Runner parameter optimization method, device and equipment of pump turbine and storage medium

Abstract

The invention relates to the technical field of hydroelectric generation, and discloses a pump turbine runner parameter optimization method, device and equipment and a storage medium. According to the method, initial geometric parameters are adjusted, and the adjusted parameters are input into a pre-constructed parameter optimization model; the parameter optimization model outputs the adjusted pressure pulsation amplitude value of the vaneless area corresponding to the geometric parameter until the obtained amplitude value of the pressure pulsation of the vaneless area corresponding to the adjusted geometric parameter is smaller than a preset threshold value, the geometric parameter serves as an optimized target parameter to be stored, and the target parameter is obtained based on an artificial intelligence optimization algorithm. The optimal geometric parameters for inhibiting the pressure pulsation of the vaneless area can be quickly and efficiently found, the optimization efficiency and precision of the geometric parameters are greatly improved, and the time and cost required by a traditional trial and error method are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of hydroelectric power generation, and in particular to a method, device, equipment and storage medium for optimizing runner parameters of a water pump turbine. Background Art

[0002] The stable operation of the pump-turbine is of great significance to the stability and reliability of the hydropower station. The geometric parameters of the runner of the pump-turbine have a great influence on the stable operation of the pump-turbine. Therefore, the optimization of the runner geometric parameters is very important. In the related technology, under non-design conditions and transition processes, the pressure pulsation amplitude in the bladeless area of ​​the pump-turbine is large, which will cause the unit to vibrate and affect the stability. Traditional solutions often rely on experience to optimize the runner geometric parameters. This method is inefficient, has large errors, and it is difficult to find the best design solution. Summary of the invention

[0003] In view of this, the present invention provides a method, device, equipment and storage medium for optimizing the impeller parameters of a pump turbine to solve the problems in the related art of optimizing the geometric parameters of the impeller based on human experience, which has low efficiency, large errors and difficulty in finding the best design solution.

[0004] In a first aspect, the present invention provides a method for optimizing the runner parameters of a pump-turbine, the method comprising: 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 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 bladeless area pressure pulsation amplitude 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 bladeless area pressure pulsation amplitude of the pump-turbine; if the amplitude of the bladeless area pressure pulsation is greater than or equal to a preset threshold, using the adjusted geometric parameters as the initial geometric parameters, returning to the step of adjusting the initial geometric parameters according to the preset adjustment step until the amplitude of the bladeless area pressure pulsation is less than the first preset threshold, and storing the adjusted geometric parameters as the optimized target parameters.

[0005] The method for optimizing the runner parameters of a pump turbine provided by the present invention adjusts the initial geometric parameters according to a preset adjustment step to obtain the adjusted geometric parameters; the adjusted geometric parameters are input into a pre-constructed parameter optimization model so that the parameter optimization model outputs the bladeless area pressure pulsation amplitude corresponding to the adjusted geometric parameters, and the parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the bladeless area pressure pulsation amplitude of the pump turbine; if the amplitude of the bladeless area pressure pulsation is greater than or equal to a preset threshold value, the adjusted geometric parameters are used as the initial geometric parameters, and the step of adjusting the initial geometric parameters according to the preset adjustment step is returned until the amplitude of the bladeless area pressure pulsation is less than the first preset threshold value, and the adjusted geometric parameters are stored as the optimized target parameters. The method provided by the present invention adjusts the initial geometric parameters and inputs the adjusted parameters into a pre-constructed parameter optimization model, so that the parameter optimization model outputs the pressure pulsation amplitude of the leafless area corresponding to the adjusted geometric parameters, until the amplitude of the pressure pulsation of the leafless area corresponding to the adjusted geometric parameters is less than a preset threshold value, and the geometric parameters are stored as optimized target parameters. Based on the artificial intelligence optimization algorithm, the optimal geometric parameters for suppressing the pressure pulsation of the leafless area can be found quickly and efficiently, which greatly improves the optimization efficiency and accuracy of the geometric parameters and reduces the time and cost required for the traditional trial and error method.

