stored as the optimized target parameter. Based on an artificial intelligence optimization algorithm, the optimal geometric parameters for suppressing pressure pulsation in the bladeless region can be found quickly and efficiently, greatly improving the optimization efficiency and accuracy of geometric parameters and reducing the time and cost required by traditional trial-and-error methods.

[0029] According to an embodiment of the present invention, an embodiment of a method for optimizing the runner parameters of a water pump turbine is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides a method for optimizing the runner parameters of a water pump turbine, which can be used in the aforementioned server. Figure 1 This is a flowchart of a method for optimizing the runner parameters of a water pump turbine according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps:

[0031] Step S101: Obtain the initial geometric parameters of the runner of the pump turbine to be optimized.

[0032] For example, the pump-turbine to be optimized can be a pump-turbine that requires optimization of its runner geometry parameters. The initial runner geometry parameters may include, but are not limited to, the number of blades, blade wrap angle, blade thickness, camber, and runner inlet diameter.

[0033] Step S102: Adjust the initial geometric parameters according to the preset adjustment step size to obtain the adjusted geometric parameters.

[0034] For example, the preset adjustment step size can be determined based on actual needs, and the specific content of the preset adjustment step size is not limited in the embodiments of this application.

[0035] Step S103: Input the adjusted geometric parameters into the pre-built parameter optimization model so that the parameter optimization model outputs the pressure pulsation amplitude of the bladeless region corresponding to the adjusted geometric parameters.

[0036] For example, in the embodiments of this application, the parameter optimization model can be obtained by training a preset model. The parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the pressure pulsation amplitude in the bladeless zone of the water pump turbine. When the adjusted geometric parameters are input into the parameter optimization model, the model will output the corresponding pressure pulsation amplitude in the bladeless zone.

[0037] Step S104: If the amplitude of the pressure pulsation in the bladeless zone is greater than or equal to the preset threshold, the adjusted geometric parameters are used as the initial geometric parameters, and the process of adjusting the initial geometric parameters according to the preset adjustment step size is returned until the amplitude of the pressure pulsation in the bladeless zone is less than the first preset threshold. The adjusted geometric parameters are then stored as the optimized target parameters.

[0038] For example, the first preset threshold can be a value determined based on optimization requirements. This application embodiment does not limit the specific content of the first preset threshold, and those skilled in the art can determine it according to their needs. This application embodiment adjusts the initial geometric parameters multiple times until the amplitude of the pressure pulsation in the bladeless region corresponding to an adjusted geometric parameter is less than a preset threshold, and then stores this geometric parameter as the optimized target parameter.

[0039] The method for optimizing the runner parameters of a pump-turbine provided in this embodiment adjusts the initial geometric parameters and inputs the adjusted parameters into a pre-constructed parameter optimization model. The model outputs the amplitude of pressure pulsation in the bladeless region corresponding to the adjusted geometric parameters until the amplitude of pressure pulsation in the bladeless region corresponding to an adjusted geometric parameter is less than a preset threshold. This geometric parameter is then stored as the optimized target parameter. Based on an artificial intelligence optimization algorithm, the optimal geometric parameters for suppressing pressure pulsation in the bladeless region can be found quickly and efficiently, greatly improving the optimization efficiency and accuracy of geometric parameters and reducing the time and cost required by traditional trial-and-error methods.

[0040] This embodiment provides a method for optimizing the runner parameters of a water pump turbine, which can be used in the aforementioned server. Figure 2 This is a flowchart of a method for optimizing the runner parameters of a water pump turbine according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps:

[0041] Step S201: Obtain the initial geometric parameters of the runner of the pump-turbine to be optimized. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0042] Step S202: Adjust the initial geometric parameters according to the preset adjustment step size to obtain the adjusted geometric parameters.

[0043] Specifically, the initial geometric parameters include the runner inlet diameter, the number of long and short blades, the blade wrap angle, the blade thickness, and the blade camber. Step S202 includes:

[0044] Step S2021: Adjust the inlet diameter of the runner, the number, length, thickness, curvature, and wrap angle of the long and short blades to obtain the adjusted geometric parameters.

