Compressor blade shape generation method, device, equipment and storage medium

Abstract

The present disclosure provides a blade shape generation method of a compressor, which can be applied to the field of artificial intelligence technology. The blade shape generation method of the compressor comprises: inputting a user's expected performance parameter of a blade into a shape design model to design the shape of the blade, to obtain a plurality of geometric shapes; inputting the plurality of geometric shapes into a performance evaluation model to determine a performance evaluation value of each geometric shape; and screening the plurality of geometric shapes according to the performance evaluation value to obtain at least one target shape meeting the expected performance parameter; wherein the shape design model and the performance evaluation model are both trained by using sample blade performance and flow field simulation data in a training set, and the flow field simulation data is obtained by numerical simulation after analyzing the geometric parameters of the sample blade. The present disclosure also provides a blade shape generation device, equipment and storage medium of a compressor.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and more specifically, to a method, device, equipment, and storage medium for generating a blade shape for a compressor. Background Art

[0002] As a core component of an aircraft engine, the compressor / fan requires excellent aerodynamic design, crucial to overall engine performance. Aerodynamic design, particularly the three-dimensional blade shape, plays a crucial role in the overall engine design process. Currently, the use of large models for compressor blade design presents challenges such as long design cycles and high designer experience requirements. Summary of the Invention

[0003] In view of this, the present disclosure provides a method, apparatus, device, storage medium, and program product for generating a blade shape for a compressor.

[0004] One aspect of the present disclosure provides a method for generating a blade shape for a compressor, comprising: inputting a user's desired performance parameters for a blade into a shape design model to design the shape of the blade to obtain a plurality of geometric shapes; inputting the plurality of geometric shapes into a performance evaluation model to determine a performance evaluation value for each of the geometric shapes; screening the plurality of geometric shapes according to the performance evaluation values ​​to obtain at least one target shape that meets the desired performance parameters; wherein the shape design model and the performance evaluation model are both trained using sample blade performance and flow field simulation data in a training set, and the flow field simulation data is obtained by numerically simulating the geometric parameters of the sample blades after analysis.

[0005] According to an embodiment of the present disclosure, the above-mentioned shape design model includes a diffusion model, a latent space representation and a decoder. The above-mentioned method inputs the user's expected performance parameters of the blade into the shape design model to design the shape of the above-mentioned blade to obtain multiple geometric shapes, including: inputting the above-mentioned expected performance parameters as constraints into the above-mentioned diffusion model, and outputting the latent space representation of the shape design data of the above-mentioned blade; inputting the latent space representation of the above-mentioned shape design data into the above-mentioned decoder to convert the above-mentioned shape design data into a three-dimensional geometric shape.

[0006] According to an embodiment of the present disclosure, the above-mentioned performance evaluation model includes an encoder and a prediction network. The above-mentioned multiple geometric shapes are respectively input into the performance evaluation model to determine the performance evaluation value of each of the above-mentioned geometric shapes, including: for each of the above-mentioned geometric shapes, the above-mentioned geometric shape is input into the encoder to convert the above-mentioned geometric shape into a latent space representation; the latent space representation obtained by conversion is input into the above-mentioned prediction network, and the performance evaluation value of the above-mentioned geometric shape is output.

[0007] According to an embodiment of the present disclosure, the method further comprises: inputting the target shape into a flow field prediction model to perform feature extraction, and outputting a plurality of blade surface parameters predicted for the target shape; and reconstructing a three-dimensional flow field for the target shape according to the blade surface parameters to obtain three-dimensional flow field information, wherein the flow field information comprises temperature information and pressure information.

[0008] According to an embodiment of the present disclosure, the method further comprises: integrating the shape design model, the performance evaluation model, and the flow field prediction model to construct an integrated design platform, wherein the integrated design platform is configured to perform flow field visualization display on three-dimensional flow field information that meets expected performance parameters according to the expected performance parameters input by the user.