[0006] In an optional embodiment, the parameter optimization model is constructed by the following steps: obtaining multiple geometric parameters and the bladeless area pressure pulsation amplitude corresponding to each geometric parameter; associating each geometric parameter with the corresponding bladeless area pressure pulsation amplitude to obtain an associated data set; using the associated data set to train a preset model until the accuracy of the model meets the preset conditions, thereby obtaining a parameter optimization model.

[0007] The method provided in this optional implementation manner trains a preset model so that the model can better predict the influence of the geometric parameters of the impeller on the pressure pulsation, thereby improving the accuracy of the prediction.

[0008] In an optional embodiment, 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 mechanics calculation software for three-dimensional simulation to obtain the pressure pulsation amplitude of the bladeless area of ​​the target pump-turbine; if the pressure pulsation amplitude of the bladeless area of ​​the target pump-turbine is greater than or equal to a second preset threshold value, the target pump-turbine is used as the pump-turbine to be optimized.

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

[0010] In an optional embodiment, the initial geometric parameters include the impeller inlet diameter, the number of long and short blades, the blade wrap angle, the blade thickness and the blade curvature, and the initial geometric parameters are adjusted according to a preset adjustment step to obtain the adjusted geometric parameters, including: adjusting the impeller inlet diameter, the number of long and short blades, the length, thickness, curvature and wrap angle to obtain the adjusted geometric parameters.

[0011] In an optional embodiment, the preset model is an adaptive gradient optimization model, and the preset model is trained using an associated data set until the accuracy of the model meets preset conditions, thereby obtaining a parameter optimization model, including: dynamically adjusting the learning rate during the adaptive training process, updating different learning rates for different geometric parameters according to the cumulative gradient square sum, until the model parameters meet the preset conditions, thereby obtaining a parameter optimization model.

[0012] The method provided by this optional implementation method dynamically adjusts the learning rate mechanism, thereby ensuring the convergence of the algorithm and the accuracy of the optimization results.

[0013] In a second aspect, the present invention provides a device for optimizing the runner parameters of a pump-turbine, the device comprising: an acquisition module for acquiring the initial geometric parameters of the runner of the pump-turbine to be optimized; an adjustment module for adjusting the initial geometric parameters according to a preset adjustment step to obtain the 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 bladeless area pressure pulsation amplitude 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 bladeless area pressure pulsation amplitude of the pump-turbine; a second determination module for using the adjusted geometric parameters as the initial geometric parameters if the amplitude of the bladeless area pressure pulsation is greater than or equal to a preset threshold, returning to the step of adjusting the initial geometric parameters according to the preset adjustment step until the amplitude of the bladeless area pressure pulsation is less than the first preset threshold, and storing the adjusted geometric parameters as the optimized target parameters.

[0014] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for optimizing the impeller parameters of a pump turbine according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for optimizing the runner parameters of a pump turbine according to the first aspect or any corresponding embodiment thereof.

[0016] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for optimizing the runner parameters of a pump turbine according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a schematic flow chart of a method for optimizing runner parameters of a pump turbine according to an embodiment of the present invention;

[0019] Figure 2 is a schematic flow chart of another method for optimizing runner parameters of a pump turbine according to an embodiment of the present invention;

[0020] Figure 3 is a schematic flow chart of another method for optimizing runner parameters of a pump-turbine according to an embodiment of the present invention;

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

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

[0023] Figure 6 Schematic diagram of the shape of the long and short blades of the runner of the water pump turbine in an embodiment of the present invention;

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

[0025] Figure 8 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] In the related art, in non-design conditions and transition processes, the pressure pulsation amplitude in the bladeless area of ​​the pump-turbine is large, which will cause unit vibration and affect stability. Traditional solutions often rely on experience to optimize the geometric parameters of the runner, which is inefficient, has large errors, and is difficult to find the best design solution.

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

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

[0030] In this embodiment, a method for optimizing runner parameters of a pump turbine is provided, which can be used for the above-mentioned server. Figure 1 is a flow chart of a method for optimizing runner parameters of a pump turbine according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0031] Step S101, obtaining the initial geometric parameters of the runner of the pump-turbine to be optimized.

[0032] For example, the pump turbine to be optimized may be a pump turbine that needs to optimize the runner geometry parameters. The runner initial 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, adjusting the initial geometric parameters according to a preset adjustment step length to obtain adjusted geometric parameters.