[0045] For example, in this optional embodiment, the configuration of the long and short blades of the impeller is adjusted, wherein the long blades provide the main hydrodynamic force and the short blades are used to optimize the flow field and reduce pressure pulsation in the bladeless region; the geometric parameters such as the number of blades, blade wrap angle, blade thickness and camber of the long and short blades are optimized to achieve the best suppression effect on the amplitude of pressure pulsation in the bladeless region.

[0046] Step S203: Input the adjusted geometric parameters into the pre-built parameter optimization model so that the parameter optimization model outputs the pressure pulsation amplitude in the bladeless region corresponding to the adjusted geometric parameters. The parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the pressure pulsation amplitude in the bladeless region of the pump turbine.

[0047] Specifically, the above parameter optimization model is constructed through the following steps:

[0048] Step a1: Obtain multiple geometric parameters and the pressure pulsation amplitude of the bladeless region corresponding to each geometric parameter.

[0049] For example, in the embodiments of this application, CFD software is used to perform three-dimensional simulation of water pump turbines with different geometric parameters to obtain the pressure pulsation amplitude in the bladeless region corresponding to different geometric parameters.

[0050] Step a2 involves correlating each geometric parameter with the corresponding pressure pulsation amplitude in the bladeless region to obtain a correlated dataset.

[0051] Step a3: Train the preset model using the associated dataset until the model's accuracy meets the preset conditions, and obtain the parameter-optimized model.

[0052] For example, in this embodiment of the application, an adaptive gradient optimization model is trained using data obtained from CFD simulations to optimize the model's parameters, enabling it to accurately predict the amplitude of pressure fluctuations. The model is trained using a large amount of data obtained from CFD simulations to minimize the error between predicted and actual values.

[0053] Step S204: If the amplitude of the pressure pulsation in the bladeless zone is greater than or equal to the preset threshold, the adjusted geometric parameters are used as the initial geometric parameters, and the process of adjusting the initial geometric parameters according to the preset adjustment step size is returned until the amplitude of the pressure pulsation in the bladeless zone is less than the first preset threshold. The adjusted geometric parameters are then stored as the optimized target parameters.

[0054] This embodiment provides a method for optimizing the runner parameters of a water pump turbine, which can be used in the aforementioned server. Figure 3 This is a flowchart of a method for optimizing the runner parameters of a water pump turbine according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps:

[0055] Step S301: Obtain the initial geometric parameters of the runner of the pump turbine to be optimized.

[0056] Specifically, step S301 includes:

[0057] Step S3011: Obtain the initial geometric parameters of the runner of the target water pump turbine.

[0058] For example, the target pump-turbine can be any pump-turbine that needs to be determined to meet design requirements.

[0059] Step S3012: Input the initial geometric parameters of the runner of the target water pump turbine into the preset fluid dynamics calculation software for three-dimensional simulation to obtain the pressure pulsation amplitude in the bladeless region of the target water pump turbine.

[0060] For example, in the embodiments of this application, the preset fluid dynamics calculation software may include, but is not limited to, CFD software. The CFD software is used to perform three-dimensional simulation of the target water pump turbine, analyze the pressure pulsation characteristics in the bladeless region, and obtain the pressure pulsation amplitude under the initial geometry.

[0061] Step S3013: If the pressure pulsation amplitude in the bladeless zone of the target water pump turbine is greater than or equal to the second preset threshold, the target water pump turbine is selected as the water pump turbine to be optimized.

[0062] For example, the embodiments of this application do not limit the specific content of the second preset threshold, which can be determined by those skilled in the art according to their needs. In the embodiments of this application, Figure 4 This is a schematic diagram of the pump-turbine structure in an embodiment of this application, including a volute 1, fixed guide vanes 2, movable guide vanes 3, a pump-turbine runner 4, and a tailrace pipe 5. A cross-sectional view of the pump-turbine runner can be shown as follows: Figure 5 As shown.