[0009] According to an embodiment of the present disclosure, the shape design model and the performance evaluation model are trained in the following manner: determining a sample space according to the obtained geometric parameters of the sample blades; performing Latin hypercube sampling on the geometric parameters in the sample space to obtain a plurality of sample data; performing batch grid processing and single-channel calculation on the sample data by using a performance simulation method to obtain flow field simulation data, wherein the flow field simulation data comprises a plurality of sample shapes; performing format conversion on the sample shapes by using a point cloud method to obtain shape training data; and performing model training on the shape training data and the performance of the corresponding sample blades to obtain the shape design model and the performance evaluation model.

[0010] According to an embodiment of the present disclosure, the model training on the shape training data and the performance of the corresponding sample blades comprises: performing noise elimination on the shape training data to perform model training on the eliminated data after noise elimination and the performance of the corresponding sample blades; and in the process of training the shape design model, introducing the shape design experience of the blade and the related alignment strategy.

[0011] Another aspect of the present disclosure provides a blade shape generation device of a compressor, comprising: a shape design module configured to input expected performance parameters of a blade input by a user into a shape design model to design a shape of the blade to obtain a plurality of geometric shapes; a performance evaluation module configured to input the plurality of geometric shapes into a performance evaluation model to determine a performance evaluation value of each geometric shape; and a shape screening module configured to screen the plurality of geometric shapes according to the performance evaluation value to obtain at least one target shape that meets the expected performance parameters; wherein the shape design model and the performance evaluation model are both trained by using sample blade performance and flow field simulation data in a training set, and the flow field simulation data is obtained by numerical simulation after analyzing geometric parameters of the sample blades.

[0012] Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0013] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0014] Another aspect of the present disclosure provides a computer program product, which includes computer-executable instructions. When the instructions are executed, they are used to implement the method described above.

[0015] According to the embodiments of the present disclosure, a shape design model is used to achieve automated blade design. By evaluating the performance of the designed geometry, desired design results can be quickly obtained. During model training, a training set consisting of sample blade performance and flow field simulation data is established, enabling the model to learn features more efficiently. By designing the blade shape based on the user's desired performance parameters and evaluating the performance of the geometry during the design process, blade design efficiency is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 An exemplary system architecture to which the method and apparatus for generating a blade shape for a compressor disclosed herein can be applied is schematically shown;

[0018] Figure 2 A flowchart of a method for generating a blade shape for a compressor according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 3 A flowchart schematically illustrates a process of a shape design model in a method for generating a blade shape for a compressor according to an embodiment of the present disclosure;

[0020] Figure 4 A schematic diagram shows a processing flow chart of a performance evaluation model in a method for generating a blade shape for a compressor according to an embodiment of the present disclosure;

[0021] Figure 5 A schematic diagram of three-dimensional flow field information prediction in a method for generating a blade shape for a compressor according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 6A schematic diagram of an integrated design platform in a method for generating a blade shape for a compressor according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 7 The following schematically shows a model training flow chart in a method for generating a blade shape for a compressor according to an embodiment of the present disclosure;

[0024] Figure 8 A block diagram schematically shows a shape generating device according to an embodiment of the present disclosure; and

[0025] Figure 9 A block diagram of an electronic device suitable for implementing a shape generation method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0027] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0029] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0030] In the embodiments of this disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of all data involved (including, but not limited to, user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and maintain the security of user personal information and network security.

[0031] In the embodiments of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0032] As the thrust-to-weight ratio of aircraft engines continues to increase, the load on the compressor stage, their core component, is approaching its limits, leaving the maximum efficiency and optimal design range of existing compressor products. This poses certain challenges to the current compressor design system for typical load levels. Traditional one-, two-, and three-dimensional iterative blade design methods are increasingly unable to meet the requirements of wide stability margins. Furthermore, the lack of an effective mapping between different blade shapes and specific performance parameters further increases the design difficulty.

[0033] Therefore, improving traditional design processes and developing new, fast and efficient 3D blade design models are crucial for high-load compressor design. Currently, generative models, represented by large models, are sweeping across various fields with their powerful generative capabilities. They train on large datasets, abstracting the essential patterns and probability distributions of data and generating new data.