[0034] Exemplarily, the preset adjustment step size may be determined based on actual needs, and the embodiments of the present application do not limit the specific content of the preset adjustment step size.

[0035] Step S103, inputting the adjusted geometric parameters into a pre-built parameter optimization model, so that the parameter optimization model outputs the pressure pulsation amplitude of the bladeless area corresponding to the adjusted geometric parameters.

[0036] Exemplarily, in an embodiment of the present 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 impeller and the pressure pulsation amplitude of the bladeless area of ​​the pump turbine. The adjusted geometric parameters are input into the parameter optimization model, and the model will output the corresponding pressure pulsation amplitude of the bladeless area.

[0037] Step S104: if the amplitude of the pressure pulsation in the leafless area is greater than or equal to the 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 until the amplitude of the pressure pulsation in the leafless area is less than the first preset threshold, and the adjusted geometric parameters are stored as the optimized target parameters.

[0038] Exemplarily, the first preset threshold value may be a value determined based on optimization requirements. The specific content of the first preset threshold value is not limited in the embodiment of the present application, and those skilled in the art may determine it according to requirements. The embodiment of the present application adjusts the initial geometric parameters multiple times until the amplitude of the pressure pulsation in the leafless area corresponding to the adjusted geometric parameters is less than the preset threshold value, and the geometric parameters are stored as the optimized target parameters.

[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, so that the parameter optimization model outputs the pressure pulsation amplitude of the bladeless area corresponding to the adjusted geometric parameters, until an amplitude of the pressure pulsation in the bladeless area corresponding to the adjusted geometric parameters is obtained that is smaller than a preset threshold value, and the geometric parameters are stored as optimized target parameters. Based on an artificial intelligence optimization algorithm, the optimal geometric parameters for suppressing the pressure pulsation in the bladeless area can be found quickly and efficiently, thereby greatly improving the optimization efficiency and accuracy of the geometric parameters and reducing the time and cost required for the traditional trial and error method.

[0040] In this embodiment, a method for optimizing runner parameters of a pump turbine is provided, which can be used for the above-mentioned server. Figure 2 is a flow chart of a method for optimizing runner parameters of a pump turbine according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0041] Step S201, obtaining the initial geometric parameters of the runner of the pump turbine to be optimized. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0042] Step S202: adjusting the initial geometric parameters according to a preset adjustment step to obtain 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 curvature. The above step S202 includes:

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

[0045] Exemplarily, in this optional embodiment, the long and short blade configuration of the impeller is adjusted, wherein the long blades provide the main fluid power and the short blades are used to optimize the flow field and reduce the pressure pulsation in the bladeless area; the geometric parameters such as the number of blades, blade wrap angle, blade thickness and curvature of the long and short blades are optimized to achieve the optimal suppression effect of the pressure pulsation amplitude in the bladeless area.

[0046] Step S203, input the adjusted geometric parameters into a pre-built parameter optimization model, so that the parameter optimization model outputs the pressure pulsation amplitude in the bladeless area corresponding to the adjusted geometric parameters. The parameter optimization model is used to characterize the correlation between the geometric parameters of the impeller and the pressure pulsation amplitude in the bladeless area of ​​the pump-turbine.

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

[0048] Step a1, obtaining a plurality of geometric parameters and the pressure pulsation amplitude of the bladeless area corresponding to each geometric parameter.

[0049] For example, in an embodiment of the present application, CFD software is used to perform three-dimensional simulation of a pump-turbine with different geometric parameters to obtain the pressure pulsation amplitude of the bladeless area corresponding to the different geometric parameters.

[0050] Step a2, correlating each geometric parameter with the corresponding leafless area pressure pulsation amplitude to obtain a correlation data set.

[0051] Step a3, using the associated data set to train the preset model until the accuracy of the model meets the preset conditions, and obtain a parameter optimization model.

[0052] For example, in the embodiment of the present application, the adaptive gradient optimization model is trained using the data obtained by CFD simulation, and the parameters of the model are optimized so that it can accurately predict the pressure pulsation amplitude. The model is trained using a large amount of data obtained by CFD simulation to minimize the error between the predicted value and the actual value.