[0063] Step S302: Adjust the initial geometric parameters according to the preset adjustment step size to obtain the adjusted geometric parameters. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0064] Step S303: Input the adjusted geometric parameters into the pre-built parameter optimization model so that the parameter optimization model outputs the pressure pulsation amplitude in the bladeless region corresponding to the adjusted geometric parameters. The parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the pressure pulsation amplitude in the bladeless region of the pump-turbine. For details, please refer to... Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0065] Step S304: If the amplitude of the pressure pulsation in the bladeless zone is greater than or equal to the preset threshold, the adjusted geometric parameters are used as the initial geometric parameters, and the process of adjusting the initial geometric parameters according to the preset adjustment step size is returned until the amplitude of the pressure pulsation in the bladeless zone is less than the first preset threshold. The adjusted geometric parameters are then stored as the optimized target parameters.

[0066] Specifically, the preset model is an adaptive gradient optimization model, and step S204 above includes:

[0067] Step S3041: During adaptive training, the learning rate is dynamically adjusted. Different learning rates are updated for different geometric parameters based on the cumulative sum of squared gradients until the model parameters meet the preset conditions, thus obtaining the parameter-optimized model.

[0068] In an exemplary embodiment of this application, the input of the adaptive gradient optimization model is the geometric parameters of the impeller, and the output is the predicted value of the pressure pulsation in the bladeless region. The input layer includes the geometric parameters of the impeller, such as the number of blades, blade wrap angle, blade thickness, and camber; the output layer is the amplitude prediction of the pressure pulsation in the bladeless region; the learning rate is dynamically adjusted during adaptive training, and different learning rates are updated for different parameters based on the cumulative sum of squared gradients.

[0069] In this embodiment of the application, the inlet diameter of the turbine runner is 2000 mm in the target geometric parameters. A schematic diagram of the shape of the long and short blades of the water pump turbine runner is shown below. Figure 6 As shown, the equation of the center curve of the long blade is:

[0070] y = 7E - 16x 6 -3E-12x 5 +5E-09x 4 -4E-06x 3 +0.0023x 2 -0.4905x+46.564

[0071] Where x is the x-coordinate, y is the y-coordinate, and E is the scientific notation E, representing 10.

[0072] The equation for the thickness of a long blade is:

[0073] m = 7E - 15x 6 -2E-11x 5 +3E-08x 4 -2E-05x 3 +0.0078x 2 -1.3954x+410.96

[0074] Where m represents the thickness of the blade, and x is the x-coordinate.

[0075] The equation for the center curve of the short blade is:

[0076] y = 6E-09x 4 -2E-05x 3 +0.0203x 2 -10.328x+2226.6

[0077] The equation for the thickness of the short blade is:

[0078] m = 2E-09x 4 -6E-06x 3 +0.0063x 2 -2.7934x+449.29

[0079] The meanings of the variables are the same as above, and will not be repeated here.

[0080] Step S305: Input the target parameters into the preset fluid dynamics calculation software for three-dimensional simulation to obtain the simulated value of the pressure pulsation amplitude in the bladeless region.

[0081] Step S306: Compare the simulated value with the preset threshold, and evaluate the optimization effect of the target parameter based on the comparison result.

[0082] For example, in this embodiment of the application, the optimized impeller geometry parameters are input again into the CFD simulation to verify whether the pressure pulsation amplitude meets the optimization requirements. If it does not meet the optimization requirements, the parameters of the adaptive gradient optimization model are adjusted, and a new round of optimization iteration is performed until the optimization requirements are met.

[0083] Furthermore, data acquisition devices can be set up to monitor the operating status of the pump-turbine designed according to the optimized geometric parameters.

[0084] The method provided in this application embodiment can achieve precise control of the pressure pulsation amplitude in the bladeless zone of a water pump turbine, improve the performance and efficiency of the water pump turbine, and at the same time monitor and control the operating status of the water pump turbine in real time to ensure its safe and stable operation.

[0085] This embodiment also provides a device for optimizing the runner parameters of a water pump turbine. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0086] This embodiment provides a device for optimizing the runner parameters of a water pump turbine, such as... Figure 7 As shown, it includes:

[0087] The acquisition module 701 is used to acquire the initial geometric parameters of the runner of the pump-turbine to be optimized;

[0088] The adjustment module 702 is used to adjust the initial geometric parameters according to a preset adjustment step size to obtain the adjusted geometric parameters;

[0089] The first determining module 703 is used to input the adjusted geometric parameters into the pre-constructed parameter optimization model so that the parameter optimization model outputs the pressure pulsation amplitude in the bladeless region corresponding to the adjusted geometric parameters. The parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the pressure pulsation amplitude in the bladeless region of the pump turbine.