[0034] Embodiments of the present disclosure provide a method, apparatus, device, storage medium, and program product for generating blade shapes for a compressor. The method comprises: inputting a user's desired performance parameters for a blade into a shape design model to design the blade's shape and obtain multiple geometric shapes; inputting the multiple geometric shapes into a performance evaluation model to determine a performance evaluation value for each geometric shape; and screening the multiple geometric shapes based on the performance evaluation values ​​to obtain at least one target shape that meets the desired performance parameters. The shape design model and the performance evaluation model are both trained using sample blade performance and flow field simulation data in a training set, and the flow field simulation data is obtained by analyzing the geometric parameters of the sample blades and performing numerical simulation.

[0035] Figure 1 The following schematically illustrates an exemplary system architecture to which the disclosed method and apparatus for generating blade shapes for a compressor can be applied. Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0036] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0037] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software (for example only).

[0038] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0039] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0040] It should be noted that the compressor blade shape generation method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the compressor blade shape generation device provided in the embodiment of the present disclosure can generally be set in the server 105. The compressor blade shape generation method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the compressor blade shape generation device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Alternatively, the compressor blade shape generation method provided in the embodiment of the present disclosure can also be executed by the first terminal device 101, the second terminal device 102 or the third terminal device 103, or can also be executed by other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103. Correspondingly, the blade shape generating device of the compressor provided in the embodiment of the present disclosure can also be set in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0041] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0042] Figure 2 A flowchart of a method for generating a blade shape for a compressor according to an embodiment of the present disclosure is schematically shown.

[0043] like Figure 2 As shown, the method includes operations S210 to S230.

[0044] In operation S210 , the user's expected performance parameters for the blade are input into a shape design model to design the shape of the blade and obtain a plurality of geometric shapes.

[0045] In operation S220 , a plurality of geometric shapes are input into a performance evaluation model, and a performance evaluation value of each geometric shape is determined.

[0046] In operation S230 , the plurality of geometric shapes are screened according to the performance evaluation values ​​to obtain at least one target shape that meets the desired performance parameters.

[0047] According to embodiments of the present disclosure, a user's desired blade performance parameters, such as mass flow rate, isentropic efficiency, and total pressure ratio, are obtained. The total pressure ratio, which refers to the ratio of the total pressure at the blade outlet to the total pressure at the blade inlet, is used to evaluate the performance and efficiency of the blade, which is intended for use in a high-load compressor. The desired performance parameters are input into a shape design model, and the blade shape is designed to produce multiple geometric shapes. These geometric shapes can be represented using three-dimensional coordinate points (x, y, z).

[0048] According to an embodiment of the present disclosure, a geometric shape is input into a performance evaluation model, and a performance evaluation value of the geometric shape is predicted. The performance evaluation values ​​include flow rate, pressure ratio, and efficiency. The multiple performance evaluation values ​​output by the performance evaluation model are compared with the desired performance parameters, and a geometric shape that is greater than or equal to the desired performance parameters can be screened out as a target shape. If a geometric shape that is greater than or equal to the desired performance parameters does not exist among the multiple performance evaluation values, the shape design model can be used to perform multiple iterations to gradually improve the performance of the blade until a target shape that meets the desired performance parameters is obtained.

[0049] According to the embodiments of the present disclosure, both the shape design model and the performance evaluation model are trained using sample blade performance and flow field simulation data from a training set. Because the training process requires a large amount of high-fidelity data, a training set for the model must be constructed. Specifically, this construction process involves studying the geometric parameters of sample blades from a highly loaded compressor to determine the boundary conditions, parameter variation range, and simulation methods required to construct the training set.

[0050] According to the embodiments of the present disclosure, after analyzing the geometric parameters of a sample blade, a simulation method is used to perform performance simulation of the geometric parameters based on the parameter variation range in the analysis results to obtain flow field simulation data. The geometric parameters include the inlet metal angle, outlet metal angle, chord length, maximum thickness, maximum thickness location, curvature, and sweep shape of three typical sections of the sample blade: the root, the middle, and the tip.

[0051] According to embodiments of the present disclosure, after constructing a training set, the shape design model can be trained using sample blade performance as input to learn from the geometric data. The performance evaluation model, on the other hand, uses blade geometry data as input to learn from the sample blade performance.