[0053] Step S204: if the amplitude of the pressure pulsation in the leafless area is greater than or equal to the 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 until the amplitude of the pressure pulsation in the leafless area is less than the first preset threshold, and the adjusted geometric parameters are stored as the optimized target parameters.

[0054] In this embodiment, a method for optimizing runner parameters of a pump turbine is provided, which can be used for the above-mentioned server. Figure 3 is a flow chart of a method for optimizing runner parameters of a pump turbine according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0055] Step S301, obtaining the initial geometric parameters of the runner of the pump-turbine to be optimized.

[0056] Specifically, the above step S301 includes:

[0057] Step S3011, obtaining the initial geometric parameters of the runner of the target pump-turbine.

[0058] Exemplarily, the target pump-turbine may be any pump-turbine that needs to be judged whether it meets the design requirements.

[0059] Step S3012, inputting the initial geometric parameters of the runner of the target pump-turbine into a preset fluid mechanics calculation software for three-dimensional simulation, and obtaining the pressure pulsation amplitude of the bladeless area of ​​the target pump-turbine.

[0060] Exemplarily, in an embodiment of the present application, the preset fluid mechanics calculation software may include but is not limited to CFD software, which is used to perform three-dimensional simulation of the target pump turbine, analyze the pressure pulsation characteristics of the bladeless area, and obtain the pressure pulsation amplitude under the initial geometry.

[0061] Step S3013: If the pressure pulsation amplitude of the bladeless area of ​​the target pump-turbine is greater than or equal to the second preset threshold, the target pump-turbine is used as the pump-turbine to be optimized.

[0062] For example, the embodiment of the present application does not limit the specific content of the second preset threshold, and those skilled in the art can determine it according to needs. Figure 4 Schematic diagram of the structure of the pump turbine in the embodiment of the present application, including a volute 1, a fixed guide vane 2, a movable guide vane 3, a pump turbine runner 4, a tailwater pipe 5, etc. The cross-sectional diagram of the runner of the pump turbine can be as follows Figure 5 shown.

[0063] Step S302: Adjust the initial geometric parameters according to the preset adjustment step length to obtain the adjusted geometric parameters. Figure 2 Step S202 of the illustrated embodiment will not be described in detail 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 of the bladeless area 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 of the bladeless area of ​​the pump turbine. For details, please refer to Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0065] Step S304: if the amplitude of the pressure pulsation in the leafless area is greater than or equal to the 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 until the amplitude of the pressure pulsation in the leafless area is less than the first preset threshold, and the adjusted geometric parameters are stored as the optimized target parameters.

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

[0067] Step S3041, dynamically adjust the learning rate during the adaptive training process, and update different learning rates for different geometric parameters according to the cumulative sum of squared gradients until the model parameters meet preset conditions, thereby obtaining a parameter optimization model.

[0068] In the embodiment of the present 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 area. The input layer includes the geometric parameters of the impeller, such as the number of blades, blade wrap angle, blade thickness and curvature, etc.; the output layer is the amplitude prediction of the pressure pulsation in the bladeless area; the learning rate is dynamically adjusted during the adaptive training process, and different learning rates are updated for different parameters according to the cumulative gradient square sum.

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

[0071] y=7E-16x 6 -3E-12x 5 +5E-09x 4 -4E-06x 3 +0.0023x 2 -0.4905x+46.564

[0072] Among them, x is the x-coordinate, y is the y-coordinate, and E is the E in scientific notation, which means 10.

[0073] The equation for the thickness of a long blade is:

[0075] m=7E-15x 6 -2E-11x 5 +3E-08x 4 -2E-05x 3 +0.0078x 2 -1.3954x+410.96

[0076] Where m represents the thickness of the blade and x is the x-direction coordinate.

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

[0079] y=6E-09x 4 -2E-05x 3 +0.0203x 2 -10.328x+2226.6

[0080] The equation for the thickness of the short blade is:

[0082] m=2E-09x 4 -6E-06x 3 +0.0063x 2 -2.7934x+449.29

[0083] The meanings of the variables are the same as above and will not be repeated here.