[0090] The second determining module 704 is used to, if the amplitude of the pressure pulsation in the bladeless zone is greater than or equal to a preset threshold, use the adjusted geometric parameters as the initial geometric parameters, return to the step of adjusting the initial geometric parameters according to a preset adjustment step size, until the amplitude of the pressure pulsation in the bladeless zone is less than the first preset threshold, and store the adjusted geometric parameters as the optimized target parameters.

[0091] In some alternative implementations, the parameter optimization model is constructed through the following steps:

[0092] Obtain multiple geometric parameters and the corresponding pressure pulsation amplitude in the bladeless region for each geometric parameter;

[0093] By correlating each geometric parameter with the corresponding pressure pulsation amplitude in the bladeless region, a correlated dataset is obtained;

[0094] The pre-defined model is trained using the associated dataset until the model's accuracy meets the pre-defined conditions, thus obtaining the parameter-optimized model.

[0095] In some alternative implementations, the acquisition module 701 includes:

[0096] The acquisition submodule is used to acquire the initial geometric parameters of the runner of the target water pump turbine;

[0097] The first determining submodule is used to input the initial geometric parameters of the runner of the target water pump turbine into a preset fluid dynamics calculation software for three-dimensional simulation, and obtain the pressure pulsation amplitude of the bladeless region of the target water pump turbine.

[0098] The second determining submodule is used to identify the target pump turbine as the pump turbine to be optimized if the pressure pulsation amplitude in the bladeless zone of the target pump turbine is greater than or equal to the second preset threshold.

[0099] In some alternative embodiments, the device further includes:

[0100] The third determination module is used to input the target parameters into the preset fluid dynamics calculation software for three-dimensional simulation to obtain the simulated value of the pressure pulsation amplitude in the bladeless region.

[0101] The evaluation module is used to compare simulated values ​​with preset thresholds and evaluate the optimization effect of the target parameters based on the comparison results.

[0102] In some optional implementations, the initial geometric parameters include the runner inlet diameter, the number of long and short blades, the blade wrap angle, the blade thickness, and the blade camber. The adjustment module 702 includes:

[0103] The adjustment submodule is used to adjust the runner inlet diameter, the number, length, thickness, curvature, and wrap angle of the long and short blades to obtain the adjusted geometric parameters.

[0104] In some optional implementations, the preset model is an adaptive gradient optimization model, and the second determining module 704 includes:

[0105] The fourth submodule is used to dynamically adjust the learning rate during adaptive training. It updates the learning rate for different geometric parameters based on the cumulative sum of squared gradients until the model parameters meet the preset conditions, thus obtaining the parameter-optimized model.

[0106] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0107] In this embodiment, the pump turbine runner parameter optimization device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0108] This invention also provides a computer device having the above-described features. Figure 7 The device shown is for optimizing the runner parameters of a water pump turbine.