[0052] According to an embodiment of the present disclosure, the shape design model is used to realize the automatic design of the blade, and the expected design result can be quickly obtained through the performance evaluation of the designed geometric shape. During the model training process, the training set including the performance of the sample blade and the flow field simulation data is established, so that the learning efficiency of the model for the characteristics is higher. By designing the blade shape based on the expected performance parameters of the user for the blade, and evaluating the performance of the geometric shape during the design process, the design efficiency of the blade is significantly improved.

[0053] Figure 3 A processing flowchart of a shape design model in a blade shape generation method of a compressor according to an embodiment of the present disclosure is schematically shown.

[0054] According to an embodiment of the present disclosure, the expected performance parameters of the user for the blade are input into the shape design model to design the shape of the blade, and a plurality of geometric shapes are obtained, including: inputting the expected performance parameters as constraint conditions into the diffusion model, outputting the latent space representation of the shape design data of the blade; inputting the latent space representation of the shape design data into the decoder to convert the shape design data into a three-dimensional geometric shape.

[0055] According to an embodiment of the present disclosure, the expected performance parameters 310 are input into the diffusion model 320 as constraint conditions. Since the diffusion model 320 sufficiently learns the distribution characteristics of the data during the training process, the diffusion model 320 can be used to design the shape of the blade to obtain shape design data. Then, the shape design data is converted into a latent space representation 330, which is a compressed data form containing the information required to generate the blade shape.

[0056] According to an embodiment of the present disclosure, the key features of the shape design data in the latent space representation 330 are captured and input into the decoder 340. The decoder 340 can be a 3D encoder. The decoder 340 is a neural network that can convert the latent space representation 330 into a blade shape in a three-dimensional space. Finally, the decoder 340 outputs a plurality of three-dimensional geometric shapes 350. By using the shape design model to design the geometric shape of the blade, a plurality of design schemes can be generated in a short time, effectively shortening the design cycle.

[0057] Figure 4 A processing flowchart of a performance evaluation model in a blade shape generation method of a compressor according to an embodiment of the present disclosure is schematically shown.

[0058] According to an embodiment of the present disclosure, the plurality of geometric shapes are respectively input into a performance evaluation model, and a performance evaluation value of each geometric shape is determined, including: for each geometric shape, inputting the geometric shape into an encoder to convert the geometric shape into a latent space representation; inputting the converted latent space representation into a prediction network to output a performance evaluation value of the geometric shape.

[0059] According to an embodiment of the present disclosure, the geometric shape 410 is input into an encoder 420, where the encoder 420 can be a 3D encoder. The encoder 420 is responsible for encoding the geometric shape into a format that can be processed by a machine learning model, for example, converting the geometric shape 410 into a feature vector. After being encoded into a feature vector, the model can take into account additional conditional information, such as fluid parameters, referred to as "inflow conditions 430", which can include environmental factors, operating conditions, or other performance-affecting variables.

[0060] According to an embodiment of the present disclosure, the encoded feature vector and the fluid conditions 430 are input together into a prediction network 440. The prediction network 440 is a machine learning model that learns how to predict performance parameters from the input features. The prediction network 440 outputs a key indicator for evaluating the performance of the geometric shape, which can be a performance evaluation value 450. By predicting the performance of the geometric shape during the process of designing the shape of the blade, immediate feedback can be provided at each stage of the design process, thereby ensuring the optimization and accuracy of the design scheme.

[0061] Figure 5 A schematic diagram of three-dimensional flow field information in a blade shape generation method of a compressor according to an embodiment of the present disclosure is shown schematically.

[0062] According to an embodiment of the present disclosure, the blade shape generation method of the compressor further includes: inputting a target shape into a flow field prediction model for feature extraction, and outputting a plurality of blade surface parameters predicted for the target shape; and reconstructing a three-dimensional flow field for the target shape according to the blade surface parameters, to obtain three-dimensional flow field information, wherein the flow field information includes temperature information and pressure information.