[0084] Step S305, inputting the target parameters into a preset fluid mechanics calculation software to perform a three-dimensional simulation, and obtaining a simulated value of the pressure pulsation amplitude of the bladeless area.

[0085] Step S306, comparing the simulation value with the preset threshold value, and evaluating the optimization effect of the target parameter based on the comparison result.

[0086] For example, in the embodiment of the present application, the optimized runner geometric parameters are input into the CFD simulation again 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 to perform a new round of optimization iterations until the optimization requirements are met.

[0087] Furthermore, a data acquisition device may be provided to monitor the working state of the pump turbine designed according to the optimized geometric parameters.

[0088] The method provided in the embodiment of the present application can achieve precise control of the pressure pulsation amplitude in the bladeless area of ​​the pump-turbine, improve the performance and efficiency of the pump-turbine, and at the same time can monitor and control the operating status of the pump-turbine in real time to ensure its safe and stable operation.

[0089] In this embodiment, a pump turbine runner parameter optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0090] This embodiment provides a pump turbine runner parameter optimization device, such as Figure 7 As shown, including:

[0091] An acquisition module 701 is used to acquire initial geometric parameters of a runner of a pump-turbine to be optimized;

[0092] An adjustment module 702 is used to adjust the initial geometric parameters according to a preset adjustment step to obtain adjusted geometric parameters;

[0093] The first determination module 703 is used to input the adjusted geometric parameters into a pre-built parameter optimization model so that the parameter optimization model outputs the bladeless area pressure pulsation amplitude corresponding to the adjusted geometric parameters, and the parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the bladeless area pressure pulsation amplitude of the pump-turbine;

[0094] The second determination module 704 is used to use the adjusted geometric parameters as the initial geometric parameters if the amplitude of the pressure pulsation in the leafless area is greater than or equal to a preset threshold, and return to the step of adjusting the initial geometric parameters according to a preset adjustment step until the amplitude of the pressure pulsation in the leafless area is less than the first preset threshold, and store the adjusted geometric parameters as the optimized target parameters.

[0095] In some optional implementations, the parameter optimization model is constructed by the following steps:

[0096] Acquire multiple geometric parameters and the pressure pulsation amplitude of the bladeless area corresponding to each geometric parameter;

[0097] Correlating each geometric parameter with the corresponding pressure pulsation amplitude of the bladeless area to obtain a correlation data set;

[0098] The preset model is trained using the associated data set until the accuracy of the model meets the preset conditions, and a parameter optimization model is obtained.

[0099] In some optional implementations, the acquisition module 701 includes:

[0100] An acquisition submodule, used for acquiring the initial geometric parameters of the runner of the target pump-turbine;

[0101] The first determination submodule is used to input the initial geometric parameters of the runner of the target pump-turbine into a preset fluid mechanics calculation software to perform a three-dimensional simulation, and obtain the pressure pulsation amplitude of the bladeless area of ​​the target pump-turbine;

[0102] The second determination submodule is used to use the target pump-turbine as the pump-turbine to be optimized if the pressure pulsation amplitude of the bladeless area of ​​the target pump-turbine is greater than or equal to a second preset threshold.

[0103] In some optional embodiments, the device further comprises:

[0104] The third determination module is used to input the target parameters into the preset fluid mechanics calculation software for three-dimensional simulation to obtain the simulated value of the pressure pulsation amplitude of the bladeless area;

[0105] The evaluation module is used to compare the simulation value with the preset threshold value and evaluate the optimization effect of the target parameter based on the comparison result.

[0106] In some optional embodiments, the initial geometric parameters include the impeller inlet diameter, the number of long and short blades, the blade wrap angle, the blade thickness and the blade curvature, and the adjustment module 702 includes:

[0107] 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.

[0108] In some optional implementations, the preset model is an adaptive gradient optimization model, and the second determination module 704 includes:

[0109] The fourth determination submodule is used to dynamically adjust the learning rate during the adaptive training process, and update different learning rates for different geometric parameters according to the cumulative gradient square sum until the model parameters meet the preset conditions to obtain a parameter optimization model.