[0109] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0110] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0111] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0112] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0113] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0114] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0115] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0116] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0117] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for optimizing the runner parameters of a water pump turbine, characterized in that, The method includes: Obtain the initial geometric parameters of the runner of the pump-turbine to be optimized; The initial geometric parameters are adjusted according to the preset adjustment step size to obtain the adjusted geometric parameters; The adjusted geometric parameters are input into a pre-built parameter optimization model so that the parameter optimization model outputs the pressure pulsation amplitude in the bladeless region corresponding to the adjusted geometric parameters. The parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the pressure pulsation amplitude in the bladeless region of the pump turbine. If the amplitude of the pressure pulsation in the bladeless zone is greater than or equal to a preset threshold, the adjusted geometric parameters are used as the initial geometric parameters, and the process of adjusting the initial geometric parameters according to a preset adjustment step size is returned until the amplitude of the pressure pulsation in the bladeless zone is less than the first preset threshold. The adjusted geometric parameters are then stored as the optimized target parameters. The step of adjusting the initial geometric parameters according to the preset adjustment step size to obtain the adjusted geometric parameters includes: adjusting the inlet diameter of the rotor, the number, length, thickness, curvature and wrap angle of the long and short blades to obtain the adjusted geometric parameters; The parameter optimization model is constructed through the following steps: Obtain multiple geometric parameters and the corresponding pressure pulsation amplitude in the bladeless region for each geometric parameter; By correlating each geometric parameter with the corresponding pressure pulsation amplitude in the bladeless region, a correlated dataset is obtained; The preset model is trained using the associated dataset until the model's accuracy meets the preset conditions, thus obtaining the parameter-optimized model. The preset model is an adaptive gradient optimization model. The process of training the preset model using the associated dataset until the model's accuracy meets preset conditions to obtain the parameter optimization model includes: During adaptive training, the learning rate is dynamically adjusted, and different learning rates are updated for different geometric parameters based on the cumulative sum of squared gradients until the model parameters meet the preset conditions, thus obtaining the parameter-optimized model. The step of obtaining the initial geometric parameters of the runner of the pump-turbine to be optimized includes: Obtain the initial geometric parameters of the runner of the target water pump turbine; The initial geometric parameters of the runner of the target water pump turbine are input into a preset fluid dynamics calculation software for three-dimensional simulation to obtain the pressure pulsation amplitude in the bladeless region of the target water pump turbine. If the pressure pulsation amplitude in the bladeless zone of the target pump turbine is greater than or equal to the second preset threshold, the target pump turbine will be selected as the pump turbine to be optimized.

2. A device for optimizing the runner parameters of a water pump turbine, characterized in that, The device includes: The acquisition module is used to acquire the initial geometric parameters of the runner of the pump-turbine to be optimized; The adjustment module is used to adjust the initial geometric parameters according to a preset adjustment step size to obtain the adjusted geometric parameters; The first determining module is used to input the adjusted geometric parameters into a pre-constructed parameter optimization model so that the parameter optimization model outputs the bladeless pressure pulsation amplitude corresponding to the adjusted geometric parameters. The parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the bladeless pressure pulsation amplitude of the pump turbine. The second determining module is used to, if the amplitude of the pressure pulsation in the bladeless zone is greater than or equal to a preset threshold, take the adjusted geometric parameters as the initial geometric parameters, return to the step of adjusting the initial geometric parameters according to a preset adjustment step size, until the amplitude of the pressure pulsation in the bladeless zone is less than the first preset threshold, and store the adjusted geometric parameters as the optimized target parameters. The step of adjusting the initial geometric parameters according to the preset adjustment step size to obtain the adjusted geometric parameters includes: adjusting the inlet diameter of the rotor, the number, length, thickness, curvature and wrap angle of the long and short blades to obtain the adjusted geometric parameters; The parameter optimization model is constructed through the following steps: Obtain multiple geometric parameters and the corresponding pressure pulsation amplitude in the bladeless region for each geometric parameter; By correlating each geometric parameter with the corresponding pressure pulsation amplitude in the bladeless region, a correlated dataset is obtained; The preset model is trained using the associated dataset until the model's accuracy meets the preset conditions, thus obtaining the parameter-optimized model. The preset model is an adaptive gradient optimization model. The process of training the preset model using the associated dataset until the model's accuracy meets preset conditions to obtain the parameter optimization model includes: During adaptive training, the learning rate is dynamically adjusted, and different learning rates are updated for different geometric parameters based on the cumulative sum of squared gradients until the model parameters meet the preset conditions, thus obtaining the parameter-optimized model. The process of obtaining the initial geometric parameters of the runner of the pump-turbine to be optimized includes: Obtain the initial geometric parameters of the runner of the target water pump turbine; The initial geometric parameters of the runner of the target water pump turbine are input into a preset fluid dynamics calculation software for three-dimensional simulation to obtain the pressure pulsation amplitude in the bladeless region of the target water pump turbine. If the pressure pulsation amplitude in the bladeless zone of the target pump turbine is greater than or equal to the second preset threshold, the target pump turbine will be selected as the pump turbine to be optimized.

3. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to perform the method for optimizing the runner parameters of a water pump turbine as described in claim 1.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for optimizing the runner parameters of the water pump turbine as described in claim 1.

5. A computer program product, characterized in that, The method includes computer instructions for causing a computer to execute the method for optimizing the runner parameters of a water pump turbine as described in claim 1.

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