[0063] According to an embodiment of the present disclosure, in order to quickly evaluate the target shape, a deep learning prediction technology suitable for the strong nonlinearity and non-stationary flow field inside the compressor can be constructed, and research on the rapid reconstruction of the three-dimensional flow field of the compressor blade passage can be carried out based on deep learning related algorithms. Specifically, the flow field prediction model is used to predict blade surface parameters, including physical parameters such as temperature, pressure, and Mach number. Predicting the blade surface parameters can give the designer sufficient feedback.

[0064] According to the embodiments of the present disclosure, the flow field prediction model is trained based on the three-dimensional flow field of the blade passage and the performance of the sample blade. After obtaining the blade surface parameters, numerical simulation can be performed to simulate the behavior of the fluid within the blade passage and obtain fluid information. This includes simulating the fluid flow, pressure distribution, and temperature changes.

[0065] According to an embodiment of the present disclosure, after obtaining the fluid information and the flow field information when the blades rotate, the flow field is reconstructed using a deep learning model. A deep learning algorithm is used to model the strongly nonlinear and unsteady flow field in the compressor blade channel to ensure accurate capture and reconstruction of complex flow field characteristics. This model can accurately predict the static temperature, static pressure and Mach number at different positions of the blade, with a relative error of no more than 3%, thereby achieving rapid and accurate reconstruction of the compressor stator flow field. During the reconstruction process, physical knowledge, such as the basic principles of fluid dynamics, can be embedded to improve the accuracy and efficiency of the reconstruction.

[0066] According to the embodiment of the present disclosure, after the reconstruction is completed, the distribution and characteristics of the flow field are intuitively displayed through flow field visualization technology, such as three-dimensional flow field information. Figure 5 As shown, the three-stream flow field animation is displayed in multiple dimensions, including a side view 510 of the compressor blade, a topology optimization concept diagram 520 of the compressor blade, a three-dimensional model of the compressor rotor 530, and a cross-sectional view of the compressor 540. By reconstructing the three-dimensional flow field within the blade passage, important information is provided for blade design and performance analysis.

[0067] Figure 6 A schematic diagram of an integrated design platform in a method for generating a blade shape for a compressor according to an embodiment of the present disclosure is schematically shown.

[0068] According to an embodiment of the present disclosure, the method for generating the blade shape of a compressor also includes: integrating the shape design model, the performance evaluation model and the flow field prediction model to construct an integrated design platform, wherein the integrated design platform is used to visualize the flow field of three-dimensional flow field information that meets the expected performance parameters based on the expected performance parameters input by the user.

[0069] According to the embodiments of the present disclosure, the shape design model 610, performance evaluation model 620, and flow field prediction model 630 are integrated to enable the model to generate multiple target shapes in a very short time after the user inputs desired performance parameters. Furthermore, by displaying three-dimensional flow field information 640 on the integrated design platform 600, the design results are presented using virtual display technology, making it easier for designers to interact with and adjust the design results.

[0070] Figure 7The figure schematically shows a flow chart of model training in the method for generating blade shape of a compressor according to an embodiment of the present disclosure.

[0071] like Figure 7 As shown, the method includes operations S710 to S750.

[0072] In operation S710 , a sample space is determined according to the acquired geometric parameters of the sample leaf.

[0073] In operation S720 , Latin hypercube sampling is performed on the geometric parameters in the sample space to obtain a plurality of sample data.

[0074] In operation S730 , batch grid processing and single-channel calculation are performed on the sample data using a performance simulation method to obtain flow field simulation data, wherein the flow field simulation data includes a plurality of sample shapes.

[0075] In operation S740 , the sample shape is converted into a format using a point cloud method to obtain shape training data.

[0076] In operation S750 , model training is performed using the shape training data and the corresponding sample blade performance to obtain a shape design model and a performance evaluation model.

[0077] According to an embodiment of the present disclosure, the geometric parameters of the acquired sample blades are analyzed to determine the boundary conditions and parameter variation range, such as the inlet total temperature and pressure and the outlet static pressure. The geometric parameters are statistically analyzed to obtain a sample space, and the geometric parameters in the sample space are subjected to Latin hypercube sampling to obtain a plurality of sample data. Specifically, the parameter variation range can be divided into several layers, and the number of layers is usually equal to the required number of samples. A sample point is randomly selected in each layer of each variable to ensure that only one sample point is selected in each layer. The sample points of each variable are combined into a sample vector, so that a sample data uniformly distributed in the multidimensional sample space is obtained. Repeat the above steps until the required number of sample data is obtained. In order to further improve the randomness of the sample, the order of the generated sample vectors can be disrupted.