[0110] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0111] The impeller parameter optimization device of the pump turbine in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0112] The embodiment of the present invention also provides a computer device having the above Figure 7 The runner parameter optimization device of the pump turbine is shown.

[0113] See also Figure 8 , Figure 8 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 8 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 8 A processor 10 is taken as an example.

[0114] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0115] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0116] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0117] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0118] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0119] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0120] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0121] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing runner parameters of a pump turbine, characterized in that: The method comprises: Obtaining initial geometric parameters of the runner of the pump-turbine to be optimized; Adjust the initial geometric parameters according to a preset adjustment step to obtain adjusted geometric parameters; Inputting the adjusted geometric parameters into a pre-built parameter optimization model so that the parameter optimization model outputs the bladeless area pressure pulsation amplitude 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 bladeless area pressure pulsation amplitude of the pump-turbine; If the amplitude of the pressure pulsation in the leafless area 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 until the amplitude of the pressure pulsation in the leafless area is less than the first preset threshold, and the adjusted geometric parameters are stored as the optimized target parameters.

2. The method according to claim 1, characterized in that The parameter optimization model is constructed by the following steps: Acquire multiple geometric parameters and the pressure pulsation amplitude of the bladeless area corresponding to each geometric parameter; Correlating each geometric parameter with the corresponding pressure pulsation amplitude of the bladeless area to obtain a correlation data set; The preset model is trained using the associated data set until the accuracy of the model meets the preset conditions, thereby obtaining the parameter optimization model.

3. The method according to claim 1, characterized in that: The step of obtaining the initial geometric parameters of the runner of the pump-turbine to be optimized comprises: Obtaining 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 mechanics calculation software to perform a three-dimensional simulation, and obtaining the pressure pulsation amplitude of the bladeless area of ​​the target pump-turbine; If the pressure pulsation amplitude of the bladeless area of ​​the target pump-turbine is greater than or equal to the second preset threshold, the target pump-turbine is used as the pump-turbine to be optimized.

4. The method according to claim 2, characterized in that: The method further comprises: The target parameters are input into a preset fluid mechanics calculation software for three-dimensional simulation to obtain a simulated value of the pressure pulsation amplitude of the bladeless area; The simulation value is compared with the preset threshold value, and the optimization effect of the target parameter is evaluated based on the comparison result.

5. The method according to any one of claims 1 to 4, characterized in that: 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 curvature. The step of adjusting the initial geometric parameters according to the preset adjustment step to obtain the adjusted geometric parameters includes: The runner inlet diameter, the number, length, thickness, curvature and wrap angle of the long and short blades are adjusted to obtain adjusted geometric parameters.

6. The method according to claim 2, characterized in that The preset model is an adaptive gradient optimization model, and the preset model is trained by using the associated data set until the accuracy of the model meets the preset conditions to obtain the parameter optimization model, including: The learning rate is dynamically adjusted during the adaptive training process, and different learning rates are updated for different geometric parameters according to the cumulative sum of squared gradients until the model parameters meet preset conditions, thereby obtaining the parameter optimization model.

7. A pump turbine runner parameter optimization device, characterized in that: The device comprises: An acquisition module, used for acquiring initial geometric parameters of the runner of the pump-turbine to be optimized; An adjustment module, used to adjust the initial geometric parameters according to a preset adjustment step length to obtain adjusted geometric parameters; A first determination 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 area pressure pulsation amplitude corresponding to the adjusted geometric parameters, and the parameter optimization model is used to characterize the correlation between the geometric parameters of the runner and the bladeless area pressure pulsation amplitude of the pump-turbine; The second determination module is used to use the adjusted geometric parameters as initial geometric parameters if the amplitude of the pressure pulsation in the leafless area is greater than or equal to a preset threshold, and return to the step of adjusting the initial geometric parameters according to a preset adjustment step until the amplitude of the pressure pulsation in the leafless area is less than a first preset threshold, and store the adjusted geometric parameters as optimized target parameters.

8. A computer device, characterized in that: include: 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 method for optimizing the runner parameters of a pump turbine according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the pump-turbine runner parameter optimization method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for optimizing runner parameters of a pump turbine according to any one of claims 1 to 6.

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