[0078] According to the embodiments of the present disclosure, the main advantage of Latin Hypercube sampling is that it can more efficiently cover the sample space, especially in non-uniformly distributed scenarios. Through the stratified random sampling method, it can ensure that there are sufficient samples in different areas of the sample space, avoiding the bias problem that may exist in random sampling.

[0079] According to the embodiments of the present disclosure, after determining the sample data, the simulation method used during the simulation is verified to check the effectiveness of the simulation prevention. If the simulation method is confirmed to be effective, the sample data is subjected to batch grid processing and single-channel calculation to form the geometric shapes of the blades under different operating conditions, thereby obtaining a flow field simulation data set.

[0080] According to an embodiment of the present disclosure, before model training, it is necessary to convert the format of the sample shape using a point cloud method. The sample shape is represented by a point cloud method, and modeling is performed using the point cloud method. Modeling using the point cloud method does not rely on a specific parameterization method, and can make maximum use of previous sample shapes. The training data converted using the point cloud method and the corresponding sample blade performance are used for model training to obtain a shape design model and a performance evaluation model. By expressing the sample shape through point cloud technology and using the coordinate points of the sample shape to train the model, the training data is not restricted to a single source, thereby increasing the utilization rate of existing data.

[0081] According to an embodiment of the present disclosure, model training is performed using shape training data and corresponding sample blade performance, including: performing noise elimination on the shape training data to perform model training using the eliminated data after noise elimination and the corresponding sample blade performance; wherein, in the process of training the shape design model, the shape design experience of the blade and related alignment strategies are introduced.

[0082] According to embodiments of the present disclosure, during model training, algorithms are introduced to eliminate noise signals in shape training data, ensuring that the generated shape training data is sufficiently smooth. For shape design model training, expert experience and alignment strategies are incorporated into the model training process to ensure that the network generates geometric shapes in a favorable direction. This integration of expert experience and alignment strategies ensures that the generated geometric shapes do not violate existing physical laws and fully utilize previous design experience.

[0083] Figure 8 The figure schematically shows a block diagram of a shape generating device according to an embodiment of the present disclosure.

[0084] like Figure 8 As shown, the compressor blade shape generating device 800 includes a shape design module 810 , a performance evaluation module 820 , and a shape screening module 830 .

[0085] The shape design module 810 is used to input the user's expected performance parameters for the blade into the shape design model to design the shape of the blade and obtain multiple geometric shapes.

[0086] The performance evaluation module 820 is configured to input a plurality of geometric shapes into a performance evaluation model and determine a performance evaluation value for each geometric shape.

[0087] The shape screening module 830 is used to screen multiple geometric shapes according to the performance evaluation value to obtain at least one target shape that meets the expected performance parameters.

[0088] Among them, the shape design model and performance evaluation model are both trained using the sample blade performance and flow field simulation data in the training set. The flow field simulation data is obtained by numerical simulation after analyzing the geometric parameters of the sample blades.

[0089] According to an embodiment of the present disclosure, the shape design module 810 includes a data determination submodule and a shape determination submodule.

[0090] A data determination submodule is used to input the desired performance parameters as constraints into the diffusion model and output a latent space representation of the blade shape design data;

[0091] The shape determination submodule is used to input the latent space representation of the shape design data into the decoder to convert the shape design data into a three-dimensional geometric shape.

[0092] According to an embodiment of the present disclosure, the performance evaluation module 820 includes a shape conversion submodule and an evaluation determination submodule.

[0093] The shape conversion submodule is used to input the geometric shape into the encoder for each geometric shape to convert the geometric shape into a latent space representation.

[0094] The evaluation and determination submodule is used to input the transformed latent space representation into the prediction network and output the performance evaluation value of the geometric shape.

[0095] According to an embodiment of the present disclosure, the compressor blade shape generation device 800 further includes a feature extraction module and a flow field reconstruction module.

[0096] The feature extraction module is used to input the target shape into the flow field prediction model for feature extraction and output multiple blade surface parameters predicted for the target shape.

[0097] The flow field reconstruction module is used to reconstruct the three-dimensional flow field of the target shape according to the blade surface parameters to obtain three-dimensional flow field information, wherein the flow field information includes temperature information and pressure information.

[0098] According to an embodiment of the present disclosure, the compressor blade shape generation device 800 also includes a platform construction module for integrating the shape design model, the performance evaluation model and the flow field prediction model to construct an integrated design platform, wherein the integrated design platform is used to visualize the flow field of three-dimensional flow field information that meets the expected performance parameters according to the expected performance parameters input by the user.

[0099] According to an embodiment of the present disclosure, the compressor blade shape generation device 800 further includes a space determination module, a parameter sampling module, a data simulation module, a format conversion module and a model training module.

[0100] The space determination module is used to determine the sample space according to the acquired geometric parameters of the sample leaf.

[0101] The parameter sampling module is used to perform Latin hypercube sampling on the geometric parameters in the sample space to obtain multiple sample data.

[0102] The data simulation module is used to perform batch grid processing and single-channel calculation on the sample data using a performance simulation method to obtain flow field simulation data, wherein the flow field simulation data includes multiple sample shapes.

[0103] The format conversion module is used to convert the sample shape format using point clouding to obtain shape training data.

[0104] The model training module is used to perform model training using shape training data and corresponding sample blade performance to obtain a shape design model and a performance evaluation model.

[0105] According to an embodiment of the present disclosure, the model training module includes a noise elimination submodule and a strategy introduction submodule.

[0106] The noise elimination submodule is used to eliminate noise from the shape training data so as to use the noise-eliminated data and the corresponding sample leaf performance for model training.

[0107] The strategy introduction submodule is used to introduce the blade shape design experience and related alignment strategies during the training of the shape design model.

[0108] According to the embodiments of the present invention, any number of modules, sub-modules, units, and sub-units, or at least part of the functions of any number of them, can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, sub-modules, units, and sub-units can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, sub-modules, units, and sub-units can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, sub-modules, units, and sub-units can be at least partially implemented as a computer program module, which can perform the corresponding functions when the computer program module is executed.

[0109] For example, any number of the shape design module 810, the performance evaluation module 820, and the shape screening module 830 can be combined into a single module / unit / sub-unit, or any one of these modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functionality of one or more of these modules / units / sub-units can be combined with at least part of the functionality of other modules / units / sub-units and implemented in a single module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the shape design module 810, the performance evaluation module 820, and the shape screening module 830 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented in hardware or firmware by any other reasonable means of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the shape design module 810 , the performance evaluation module 820 , and the shape screening module 830 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be performed.

[0110] It should be noted that the blade shape generating device part of the compressor in the embodiment of the present disclosure corresponds to the blade shape generating method part of the compressor in the embodiment of the present disclosure. The description of the blade shape generating device part of the compressor specifically refers to the blade shape generating method part of the compressor, which will not be repeated here.

[0111] Figure 9A block diagram of an electronic device suitable for implementing a shape generation method according to an embodiment of the present disclosure is schematically shown. Figure 9 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0112] like Figure 9 As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0113] Various programs and data required for the operation of the electronic device 900 are stored in the RAM 903. The processor 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. The processor 901 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 902 and / or the RAM 903. It should be noted that the programs may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0114] According to an embodiment of the present disclosure, electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to bus 904. Electronic device 900 may also include one or more of the following components connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 908 including a hard disk; and a communication section 909 including a network interface card such as a LAN card or modem. Communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 910 as needed, so that computer programs read from the removable media can be installed into storage section 908 as needed.

[0115] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0116] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0117] According to embodiments of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0118] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 902 and / or the RAM 903 described above and / or one or more memories other than the ROM 902 and the RAM 903 .

[0119] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the compressor blade shape generation method provided by the embodiment of the present disclosure.

[0120] When the computer program is executed by the processor 901, the above functions defined in the system / device of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0121] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0122] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, and all of these combinations and / or couplings fall within the scope of the present disclosure.

[0124] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for generating a blade shape for a compressor, comprising: Inputting the user's expected performance parameters for the blade into the shape design model to design the shape of the blade to obtain multiple geometric shapes; Inputting a plurality of the geometric shapes into a performance evaluation model to determine a performance evaluation value for each of the geometric shapes; screening the plurality of geometric shapes according to the performance evaluation value to obtain at least one target shape that meets the desired performance parameters; The shape design model and the performance evaluation model are both trained using the performance of sample blades and flow field simulation data in the training set. The flow field simulation data is obtained by numerically simulating the geometric parameters of the sample blades after analysis. The shape design model includes a diffusion model, a latent space representation, and a decoder. The user's expected performance parameters for the blade are input into the shape design model to design the shape of the blade, thereby obtaining multiple geometric shapes, including: Inputting the desired performance parameters as constraints into the diffusion model, and outputting a latent space representation of the blade shape design data; The latent space representation of the shape design data is input into the decoder to convert the shape design data into a three-dimensional geometric shape.

2. The method according to claim 1, wherein The performance evaluation model includes an encoder and a prediction network, and the plurality of geometric shapes are respectively input into the performance evaluation model to determine a performance evaluation value of each geometric shape, including: For each of the geometric shapes, input the geometric shape into an encoder to convert the geometric shape into a latent space representation; The latent space representation obtained by the transformation is input into the prediction network, and a performance evaluation value of the geometric shape is output.

3. The method according to claim 1, further comprising: Inputting the target shape into a flow field prediction model for feature extraction, and outputting a plurality of blade surface parameters predicted for the target shape; The target shape is reconstructed into a three-dimensional flow field according to the blade surface parameters to obtain three-dimensional flow field information, wherein the flow field information includes temperature information and pressure information.

4. The method according to claim 3, further comprising: The shape design model, the performance evaluation model and the flow field prediction model are integrated to construct an integrated design platform, wherein the integrated design platform is used to visualize the flow field of three-dimensional flow field information that meets the expected performance parameters input by the user.

5. The method according to claim 1, wherein The shape design model and the performance evaluation model are trained in the following manner: determining a sample space according to the acquired geometric parameters of the sample leaf; Performing Latin hypercube sampling on the geometric parameters in the sample space to obtain a plurality of sample data; Performing batch grid processing and single-channel calculation on the sample data using a performance simulation method to obtain the flow field simulation data, wherein the flow field simulation data includes a plurality of sample shapes; Converting the sample shape into a point cloud to obtain shape training data. Model training is performed using the shape training data and the corresponding sample blade performance to obtain the shape design model and the performance evaluation model.

6. The method according to claim 5, wherein: The performing model training using the shape training data and the corresponding sample blade performance includes: performing noise elimination on the shape training data, so as to perform model training using the noise eliminated data and corresponding sample blade performance; In the process of training the shape design model, blade shape design experience and related alignment strategies are introduced.

7. A compressor blade shape generating device, comprising: A shape design module, configured to input a user's desired performance parameters for a blade into a shape design model to design the shape of the blade and obtain a plurality of geometric shapes; a performance evaluation module, configured to input the plurality of geometric shapes into a performance evaluation model and determine a performance evaluation value for each of the geometric shapes; a shape screening module, configured to screen the plurality of geometric shapes according to the performance evaluation value to obtain at least one target shape that meets the desired performance parameters; The shape design model and the performance evaluation model are both trained using the performance of sample blades and flow field simulation data in the training set. The flow field simulation data is obtained by numerically simulating the geometric parameters of the sample blades after analysis. The shape design model includes a diffusion model, a latent space representation and a decoder, and the shape design module includes: A data determination submodule is used to input the desired performance parameters as constraints into the diffusion model and output a latent space representation of the blade shape design data; The shape determination submodule is used to input the latent space representation of the shape design data into the decoder to convert the shape design data into a three-dimensional geometric shape.

8. An electronic device comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to implement the method according to any one of claims 1 to 6.

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